two types of research studies often used by behavioral geneticists

Module 3: Prenatal Development

Behavioral genetics, learning outcomes.

• Describe the interaction between genetics and the environment
• Compare monozygotic and dizygotic twins

Behavioral geneticists study how individual differences arise, in the present, through the interaction of genes and the environment. When studying human behavior, behavioral geneticists often employ twin and adoption studies to research questions of interest. Twin studies compare the rates that a given behavioral trait is shared among identical and fraternal twins; adoption studies compare those rates among biologically related relatives and adopted relatives. Both approaches provide some insight into the relative importance of genes and environment for the expression of a given trait.

Nature or Nurture?

For decades, scholars have carried on the “nature/nurture” debate. For any particular feature, those on the “nature” side would argue that heredity plays the most important role in bringing about that feature. Those on the “nurture” side would argue that one’s environment is most significant in shaping the way we are. This debate continues in questions about what makes us masculine or feminine (Lippa, 2002), concerns about vision (Mutti, Kadnik, & Adams, 1996), and many other developmental issues.

Most scholars agree that there is a constant interplay between the two forces. It is difficult to isolate the root of any single behavior as a result solely of nature or nurture, and most scholars believe that even determining the extent to which nature or nurture impacts a human feature is difficult to answer. In fact, almost all human features are polygenic (a result of many genes) and multifactorial (a result of many factors, both genetic and environmental). It is as if one’s genetic make-up sets up a range of possibilities, which may or may not be realized depending upon one’s environmental experiences. For instance, a person might be genetically predisposed to develop diabetes, but the person’s lifestyle may help bring about the disease.

When you think about your own family history, it is easy to see that there are certain personality traits, behavioral characteristics, and medical conditions that are more common than others. This is the reason that doctors ask you about your family medical history. While genetic predisposition is important to consider, there are some family members who, for a variety of reasons, seemed to defy the odds of developing these conditions. These differences can be explained in part by the effect of epigenetic (above the genome) changes.

This video explains some of the research that gives insights into the complicated relationship between nature and nurture.

You can view the transcript for “Epigenetics: Nature vs nurture” here (opens in new window) .

The Epigenetic Framework

The term “epigenetic” has been used in developmental psychology to describe psychological development as the result of an ongoing, bi-directional interchange between heredity and the environment. Gottlieb (1998; 2000; 2002) suggests an analytic framework for the nature/nurture debate that recognizes the interplay between the environment, behavior, and genetic expression. This bidirectional interplay suggests that the environment can affect the expression of genes just as genetic predispositions can impact a person’s potentials. Likewise, environmental circumstances can trigger symptoms of a genetic disorder. For example, a person predisposed genetically for type 2 diabetes may trigger the disease through poor diet and little exercise.

The developmental psychologist Erik Erikson wrote of an  epigenetic principle  in his book  Identity: Youth and Crisis  (1968), encompassing the notion that we develop through an unfolding of our personality in predetermined stages, and that our environment and surrounding culture influence how we progress through these stages. This biological unfolding in relation to our socio-cultural settings is done in stages of psychosocial development, where “progress through each stage is in part determined by our success, or lack of success, in all the previous stages.”

In typical human families, children’s biological parents raise them, so it is very difficult to know whether children act like their parents due to genetic (nature) or environmental (nurture) reasons. Nevertheless, despite our restrictions on setting up human-based experiments, we do see real-world examples of nature-nurture at work in the human sphere—though they only provide partial answers to our many questions. The science of how genes and environments work together to influence behavior is called  behavioral genetics . The easiest opportunity we have to observe this is the  adoption study . When children are put up for adoption, the parents who give birth to them are no longer the parents who raise them. Children aren’t assigned to random adoptive parents in order to suit the particular interests of a scientist but adoption still tells us some interesting things, or at least confirms some basic expectations. For instance, if the biological child of tall parents were adopted into a family of short people, do you suppose the child’s growth would be affected? What about the biological child of a Spanish-speaking family adopted at birth into an English-speaking family? What language would you expect the child to speak? And what might these outcomes tell you about the difference between height and language in terms of nature-nurture?

Monozygotic and Dizygotic Twins

Another option for observing nature-nurture in humans involves  twin studies . To analyze nature–nurture using twins, we compare the similarity of monozygotic and dizygotic pairs. Monozygotic twins occur when a single zygote or fertilized egg splits apart in the first two weeks of development. The result is the creation of two separate but genetically identical offspring. About one-third of twins are monozygotic twins. Monozygotic twins occur in birthing at a rate of about 3 in every 1000 deliveries worldwide (about 0.3% of the world population). Monozygotic twins are genetically nearly identical and they are always the same sex unless there has been a mutation during development. The children of monozygotic twins test genetically as half-siblings (or full siblings, if a pair of monozygotic twins reproduces with another pair of identical twins or with the same person), rather than first cousins.

Sometimes two eggs or ova are released and fertilized by two separate sperm. The result is  dizygotic  or fraternal twins. About two-thirds of twins are dizygotic. These two individuals share the same amount of genetic material as would any two children from the same mother and father. Older mothers are more likely to have dizygotic twins than are younger mothers and couples who use fertility drugs are also more likely to give birth to dizygotic twins. Consequently, there has been an increase in the number of fraternal twins in recent years (Bortolus et al., 1999). In vitro fertilization (IVF) techniques are more likely to create dizygotic twins. For IVF deliveries, there are nearly 21 pairs of twins for every 1,000.

In the uterus, a majority of monozygotic twins (60–70%) share the same placenta but have separate amniotic sacs. The  placenta  is a temporary organ that connects the developing fetus via the umbilical cord to the uterine wall to allow nutrient uptake, thermo-regulation, waste elimination, and gas exchange via the mother’s blood supply.  The  amniotic sac (also called the bag of waters or the membranes),  is a thin but tough transparent pair of membranes that hold a developing embryo (and later fetus) until shortly before birth. In 18–30% of monozygotic twins each fetus has a separate placenta and a separate amniotic sac. A small number (1–2%) of monozygotic twins share the same placenta and amniotic sac. Fraternal twins each have their own placenta and own amniotic sac.

Figure 1.  Monozygotic twins come from a single zygote and generally share the same placenta, although some (18-30%) have separate placentas. Dizygotic twins come from two separately fertilized eggs and have their own placentas and amniotic sacs.

Monozygotic (one egg/identical) twins can be categorized into four types depending on the timing of the separation and duplication of cells. Various types of chorionicity and amniosity (how the baby’s sac looks) in monozygotic twins are a result of when the fertilized egg divides. This is known as placentation.

Figure 2. Various types of chorionicity and amniosity (how the baby’s sac looks) in monozygotic (one egg/identical) twins as a result of when the fertilized egg divides (Author Kevin Dufenbach)

Conjoined twins

Conjoined twins are monozygotic twins whose bodies are joined together during pregnancy. This occurs when the zygote starts to split after day 12  following fertilization and fails to separate completely. This condition occurs in about 1 in 50,000 human pregnancies. Most conjoined twins are now evaluated for surgery to attempt to separate them into separate functional bodies. The degree of difficulty rises if a vital organ or structure is shared between twins, such as the brain, heart or liver.

Vanishing twins

Researchers suspect that as many as 1 in 8 pregnancies start out as multiples, but only a single fetus is brought to full term because the other fetus has died very early in the pregnancy and has not been detected or recorded.  Early obstetric ultrasonography exams sometimes reveal an “extra” fetus, which fails to develop and instead disintegrates and vanishes in the uterus. There are several reasons for the “vanishing” fetus, including it being embodied or absorbed by the other fetus, placenta or the mother. This is known as vanishing twin syndrome. Also, in an unknown proportion of cases, two zygotes may fuse soon after fertilization, resulting in a single chimeric embryo, and, later, fetus.

Twin Studies

Using the features of height and spoken language as examples, let’s take a look at how nature and nurture apply: identical twins, unsurprisingly, are almost perfectly similar for height. The heights of fraternal twins, however, are like any other sibling pairs: more similar to each other than to people from other families, but hardly identical. This contrast between twin types gives us a clue about the role genetics plays in determining height.

Figure 3.  Identical twins Laurent and Larry Nicolas Bourgeois, also known as the Les Twins, are internationally renowned dancers.

Now consider spoken language. If one identical twin speaks Spanish at home, the co-twin with whom she is raised almost certainly does too. But the same would be true for a pair of fraternal twins raised together. In terms of spoken language, fraternal twins are just as similar as identical twins, so it appears that the genetic match of identical twins doesn’t make much difference.

Twin and adoption studies are two instances of a much broader class of methods for observing nature-nurture called  quantitative genetics , the scientific discipline in which similarities among individuals are analyzed based on how biologically related they are. We can do these studies with siblings and half-siblings, cousins, and twins who have been separated at birth and raised separately (Bouchard, Lykken, McGue, & Segal, 1990). Such twins are very rare and play a smaller role than is commonly believed in the science of nature–nurture, or with entire extended families (Plomin, DeFries, Knopik, & Neiderhiser, 2012).

It would be satisfying to be able to say that nature–nurture studies have given us conclusive and complete evidence about where traits come from, with some traits clearly resulting from genetics and others almost entirely from environmental factors, such as child-rearing practices and personal will; but that is not the case. Instead,  everything  has turned out to have some footing in genetics. The more genetically-related people are, the more similar they are—for  everything : height, weight, intelligence, personality, mental illness, etc. Sure, it seems like common sense that some traits have a genetic bias. For example, adopted children resemble their biological parents even if they have never met them, and identical twins are more similar to each other than are fraternal twins. And while certain psychological traits, such as personality or mental illness (e.g., schizophrenia), seem reasonably influenced by genetics, it turns out that the same is true for political attitudes, how much television people watch (Plomin, Corley, DeFries, & Fulker, 1990), and whether or not they get divorced (McGue & Lykken, 1992).

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4.3 Behavioral Genetics

Learning outcomes.

• Describe the interaction between genetics and the environment
• Compare monozygotic and dizygotic twins

Behavioral geneticists study how individual differences arise, in the present, through the interaction of genes and the environment. When studying human behavior, behavioral geneticists often employ twin and adoption studies to research questions of interest. Twin studies compare the rates that a given behavioral trait is shared among identical and fraternal twins; adoption studies compare those rates among biologically related relatives and adopted relatives. Both approaches provide some insight into the relative importance of genes and environment for the expression of a given trait.

Nature or Nurture?

For decades, scholars have carried on the “nature/nurture” debate. For any particular feature, those on the “nature” side would argue that heredity plays the most important role in bringing about that feature. Those on the “nurture” side would argue that one’s environment is most significant in shaping the way we are. This debate continues in questions about what makes us masculine or feminine (Lippa, 2002), concerns about vision (Mutti et al. 1996), and many other developmental issues.

Most scholars agree that there is a constant interplay between the two forces. It is difficult to isolate the root of any single behavior as a result solely of nature or nurture, and most scholars believe that even determining the extent to which nature or nurture impacts a human feature is difficult to answer. In fact, almost all human features are polygenic (a result of many genes) and multifactorial (a result of many factors, both genetic and environmental). It is as if one’s genetic make-up sets up a range of possibilities, which may or may not be realized depending upon one’s environmental experiences. For instance, a person might be genetically predisposed to develop diabetes, but the person’s lifestyle may help bring about the disease.

When you think about your own family history, it is easy to see that there are certain personality traits, behavioral characteristics, and medical conditions that are more common than others. This is the reason that doctors ask you about your family medical history. While genetic predisposition is important to consider, there are some family members who, for a variety of reasons, seemed to defy the odds of developing these conditions. These differences can be explained in part by the effect of epigenetic (above the genome) changes.

You can view the transcript for “Epigenetics: Nature vs nurture” here (opens in new window) .

The Epigenetic Framework

The term “epigenetic” has been used in developmental psychology to describe psychological development as the result of an ongoing, bi-directional interchange between heredity and the environment. Gottlieb (1998; 2000; 2002) suggests an analytic framework for the nature/nurture debate that recognizes the interplay between the environment, behavior, and genetic expression. This bidirectional interplay suggests that the environment can affect the expression of genes just as genetic predispositions can impact a person’s potentials. Likewise, environmental circumstances can trigger symptoms of a genetic disorder. For example, a person predisposed genetically for type 2 diabetes may trigger the disease through poor diet and little exercise.

The developmental psychologist Erik Erikson wrote of an  epigenetic principle  in his book  Identity: Youth and Crisis  (1968), encompassing the notion that we develop through an unfolding of our personality in predetermined stages, and that our environment and surrounding culture influence how we progress through these stages. This biological unfolding in relation to our socio-cultural settings is done in stages of psychosocial development, where “progress through each stage is in part determined by our success, or lack of success, in all the previous stages.”

In typical human families, children’s biological parents raise them, so it is very difficult to know whether children act like their parents due to genetic (nature) or environmental (nurture) reasons. Nevertheless, despite our restrictions on setting up human-based experiments, we do see real-world examples of nature-nurture at work in the human sphere—though they only provide partial answers to our many questions. The science of how genes and environments work together to influence behavior is called  behavioral genetics . The easiest opportunity we have to observe this is the  adoption study . When children are put up for adoption, the parents who give birth to them are no longer the parents who raise them. Children aren’t assigned to random adoptive parents in order to suit the particular interests of a scientist but adoption still tells us some interesting things, or at least confirms some basic expectations. For instance, if the biological child of tall parents were adopted into a family of short people, do you suppose the child’s growth would be affected? What about the biological child of a Spanish-speaking family adopted at birth into an English-speaking family? What language would you expect the child to speak? And what might these outcomes tell you about the difference between height and language in terms of nature-nurture?

Monozygotic and Dizygotic Twins

Another option for observing nature-nurture in humans involves  twin studies . To analyze nature–nurture using twins, we compare the similarity of monozygotic and dizygotic pairs. Monozygotic twins occur when a single zygote or fertilized egg splits apart in the first two weeks of development. The result is the creation of two separate but genetically identical offspring. About one-third of twins are monozygotic twins. Monozygotic twins occur in birthing at a rate of about 3 in every 1000 deliveries worldwide (about 0.3% of the world population). Monozygotic twins are genetically nearly identical and they are always the same sex unless there has been a mutation during development. The children of monozygotic twins test genetically as half-siblings (or full siblings, if a pair of monozygotic twins reproduces with another pair of identical twins or with the same person), rather than first cousins.

Sometimes two eggs or ova are released and fertilized by two separate sperm. The result is  dizygotic or fraternal twins. About two-thirds of twins are dizygotic. These two individuals share the same amount of genetic material as would any two children from the same two parents. Older gestational parents are more likely to have dizygotic twins than are younger gestational parents and couples who use fertility drugs are also more likely to give birth to dizygotic twins. Consequently, there has been an increase in the number of fraternal twins in recent years (Bortolus et al., 1999). In vitro fertilization (IVF) techniques are more likely to create dizygotic twins. For IVF deliveries, there are nearly 21 pairs of twins for every 1,000.

In the uterus, a majority of monozygotic twins (60–70%) share the same placenta but have separate amniotic sacs. The  placenta is a temporary organ that connects the developing fetus via the umbilical cord to the uterine wall to allow nutrient uptake, thermo-regulation, waste elimination, and gas exchange via the gestational parent’s blood supply.  The amniotic sac (also called the bag of waters or the membranes),  is a thin but tough transparent pair of membranes that hold a developing embryo (and later fetus) until shortly before birth. In 18–30% of monozygotic twins each fetus has a separate placenta and a separate amniotic sac. A small number (1–2%) of monozygotic twins share the same placenta and amniotic sac. Fraternal twins each have their own placenta and own amniotic sac.

Monozygotic (one egg/identical) twins can be categorized into four types depending on the timing of the separation and duplication of cells. Various types of chorionicity and amniosity (how the baby’s sac looks) in monozygotic twins are a result of when the fertilized egg divides. This is known as placentation.

Conjoined twins

Conjoined twins are monozygotic twins whose bodies are joined together during pregnancy. This occurs when the zygote starts to split after day 12  following fertilization and fails to separate completely. This condition occurs in about 1 in 50,000 human pregnancies. Most conjoined twins are now evaluated for surgery to attempt to separate them into separate functional bodies. The degree of difficulty rises if a vital organ or structure is shared between twins, such as the brain, heart or liver.

Vanishing twins

Researchers suspect that as many as 1 in 8 pregnancies start out as multiples, but only a single fetus is brought to full term because the other fetus has died very early in the pregnancy and has not been detected or recorded. Early obstetric ultrasonography exams sometimes reveal an “extra” fetus, which fails to develop and instead disintegrates and vanishes in the uterus. There are several reasons for the “vanishing” fetus, including it being embodied or absorbed by the other fetus, placenta, or the gestational parent. This is known as vanishing twin syndrome. Also, in an unknown proportion of cases, two zygotes may fuse soon after fertilization, resulting in a single chimeric embryo, and, later, fetus.

Twin Studies

Using the features of height and spoken language as examples, let’s take a look at how nature and nurture apply: identical twins, unsurprisingly, are almost perfectly similar for height. The heights of fraternal twins, however, are like any other sibling pairs: more similar to each other than to people from other families, but hardly identical. This contrast between twin types gives us a clue about the role genetics plays in determining height.

Now consider spoken language. If one identical twin speaks Spanish at home, the co-twin with whom she is raised almost certainly does too. But the same would be true for a pair of fraternal twins raised together. In terms of spoken language, fraternal twins are just as similar as identical twins, so it appears that the genetic match of identical twins doesn’t make much difference.

Twin and adoption studies are two instances of a much broader class of methods for observing nature-nurture called  quantitative genetics , the scientific discipline in which similarities among individuals are analyzed based on how biologically related they are. We can do these studies with siblings and half-siblings, cousins, and twins who have been separated at birth and raised separately (Bouchard et al., 1990). Such twins are very rare and play a smaller role than is commonly believed in the science of nature–nurture, or with entire extended families (Plomin et al., 2012).

It would be satisfying to be able to say that nature–nurture studies have given us conclusive and complete evidence about where traits come from, with some traits clearly resulting from genetics and others almost entirely from environmental factors, such as child-rearing practices and personal will; but that is not the case. Instead,  everything  has turned out to have some footing in genetics. The more genetically-related people are, the more similar they are—for  everything : height, weight, intelligence, personality, mental illness, etc. Sure, it seems like common sense that some traits have a genetic bias. For example, adopted children resemble their biological parents even if they have never met them, and identical twins are more similar to each other than are fraternal twins. And while certain psychological traits, such as personality or mental illness (e.g., schizophrenia), seem reasonably influenced by genetics, it turns out that the same is true for political attitudes, how much television people watch (Plomin et al. 1990), and whether or not they get divorced (McGue & Lykken, 1992).

Bortolus, R., Parazzini, F., Chatenoud, L., Benzi, G., Bianchi, M. M., & Marini, A. (1999). The epidemiology of multiple births. Human Reproduction Update, 5, 179-187.

Bouchard Jr, T. J., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart.  Science ,  250 (4978), 223-228.

Erikson, E. H. (1968).  Identity: Youth and Crisis  (No. 7). WW Norton & company.

Gottlieb, G. (1998). Normally occurring environmental and behavioral influences on gene activity: From central dogma to probabilistic epigenesis. Psychological Review, 105 , 792-802.

Gottlieb, G. (2000). Environmental and behavioral influences on gene activity. Current Directions in Psychological Science, 9 , 93-97.

Gottlieb, G. (2002). Individual development and evolution: The genesis of novel behavior. New York: Oxford University Press.

Lippa, R. A. (2002). Gender, nature, and nurture . Mahwah, NJ: L. Erlbaum.

McGue, M., & Lykken, D. T. (1992). Genetic influence on risk of divorce. Psychological Science,  3 (6), 368-373.

Mutti, D. O., Zadnik, K., & Adams, A. J. (n.d.). Myopia. The nature versus nurture debate goes on. Investigative Ophthalmology & Visual Science. Retrieved May 03, 2011, from  http://www.iovs.org/cgi/reprint/37/6/952

Plomin, R., Corley, R., DeFries, J. C., & Fulker, D. W. (1990). Individual differences in television viewing in early childhood: Nature as well as nurture.  Psychological Science , 1(6), 371-377.

Plomin R. DeFries J. C. Knopik V. S. & Neiderhiser J. M. (2013).  Behavioral genetics  (Sixth). Worth.

CC Licensed Content

• Behavioral Genetics. Authored by : Julie Lazzara for Lumen Learning.  Provided by : Lumen Learning.  Located at: https://courses.lumenlearning.com/wm-lifespandevelopment/chapter/behavioral-genetics/ .  License :  CC BY: Attribution
• Psyc 200 Lifespan Psychology.  Authored by : Laura Overstreet.  Located at :  http://opencourselibrary.org/econ-201/ .  License :  Public Domain: No Known Copyright
• The Nature Nurture Question.  Provided by : Lumen Learning.  Located at :  https://courses.lumenlearning.com/waymaker-psychology/chapter/the-nature-nurture-question/ .  License :  CC BY: Attribution
• Epigenetics.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Epigenetics .  License :  CC BY-SA: Attribution-ShareAlike

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• Les Twins.  Authored by : Shawn Welling.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/File:Les_Twins_profile.jpg .  License :  CC BY-SA: Attribution-ShareAlike
• Identical-Fraternal Twins. Provided by : Wikipedia.  Located at : https://commons.wikimedia.org/wiki/File:Identical-fraternal-sperm-egg.svg .  License :  CC BY-SA: Attribution-ShareAlike
• Placentation.  Provided by : Wikipedia.  Located at : https://commons.wikimedia.org/wiki/File:Placentation.svg . License :  CC BY-SA: Attribution-ShareAlike
• Epigenetics: Nature vs nurture.  Authored by : Det medisinske fakultet UiO.  Located at :  https://www.youtube.com/watch?v=k50yMwEOWGU .  License :  Other .  License Terms : Standard YouTube License

4.3 Behavioral Genetics Copyright © by Meredith Palm is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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53.3: Behavioral Genetics

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Skills to Develop

• Compare innate and learned behavior
• Discuss how movement and migration behaviors are a result of natural selection
• Discuss the different ways members of a population communicate with each other
• Give examples of how species use energy for mating displays and other courtship behaviors
• Differentiate between various mating systems
• Describe different ways that species learn

Behavior is the change in activity of an organism in response to a stimulus. Behavioral biology is the study of the biological and evolutionary bases for such changes. The idea that behaviors evolved as a result of the pressures of natural selection is not new. Animal behavior has been studied for decades, by biologists in the science of ethology , by psychologists in the science of comparative psychology, and by scientists of many disciplines in the study of neurobiology. Although there is overlap between these disciplines, scientists in these behavioral fields take different approaches. Comparative psychology is an extension of work done in human and behavioral psychology. Ethology is an extension of genetics, evolution, anatomy, physiology, and other biological disciplines. Still, one cannot study behavioral biology without touching on both comparative psychology and ethology.

One goal of behavioral biology is to dissect out the innate behaviors , which have a strong genetic component and are largely independent of environmental influences, from the learned behaviors , which result from environmental conditioning. Innate behavior, or instinct, is important because there is no risk of an incorrect behavior being learned. They are “hard wired” into the system. On the other hand, learned behaviors, although riskier, are flexible, dynamic, and can be altered according to changes in the environment.

Innate Behaviors: Movement and Migration

Innate or instinctual behaviors rely on response to stimuli. The simplest example of this is a reflex action , an involuntary and rapid response to stimulus. To test the “knee-jerk” reflex, a doctor taps the patellar tendon below the kneecap with a rubber hammer. The stimulation of the nerves there leads to the reflex of extending the leg at the knee. This is similar to the reaction of someone who touches a hot stove and instinctually pulls his or her hand away. Even humans, with our great capacity to learn, still exhibit a variety of innate behaviors.

Kinesis and Taxis

Another activity or movement of innate behavior is kinesis , or the undirected movement in response to a stimulus. Orthokinesis is the increased or decreased speed of movement of an organism in response to a stimulus. Woodlice, for example, increase their speed of movement when exposed to high or low temperatures. This movement, although random, increases the probability that the insect spends less time in the unfavorable environment. Another example is klinokinesis, an increase in turning behaviors. It is exhibited by bacteria such as E. coli which, in association with orthokinesis, helps the organisms randomly find a more hospitable environment.

A similar, but more directed version of kinesis is taxis : the directed movement towards or away from a stimulus. This movement can be in response to light (phototaxis), chemical signals (chemotaxis), or gravity (geotaxis) and can be directed toward (positive) or away (negative) from the source of the stimulus. An example of a positive chemotaxis is exhibited by the unicellular protozoan Tetrahymena thermophila . This organism swims using its cilia, at times moving in a straight line, and at other times making turns. The attracting chemotactic agent alters the frequency of turning as the organism moves directly toward the source, following the increasing concentration gradient.

Fixed Action Patterns

A fixed action pattern is a series of movements elicited by a stimulus such that even when the stimulus is removed, the pattern goes on to completion. An example of such a behavior occurs in the three-spined stickleback, a small freshwater fish (Figure \(\PageIndex{1}\)). Males of this species develop a red belly during breeding season and show instinctual aggressiveness to other males during this time. In laboratory experiments, researchers exposed such fish to objects that in no way resemble a fish in their shape, but which were painted red on their lower halves. The male sticklebacks responded aggressively to the objects just as if they were real male sticklebacks.

Migration is the long-range seasonal movement of animals. It is an evolved, adapted response to variation in resource availability, and it is a common phenomenon found in all major groups of animals. Birds fly south for the winter to get to warmer climates with sufficient food, and salmon migrate to their spawning grounds. The popular 2005 documentary March of the Penguins followed the 62-mile migration of emperor penguins through Antarctica to bring food back to their breeding site and to their young. Wildebeests (Figure \(\PageIndex{2}\)) migrate over 1800 miles each year in search of new grasslands.

Although migration is thought of as innate behavior, only some migrating species always migrate (obligate migration). Animals that exhibit facultative migration can choose to migrate or not. Additionally, in some animals, only a portion of the population migrates, whereas the rest does not migrate (incomplete migration). For example, owls that live in the tundra may migrate in years when their food source, small rodents, is relatively scarce, but not migrate during the years when rodents are plentiful.

Foraging is the act of searching for and exploiting food resources. Feeding behaviors that maximize energy gain and minimize energy expenditure are called optimal foraging behaviors, and these are favored by natural section. The painted stork, for example, uses its long beak to search the bottom of a freshwater marshland for crabs and other food (Figure \(\PageIndex{3}\)).

Innate Behaviors: Living in Groups

Not all animals live in groups, but even those that live relatively solitary lives, with the exception of those that can reproduce asexually, must mate. Mating usually involves one animal signaling another so as to communicate the desire to mate. There are several types of energy-intensive behaviors or displays associated with mating, called mating rituals. Other behaviors found in populations that live in groups are described in terms of which animal benefits from the behavior. In selfish behavior, only the animal in question benefits; in altruistic behavior, one animal’s actions benefit another animal; cooperative behavior describes when both animals benefit. All of these behaviors involve some sort of communication between population members.

Communication within a Species

Animals communicate with each other using stimuli known as signals . An example of this is seen in the three-spined stickleback, where the visual signal of a red region in the lower half of a fish signals males to become aggressive and signals females to mate. Other signals are chemical (pheromones), aural (sound), visual (courtship and aggressive displays), or tactile (touch). These types of communication may be instinctual or learned or a combination of both. These are not the same as the communication we associate with language, which has been observed only in humans and perhaps in some species of primates and cetaceans.

A pheromone is a secreted chemical signal used to obtain a response from another individual of the same species. The purpose of pheromones is to elicit a specific behavior from the receiving individual. Pheromones are especially common among social insects, but they are used by many species to attract the opposite sex, to sound alarms, to mark food trails, and to elicit other, more complex behaviors. Even humans are thought to respond to certain pheromones called axillary steroids. These chemicals influence human perception of other people, and in one study were responsible for a group of women synchronizing their menstrual cycles. The role of pheromones in human-to-human communication is still somewhat controversial and continues to be researched.

Songs are an example of an aural signal, one that needs to be heard by the recipient. Perhaps the best known of these are songs of birds, which identify the species and are used to attract mates. Other well-known songs are those of whales, which are of such low frequency that they can travel long distances underwater. Dolphins communicate with each other using a wide variety of vocalizations. Male crickets make chirping sounds using a specialized organ to attract a mate, repel other males, and to announce a successful mating.

Courtship displays are a series of ritualized visual behaviors (signals) designed to attract and convince a member of the opposite sex to mate. These displays are ubiquitous in the animal kingdom. Often these displays involve a series of steps, including an initial display by one member followed by a response from the other. If at any point, the display is performed incorrectly or a proper response is not given, the mating ritual is abandoned and the mating attempt will be unsuccessful. The mating display of the common stork is shown in Figure \(\PageIndex{4}\).

Aggressive displays are also common in the animal kingdom. An example is when a dog bares its teeth when it wants another dog to back down. Presumably, these displays communicate not only the willingness of the animal to fight, but also its fighting ability. Although these displays do signal aggression on the part of the sender, it is thought that these displays are actually a mechanism to reduce the amount of actual fighting that occurs between members of the same species: they allow individuals to assess the fighting ability of their opponent and thus decide whether it is “worth the fight.” The testing of certain hypotheses using game theory has led to the conclusion that some of these displays may overstate an animal’s actual fighting ability and are used to “bluff” the opponent. This type of interaction, even if “dishonest,” would be favored by natural selection if it is successful more times than not.

Distraction displays are seen in birds and some fish. They are designed to attract a predator away from the nest that contains their young. This is an example of an altruistic behavior: it benefits the young more than the individual performing the display, which is putting itself at risk by doing so.

Many animals, especially primates, communicate with other members in the group through touch. Activities such as grooming, touching the shoulder or root of the tail, embracing, lip contact, and greeting ceremonies have all been observed in the Indian langur, an Old World monkey. Similar behaviors are found in other primates, especially in the great apes.

Link to Learning

The killdeer bird distracts predators from its eggs by faking a broken wing display in this video taken in Boise, Idaho.

Altruistic Behaviors

Behaviors that lower the fitness of the individual but increase the fitness of another individual are termed altruistic. Examples of such behaviors are seen widely across the animal kingdom. Social insects such as worker bees have no ability to reproduce, yet they maintain the queen so she can populate the hive with her offspring. Meerkats keep a sentry standing guard to warn the rest of the colony about intruders, even though the sentry is putting itself at risk. Wolves and wild dogs bring meat to pack members not present during a hunt. Lemurs take care of infants unrelated to them. Although on the surface, these behaviors appear to be altruistic, it may not be so simple.

There has been much discussion over why altruistic behaviors exist. Do these behaviors lead to overall evolutionary advantages for their species? Do they help the altruistic individual pass on its own genes? And what about such activities between unrelated individuals? One explanation for altruistic-type behaviors is found in the genetics of natural selection. In the 1976 book, The Selfish Gene, scientist Richard Dawkins attempted to explain many seemingly altruistic behaviors from the viewpoint of the gene itself. Although a gene obviously cannot be selfish in the human sense, it may appear that way if the sacrifice of an individual benefits related individuals that share genes that are identical by descent (present in relatives because of common lineage). Mammal parents make this sacrifice to take care of their offspring. Emperor penguins migrate miles in harsh conditions to bring food back for their young. Selfish gene theory has been controversial over the years and is still discussed among scientists in related fields.

Even less-related individuals, those with less genetic identity than that shared by parent and offspring, benefit from seemingly altruistic behavior. The activities of social insects such as bees, wasps, ants, and termites are good examples. Sterile workers in these societies take care of the queen because they are closely related to it, and as the queen has offspring, she is passing on genes from the workers indirectly. Thus, it is of fitness benefit for the worker to maintain the queen without having any direct chance of passing on its genes due to its sterility. The lowering of individual fitness to enhance the reproductive fitness of a relative and thus one’s inclusive fitness evolves through kin selection . This phenomenon can explain many superficially altruistic behaviors seen in animals. However, these behaviors may not be truly defined as altruism in these cases because the actor is actually increasing its own fitness either directly (through its own offspring) or indirectly (through the inclusive fitness it gains through relatives that share genes with it).

Unrelated individuals may also act altruistically to each other, and this seems to defy the “selfish gene” explanation. An example of this observed in many monkey species where a monkey will present its back to an unrelated monkey to have that individual pick the parasites from its fur. After a certain amount of time, the roles are reversed and the first monkey now grooms the second monkey. Thus, there is reciprocity in the behavior. Both benefit from the interaction and their fitness is raised more than if neither cooperated nor if one cooperated and the other did not cooperate. This behavior is still not necessarily altruism, as the “giving” behavior of the actor is based on the expectation that it will be the “receiver” of the behavior in the future, termed reciprocal altruism. Reciprocal altruism requires that individuals repeatedly encounter each other, often the result of living in the same social group, and that cheaters (those that never “give back”) are punished.

Evolutionary game theory, a modification of classical game theory in mathematics, has shown that many of these so-called “altruistic behaviors” are not altruistic at all. The definition of “pure” altruism, based on human behavior, is an action that benefits another without any direct benefit to oneself. Most of the behaviors previously described do not seem to satisfy this definition, and game theorists are good at finding “selfish” components in them. Others have argued that the terms “selfish” and “altruistic” should be dropped completely when discussing animal behavior, as they describe human behavior and may not be directly applicable to instinctual animal activity. What is clear, though, is that heritable behaviors that improve the chances of passing on one’s genes or a portion of one’s genes are favored by natural selection and will be retained in future generations as long as those behaviors convey a fitness advantage. These instinctual behaviors may then be applied, in special circumstances, to other species, as long as it doesn’t lower the animal’s fitness.

Finding Sex Partners

Not all animals reproduce sexually, but many that do have the same challenge: they need to find a suitable mate and often have to compete with other individuals to obtain one. Significant energy is spent in the process of locating, attracting, and mating with the sex partner. Two types of selection occur during this process and can lead to traits that are important to reproduction called secondary sexual characteristics: intersexual selection , the choosing of a mate where individuals of one sex choose mates of the other sex, and intrasexual selection , the competition for mates between species members of the same sex. Intersexual selection is often complex because choosing a mate may be based on a variety of visual, aural, tactile, and chemical cues. An example of intersexual selection is when female peacocks choose to mate with the male with the brightest plumage. This type of selection often leads to traits in the chosen sex that do not enhance survival, but are those traits most attractive to the opposite sex (often at the expense of survival). Intrasexual selection involves mating displays and aggressive mating rituals such as rams butting heads—the winner of these battles is the one that is able to mate. Many of these rituals use up considerable energy but result in the selection of the healthiest, strongest, and/or most dominant individuals for mating. Three general mating systems, all involving innate as opposed to learned behaviors, are seen in animal populations: monogamous, polygynous, and polyandrous.

In monogamous systems, one male and one female are paired for at least one breeding season. In some animals, such as the gray wolf, these associations can last much longer, even a lifetime. Several explanations have been proposed for this type of mating system. The “mate-guarding hypothesis” states that males stay with the female to prevent other males from mating with her. This behavior is advantageous in such situations where mates are scarce and difficult to find. Another explanation is the “male-assistance hypothesis,” where males that remain with a female to help guard and rear their young will have more and healthier offspring. Monogamy is observed in many bird populations where, in addition to the parental care from the female, the male is also a major provider of parental care for the chicks. A third explanation for the evolutionary advantages of monogamy is the “female-enforcement hypothesis.” In this scenario, the female ensures that the male does not have other offspring that might compete with her own, so she actively interferes with the male’s signaling to attract other mates.

Polygynous mating refers to one male mating with multiple females. In these situations, the female must be responsible for most of the parental care as the single male is not capable of providing care to that many offspring. In resourced-based polygyny, males compete for territories with the best resources, and then mate with females that enter the territory, drawn to its resource richness. The female benefits by mating with a dominant, genetically fit male; however, it is at the cost of having no male help in caring for the offspring. An example is seen in the yellow-rumped honeyguide, a bird whose males defend beehives because the females feed on their wax. As the females approach, the male defending the nest will mate with them. Harem mating structures are a type of polygynous system where certain males dominate mating while controlling a territory with resources. Elephant seals, where the alpha male dominates the mating within the group are an example. A third type of polygyny is a lek system. Here there is a communal courting area where several males perform elaborate displays for females, and the females choose their mate from this group. This behavior is observed in several bird species including the sage grouse and the prairie chicken.

In polyandrous mating systems, one female mates with many males. These types of systems are much rarer than monogamous and polygynous mating systems. In pipefishes and seahorses, males receive the eggs from the female, fertilize them, protect them within a pouch, and give birth to the offspring (Figure \(\PageIndex{5}\)). Therefore, the female is able to provide eggs to several males without the burden of carrying the fertilized eggs.

Simple Learned Behaviors

The majority of the behaviors previously discussed were innate or at least have an innate component (variations on the innate behaviors may be learned). They are inherited and the behaviors do not change in response to signals from the environment. Conversely, learned behaviors, even though they may have instinctive components, allow an organism to adapt to changes in the environment and are modified by previous experiences. Simple learned behaviors include habituation and imprinting—both are important to the maturation process of young animals.

Habituation

Habituation is a simple form of learning in which an animal stops responding to a stimulus after a period of repeated exposure. This is a form of non-associative learning, as the stimulus is not associated with any punishment or reward. Prairie dogs typically sound an alarm call when threatened by a predator, but they become habituated to the sound of human footsteps when no harm is associated with this sound, therefore, they no longer respond to them with an alarm call. In this example, habituation is specific to the sound of human footsteps, as the animals still respond to the sounds of potential predators.

Imprinting is a type of learning that occurs at a particular age or a life stage that is rapid and independent of the species involved. Hatchling ducks recognize the first adult they see, their mother, and make a bond with her. A familiar sight is ducklings walking or swimming after their mothers (Figure \(\PageIndex{6}\)). This is another type of non-associative learning, but is very important in the maturation process of these animals as it encourages them to stay near their mother so they will be protected, greatly increasing their chances of survival. However, if newborn ducks see a human before they see their mother, they will imprint on the human and follow it in just the same manner as they would follow their real mother.

The International Crane Foundation has helped raise the world’s population of whooping cranes from 21 individuals to about 600. Imprinting hatchlings has been a key to success: biologists wear full crane costumes so the birds never “see” humans. Watch this video to learn more.

Conditioned Behavior

Conditioned behaviors are types of associative learning, where a stimulus becomes associated with a consequence. During operant conditioning, the behavioral response is modified by its consequences, with regards to its form, strength, or frequency.

Classical Conditioning

In classical conditioning , a response called the conditioned response is associated with a stimulus that it had previously not been associated with, the conditioned stimulus. The response to the original, unconditioned stimulus is called the unconditioned response. The most cited example of classical conditioning is Ivan Pavlov’s experiments with dogs (Figure \(\PageIndex{7}\)). In Pavlov’s experiments, the unconditioned response was the salivation of dogs in response to the unconditioned stimulus of seeing or smelling their food. The conditioning stimulus that researchers associated with the unconditioned response was the ringing of a bell. During conditioning, every time the animal was given food, the bell was rung. This was repeated during several trials. After some time, the dog learned to associate the ringing of the bell with food and to respond by salivating. After the conditioning period was finished, the dog would respond by salivating when the bell was rung, even when the unconditioned stimulus, the food, was absent. Thus, the ringing of the bell became the conditioned stimulus and the salivation became the conditioned response. Although it is thought by some scientists that the unconditioned and conditioned responses are identical, even Pavlov discovered that the saliva in the conditioned dogs had characteristic differences when compared to the unconditioned dog.

It had been thought by some scientists that this type of conditioning required multiple exposures to the paired stimulus and response, but it is now known that this is not necessary in all cases, and that some conditioning can be learned in a single pairing experiment. Classical conditioning is a major tenet of behaviorism, a branch of psychological philosophy that proposes that all actions, thoughts, and emotions of living things are behaviors that can be treated by behavior modification and changes in the environment.

Operant Conditioning

In operant conditioning , the conditioned behavior is gradually modified by its consequences as the animal responds to the stimulus. A major proponent of such conditioning was psychologist B.F. Skinner, the inventor of the Skinner box. Skinner put rats in his boxes that contained a lever that would dispense food to the rat when depressed. While initially the rat would push the lever a few times by accident, it eventually associated pushing the lever with getting the food. This type of learning is an example of operant conditioning. Operant learning is the basis of most animal training. The conditioned behavior is continually modified by positive or negative reinforcement, often a reward such as food or some type of punishment, respectively. In this way, the animal is conditioned to associate a type of behavior with the punishment or reward, and, over time, can be induced to perform behaviors that they would not have done in the wild, such as the “tricks” dolphins perform at marine amusement park shows (Figure \(\PageIndex{8}\)).

Cognitive Learning

Classical and operant conditioning are inefficient ways for humans and other intelligent animals to learn. Some primates, including humans, are able to learn by imitating the behavior of others and by taking instructions. The development of complex language by humans has made cognitive learning , the manipulation of information using the mind, the most prominent method of human learning. In fact, that is how students are learning right now by reading this book. As students read, they can make mental images of objects or organisms and imagine changes to them, or behaviors by them, and anticipate the consequences. In addition to visual processing, cognitive learning is also enhanced by remembering past experiences, touching physical objects, hearing sounds, tasting food, and a variety of other sensory-based inputs. Cognitive learning is so powerful that it can be used to understand conditioning in detail. In the reverse scenario, conditioning cannot help someone learn about cognition.

Classic work on cognitive learning was done by Wolfgang Köhler with chimpanzees. He demonstrated that these animals were capable of abstract thought by showing that they could learn how to solve a puzzle. When a banana was hung in their cage too high for them to reach, and several boxes were placed randomly on the floor, some of the chimps were able to stack the boxes one on top of the other, climb on top of them, and get the banana. This implies that they could visualize the result of stacking the boxes even before they had performed the action. This type of learning is much more powerful and versatile than conditioning.

Cognitive learning is not limited to primates, although they are the most efficient in using it. Maze running experiments done with rats by H.C. Blodgett in the 1920s were the first to show cognitive skills in a simple mammal. The motivation for the animals to work their way through the maze was a piece of food at its end. In these studies, the animals in Group I were run in one trial per day and had food available to them each day on completion of the run (Figure \(\PageIndex{9}\)). Group II rats were not fed in the maze for the first six days and then subsequent runs were done with food for several days after. Group III rats had food available on the third day and every day thereafter. The results were that the control rats, Group I, learned quickly, and figured out how to run the maze in seven days. Group III did not learn much during the three days without food, but rapidly caught up to the control group when given the food reward. Group II learned very slowly for the six days with no reward to motivate them, and they did not begin to catch up to the control group until the day food was given, and then it took two days longer to learn the maze.

It may not be immediately obvious that this type of learning is different than conditioning. Although one might be tempted to believe that the rats simply learned how to find their way through a conditioned series of right and left turns, E.C. Tolman proved a decade later that the rats were making a representation of the maze in their minds, which he called a “cognitive map.” This was an early demonstration of the power of cognitive learning and how these abilities were not just limited to humans.

Sociobiology

Sociobiology is an interdisciplinary science originally popularized by social insect researcher E.O. Wilson in the 1970s. Wilson defined the science as “the extension of population biology and evolutionary theory to social organization.” 1 The main thrust of sociobiology is that animal and human behavior, including aggressiveness and other social interactions, can be explained almost solely in terms of genetics and natural selection. This science is controversial; noted scientist such as the late Stephen Jay Gould criticized the approach for ignoring the environmental effects on behavior. This is another example of the “nature versus nurture” debate of the role of genetics versus the role of environment in determining an organism’s characteristics.

Sociobiology also links genes with behaviors and has been associated with “biological determinism,” the belief that all behaviors are hardwired into our genes. No one disputes that certain behaviors can be inherited and that natural selection plays a role retaining them. It is the application of such principles to human behavior that sparks this controversy, which remains active today.

Behaviors are responses to stimuli. They can either be instinctual/innate behaviors, which are not influenced by the environment, or learned behaviors, which are influenced by environmental changes. Instinctual behaviors include mating systems and methods of communication. Learned behaviors include imprinting and habituation, conditioning, and, most powerfully, cognitive learning. Although the connection between behavior, genetics, and evolution is well established, the explanation of human behavior as entirely genetic is controversial.

• 1 Edward O. Wilson. On Human Nature (1978; repr., Cambridge: Harvard University Press, 2004), xx.

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• Published: 11 May 2020

Using genetics for social science

• K. Paige Harden   ORCID: orcid.org/0000-0002-1557-6737 1 &
• Philipp D. Koellinger   ORCID: orcid.org/0000-0001-7413-0412 2  

Nature Human Behaviour volume  4 ,  pages 567–576 ( 2020 ) Cite this article

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Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences.

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Acknowledgements

We thank C. Burik for preparing Fig. 1 and the Social Science Genetic Association Consortium ( https://www.thessgac.org/ ) for Fig. 3 . P.D.K. was financially supported by an ERC consolidator grant (647648 EdGe). K.P.H. was supported by the Jacobs Foundation, the Templeton Foundation and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grants R01-HD083613 and 5-R24-HD042849 (to the Population Research Center at the University of Texas at Austin). The funders had no role in the conceptualization, preparation or decision to publish this work.

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Harden, K.P., Koellinger, P.D. Using genetics for social science. Nat Hum Behav 4 , 567–576 (2020). https://doi.org/10.1038/s41562-020-0862-5

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Behavioural genetics methods

Vol: 3 (2006) > Issue: 1 (January)

Primer | DOI: 10.1038/s43586-022-00191-x

• John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
• Department of Child and Adolescent Psychiatry & Social Care, Amsterdam UMC, Amsterdam, Netherlands
• Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
• School of Applied Sciences, University of Mississippi, University, MS, USA
• Department of Clinical Developmental Psychology, Vrije Universiteit, Amsterdam, Netherlands

The question of why people show individual differences in their behaviours and capacities has intrigued researchers for centuries. Behaviour genetics offers us various methods to address this question. The answers are interesting for a range of

The question of why people show individual differences in their behaviours and capacities has intrigued researchers for centuries. Behaviour genetics offers us various methods to address this question. The answers are interesting for a range of research fields, varying from medicine to psychology, economics and neuroscience. Starting with twin and family studies in the late 1970s, the field of behaviour genetics has rapidly developed by applying molecular genetic techniques next to, and sometimes combined with, family data. The overarching conclusion at this point in time is that all measured human traits are to some extent heritable, and that many genetic variants, with each exerting a small effect, explain this heritability. Against this backdrop, we offer readers who might be less familiar with behaviour genetics a brief Primer on the topic. Sitting atop our list of goals is to be a resource for scholars interested in applying the widely useful techniques of the field to their particular specialty, regardless of what that might be.

Figures ( 0 ) & Videos ( 0 )

Fig. 1 : Three common twin family models.

Fig. 2 : Correlations in cognitive ability between twin pairs.

Fig. 3 : Example raw and standardized variance path diagrams.

Fig. 4 : A cross-lagged longitudinal MZ discordant twin design.

Fig. 5 : The Wilson effect for cognitive ability.

Fig. 6 : Estimates of within-family shrinkage for GWAS predictors across a range of phenotypes from a within-sibship study 52 .

Experimental Specifications

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10 Twin Studies and Behavior Genetics

Gabriëlla A.M. Blokland, Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, School of Psychology and Centre for Advanced Imaging, University of Queensland, Brisbane, Australia

Miriam A. Mosing, Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, School of Psychology, University of Queensland, Brisbane, Australia

Karin J.H. Verweij, Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, School of Psychology, University of Queensland, Brisbane, Australia

Sarah E. Medland, Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, School of Psychology, University of Queensland, Brisbane, Australia

• Published: 01 October 2013
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Twin studies and behavior genetics address the questions raised by the nature versus nurture debate. Their aim is to estimate the extent to which individual differences in complex traits or phenotypes result from genetic and environmental influences. The vast majority of human behaviors and characteristics are complex traits and are influenced by both genetic and environmental influences, as well as the interplay between these two. Based on the differing genetic relatedness of monozygotic co-twins and dizygotic co-twins, the classical twin model allows for an estimation of the relative importance of these etiological factors. The classical twin model can be extended in multiple ways, depending on the phenotype, research question, and research design. In addition to the classical twin methodology, several such extensions are described in this chapter.

Introduction

In this chapter we will discuss some of the methodologies used in the genetic analysis of quantitative traits. The aim of this chapter is to provide an introduction to basic genetics and to the twin design as a method to study the etiology of individual differences in complex traits.

The phenotypic variation among species is extensive. In humans, this variation is observed across physical (e.g., height or weight), physiological (e.g., blood pressure or brain volume), cognitive (e.g., intelligence), and psychological (e.g., personality or depression) domains. The question of whether these individual differences in complex behavioral traits are caused by genetic (heritable) or environmental influences, or a combination of the two, is referred to as the nature versus nurture debate (Fig. 10.1 ), and dates back to ancient Greece ( Loehlin, 2009 ).

Comparing individual variation at the population level to the variation within a family shows that there is less variation within families than between families, with the least variation observed between individuals sharing their entire genome (i.e., identical twins). In the late 1800s, Francis Galton developed a number of statistical techniques including the correlation coefficient and regression, in order to study the way in which family resemblance for many traits increased with family relatedness ( Galton, 1889 ). These statistics underlie much of the behavioral and quantitative genetic techniques used today ( Plomin, DeFries, McClearn, & McGuffin, 2001 ).

The broad aim of quantitative genetic analyses is to estimate the extent to which differences observed among individuals result from genetic and environmental influences, and can thus directly address the questions raised by the nature-nurture debate. As shown in Figure 10.1 , within the scientific community it is generally accepted that the vast majority of human behaviors and characteristics are complex traits and are influenced by both genetic and environmental influences, as well as the interplay between these two.

Nature versus Nurture.

Notes: Individual differences on traits result from genetic and/or environmental influences, or a combination of both. Mendelian traits, such as Huntington’s disease, are (almost) entirely inherited, while traumatic brain injury can be caused by environmental exposures, such as a car accident. Quantitative traits are generally influenced by a combination of genetic and environmental influences.

Although the degree of sharing of environmental factors among related (as well as unrelated) individuals is hard to measure, the sharing of genetic factors between individuals is easy to quantify, because inheritance of most genetic material follows very simple rules. These rules were first postulated by Gregor Mendel in 1866 and have come to be referred to as the basic laws of heredity ( Plomin, DeFries, McClearn, & McGuffin, 2001 ). In his experimentation with pea plants, Mendel found that when crossing plants with different colored flowers (white and purple) the flowers of the resulting plant would still be purple (rather than lavender). These results led him to develop the idea of genetic loci (which he termed “heredity units"), which could either be additive or dominant. He concluded that each individual has two alleles , or versions of the genetic locus, one from each parent (note that didactic examples such as this one, are usually illustrated using the example of a bi-allelic locus with two variants, A and a; however, some types of loci have many more than two possible variants).

Within somatic cells, the DNA is arranged in two paired strands. Mendel established that, following the binomial distribution, within each individual the alleles at each locus can be paired as aa, Aa , or AA , with each pairing being referred to as a genotype . Cases where the genotype is composed of two copies of the same allele (i.e., AA or aa ), are denoted homozygotes , while those with differing alleles (i.e., Aa ), are referred to as heterozygotes . The frequency of each genotype reflects the frequency of each allele in the population. For example, if a has an allele frequency of p, and A has a frequency of q in the population, the frequencies of the three genotypes, aa, Aa , or AA , are p 2 , 2pq and q 2 . In didactic examples, q = 1 − p , however, this is not always true. Note also, that the frequency of the heterozygote is twice pq as this genotype can arise in two ways, Aa and aA , where the A allele can be inherited from either the mother or the father.

Mendel’s findings are summarized in two laws: (1) the law of segregation; and (2) the law of independent assortment ( Plomin, DeFries, McClearn, & McGuffin, 2001 ). The process of segregation occurs during gametogenesis , when the gametes or sex cells (egg and sperm) are formed. During this process the genotype separates; for example, a male with a heterozygous Aa genotype will usually develop approximately equal numbers of sperm carrying the A allele and the a allele. Thus, each of the parental alleles has an equal chance of being transmitted, regardless of the frequency of each allele within the population. Assortment refers to the process of segregation among many loci. This principle states that the inheritance of these loci is independent such that the process of segregation is random at each locus. An important caveat is that this principle does not hold if the loci are closely located on the same chromosome. This is because at the physical level stretches of DNA are co-inherited. This fact forms the basis of linkage analysis, which will be discussed in Chapter 11 .

A genetic effect is described as dominant if the heterozygous individuals show the same behavior or phenotype as one of the homozygotes. By convention, a capital letter (e.g., A ), is used to represent the dominant allele, while a lower case letter (e.g., a ), is used to describe the recessive allele. However, if the gene effects are additive (i.e., the trait value increases with each additional increasing allele, denoted A ), the observed trait or phenotype in the heterozygote will lie midway between the two homozygotes. While Mendelian laws were initially thought to apply only to traits influenced by single genes, it was subsequently shown by R.A. Fisher (1918 ) that they also apply to many complex and quantitative traits, where the phenotype results in part from the combined influence of multiple genes (Fig. 10.2 ).

At a genome-wide level the average amount of genetic sharing between two relatives can be calculated based on biometric genetic theory. A child shares 50% of their autosomal (i.e., non-sex chromosome) DNA with each of his parents. Similarly, siblings share on average 50% of their genetic material, and grandparents share on average 25% of their genetic material with their grandchildren (the same applies for half siblings and avuncular relationships). Analysis of data from related individuals, making use of the differences in genetic sharing between relatives, provides one way of estimating the relative magnitude of genetic (or heritable) and environmental influences on trait variation.

Heritability can be defined as the proportion of the phenotypic variance in a trait that is attributable to the effects of genetic variation ( Neale & Maes, 2004 ). Generally, the more diverse the relatedness of the participants included (i.e., parents, siblings, and cousins), the greater the power to disentangle genetic and environmental influences on trait variation ( Medland & Hatemi, 2009 ). A particularly attractive design to investigate genetic and environmental influences on trait variation is the adoption study. By comparing the resemblance between the adoptee and the adoptive family (environmental influence) versus the resemblance between the adoptee and the biological family (genetic influence) for a given trait, the relative contribution of genes and environment to variance in this trait can be estimated. However, this design is complicated by the difficulties associated with collecting data from the biological family, the nonrandom placement of adoptees and the effects of pre-adoptive experiences.

Genotypes to phenotypes: From single gene action to complex polygenic traits.

Notes: Given that each gene has 3 possible combinations of alleles ( aa, Aa , and AA ), under the assumption of additive genetic effects the homozygotes would be on the lower ( aa ) and the upper ( AA ) end of the phenotypic distribution, while the heterozygote is in the middle. If we extend this to include multiple genes, as would be the case for complex polygenic traits, with the inclusion of each new gene the distribution of phenotypic values in the sample increasingly resembles a normal distribution.

Arguably, the classical twin study represents the most practical and powerful family design available to researchers. This method compares the within-pair similarity of identical (monozygotic ; MZ) and non-identical (dizygotic; DZ) twins. Monozygotic twins develop when the developing zygote (fertilized egg) divides, usually within 2 weeks of fertilization, and the two parts continue to develop independently (Fig. 10.3 ). In this case, both twins originate from the same sperm and egg, which makes them genetically identical and, therefore, they are always of the same sex. The later the twinning event occurs, the more likely the twins are to share chorion (which is comprised of the placenta and related membranes) and amniotic sacs ( Derom et al., 2001 ; Baergen, 2011 ). In contrast, DZ twinning occurs when more than one egg is released by the ovaries at the same time and, subsequently, each of the eggs is fertilized by a separate sperm cell. As a result, DZ twins do not differ from normal siblings genetically, sharing on average 50% of their genetic loci. However, they do have shared prenatal environments, as they were conceived at the same time and shared the womb. Dizygotic twins, like normal siblings, can either be of the same or of the opposite sex (i.e., a male and a female).

In order to facilitate the use of twin designs many countries have set up twin registries, collecting information on twins and their families. The oldest national twin register is the Danish Twin Registry, initiated in 1954, currently listing more than 75,000 twin pairs ( Skytthe et al., 2006 ). Subsequently, many more countries have followed the Danish example by setting up large nationwide twin registries (e.g., Sweden, Australia, and the Netherlands). One of the biggest challenges for twin registries is the correct ascertainment of the zygosity of twins (MZ versus DZ). Until recently, zygosity was mainly determined by means of a questionnaire assessing twin similarity between same-sex twins. This method has proven to be efficient in 95% of cases ( Kasriel & Eaves, 1976 ). Over the past thirty years, however, gene-finding methods became available, enabling the precise determination of zygosity status; these have largely been used to confirm and replace zygosity determination based on questionnaires ( Plomin, DeFries, McClearn, & McGuffin, 2001 ). To date numerous twin studies on a very large variety of traits have been conducted. Although originally the focus was on “simple” (physical) traits such as height, soon twin studies were used to explore the variation in increasingly complex traits, such as intelligence, personality, psychiatric disorders, etc.

The development of monozygotic versus dizygotic twins.

As mentioned above, the phenotypes and genotypes of related individuals are not independent, nor are they identically distributed; therefore, many standard statistical tests cannot and/or should not be applied in the analyses of relatives. Most analyses based on related individuals use statistical approaches based on likelihood, as this very general statistical framework has high modeling flexibility (e.g., Maes et al., 2009 ; Neale & Maes, 2004 ). These statistical approaches will be explained in this chapter.

The Classical Twin Model

As mentioned above, the classical twin design draws its explanatory power from the differences in genetic sharing of MZ and DZ twins. Using simultaneous equations, this knowledge can be used to partition the variance in a phenotype into that which results from additive genetic (A), dominant genetic (D), common or shared environmental (C) and unique or unshared environmental (E) influences. Additive and dominant genetic influences refer to the cumulative effect of genes acting in an additive or dominant manner. Common environmental influences refer to experiences shared by co-twins, including the intrauterine environment, and the social and cultural rearing environment (i.e., same socio-economic status, parents, diet, etc.) Unique environmental factors comprise all aspects of the physical and social environment experienced differentially by individuals in a family, such as illness, physical and psychological trauma, peers, teachers, etc. This component also includes measurement error and gene–environment interactions when not accounted for in the modeling ( Eaves, Last, Martin, & Jinks, 1977 ; Jinks & Fulker, 1970 ).

The classical twin model assumes that phenotypic variation results from the sum of these sources, such that the total variance can be written as: A + C + D + E. Monozygotic twins share approximately 100% of their genetic information (A and D), as well as 100% of their common or shared environment (C).Thus, the MZ covariance (i.e., the covariance between twin 1 and 2 of an MZ pair) can be written as: A + C + D. Conversely, DZ twins are assumed to share, on average, 50% of their segregating genes, and 25% of the time they share the same paternal and maternal alleles (which are required to share dominant effects). In addition, they are assumed to share 100% of the common environment. Thus, the DZ covariance can be written as: 0.5A + C + 0.25D.

As will be obvious from these three equations, there is insufficient information within the classical twin model to simultaneously estimate the magnitude of all four sources of variance. As such, twin studies tend to estimate either C or D. This is because these measures are negatively confounded; that is, dominance effects tend to reduce the DZ correlation relative to the MZ correlation (i.e., make MZ twins more similar), whereas the common environment tends to increase the DZ correlation relative to the MZ correlation (i.e., makes DZ twins more similar). One or the other source can be assumed absent depending on whether the DZ twin correlation is greater or less than half the MZ correlation. In general an ACE model would be estimated if the DZ correlation is greater than half of the MZ correlation, and an ADE model if the DZ correlation is less than half of the MZ correlation.

In either case, the extent to which MZ twin pairs resemble each other more for a trait (i.e., show higher twin correlations) than DZ twin pairs gives information on the relative influence of genetic versus environmental factors on a trait. Under the ACE model, the proportion of variance resulting from additive effects (A) or the heritability ( a 2 ), can be calculated as twice the difference between the MZ and DZ correlations ( Holzinger, 1929 ): a 2 = 2 ( r MZ ∼ r DZ ). An estimate of shared environment (C or c 2 ) can be calculated via twice the DZ correlation minus the MZ correlation: c 2 = 2 r DZ ∼ r MZ . Because MZ twins do not share the non-shared environmental variance (E or e 2 ), 1 minus the MZ correlation gives the contribution of the non-shared environment: e 2 = 1 − r MZ . Because correlations are standardized (with unit variance), the total phenotypic variance (A + C + E) is also standardized. Therefore, each variance component represents the relative contribution to a trait.

Twin correlations.

Notes: Scatter plots showing MZ and DZ twin pair correlations for (a) height in cm (males only) and (b) adolescent misconduct based on questionnaire data. Twin correlations for height indicate a high heritability for this trait, whereas twin correlations for adolescent misconduct point to moderate heritability. Data were provided by the Genetic Epidemiology Laboratory, Queensland Institute of Medical Research.

If we apply these formulas to the example data for height in Figure 10.4a , where the MZ twin correlation is 0.88 and the DZ correlation is 0.44, the heritability would be a 2 = 2 * (0.88 − 0.44) = 0.88, and the common environmental influence would be c 2 = (2 * 0.44) − 0.88 = 0. A heritability of 0.88 should be interpreted to mean that 88% of the population variance in a trait can be attributed to variation at the genetic level. Importantly, this cannot be interpreted as height being genetically controlled for 88% of individuals. The estimate of the proportion of variance accounted for by E for this trait is 12%; notably, variance resulting from measurement error is also included in this estimate. For adolescent misconduct (Fig. 10.4b ), where the MZ twin correlation is 0.70 and the DZ correlation is 0.47, the heritability would be a 2 = 2 * (0.70 − 0.47) = 0.46 and the common environmental influence would be c 2 = (2*0.47)− 0.70 = 0.24.

Figure 10.4b also illustrates how the range of values for the trait under investigation can affect the data distribution. In twin modeling it is important that the trait of interest shows a normal distribution in the entire sample, as well as in the MZ and DZ subsamples. If this is not the case, transformation of the data may be necessary. Otherwise, alternative models are available for data that violate this assumption ( see section on the liability threshold model).

Structural Equation Modeling

The formulas developed by Holzinger (1929 ) are limited in their application to continuous phenotypes and univariate contexts. As much of the focus of modern quantitative genetics is on estimating the contribution of genetic effects to the covariation between phenotypes, the Holzinger method is seldom used in contemporary studies. The majority of current studies now use more sophisticated structural equation models to estimate these influences ( Eaves, 1969 ; Eaves, Last, Martin, & Jinks, 1977 ; Martin & Eaves, 1977 ). These new methodologies allowed the development of models that more accurately reflect the complexities of human behavior and development ( Mehta & Neale, 2005 ). Structural equation modeling (SEM) is used to test complex relationships between observed (measured) and unobserved (latent) variables and also relationships between two or more latent variables ( Wright, 1921 ). For a more detailed explanation of structural equation modeling methodology, please refer to Chapter 15 . The parameters of the structural equation model for the pattern of MZ and DZ variances and covariances can be estimated by several approaches, including maximum likelihood and weighted least squares . In this chapter we will assume that maximum likelihood methods are used.

Path diagrams (Fig. 10.5 ) provide a graphical representation of models. Path diagrams can be mapped directly to mathematical equations and are sometimes easier to understand. Structural equation modeling allows us to obtain maximum likelihood estimates of phenotypic means and genetic and environmental variance components, while also allowing for the explicit modeling of effects of covariates (e.g., sex, age, IQ) and interaction effects. The aim of maximum likelihood estimation is to find the parameter values that explain the observed data best. Likelihood ratio tests, which are asymptotically distributed as chi-square (χ 2 ), are used to compare the goodness of fit of reduced submodels (i.e., AE, CE, and E models) with that of the full ACE model. Model fit is evaluated according to the principle of parsimony , in which models with fewer parameters are considered preferable if they show no significant worsening of fit ( p > 0.05) when compared to a full ACE model. A larger χ 2 (corresponding to a low probability) indicates a poor fit of the submodel; a smaller x 2 (accompanied by a non-significant p value) indicates that the data are consistent with the fitted model.

For example, if dropping the A parameter from the ACE model (i.e., by equating the additive genetic path coefficient to zero) results in a significant worsening of model fit ( p < 0.05), this signifies that the simpler CE model is not an accurate description of the observed data, and thereby indicates the significance of the genetic influences. Components of variance (A, C, or E) are calculated by dividing the squared value of the corresponding path coefficient by the total variance (i.e., the summed squared values of all path coefficients).

From Figure 10.5 , the following algebraic statements can be derived for the variance/covariance matrices of MZ and DZ twins (Matrix 10.1 ), where the variance for each twin is located on the diagonal (shaded dark gray) with the covariance between twins on the off-diagonal (shaded light grey).

As mentioned previously, when estimating an ACE model it is assumed that there is no variance resulting from non-additive genetic influences (D).

Path diagram depicting the classical twin model. Notes: P = phenotype; T1 = twin 1 of a pair; T2 = twin 2 of a pair; MZ = monozygotic; DZ = dizygotic; A = additive genetic influences; C = common environmental influences; E = unique environmental influences; a = additive genetic path coefficient; c = common environmental path coefficient; e = unique environmental path coefficient. Circles represent latent, unobserved variables; squares represent observed phenotypes; single-headed arrows represent influences of latent variables on observed variables; double-headed arrows represent (co) variances. Correlations between additive genetic factors (A) are fixed at 1 for MZ pairs and 0.5 for DZ pairs, because MZ twins share 100% of their segregating genes and DZ twins on average 50%. Correlations between common environmental factors (C) are fixed at 1 for both MZ and DZ twins, because both types of twins share 100% of their familial environment. By definition, E factors are left uncorrelated in both MZ and DZ twins because they are unique for each individual.

Variance resulting from non-additive genetic influences (D) may also be estimated, where correlations between MZ twins are fixed at 1 and correlations between DZ twins are fixed at 0.25. The covariance structure of an ADE model is summarized in Figure 10.6 and in the matrix below (Matrix 10.2 ), where the variance for each twin is located on the diagonal (shaded dark gray) with the covariance between twins on the off-diagonal (shaded light grey).

The most commonly used software package for twin modeling is the flexible matrix algebra program, Mx ( Neale, Boker, Xie, & Maes, 2002 ); Mx can be downloaded from: http://www.vcu.edu/mx/ . The Mx website also contains (links to) example code for various models. Recently, Mx has been implemented within the R programming environment under the new name OpenMx ( Boker et al., 2011 ); OpenMx and R can be downloaded from the following pages: http://openmx.psyc.virginia.edu/installingopenmx and http://www.r-project.org/ , respectively. The OpenMx website also contains example code as well as a forum where OpenMx-related topics can be discussed. Another program suitable for twin modeling is Mplus ( Muthen & Muthen, 1998-2010 ); the Mplus homepage can be found at: http://www.statmodel.com/ . For family studies, when not utilizing twin data, SOLAR (Sequential Oligogenic Linkage Analysis Routines) can be used; the software can be downloaded from: http://solar.sfbrgenetics.org/download.html .

Path diagram depicting the ADE model.

Notes: P = phenotype; T1 = twin 1 of a pair; T2 = twin 2 of a pair; MZ = monozygotic; DZ = dizygotic; A = additive genetic influences; D = dominance genetic influences; E = unique environmental influences; a = additive genetic path coefficient; d = dominance genetic path coefficient; e = unique environmental path coefficient. Circles represent latent, unobserved variables; squares represent observed phenotypes; single-headed arrows represent influences of latent variables on observed variables; double-headed arrows represent (co)variances.

Assumptions of the Classical Twin Model

Several assumptions underlie the classical twin design, including generalizability, random mating, equal environments, and absence of genotype-environment interaction or genotype-environment correlation. These assumptions will be explained below.

Generalizability

A frequently asked question regarding twin studies is whether their results can be generalized to the general population (i.e., singletons). The experience of being a twin, including the sharing of limited space and resources during gestation, and the differences in the birth process, may cause twins to be different from singletons. Generalizability can be assessed by comparing means and variances for a trait between twins and members of the general population, which are matched for age and sex. However, the best method of assessing generalizability is by extending the twin design to include the twins’ own siblings within the analysis. Comparing the DZ co-twin correlation with twin-sibling correlations allows an examination of the role of pre-or perinatal factors on the trait of interest (correcting for age). One of the advantages of comparing twins with their own non-twin siblings is that by using siblings as the control group we can, at least partly, control for variance in maternal size (i.e., intrauterine size and body shape, which may influence the length of gestation and ease of delivery) and the effects of genetic transmission (as both DZ twins and their full siblings share, on average, 50% of their genetic material). Although twins do differ from singletons for some traits, especially those related to prenatal growth, most studies generally do not find differences in personality and social traits ( Evans, Gillespie, & Martin, 2002 ). If this assumption is violated, additional twin-specific effects will need to be incorporated in the model.

Random Mating

The assumption that DZ twins share on average 50% of their genes no longer holds true in the case of assortative mating . Visscher et al. (2006 ) used molecular data to get exact measures of average genetic sharing of sibling pairs, which in a sample of 4,401 sibling pairs ranged from 37% to 61%. Assortative mating may be based on phenotypic similarity (positive assortment) or dissimilarity (negative assortment). Positive assortative mating refers to the situation where prospective mating partners are more likely to select each other when they possess similar traits. As these traits will probably be at least partly caused by similar gene variants, their children are likely to share more than 50% of their genetic information, for genetic loci influencing the trait of interest. To illustrate, Maes et al. (1998 ) investigated assortative mating in the context of major depression, generalized anxiety disorder, panic disorder, and phobias, and found considerable associations between partners for most psychiatric diagnoses. Assortment was observed both within and between classes of psychiatric disorders. Variables correlated with the psychiatric diagnoses, such as age, religious attendance, and education, did explain part, but not all, of the assortment between partners.

Because assortative mating increases the correlations between mates, estimates of the relative genetic and environmental influences based on a twin design will be biased if assortative mating is present and is not appropriately accounted for. When parents are more genetically alike than expected by chance, the DZ twins’ genetic resemblance will on average be more than 50% because of the transmission of the correlated parental genes. As a result, the resemblance of DZ twin pairs will increase relative to MZ twin pairs. Unmodeled assortative mating will therefore result in artificially inflated estimates of the shared environmental component and an underestimation of heritability. The presence of assortative mating can be studied by calculation of the phenotypic correlation between the parents of twins, or the phenotypic correlation between twins and their spouses, assuming that the extent of assortative mating does not change across generations.

The Degree of Genetic Similarity Between MZ Twins

Although MZ twins are assumed to be genetically identical, a study of19 MZ twin pairs detected subtle differences in copy number variations of the DNA ( Bruder et al., 2008 ). These differences occur when a set of coding nucleotide bases in DNA are missing or when extra copies appear. It is currently theorized that at the time of conception MZ twins are genetically identical; however, during subsequent DNA replications and cell division, a small number of mutations may occur. The same phenomenon would also decrease the “known” degree of relatedness between DZ twins (50%), parents and children (50%), half siblings (25%), etc. This would also mean that age differences would influence the degree of relatedness in family studies (i.e., newborns would have fewer mutations than their older family members simply because they are younger).

Equal Environments

The twin method partitions the environment into that which is shared between co-twins and that which is unshared. Generally the shared environment is assumed to include prenatal effects and the effects of growing up in the same household. This interpretation relies on the assumption that MZ and DZ twins experience shared environments to the same extent (i.e., that trait-relevant environmental influences contribute equally to the resemblance of MZ and DZ twin pairs). This assumption has received much attention. It has been found that MZ twins are treated more similarly than DZ twins in certain aspects; as young children they share a bedroom and are dressed alike more often, and they are more likely to share the same friends and stay in closer contact once they leave home ( Cohen, Dibble, Grawe, & Pollin, 1973 ; Kendler, Heath, Martin, & Eaves, 1987 ; Loehlin & Nichols, 1976 ). However, it is not clear whether greater environmental similarity results in greater phenotypic similarity.

Furthermore, as highlighted by Heath et al. (1989 ), environmental inequality would only result in bias if the trait of interest happened to be affected by those environmental factors that differ between twins. Salient environmental influences that are more similar for MZ compared to DZ twins would increase twin correlations in MZ twins, inappropriately inflating estimates of trait heritability. Several methods have been used to test the equal environments assumption, including correlating perceived zygosity with the trait while controlling for actual zygosity ( Kendler et al., 1993 ; Matheny, Wilson, & Dolan, 1976 ; Plomin, Willerman, & Loehlin, 1976 ; Scarr, 1982 ; Scarr & Carter-Saltzman, 1979 ), direct observation of family members and others to examine their self-initiated and twin-initiated behaviors toward the different twin types ( Lytton, Martin, & Eaves, 1977 ), and correlating the similarity of the twin environments with the trait while controlling for actual zygosity ( Borkenau, Riemann, Angleitner, & Spinath, 2002 ; Heath, Jardine, & Martin, 1989 ; Kendler et al., 1987 ; Martin et al., 1986 ).

A modeling-based approach is the extension of the classical ACE model by partitioning the common environment into the usual common environment, C residual , which is completely correlated for all twin pairs, and that which is influenced by the perceived zygosity, C specigc , which is parameterized to be completely correlated if both twins perceive themselves to be MZ, completely uncorrelated if both twins perceive themselves to be DZ, and correlated at 0.5 if the twins disagree about their perceived zygosity ( Hettema, Neale, & Kendler, 1995 ; Kendler et al., 1993 ; Scarr & Carter-Saltzman, 1979 ; Xian et al., 2000 ).

Furthermore, when data have been collected from non-twin siblings, checking for differences between the DZ covariance and the twin–sibling and sibling–sibling covariances can provide an additional test of the equal environments assumption. Arguably, if the more similar treatment of MZ twins were affecting their trait values, one might also expect more similar treatment of DZ twins as compared to regular siblings. When using ordinal data, equality of the thresholds of MZ and DZ twins indicates there are no differences in variances between MZ and DZ twin pairs, excluding the possibility of an extra environmental influence specific to MZ twins. The most recent method to remove equal environment biases allows heritability to be estimated from non-twin siblings ( Visscher et al., 2006 ).

Although MZ and DZ mean differences have been found for traits such as birth weight ( Koziel, 1998 ) and similar dress ( Matheny, Wilson, & Dolan, 1976 ), rigorous and frequent testing of characteristics such as physical twin similarity( Hettema, Neale, & Kendler, 1995 ), self-perceived zygosity ( Xian et al., 2000 ), perceived zygosity and associated parental approach to rearing their twins ( Cronk et al., 2002 ; Kendler & Gardner, 1998 ; Kendler et al., 1993 ; Kendler et al., 1994 ), self-reported similarity of childhood experiences ( Borkenau, Riemann, Angleitner, & Spinath, 2002 ), and physical and emotional closeness between the twins ( Cronk et al., 2002 ; Kendler & Gardner, 1998 ; LaBuda, Svikis, & Pickens, 1997 ), has shown that these traits are uncorrelated with zygosity differences in intelligence, personality, and psychiatric disorders such as alcohol and illicit drug dependence, major depression, anxiety, and externalizing disorders, thereby supporting the validity of the equal environmental assumption in twin studies assessing these phenotypes.

Genotype-Environment Interaction

The classical twin model does not take the possible presence of genotype–environment (GxE) interaction into account. Gene–environment interaction occurs when environments have differential effects on different genotypes. For example, Boomsma and colleagues (1999) found that a religious upbringing reduces the influence of genetic factors on disinhibition. A recent study of borderline personality disorder by Distel et al. (2011 ) also found evidence for GxE interaction. For individuals who had experienced a divorce/break-up, violent assault, sexual assault, or job loss, environmental variance for borderline personality disorder features was higher, leading to a lower heritability in exposed individuals. Jinks and Fulker (1970 ) suggested a screening test for GxE interaction using data from MZ twin pairs, whereby the intrapair differences are plotted against the sum of the co-twins’ phenotypic values. A significant correlation between these two indicates the presence of GxE interaction. However, to avoid spurious results, this test requires data from MZ twins reared apart, and it is unsuitable for binary data. Purcell (2002 ) proposed another approach to the detection of GxE interaction, which allows the explicit modeling of the interaction through extension of the classical twin design. In order to model the interaction, the environmental covariate(s) must be entered into the analysis as an observed variable, thus limiting the application of this approach to the study of already known or suspected environmental covariates.

Genotype-Environment Correlation

Gene–environment correlation ( r GE ) occurs when individuals actively or passively expose themselves to different environments depending on their genotype, or when individuals’ genotypes affect their social interactions or influence the responses they elicit from other individuals ( Falconer & Mackay, 1996 ; Plomin, DeFries, & Loehlin, 1977 ). If r GE is positive it could result in an increase in the total phenotypic variance of the trait. Alternatively, in the case of a negative r GE , the total phenotypic variance would be decreased. Distel et al. (2011 ) also found evidence for gene–environment correlation. The genetic effects that influence borderline personality disorder features also increased the likelihood of being exposed to certain life events. Three types of r GE have been described by Plomin et al. (1977 ), namely cultural transmission, autocorrelation, and sibling effects .

Cultural transmission refers to the environmental effect of the parental phenotype on the offspring’s phenotype ( Neale & Maes, 2004 ; i.e., resemblance between parents and offspring that results from a home environment created by the parents). To use a simplistic example, imagine two children taking part in a study of reading ability. Both children come from the same socio-economic strata and have very similar backgrounds. The parents of child A enjoy reading and have many books in their home, thus child A is read to as young child, observes his/her parents reading, and grows up in an environment where books are accessible. The parents of child B do not enjoy reading; they do not own many books and do not use the local library. Child B thus grows up in an environment where books are less accessible, and despite being read to as young child because the parents feel this is important, child B does not often observe his/her parents reading. As it is likely that the environmental differences between the two children are related to the genetic variants influencing reading ability, the environmental and genetic effects become correlated. Failure to model this correlation can inflate the heritability of reading ability in the children. The effects of cultural transmission may be examined by extending the twin design to include parental data. Such a design also allows for a test of the assumption of random or non-assortative mating ( Neale & Maes, 2004 ).

Gene–environment autocorrelation occurs when environments are not randomly assigned to each individual but are, in part, individually selected on the basis of genetically influenced preferences. For example, when gifted individuals create or evoke situations that further enhance their intellectual ability, or when genetically introverted individuals choose to avoid situations where they may be the focus of attention.

Sibling interactions may be either cooperative, increasing the trait value of the co-twin (imitation effect), or competitive, decreasing the trait value in the co-twin (contrast effect; Carey, 1986 ). Cooperation effects increase the variance and decrease the covariance of MZ twins relative to DZ twins, while competition produces the opposite effects.

Correlated effects of genotypes and environments are difficult to detect. If not explicitly modeled, r GE between the latent A and E variables behave like additive effects, whereas r GE between the latent A and C variables acts like C.

Extensions to the Classical Twin Model

The classical twin model is the most basic twin model one can employ. There are many extensions available, the most basic of which is the incorporation of covariates to improve the estimation of phenotypic means. This allows for correction for effects such as age and gender, but also for effects of other variables that may confound the estimation of heritability. For example, if our trait of interest is cerebro-vascular disease in addition to age and gender, we may want to include smoking behavior as a covariate; if our trait of interest is education, we may want to include socio-economic status as a covariate.

Sex Limitation

Sex differences may obscure the data in different ways. Opposite-sex DZ twins can reduce the overall DZ twin covariance significantly if males and females differ greatly in their phenotypic values. Sex limitation refers to sex differences in the magnitude and/or proportion of the variance accounted for by genetic and environmental effects ( Neale & Maes, 2004 ). If twin pair correlations differ between the sexes within zygosity, it is better to estimate A, C, and E separately for males and females. Three types of sex limitation have been described: quantitative, qualitative, and scalar.

In the quantitative sex limitation model the genetic and environmental sources of variance and covariance in males and females are assumed to be the same (i.e., sex-specific pathways are fixed to zero) but the magnitudes of these effects are allowed to differ and the correlations for additive genetic and common environmental influences in the opposite-sex DZ pairs are assumed to be 0.5 and 1, respectively. If data from opposite-sex DZ twins have been collected, the difference in fit (χ 2 ) between this model and the qualitative sex limitation model can be used to examine whether the same genetic or environmental factors are influencing males and females ( Neale & Maes, 2004 ). Silventoinen et al. (2001 ) did this for height in two cohorts of twins (born in 1938-1949 and in 1975-1979) and found that the heritability estimates were higher among men ( h 2 = 0.87 in the older cohort and h 2 = 0.82 in the younger cohort) than women ( h 2 = 0.78 and h 2 = 0.67, respectively). Sex-specific genetic factors were not statistically significant in either cohort, suggesting that the same genes contribute to variation in body height for both men and women.

The hypothesis underlying qualitative sex limitation models is that different genetic or environmental factors influence trait variation in males and females. This model includes an extra genetic or environmental component ( m 2 ) that contributes to either males or females. Differences in both genetic and environmental effects cannot be tested simultaneously when working with twin and sibling data. Therefore, one would usually run this model twice; once specifying m 2 as an additive genetic parameter ( r = 0.5 for DZ twins) and once specifying m 2 as a common environment parameter ( r = 1 for DZ twins). Derks and colleagues (2007 ) found this to be the case for attention deficit-hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). The heritabilities for both ADHD and ODD were high and of a similar magnitude for boys and girls. However, the source of this genetic variation differed between boys and girls, indicating that some genetic loci may be having sex-specific influences on these traits.

The scalar sex limitation is the simplest and most restrictive of the three models. Here the absolute magnitude of the total variance, and thus the unstandardized variance components, differ between males and females while the proportion of variance accounted for by genetic and environmental effects, that is, the standardized variance components, are equal across sexes. In the scalar model not only are the sex-specific effects removed, but the variance components for females are all constrained to be equal to a scalar multiple ( k ) of the male variance components, such that a f 2   =   k a m 2 , , and e f 2   =   k e m 2 ( Neale & Maes, 2004 ). This model is a submodel of both the quantitative and qualitative sex limitation models, and can only be tested using continuous data, as variances are fixed to unity when working with ordinal data.

Normally you would test for the presence of sex limitation as part of testing a series of assumptions prior to fitting the ACE or ADE model. These assumptions include the equality of means and variances across zygosity and birth order, the equality of means and variances between twins and siblings, and the equality of means, variances, and covariances across the two sexes.

Liability Threshold Model

The classical twin design assumes that the trait of interest is a continuous variable, with a normal distribution. However, many traits that may be of interest are ordinal or dichotomous variables, such as medical or psychiatric diagnoses. For such variables, a liability threshold model can be used to estimate twin correlations and heritability. Threshold models assume that there is an underlying continuum of liability (e.g., to depression or ADHD) that is normally distributed in the population, and that our measurement categories (e.g., depressed/not depressed) result from one or more artificial divisions (thresholds) overlaying this normal distribution. Analyses are effectively performed on the underlying liability to the trait, resulting in estimates of the heritability of the liability. Figure 10.7 illustrates the threshold model. Panel A shows a model with a single threshold, separating persons into two classes, unaffected or affected, such as children with ADHD and controls. Panel B shows a liability threshold model with four thresholds (i.e., five categories), which could apply to a study of self-rated health, where the response categories were “very good,” “good,” “fair,” “poor", and “very poor” (e.g., Mosing etal., 2009 ). Liability to psychiatric disorders such as ADHD, depression, anxiety, and schizophrenia has been found to be influenced by genetic factors ( Hettema, Neale, & Kendler, 2001 ; Jepsen & Michel, 2006 ; Kendler, Gatz, Gardner, & Pedersen, 2006a , 2006b ; Sullivan, Kendler, & Neale, 2003 ; Sullivan, Neale, & Kendler, 2000 ), with heritability estimates of >70%.

The threshold model.

Notes: (a) Univariate normal distribution for dichotomous phenotype. One threshold is shown (at z-value +0.5) corresponding to 2 categories with the frequencies 69% and 31%. (b) Univariate normal distribution with thresholds distinguishing ordered response categories. Four thresholds are shown (at z-values −2.30, −1.70, −0.5, and +1) corresponding to 5 categories with the frequencies, 1%, 3%, 27%, 53%, and 16%.

Including Data from Additional Family Members

As briefly mentioned above, the classical twin design can be extended by including singleton (non-twin) siblings, parents, children, and spouses. Including additional family members substantially enhances the statistical power to detect non-additive genetic and common environmental influences resulting from a greater number of observed covariance statistics ( Posthuma et al., 2003 ). The power to detect common environmental influences is maximized when there are four times as many DZ pairs as MZ pairs ( Nance & Neale, 1989 ). As siblings have the same amount of genetic material in common as DZ twins (on average 50%), including data from extra siblings in the model effectively increases the DZ to MZ ratio. As discussed above, adding data from non-twin siblings makes it possible to test for twin-specific environmental influences. The variance and covariance of additional siblings are modeled in the same way as for a DZ twin (Fig. 10.8 ). If we were to include the data of one extra sibling the typical variance-covariance matrix would

Path diagram depicting the extended twin model.

Notes: P = phenotype; T1 = twin 1 of a pair; T2 = twin 2 of a pair; SIB = singleton sibling; MZ = monozygotic; DZ = dizygotic; A = additive genetic influences; C = common environmental influences; E = unique environmental influences; a = additive genetic path coefficient; c = common environmental path coefficient; e = unique environmental path coefficient. Circles represent latent, unobserved variables; squares represent observed phenotypes; single-headed arrows represent influences of latent variables on observed variables; double-headed arrows represent (co)variances.

be extended as shown in Matrix 10.3 . Additional siblings can be added in the same way. Variances are on the diagonal highlighted in the darkest shade of gray, the intrapair covariances are a shade lighter on the off-diagonal, and the twin–sibling covariances are highlighted in the lightest shade of grey on the outermost row and column of the matrix.

The extended twin family model or the nuclear family model also allows for the estimation of more parameters and relaxed assumptions regarding mating and cultural transmission. For example, adding parental data to the model makes it possible to estimate effects from assortative mating, familial transmission, sibling environment, and the correlation between additive genetic effects and family environment ( Keller et al., 2009 ), as well as allowing for the simultaneous estimation of C and D influences.

Another method allowing for the estimation of A, C, D and E in the same model is the twin adoption design. Here, twins raised apart (with no shared environmental influences) are compared to twins raised together. This design has a great explanatory power to facilitate separation of biological from environmental influences ( Medland & Hatemi, 2009 ). However, because of ethical and legal hurdles, twin adoption studies are increasingly difficult to conduct. Also, modern adoption policies facilitate twins being adopted together, rapidly decreasing the number of twins reared apart. Finally, there are other methodological factors that have to be taken into account, such as contact with the biological family, age of adoption, time spent in state care or protective custody, and selective placement (i.e., matching of the infants’ biological and adoptive environments), each of which may bias the sample. As a result of these caveats, which are hard to overcome, the twin adoption design is used only rarely and will not be explained here in further detail.

Multivariate Modeling

The twin model can also be extended to include multiple phenotypes. In the case of a multivariate design the aim is to decompose the covariance between traits into that caused by A, C, and E in the same way as one would with the phenotypic variance of a single trait. A multivariate design allows us to investigate the extent to which common sets of genes (genetic correlation, r g ), shared environmental factors (common environmental correlation, r c ) or unshared environmental factors (unique environmental correlation, r e ) underlie correlations between phenotypes. Matrix 10.4 shows a schematic representation of the variance/covariance matrix for a bivariate model. The corresponding path diagram is shown in Figure 10.9 .

The model in Figure 10.9 employs Cholesky decomposition (named after its developer Andre-Louis Cholesky) and can be extended in a similar way to include many more phenotypes. In linear algebra, the Cholesky decomposition or Cholesky triangle is a decomposition of a symmetric, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose. Cholesky decomposition or triangular decomposition, illustrated in Figure 10.9 for two variables, can represent a multivariate analysis of simultaneously measured variables considered in some rationally defined order of priority ( Loehlin, 1996 ; Neale & Maes, 2004 ). The first latent additive genetic variable (A1) explains the genetic influences on the first phenotype (P1) and the correlated genetic influences on the second phenotype (P2). The second latent additive genetic variable (A2) is uncorrelated with A1 and explains the remaining heritability of P2. Similar latent structures are estimated for E and C or D. From this basic model, parameters can be dropped or equated to test specific hypotheses regarding those parameters. The goal is to explain the data with as few underlying factors as possible, by testing which paths are significant (i.e., by setting the path coefficients to zero and noting whether this results in a significant decrease in model fit). For a review on how to use SEM programs to perform Cholesky decomposition please see, for example, Raykov, Marcoulides, and Boyd (2003 ).

Multivariate modeling can accommodate numerous variables, and can be used for both exploratory and confirmatory factor analysis, as well as longitudinal and causal analyses. It should be emphasized that the final results depend on the ordering—if we had considered the latent variables in the reverse order, A2 would be a factor with paths to both variables, and A1 a residual. Only in the case of uncorrelated variables is the order of selection irrelevant ( Loehlin, 1996 ), but in that case multivariate modeling should not be used anyway.

One example application of multivariate twin modeling is the use of this method to examine genetic contributions to the comorbidity between psychiatric disorders. More than a dozen studies have revealed a shared genetic vulnerability between anxiety and depression, particularly between major depressive disorder and generalized anxiety disorder ( see   Cerda, Sagdeo, Johnson, & Galea, 2010 , for a review).

Common Pathway Model

The common and independent pathway models can be considered submodels of the standard Cholesky decomposition. The common pathway model hypothesizes that the covariation between variables results from a single underlying “phe-notypic” latent variable. A frequent application of this model is the examination of symptom dimensions in complex, heterogeneous diseases. For example, van Grootheest et al. (2008 ) applied this model to obsessive-compulsive behavior and found that the three symptom dimensions—Rumination, Contamination, and Checking—share variation with a latent common factor, denoted obsessive-compulsive behavior. Variation in this common factor was explained by both genes (36%) and environmental factors (64%). Only the Contamination dimension was influenced by specific genes and seemed to be a relatively independent dimension. The results suggest that a broad obsessive-compulsive behavioral phenotype exists, influenced by both genes and unshared environment. However, the common pathway model, although it is conceptually attractive, often does not fit the observed data well because the amount of genetic and environmental variation transmitted from the latent factor is defined by the phenotypic correlation between the measured and latent variables ( Medland & Hatemi, 2009 ).

Path diagram depicting the bivariate twin model.

Notes: P1 + phenotype 1; P2 + phenotype 2; T1 + twin 1 of a pair; T2 + twin 2 of a pair; MZ + monozygotic; DZ + dizygotic; A1 + additive genetic influence 1; C1 + common environmental influence 1; E1 + unique environmental influence 1; A2 + additive genetic influence 2; C2 + common environmental influence 2; E2 + unique environmental influence 2; a + additive genetic path coefficient; c=common environmental path coefficient; e=unique environmental path coefficient. Circles represent latent, unobserved variables; squares represent observed phenotypes; single-headed arrows represent influences of latent variables on observed variables; double-headed arrows represent (co)variances.

Independent Pathway Model

On the other hand, the independent pathway model hypothesizes that the variance and covariance between the variables is expected to result from one (or sometimes two) common factor(s) with the residual variance reflecting variable-specific genetic and environmental effects. This is the case, for example, with cognitive domains and latency of event-related potentials ( Hansell et al., 2005 ; Luciano et al., 2004 ). Both the common and independent pathway models are nested within the previously described Cholesky decomposition. The fit of these models may therefore be compared to the “saturated model” using a likelihood ratio test, which is asymptotically distributed as x 2 with the degrees of freedom equal to the difference in the number of estimated parameters between the nested and saturated models.

Cross-Sectional Cohort and Longitudinal Designs

Once the role of genetic factors in the variance of a particular trait has been established, an additional question that can be addressed is whether the magnitude of these genetic influences is stable over time. Instead of a costly and time-consuming longitudinal study (which is another possibility; see below), this can be investigated with a cohort design , in which genetic and environmental estimates are obtained from different cohorts. In such a design, subjects from different age cohorts are assessed on one or more phenotypes. For example, Lyons et al. (1998 ) used a cohort design to examine the diagnosis of early-and late-onset major depression in men. Early-onset (before 30 years of age) and late-onset (after 30 years of age) major depression were both significantly influenced by genetic factors (early-onset: h 2 = 0.47; late-onset: h 2 = 0.10) and unique environmental factors (early-onset: e 2 = 0.53; late-onset: e 2 = 0.90), but early-onset major depression (95% CI: 0.32, 0.61) was significantly more heritable than late-onset major depression (95% CI: 0.01, 0.29). However, determining whether the same genes are involved at different stages of life is not possible with a cohort design. In addition, phenotypic differences resulting from age are confounded with any other differences between the cohorts.

With longitudinal twin data it is possible to estimate to what extent the relative contributions of genetic and environmental factors to the observed phenotypic variance are stable over time, and to what extent these genetic and environmental contributions are specific to a certain time of life. One use of the Cholesky decomposition is in temporal contexts ( Loehlin, 1996 ). For example, phenotypes P1 to P3 might represent measurements of a trait at three successive times. In this case, A1 would represent genetic influences present at time 1, affecting the observed trait at time 1 and on subsequent occasions; A2 would represent additional genetic influences that have arisen by time 2 and whose effects are added to those of A1; and, finally, A3 represents additional genetic influences, affecting only the third measurement (P3). To illustrate, studies on the heritability of cognitive abilities have repeatedly shown an increase in genetic influences and a decrease in common environmental influences over the life span ( Ando, Ono, & Wright, 2001 ; Bartels, Rietveld, van Baal, & Boomsma, 2002 ; Boomsma & van Baal, 1998 ; Luciano et al., 2001 ; Petrill et al., 2004 ; Plomin, 1999 ; Posthuma, de Geus, &Boomsma, 2001 ).

Increasing heritability over the life span could result from genes that are activated or become more active later in life, or may result from a decrease in the influence of environmental factors, as a result of which the relative contribution of genetic influences increases. Although it is possible to use a standard Cholesky decomposition for the purposes of a longitudinal study (as mentioned above), various longitudinal models have been described, including the genetic simplex model ( Boomsma & Molenaar, 1987 ; Eaves, Long, & Heath, 1986 ) and latent growth curve models ( Baker, Reynolds, & Phelps, 1992 ; McArdle, 1986 ; Neale & McArdle, 2000 ).

The genetic simplex model is based on the frequent observation that correlations are highest among adjoining occasions and that they fall away systematically as the distance between time points increases. Such a pattern is called a simplex structure after Guttman (1955 ). The genetic simplex design allows for modeling of changes in latent true scores over time by fitting autoregressive or Markovian chains. In autoregression each latent true score is predicted to be causally related to the immediately preceding latent true score in a linear fashion (linear regression of latent factor on the previous latent factor), while allowing for genetic/environmental change or innovation that is uncorrelated with the previous latent factor at each consecutive time point. Using this design Gillespie et al. (2004 ) were able to show that although female neuroticism shows a degree of genetic continuity, there are also age-specific genetic effects (genetic innovation), which could be related to developmental or hormonal changes during puberty and psychosexual development.

Growth curve models can be applied to assess the heritability of rate of change (increase or decrease) in a trait (e.g., cognitive abilities, brain volumes) throughout development. Reynolds et al. (2005 ) applied the growth curve model to a measure of cognitive abilities in adulthood. They examined sources of variability for ability level (intercept) and rate of change (linear and quadratic effects) for verbal, fluid, memory, and perceptual speed abilities. With the exception of one verbal and two memory measures, estimated variance components indicated decreasing genetic and increasing non-shared environmental variation over age, providing support for theories of the increasing influence of the environment on cognitive abilities with age.

Causal Models

When two correlated traits have rather different modes of inheritance (e.g., family resemblance is determined largely by family background, C, for one trait and by genetic factors, A or D, for the other trait), cross-sectional family data will allow for testing of unidirectional causal hypotheses ("A and B are correlated because A causes B” versus “because B causes A"), through the pattern of cross-twin cross-trait correlations ( Gillespie & Martin, 2005 ; Heath et al., 1993 ). This model makes it possible to model specific environmental risk factors. For example, proposing a twin-family model that incorporates childhood parental loss as a specific environmental risk factor, Kendler et al. (1996 ) examined how much of the association between childhood parental loss (through separation) and alcoholism was causal (i.e., mediated by environmental factors) versus non-causal (mediated by genetic factors, with parental loss serving as an index of parental genetic susceptibility to alcoholism). Both the causal-environmental pathway and non-causal genetic paths were significant for alcoholism. However, the causal-environmental pathway consistently accounted for most of the association, suggesting childhood parental loss is a direct and significant environmental risk factor for the development of alcoholism in women. De Moor et al. (2008 ) tested the hypothesis that exercise reduces symptoms of anxiety and depression, and found that although regular exercise is associated with reduced anxious and depressive symptoms in the population, the association is not because of causal effects of exercise.

Latent Class Analysis

Latent class analysis can be used to investigate whether distinct classes of disease subtypes can be identified, which can be used to refine genetic analyses. Using this approach, Althoff et al. (2006 ) were able to identify inattentive, hyperactive, or combined subtypes for ADHD based on the Child Behavior Check List. Latent class analysis allows for modeling of etiological heterogeneity in disease subtypes; for example, it compares a model that allows for genetic heterogeneity that is expressed only in individuals exposed to a high-risk “predisposing” environment (i.e., differential sensitivity of latent classes to measured covariates) with a model that allows the environment to differentiate two forms of the disorder in individuals of high genetic risk (i.e., GxE interaction; Eaves et al., 1993 ).

The genetic models described above and the related matrix algebra have been explained in more detail elsewhere, such as in Neale and Maes’ Methodology for Genetic Studies of Twins and Families (2004) . This book is downloadable free of charge at http://ibgwww.colorado.edu/workshop2006/cdrom/HTML/BOOK.HTM .

Twin Studies and Beyond

Twin studies have shown that almost every trait is heritable to some extent. Although the behavior genetics approach allows for the determination of the ratio of genetic and environmental influences, neither the number of genetic loci influencing a trait, nor the direction of these genetic effects, nor the location, nor identity of the loci can be determined with this approach. Thus, the next interesting step in genetic research is to identify specific genetic variants underlying the trait. Identification of specific genetic variants influencing complex traits provides knowledge about underlying biological mechanisms and identified genetic variants could potentially be used as biomarkers for screening, prevention, and medical treatment.

Linkage and candidate gene association studies were the first to search for underlying genetic variants. Linkage studies test for coinheritance of genetic markers and traits within families and are used to localize regions of the genome where a locus is harbored that regulates the trait. Candidate gene association studies test for a correlation between a specific genetic marker and the trait of interest in population samples. The markers tested generally have a known function that is hypothesized to influence the trait. Linkage and candidate gene studies have identified numerous potential regions and genes underlying complex traits, but they have not always been consistently replicated ( Bosker et al., 2011 ; Verweij et al.,2012 ).

Recent technological advances have enabled genome-wide association studies (GWAS), where single-nucleotide polymorphisms (SNPs) across the entire genome are systematically tested for association with the trait of interest. Genome-wide association studies do not take prior knowledge of gene function into account, so the approach is hypothesis-free. For complex traits, the results of GWAS are mixed. Genome-wide association studies have been successful in identifying genetic variants of large effect for a number of relatively rare disease traits ( Burton et al., 2007 ; Visscher & Montgomery, 2009 ). There have also been some successes in identifying many genetic variants of small effect underlying complex traits (i.e., schizophrenia, autism, and smoking; Liu et al., 2010 ; The International Schizophrenia Consortium, 2009 ; Wang et al., 2009 ).

Other technological advances, such as next-generation sequencing, assessment of copy number variation (CNV) and methylation rates will provide new opportunities. These approaches are promising, but only the future can tell us whether these methods will enable us to better unravel the genetic etiology of complex traits. A more in-depth description of linkage and association studies and their methodological background can be found in Chapter 11 .

Twin studies have contributed greatly to our knowledge about biological pathways. Although application of the twin model has revealed that almost every conceivable trait is partly genetically influenced, understanding the source of variance does not offer any indication of the number or location of genes influencing the trait. Twin studies provide one method of investigating the ongoing nature-nurture debate and are a very important and necessary first step in genetic analyses. In addition, multivariate twin analyses remain an important way to examine the nature and magnitude of covariation between traits and across time. Technological advances in both computational and laboratory techniques have led to the integration of variance component analyses with genetic information derived from DNA. The finding that a significant proportion of the variance in the trait of interest can be explained by genetic effects allows researchers to justify requesting funds to attempt to locate the genetic loci influencing the trait, as will be discussed in Chapter 11 .

Author Note

Gabriëlla A. M. Blokland, Miriam A. Mosing, Karin J. H.Verweij, Sarah E. Medland-Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, [email protected]

Althoff, R. R. , Copeland, W. E. , Stanger, C. , Derks, E. M. , Todd, R. D. , Neuman, R. J. , et al. ( 2006 ). The latent class structure of ADHD is stable across informants.   Twin Res Hum Genet , 9(4), 507–522.

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Behavioral Genetics, Population Genetics, and Genetic Essentialism

A Survey Experiment

• SI: genetics and identity
• Published: 04 November 2020
• Volume 29 , pages 1595–1619, ( 2020 )

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• Alexandre Morin-Chassé   ORCID: orcid.org/0000-0003-3519-870X 1  

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The paper presents an experimental study that examines the conditions required for news about behavioral genetics to activate genetic essentialism beliefs. Nine hundred sixty-five adults living in the USA were randomly assigned to read either a control news article or one of the three versions of a news story about behavioral genetics. The cautious version presents a general introduction to behavioral genetics and examples while also discrediting the genetic determinist myth and clarifying that this field is not interested in studying differences between populations. Another version was identical to the cautious version, except that it mentioned high heritability estimates as supporting evidence. Finally, a third version included claims supporting Nicolas Wade’s ( 2014 ) main thesis, which argued that societies develop different institutions partly because of their population’s behavioral genetic predispositions. Compared to participants in the control group, those exposed to the high heritability version and the Wade’s thesis version reported higher scores on a scale measuring belief in genetics determinism. The results revealed no overall effect for the cautious version, but an exploratory interaction model indicates that reactions to this version vary depending on educational attainment. Implications and limitations are discussed.

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Full texts in Electronic Supplementary Material.

Heritability estimates of similar sizes were found in Fowler et al. ( 2008 ), a study that received extensive media coverage.

Our study used the MTurk platform to recruit participants. This tool is often used to conduct psychology experiments, and has been validated elsewhere (Behrend et al. 2011 ; Berinsky et al. 2012 ; Casler et al. 2013 ; Johnson and Borden 2012 ).

Unfortunately, it is impossible to conduct a multiple imputation model for these missing observations because those participants abandoned the study at the beginning of the questionnaire, when they were exposed to the reading task. As such, they left the survey before answering any question item. In the Electronic Supplementary Material, Table A1 shows that there is no significant difference in drop-out rates across experimental groups.

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Acknowledgements

Thanks to André Blais, James H. Fowler and Peter Loewen for their support at different stages of this project. Thanks also to Aaron Panofsky, Elizabeth Suhay, Maya Sabatello and Paul S. Appelbaum for their insightful feedback.

This study received funding from the Department of Political Science at the University of California, San Diego.The author benefited from a PhD scholarship from the Fonds Québécois de Recherche – Société et Culture at the time this research was conducted.

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Morin-Chassé, A. Behavioral Genetics, Population Genetics, and Genetic Essentialism. Sci & Educ 29 , 1595–1619 (2020). https://doi.org/10.1007/s11191-020-00166-y

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Evolutionary behavioral genetics

Brendan p. zietsch.

1 School of Psychology, University of Queensland, Brisbane, Queensland, Australia

2 Genetic Epidemiology Laboratory, QIMR Berghofer, Brisbane, Queensland, Australia

Teresa R de Candia

3 Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, United States of America

4 Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America

Matthew C. Keller

Associated data.

We describe the scientific enterprise at the intersection of evolutionary psychology and behavioral genetics—a field that could be termed Evolutionary Behavioral Genetics —and how modern genetic data is revolutionizing our ability to test questions in this field. We first explain how genetically informative data and designs can be used to investigate questions about the evolution of human behavior, and describe some of the findings arising from these approaches. Second, we explain how evolutionary theory can be applied to the investigation of behavioral genetic variation. We give examples of how new data and methods provide insight into the genetic architecture of behavioral variation and what this tells us about the evolutionary processes that acted on the underlying causal genetic variants.

Although both evolutionary psychology and behavioral genetics arose in the 1970s as attempts to integrate the study of human behavior with other branches of biological science, the two fields have largely developed in isolation. Evolutionary psychology has primarily focused on using evolutionary theory to explain species- or sex-typical behavioral features—why people tend to find particular traits appealing in romantic partners or friends, for example. Behavioral genetics, on the other hand, has primarily focused on understanding proximate causes of variation among individuals—to what extent genetic and environmental influences are responsible for behavioral differences between individuals, and which specific genetic polymorphisms or environmental factors are responsible. The purpose of this manuscript is to describe the scientific enterprise at the intersection of evolutionary psychology and behavioral genetics—a field that could be termed Evolutionary Behavioral Genetics —and how modern genetic data is revolutionizing our ability to test questions in this field. We first describe how methods and designs developed in behavioral and statistical genetics can be profitably applied to evolutionary psychology and the study of human ‘universals.’ Second, we explain how evolutionary theory can be applied to the investigation of human behavioral genetic variation and give examples of the types of designs and research findings that provide evidence for competing evolutionary models.

Using behavioral genetic methods to test hypotheses in evolutionary psychology

Evolutionary psychologists have often viewed genetic variation as “noise in the system” and assumed that heritability in traits relevant to reproductive success would be close to zero [ 1 ]. However, genetic variation is ubiquitous in animals, even for traits under strong selection [ 2 ], and this is no different in humans [ 3 ]. Virtually no psychological traits that vary have a near-zero heritability—including traits that are likely to be related to ancestral fitness [ 3 – 6 ]. Because evolutionary hypotheses and alternative explanations often make predictions or assumptions about the genetic variation in and covariation between traits, analyses of genetic (co)variation can be extremely helpful in testing hypotheses about how human features evolved. We highlight below several areas in which behavioural genetic data and designs have helped in testing hypotheses in evolutionary psychology.

Genetic correlation between traits

In addition to demonstrating and quantifying heritability of individual traits, behavioural geneticists often examine whether the same genes influence different traits by modelling the genetic correlation between traits. For example, sexual selection is thought to have influenced the evolution of certain human features. Given heritable variation in traits and trait preferences, this hypothesis predicts a genetic correlation between preferences for a given feature and the expression of that feature itself [ 7 , 8 ]. This is because individuals with stronger-than-average preference for a certain trait will tend to choose a mate with above-average values of that trait, with the resulting offspring tending to inherit alleles predisposing to both higher-than-average trait and higher-than-average preference. This co-inheritance leads to linkage disequilibrium between alleles influencing the preferences and those influencing the trait, which manifests as a genetic correlation between the trait and the preference. Multivariate twin analyses have shown that genetic correlation between a trait and its preference applies to several traits of interest in humans (including height, hair colour, intelligence, and creativity) [ 9 ], consistent with an influence of sexual selection on these traits.

‘Good genes’ models of sexual selection also predict that traits that serve as good genes indicators will tend to be positively genetically intercorrelated because each trait is an imperfect index of the same underlying “mutation load” [ 10 ]. In other words, for traits to be accurate indicators of mutational loads, many genes must influence them, which causes overlaps in their genes (pleiotropic genes) and hence genetic correlations between them. However, genetic correlations between sexually selected traits can also arise via linkage disequilibrium due to cross-trait assortative mating (mates choosing simultaneously on a number of indicators, as described in previous section, above). The relative importance of these alternative explanations for genetic correlations can be quantified using an extended twin-family designs [ 11 – 13 ], which has indicated that both pleiotropy and cross-trait assortative mating are roughly equally important in causing the genetic correlation between height and intelligence [ 14 ], two traits that are potential good genes indicators. Additional traits need to be tested in a similar way to understand the generality of this conclusion.

Cross-sex genetic correlation

Evolutionary hypotheses about the origin of sexual dimorphism often make predictions about cross-sex genetic correlations– that is, the extent to which the same or different genes influence a trait in males and females. An example pertains to the evolutionary basis of facial sexual dimorphism. The predominant hypothesis in evolutionary psychology is that male facial masculinity is a good genes indicator such that women can increase the quality of their offspring by choosing a facially masculine mate [ 15 , 16 ]. However, genetic analyses suggest that the genes that make male faces masculine do not improve male attractiveness but do make female relatives’ faces more masculine and less attractive, casting doubt on the good genes theory of male facial masculinity [ 4 , 17 ].

Cross-population genetic correlation

New methods allow testing genetic correlations using samples of unrelated people with measured genotypes [ 18 ]. Importantly, this enables testing genetic correlations between traits that are measured in different individuals. How might this be used to inform evolutionary questions? Standard twin analyses have shown in a Swedish population that variation in fitness (both first and second generation reproductive success) is substantially heritable [ 19 ], but it is impossible with this type of analysis to determine to what extent the genes that affect fitness in Sweden are the same or different from those that affect fitness in small-scale, natural fertility traditional societies that are more similar to our ancestral circumstances. However, this could in principle be tested with large genotyped samples of Western and traditional societies, which would shed light on the genetic differences between modern and ancestral fitness.

Controlling genetic and familial confounds

Another function of genetically informative designs is to provide crucial controls for genetic and familial confounds in tests of evolutionary hypotheses. For example, it has been hypothesised that father absence causes early physical and behavioural sexual maturation (age-of-menarche, age at first intercourse) because of an evolved mechanism that strategically calibrates development to the riskiness of the environment [ 20 ]. However, Mendle et al. [ 21 , 22 ] showed that these effects were not present when familial (including genetic) confounds were controlled using the children-of-twins design: cousins discordant for father absence showed no differences in sexual maturation. This finding is inconsistent with the evolved mechanism, but consistent with genetic or environmental factors that both predispose fathers to leave the family unit and predispose daughters to early sexual maturation. This and many other evolutionary hypotheses involving the effects of childhood environmental factors (e.g. low socioeconomic status) on later behaviour (e.g. adult risk-taking [ 23 ]) continue to be tested without controlling for genetic and familial confounds, and their conclusions generally suffer from similar (often unacknowledged) alternative explanations.

Evolution of human behavioral genetic variation

In the previous section we described how behavioural genetics methods can inform evolutionary hypotheses about species- or sex-typical human behavioural features. However, the existence of underlying genetic variation itself also requires evolutionary explanation. In this section we focus on how to investigate the evolutionary bases of genetic variation in behaviour, and some of what we have learned thus far.

The observation of pervasive genetic variation in fitness related traits is at odds with the traditional interpretation of Fisher’s Fundamental Theorem [ 24 ]. Explaining the evolutionary basis of such widespread genetic trait variation has been a central question in biology for decades [ 25 ], but, in part due to the rapid advances in technology, this question has only recently drawn significant attention in psychology and psychiatry. As long understood, there are three basic evolutionary processes that can explain the existence of genetic variation in complex traits [reviewed in 26 ]. The first is mutation-selection balance: genetic variation is the consequence of a balance between deleterious mutations arising at many loci and their eventual removal by purifying selection. The second mechanism is neutral mutation-drift : genetic variation is the balance between mutations arising at many loci that have no (or nearly no) effect on net fitness, and their eventual (albeit typically much later) removal or fixation due to chance or “drift.” The final mechanism, balancing selection, is actually a group of processes, all of which involve genetic variation being actively maintained by selection because the relative fitness of alternative genetic variants depends on variable environmental or genetic contexts.

These three evolutionary processes make different predictions about the genetic architecture of traits—i.e. the number of causal variants (CVs—the genetic polymorphisms that cause trait differences), the distributions of their frequencies and effect sizes, and their interactions between and within loci. In the following sections, we briefly review some examples of what we have learned about the genetic architectures of human behavioral phenotypes, and describe what this evidence tells us about the evolutionary forces that acted on their CVs. We use schizophrenia as an example throughout because it is perhaps the most intensively studied behavioral trait in genetics, but the methods involved should apply equally to other traits as data continues to accumulate for them.

The direction of dominance of genetic causal variants (CVs)

Purifying selection is less efficient at eliminating recessive or partially recessive deleterious alleles compared to additive or dominant deleterious alleles, since the former are “hidden” from selection when heterozygous. As a result, deleterious alleles that have not (yet) been eliminated by purifying selection tend to be more recessive than would be expected due to chance. This phenomenon, where the deleterious alleles tend to be more recessive and the fittest alleles more dominant, is called directional dominance and can be used to infer selection [ 27 ]. For example, if CVs that decrease a trait tend to be more recessive than those that increase a trait, one can infer that trait-decreasing CVs were selected against on average over evolutionary time. Because inbreeding between close genetic relatives increases the likelihood that recessive CVs will be expressed in offspring, this phenomenon has long been studied by cataloguing the traits for which inbred individuals have higher or lower average trait values [ 28 ]. However, inbreeding studies using human pedigrees are difficult to conduct and suffer from alternative explanations, including the possibility that individuals who mate with close relatives may differ genetically or environmentally from other individuals and these differences may influence their offspring.

Recently, several studies [ 6 , 9 , 29 – 34 ] have used single nucleotide polymorphisms (SNPs) measured at hundreds of thousands of locations across the genome to detect very small individual differences in ‘distant’ inbreeding (arising from common ancestors who lived 10’s of generations ago) among samples unselected for inbreeding. This is done by measuring the genome-wide burden of runs of homozygosity [ 35 ]. Because variation in the overall burden of such runs of homozygosity is small in samples unselected for inbreeding, sample sizes typically need to be large (e.g., > 10K–20K) to reliably detect associations with traits [ 36 ]. Using a large ( n ~21K) schizophrenia case-control sample, we found that total burden of runs of homozygosity is reliably but weakly associated with schizophrenia [ 37 ]. This finding suggests that, on average , CVs that increase schizophrenia risk are more recessive than expected by chance and therefore are likely to have been selected against over evolutionary time.

The number, effect sizes, and frequencies of causal variants

The findings from large-scale linkage and genome-wide association studies on a variety of complex behavioral traits (personality, psychiatric disorders, cognitive abilities, etc.) tell a consistent story: complex traits are affected by a huge number of CVs (e.g., hundreds to thousands), each of which generally explains only a miniscule amount of the phenotypic variation. Thus, findings are turning out to be roughly consistent with the so-called “infinitesimal model” developed by Fisher nearly a hundred years ago [ 38 ].

Figure 1 (see also [ 39 , 40 ]) shows a strong inverse relationship between the effect sizes of all genetic variants reliably associated with schizophrenia to date and their frequencies (which includes the largest schizophrenia GWAS conducted to date, N ~80,000 [ 41 ]). The variance accounted for by a particular allele is proportionate to 2 p (1− p )ln( OR ) 2 , where p is the minor allele frequency and ln(OR) is the effect size (log odds ratio) of the risk allele. The dashed red line in Figure 1 plots the effect size/allele frequency combinations of hypothetical loci that would each explain 0.05% of the phenotypic variation. The close fit of this line with the observed associated variants suggests that each of the reliably associated schizophrenia risk variants accounts for around five hundredths of one percent of the variation in the trait; the many more variants that have yet to be detected probably each account for this amount of variation or less (region in grey).

The relationship between the effect sizes (natural log of odds ratios) and minor allele frequencies of all genetic variants (copy number variants [CNVs] or single-nucleotide polymorphisms [SNPs]) reliably associated with schizophrenia. The dotted red line defines a constant variance explained of 0.05% assuming a 1% prevalence of schizophrenia. The lack of data points between SNPs and CNVs occurs because no common CNVs are known to be associated with schizophrenia and because SNP panels do not measure variants with minor allele frequencies under 1%. Sequencing data may fill in this gap. There is adequate statistical power to detect only those variants with the largest effect sizes (near the dotted red line), although the entire shaded region is expected to be populated with schizophrenia risk alleles. The lack of variants in the unshaded region is consistent with a purifying selection model on schizophrenia risk alleles.

What does this tell us about the evolutionary forces acting on schizophrenia CVs? The inverse relationship between schizophrenia CVs’ effect sizes and frequencies, and the fact that no single variant explains much heritability, conform to expectations under mutation-selection balance, where purifying selection is removing deleterious mutations. Under purifying selection, deleterious CVs with large effects will be selected against the strongest and therefore be rare, whereas risk alleles with small effects may be nearly neutral and can drift to higher frequencies. In either case, because of this tradeoff between frequency and effect size, no single allele can account for much population variation. Such an inverse relationship between alleles’ effect sizes and frequencies is not expected under neutral mutation-drift or balancing selection.

The allelic spectrum of causal variants

The allelic spectrum of a trait refers to the distribution of a trait’s genetic variance accounted for by all the CVs in each allele frequency bin. Under a neutral-drift model, effect sizes should be uncorrelated with allele frequencies, and the allelic spectrum should be uniform, such that each CV frequency bin accounts for an equal proportion of variance [ 42 , 43 ]. In contrast, modeling suggests that balancing selection maintains variants at intermediate frequencies, so the allelic spectrum of CVs under balancing selection should be shifted toward minor alleles of higher frequencies [ 44 , 45 ]. Finally, under a mutation selection model, the allelic spectrum should be shifted toward minor alleles of lower frequencies as previously explained.

A recent and highly influential method gives accurate estimates of the additive genetic variation explained by all SNPs together even though the true effect at each specific SNP remains unknown [ 46 ]. Although SNPs themselves are probably often not the true CVs, SNPs tend to best predict nearby CVs that are similar in frequencies [ 47 ]. Because this method has been up to now used only on SNPs that exist on modern SNP panels, and because SNP panels have virtually no information on rare (minor allele frequencies < .01) SNPs, resulting estimates give an idea of the cumulative importance of additive common CVs but are blind to the importance of rare CVs.

By comparing additive genetic variance estimates from this method, which estimates only the effects of common CVs, to those from traditional family-based studies, which estimates the effects of both rare and common CVs, scientists have gained their first insights into the relative importance of common versus rare CVs. This method has been used on a large number of behavioral traits in the last several years, and between one-tenth to one-half of total additive genetic variation estimated from family-based studies appears to be due to the additive effects of (mostly common) CVs tagged by common SNPs [ 6 , 48 – 53 ]. While family-based estimates of additive genetic variation may be inflated [ 54 ], as long as they are roughly correct, these findings are consistent with much of the remainder of the additive genetic variation being due to rare CVs. If so, substantially more variation would be due to rare CVs than expected under the uniform distribution of CV allele frequencies predicted by neutral drift (i.e., 99% of additive genetic variance explained by CVs with risk allele frequency >.01) [ 42 ]. Nevertheless, a simple model of strong purifying selection on all CVs would predict that no CVs should be common; the evidence that common CVs do in fact influence schizophenia suggests that many schizophrenia CVs are under weak purifying selection or are drifting neutrally. This observation, and the potential for rare CVs to explain much of the remaining additive genetic variation not tagged by SNPs, is again potentially consistent with a model of purifying selection of varying strength: CVs of small effect are under weak to non-existent purifying selection and drift to high frequencies whereas CVs of larger effect are under increasingly strong purifying selection and kept rare because of it ( Figure 1 ).

Finally, although we have argued that much of the remaining variation in traits that has not been explained by SNPs is likely to be due to rare CVs, there are several alternative explanations for the discrepancy. For example, it is possible that family studies have over-estimated additive genetic variation, meaning that little additive genetic variation remains to be explained and that rare variants thereby account for little trait variation. Forthcoming methods that use whole-genome sequencing data or shared identical-by-descent haplotypes, both of which can measure or tag rare CVs, should be able to put the rare variant debate largely to rest by directly estimating the importance of rare CVs.

Future directions in understanding the evolutionary basis of genetic variation in behavior

We have presented evidence from schizophrenia that is generally consistent with underlying CVs on average being under purifying selection and their frequencies being maintained by mutation-selection balance. Findings on human personality [ 6 ] and other behavioral traits appear generally consistent with this, although datasets are smaller and conclusions more tentative. However, the substantial proportion of variation accounted for by common CVs suggests that the highest frequency/smallest effect CVs may be selectively neutral or nearly neutral. These findings are not contradictory. It is important to recognize that the mutation-selection and the neutral mutation-drift models are not qualitatively distinct; they exist on the same continuum defined by the strength of purifying selection. To date, there is no convincing evidence that balancing selection plays an important role in maintaining the genetic variation in behavioral traits, and outside of the MHC region, genome-wide scans suggest a limited role for balancing selection in general [ 55 – 57 ]. Nevertheless, absence of evidence does not necessarily equate to evidence for absence, and future findings could challenge this conclusion.

Large whole-genome sequencing datasets will greatly expand our ability to understand the importance of rare variants in complex traits and inform our understanding of the evolutionary processes involved in maintaining traits’ genetic variation. Nevertheless, attempting to understand the evolutionary roots of genetic variation in traits will remain inherently difficult because selection acts on total ‘net fitness’ rather than fitness with respect to any given trait. Given that CVs may often affect multiple traits simultaneously and that many CVs affect any given trait, a trait’s CVs may often be under many different types and strengths of selection. As such, future progress is likely to involve multivariate analyses that compare the characteristics (directional dominance, effect size, allelic spectrum) of CVs that affect multiple traits in the same or opposite directions with respect to fitness.

The promise of evolutionary behavioral genetics

In this article we have given an abbreviated overview of the conceptual and methodological bases of research at the intersection of evolutionary psychology and behavioral genetics, as well as a sample of the findings in this still nascent field. We have mentioned contributions of evolutionary behavioral genetics to our understanding of mate preferences, sexual dimorphism, sexual maturation, reproductive success, personality, and schizophrenia, but of necessity omitted important research on other traits [ 58 – 63 ]. We have tried to convey some of the depth and breadth of the possibilities afforded by these approaches and hope that this might spur others to adopt these approaches in testing hypotheses in evolutionary psychology and behavioral genetics.

Purifying selection removes alleles (generally rare mutations) with lower fitness in favor of one or more alternate alleles with higher fitness.

Linkage disequilibrium refers to the statistical relationship between alleles at different loci (positions in the genome).

Heritable trait variation is that due to genetic variation. Heritability refers to the proportion of trait variation that can be attributed to genetic factors.

Genetic correlation refers to the proportion of total genetic variation in two traits that is shared due to genetic factors.

Sexual selection refers to a mode of natural selection in which certain alleles are favored over others because of their effects on attracting mates rather than survival.

Alleles are alternative versions of genetic variants at a given locus.

Mutation load refers to an individual’s aggregate burden of deleterious mutations (rare alleles) across the genome, which is heritable across generations.

Good gene indicators are traits that reflect underlying genetic fitness, e.g. low mutation load.

Pleiotropic genes influence more than one trait.

Cross-trait assortative mating occurs when two different traits correlate across mates, e.g. males of above-average height mating with females of above-average intelligence.

Extended twin-family designs take advantage of the genetic relatedness between multiple family members e.g. twins, their spouses, and their parents, in order to investigate the importance of environmental and genetic influences on one or multiple traits.

Sexual dimorphism refers to the difference between male and female phenotypes.

Fisher’s Fundamental Theorem states that “the rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time.” It has often been interpreted to mean that additive genetic variation should be low in traits related to fitness.

Phenotypes are observable characteristics or traits of an organism.

Recessive/additive/dominant refer to how likely an allele is to be expressed in the phenotype. At a diallelic locus, a fully recessive allele will not be expressed unless both copies are present, while the fully dominant allele will be fully expressed with only one copy. Many dominance relationships are partial rather than full, yielding a spectrum of dominance or recessivity. Additivity is intermediate between fully recessive and fully dominant.

SNP (single nucleotides polymorphism) is a type of allele where a single nucleotide position is variable in the population. Often, “SNP” is used for loci where the minor allele frequency is > 1% and “mutation” when the minor allele frequency is < 1%.

Homozygosity occurs when two copies of the same allele are present at a locus, as opposed to heterozygosity, in which the two alleles at a locus are different. Runs of homozygosity are stretches of contiguous SNPs (e.g. 60+) that are consistently homozygous along some stretch of an individual’s genome.

Linkage studies test for coinheritance of alleles and traits within families. They are less powerful for detecting the effects of common causal variants than genome-wide association studies but can potentially discover regions where large-effect, rare causal variants occur.

Genome-wide association studies test for associations between each of hundreds of thousands of SNPs across the genome and one or more traits, Very large sample sizes are required to detect the small effect sizes that appear to be the norm for complex traits.

An allele frequency bin includes only alleles within a fixed-size range of frequencies.

The minor allele at a given locus is the allele that is less common in the population, and for SNPs, there are usually two alleles. The minor allele frequency is the frequency of the least common allele at a locus.

A Causal Variant (CV) is an allele that influences a trait

CVs are tagged by measured SNPs to the extent that they are in linkage disequilibrium, and therefore statistically correlated, with them.

Whole-genome sequencing provides data for the complete sequence of DNA for an individual, including all frequency classes of alleles (including unique alleles).

Supplementary Material

Acknowledgments.

The authors thank Dr. Patrick Sullivan for sharing the CNV effects that are included in Figure 1 . This work was supported by National Institutes of Mental Health grants K01MH085812 and R01MH100141 to Dr. Keller and an Australian Research Council Discovery Early Career Research Award (DE120100562) to Dr Zietsch.

Financial Disclosures.

The author reports no biomedical financial interests or potential conflicts of interest.