{"id":416,"date":"2026-08-22T14:51:53","date_gmt":"2026-08-22T14:51:53","guid":{"rendered":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/?post_type=chapter&p=416"},"modified":"2026-08-24T17:44:33","modified_gmt":"2026-08-24T17:44:33","slug":"behavioral-genetics-and-addiction-relevant-traits","status":"publish","type":"chapter","link":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/chapter\/behavioral-genetics-and-addiction-relevant-traits\/","title":{"raw":"Behavioral Genetics and Addiction-Relevant Traits","rendered":"Behavioral Genetics and Addiction-Relevant Traits"},"content":{"raw":"
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Reading Objectives<\/h2>\r\nBy the end of this chapter, you should be able to:\r\n
    \r\n \t
  1. Distinguish externalizing and internalizing pathways associated with addiction risk.<\/li>\r\n \t
  2. Compare family, adoption, classical twin, and discordant-MZ study designs.<\/li>\r\n \t
  3. Explain how mental health and neurocognitive measurements become family-linked phenotypes.<\/li>\r\n \t
  4. Interpret paired scatterplots and compare MZ and DZ correlations.<\/li>\r\n \t
  5. Use Falconer equations to approximate and interpret A, C, and E.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/div>\r\n
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    Key Terms<\/h2>\r\nDNA \u00b7 gene \u00b7 genetics \u00b7 exposure \u00b7 phenotype \u00b7 externalizing \u00b7 internalizing \u00b7 familial aggregation \u00b7 monozygotic (MZ) twins \u00b7 dizygotic (DZ) twins \u00b7 zygosity \u00b7 cotwin resemblance \u00b7 paired observation \u00b7 additive genetic variance (A) \u00b7 shared environmental variance (C) \u00b7 nonshared environmental and residual variance (E) \u00b7 Falconer equations \u00b7 heritability \u00b7 discordant-MZ design\r\n\r\n<\/div>\r\n \r\n

    1. Introduction<\/h2>\r\nIn the marshmallow test<\/strong>, you place one marshmallow in front of a young child and offer a choice: eat it now, or wait and receive two marshmallows later. This is the basis of a well-known study designed to assess delayed gratification. Waiting longer has also been studied as a predictor of a range later-in-life outcomes, such as impulsive tendencies (Mischel et al., 1989). However, it should be noted that more recent research has found that associations between waiting and subsequent outcomes become smaller after accounting for family and developmental conditions (Watts et al., 2018).\r\n\r\nhttps:\/\/www.youtube.com\/watch?v=2xMgHKxukr0\r\n\r\n \r\n

    Externalizing vs Internalizing<\/h3>\r\nImpulsivity<\/strong> is a construct, which we defined in Chapter 2 as an abstract and unobservable concept used to describe human mental processes, traits, or behaviors; researchers must infer these tendencies from surveys, behavioral tasks, observations, and patterns across situations. The construct of impulsivity includes acting without sufficient forethought, favoring immediate rewards, or having difficulty stopping a response.\r\n\r\nImpulsivity is categorized within the broader construct of externalizing<\/strong>. Externalizing refers to outwardly expressed patterns of behavioral disinhibition. These patterns can include impulsivity, sensation seeking, rule-breaking, aggression, oppositional behavior, and difficulty controlling actions. A contrasting pathway is internalizing<\/strong>, which refers to distress that is directed inward, including persistent anxiety, depressed mood, worry, withdrawal, and negative affect. They exist on continua. A person can show relatively more or less of these tendencies without having a behavioral disorder.\r\n\r\n[caption id=\"attachment_109\" align=\"aligncenter\" width=\"268\"]\"Horizontal Figure 4.1. Behaviors Exist on Continuums, Rather Than Binary Yes\/No Diagnosis. There is Always a \"Grey Area.\"[\/caption]\r\n\r\nExternalizing and internalizing are two classic pathways to substance use and addiction. For externalizing, young people who are highly sensation seeking or behaviorally disinhibited may begin experimenting with substances earlier, enter more high-risk situations, or have greater difficulty limiting use once it begins. This does not mean that impulsivity inevitably produces addiction. It means that externalizing tendencies can increase risk when they combine with substance availability, peer contexts, stress, and other conditions (Iacono et al., 2008). In contrast, a person experiencing internalizing problems may use alcohol or other substances to reduce distress, escape unwanted thoughts, or feel temporary relief. This is sometimes described as a coping or self-medication pathway (Hussong et al., 2011).\r\n\r\nExternalizing and internalizing are not mutually exclusive boxes. The same person may experience both behavioral disinhibition and emotional distress. The pathways can also intersect. For example, negative urgency<\/strong> refers to a tendency to act rashly while experiencing intense negative emotion. It connects an internalizing condition, emotional distress, with an externalizing response, impulsive action.\r\n\r\nThese pathways describe patterns of risk, not predetermined routes to addiction. Most people with internalizing or externalizing tendencies do not develop a substance use disorder. Social relationships, opportunities, stressors, protective resources, substance exposure, and developmental changes all influence whether and how risk is expressed.\r\n

    Where do these differences come from?<\/h3>\r\nWhy do people differ in impulsivity, externalizing behavior, internalizing distress, and other behavioral traits and characteristics? Chapters 1 through 3 examined how constructs are measured and how social and environmental conditions contribute to different developmental pathways. This chapter introduces another part of the question: whether inherited biological differences also contribute to variation among people.\r\n\r\nFour terms are necessary for asking this question.\r\n\r\nDNA<\/strong> is the molecule that stores biological information in cells. People receive DNA from both biological parents. A gene<\/strong> is a region of DNA involved in producing or regulating a biological function. Most human DNA is shared across people, but differences in DNA sequences contribute to biological variation. There is no single gene for impulsivity, internalizing, or addiction. These complex outcomes reflect many genetic differences operating through development and in combination with lived conditions.\r\n\r\nA phenotype<\/strong> is an observed or derived characteristic that researchers measure and compare. Phenotypes can be physical or behavioral characteristics, like height or introversion and extroversion. Calling something a phenotype does not mean that it is fixed, purely genetic, or directly observable without measurement.\r\n\r\nGenetics<\/strong> is the study of DNA, inheritance, and biological variation. When researchers ask whether a phenotype is genetically influenced, they are not asking whether genes determine an individual\u2019s future. They are asking whether inherited differences help explain why measurements of that phenotype vary among people in a particular population and context.\r\n\r\nAn exposure<\/strong> is a condition, experience, or behavior whose possible relationship with an outcome is being studied. The term does not refer only to toxic chemicals. Family conditions, peer relationships, stress, neighborhood environments, and access to substances can all be exposures. Alcohol or cannabis use can also be treated as an exposure when researchers ask whether sustained use is associated with later life outcomes.\r\n\r\nIn many cases, people who share DNA often share exposures, like families and environments. Inherited tendencies may affect how people respond to the same exposure or which situations they encounter. Exposures may strengthen, weaken, or redirect the expression of inherited differences. The measurement process also affects the phenotype that researchers ultimately analyze. This makes the motivating question difficult:<\/span>\r\n
    How can researchers investigate whether genetic differences contribute to variation in externalizing, internalizing, and other addiction-related phenotypes when biological relatives also share important exposures and environments?<\/blockquote>\r\nFamily, adoption, and twin studies approach this problem by comparing people who differ in their expected genetic relatedness and rearing relationships. This chapter examines the logic and limitations of those comparisons. It then traces how family-linked observations become measured phenotypes, paired records, correlations, and estimates of genetic and environmental variation. We do not use a \"nature vs nurture\" framework, as we assume an approach that how genes and environments act together to lead to complex outcomes like addiction.\r\n

    2. From Family Resemblance to Genetically Informative Designs<\/h2>\r\nBehavioral genetics is a branch of science that studies the influence of genetic and environmental factors on behaviors. In the context of addiction, behavioral genetics helps identify why some individuals are more susceptible to substance use disorders than others. Foundational study designs include family, adoption, classical twin, and discordant-twin studies; each design addresses a different question, depends on different assumptions, and may represent a different population (Willoughby, Polderman, & Boutwell, 2023).\r\n

    2.1 Family studies: detecting familial aggregation<\/h3>\r\nA family study asks whether a diagnosis, behavior, or measured trait occurs more often, or is more similar, among relatives than would be expected in the broader population. Researchers might compare rates of alcohol use disorder among the relatives of people with and without the disorder, or examine whether parent and youth scores on addiction-relevant traits are associated.\r\n\r\nWhen elevated risk or resemblance occurs within families, the outcome is said to show familial aggregation<\/strong>. Alcohol and other substance use disorders, including cannabis use disorder, have repeatedly been found to aggregate within families (Merikangas et al., 1998). This pattern can be scientifically and clinically informative. For example, a reported family history of alcohol or cannabis problems may help identify young people with elevated risk who could benefit from additional assessment or prevention resources.\r\n\r\nFamilial aggregation does not reveal why relatives resemble one another. Biological relatives may share inherited genetic variation, but they may also share housing, resources, stressors, substance availability, cultural practices, and patterns of family interaction. Relatives can also influence one another\u2019s behavior. A family-history measure further depends on what the respondent knows, remembers, and is willing to report. It is therefore an imperfect indicator of familial liability, not a genetic measurement or evidence that an outcome is inherited.\r\n

    2.2 Adoption studies: separating some genetic and rearing relationships<\/h3>\r\nAdoption studies introduce a different comparison. An adopted person shares inherited genetics with biological relatives without sharing a household. Researchers can therefore compare resemblance with biological and adoptive relatives to investigate whether a trait follows genetic relationships, adoptive relationships, or both. However, this comparison remains observational. The design remains observational and is limited by prenatal influences, selective placement, later contact, and the limited representativeness of many adoption samples.\r\n

    2.3 Classical twin studies: comparing degrees of genetic relatedness<\/h3>\r\nThe classical twin design compares monozygotic (MZ, or identical twins)<\/strong> and dizygotic (DZ, or fraternal twins)<\/strong> twin pairs. MZ twins develop from the same fertilized egg and share nearly all their inherited DNA. DZ twins develop from separate fertilized eggs and share, on average, approximately half of their genes, the same expected proportion as ordinary biological siblings. Both sets of twins are the same age and commonly share a household, caregivers, schools, communities, and many other features of early development.\r\n\r\n \r\n\r\n[caption id=\"attachment_98\" align=\"aligncenter\" width=\"720\"]\"Diagram Figure 4.2. Identical (monozygotic) and fraternal (dizygotic) twins differ in how they develop from fertilized eggs. Source: National Human Genome Research Institute (NIH). Public domain.[\/caption]\r\n\r\nIn twin studies, researchers measure the same observed traits in both members of each twin pair. Next, they estimate how strongly cotwins resemble one another. For example, imagine that MZ twins show strong consistency in smoking cannabis, if one MZ twin is a frequent smoker, then the other is too; and we find little consistency in cannabis smoking among DZ twins, if one DZ twin smokes then it has no association to smoking behaviors in the other twin. In this example, MZ twins are more highly correlated in cannabis smoking than DZ twins, and that pattern is consistent with genetic differences contributing to variation in cannabis smoking (our trait of interest). A wealth of twin studies have shown that substance use - and nearly any behavior you can name - are genetically influenced (Dick, 2021). However, twin studies do not prove that genes caused<\/em> the trait. The genetic inference in twin studies depends on assumptions about relevant environmental similarity, mating patterns, measurement quality, and the comparability of the MZ and DZ samples (Rijsdijk & Sham, 2002).\r\n

    2.4 Discordant-MZ studies: comparing differences within pairs<\/h3>\r\nA discordant-MZ study focuses on identical twins who differ in an exposure or experience. Discordant<\/em> means different within the pair. Researchers then ask whether the twins also differ on a later outcome.\r\n\r\nFor example, suppose one MZ twin has greater cannabis exposure than the cotwin. Researchers could compare the twins\u2019 later scores on the same memory test. The analysis asks whether the twin with greater exposure also has a different memory score.\r\n\r\n[caption id=\"attachment_454\" align=\"aligncenter\" width=\"1672\"]\"Four-panel Figure 4.3. Logic and limits of a discordant-MZ comparison. Researchers identify an exposure difference within an MZ pair and compare the twins on a later outcome. Because the twins share nearly all inherited DNA, the same age, and many family conditions, the comparison accounts for much of their shared background. It does not establish that the exposure caused the outcome difference.[\/caption]\r\n\r\nThis comparison provides stronger control for shared inherited and family conditions than a comparison between unrelated participants. If the twins differ in memory scores, that difference cannot easily be attributed to characteristics that are the same for both twins.\r\n\r\nHowever, MZ twins can still have different friends, stressors, illnesses, school experiences, and mental-health conditions. Any of these individual experiences could contribute to both cannabis exposure and memory performance. The memory difference could also have existed before the exposure difference, one twin\u2019s behavior could affect the other, or imperfect measurements could make the twins appear more different or similar than they are. Only pairs that differ sufficiently in exposure are useful for the comparison, and those pairs may not represent all MZ twins.\r\n\r\nThe two twin approaches therefore answer different questions. A classical twin study<\/strong> compares MZ and DZ resemblance to investigate why people vary in a measured characteristic. A discordant-MZ study<\/strong> compares differences within MZ pairs to examine an exposure-outcome relationship while accounting for much of the twins\u2019 shared background.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    Table 4.1. What different family designs compare<\/caption>\r\n
    Design<\/th>\r\nWho or what is compared?<\/th>\r\nWhat the comparison helps researchers examine<\/th>\r\nWhat remains uncertain<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    Family study<\/strong><\/td>\r\nPeople from the same family compared with people from other families or the broader population<\/td>\r\nWhether a diagnosis, behavior, or trait tends to run in families<\/td>\r\nWhether the resemblance reflects inherited variation, shared environments, or both<\/td>\r\n<\/tr>\r\n
    Adoption study<\/strong><\/td>\r\nAn adopted person\u2019s resemblance to biological relatives compared with resemblance to adoptive relatives<\/td>\r\nWhether resemblance more closely follows biological relationships, the rearing household, or both<\/td>\r\nPrenatal influences, selective placement, later contact with biological relatives, and whether adoptive families represent the broader population<\/td>\r\n<\/tr>\r\n
    Classical twin study<\/strong><\/td>\r\nResemblance among identical, or MZ, twins compared with resemblance among fraternal, or DZ, twins<\/td>\r\nWhether greater genetic similarity is associated with greater similarity in the measured trait in MZ twins compared to DZ twins<\/td>\r\nWhether relevant environments were equally similar for MZ and DZ twins, along with measurement and sampling limitations<\/td>\r\n<\/tr>\r\n
    Discordant-MZ study<\/strong><\/td>\r\nTwo MZ twins who differ in an exposure or trait<\/td>\r\nWhether the more-exposed twin also differs in an outcome after accounting for stable inherited differences and much of the shared family background<\/td>\r\nIndividual experiences, timing, reverse causation, measurement error, and why the twins became different<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nThese comparisons become informative only when researchers know what was measured, how relatives were identified and linked, and which assumptions the design requires. The ABCD twin study illustrates how that evidence is produced.\r\n

    3. The ABCD Twin Study as a Biomedical Data Journey<\/h2>\r\nABCD Study includes a twin subsample of 772 same-sex twin pairs (N = 1,544 youth) enrolled at ages 9\u201310 at baseline. ABCD intentionally oversampled twins at four sites with established twin registries and expertise (University of Colorado Boulder, University of Minnesota, Virginia Commonwealth University, Washington University in St. Louis) using state birth records and standardized recruitment (Iacono et al., 2018). Zygosity (MZ vs DZ) was inferred from genomic markers (e.g., the Smokescreen array; Uban et al., 2018), improving classification accuracy. The twin cohort\u2014while broadly similar to the full ABCD sample\u2014tends to include families with somewhat higher parental education and income and lower racial\/ethnic diversity (Iacono et al., 2018). All procedures were IRB-approved (Auchter et al., 2018), with parental consent and child assent.\r\n

    3.1 Data Architecture and Family-Linked Records<\/h3>\r\n

    Twin data are hierarchical<\/strong> because individual participants belong to larger groups. Each child belongs to a twin pair and family, and each family is associated with a study site. The same participant may also contribute records at multiple visits. Consequently, in an ABCD-like dataset, two rows belonging to the same family are related observations, not two unrelated people sampled independently.<\/p>\r\n

    In a participant-level longitudinal table, one row might represent one child at one visit. That row would need a participant identifier, family identifier, event or visit label, measure name, and score version. A paired-participant identifier connects that child to the cotwin, while a zygosity field indicates whether the pair is classified as MZ or DZ. Current ABCD documentation provides fields such as gn_y_genrel_id__fam<\/code> for genetically related family groups, gn_y_genrel_id__paired__01<\/code> for paired participants, and gn_y_genrel_zyg__01<\/code> for relationship classification (ABCD Study genetics documentation<\/a>).<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    Table 4.2. The ABCD twin biomedical data journey<\/strong><\/caption>\r\n
    Stage<\/th>\r\nPhysical and operational infrastructure<\/th>\r\nData architecture<\/th>\r\nGovernance and provenance<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    Recruit and collect<\/strong><\/td>\r\nTwin registries and birth records, four specialized research sites, staff, study visits, digital instruments, and biospecimens<\/td>\r\nParticipant, family, birth-set, site, visit, instrument, and score-version records<\/td>\r\nParent consent, youth assent, approved recruitment procedures, and standardized data collection<\/td>\r\n<\/tr>\r\n
    Process and link<\/strong><\/td>\r\nGenotyping laboratories, equipment, computational pipelines, and quality-control procedures<\/td>\r\nGenomic relatedness and zygosity classifications; linked family records using fields such as gn_y_genrel_id__fam<\/code>, gn_y_genrel_id__paired__01<\/code>, and gn_y_genrel_zyg__01<\/code> (ABCD genetics documentation<\/a>)<\/td>\r\nSecure data transfer, access controls, quality review, and documentation of the data release and processing history<\/td>\r\n<\/tr>\r\n
    Analyze and share<\/strong><\/td>\r\nResearch teams and approved computing environments<\/td>\r\nParticipant records reorganized into pair-level datasets; separate MZ and DZ correlations; ACE model estimates; aggregate Twin Hub results<\/td>\r\nControlled access to individual-level data, aggregate public sharing, and interpretation appropriate to the sample and data release (ABCD data-access documentation<\/a>)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nRelatedness methods, variable names, scoring procedures, and available records can change across releases, making the release and processing history essential parts of data provenance.\r\n\r\n[caption id=\"attachment_427\" align=\"aligncenter\" width=\"2560\"]\"Alt Figure 4.4. One twin measure represented at three levels. Participant-event records are linked and reorganized into one row per twin pair. Researchers then summarize resemblance across qualifying MZ and DZ pairs using separate within-pair correlations. The records and values shown are fictional and simplified for instruction.[\/caption]\r\n

    4. Selected mental health and neurocognitive measures<\/h2>\r\nModules 2 and 3 introduced selected instruments and variables from the ABCD Substance Use data domain<\/a>. These included youth and caregiver reports, structured interviews, summary variables, and toxicology measures used to study substance exposure and substance-related outcomes. This chapter broadens our view of the ABCD data.\r\n\r\nSection 3 introduced the Genetics domain through the ABCD twin sample. This section turns to selected measures from two additional domains: Mental Health<\/a> and Neurocognition<\/a>. Recall that a data domain<\/strong> is a broad organizational category that groups related instruments, procedures, and variables.\r\n\r\nThe ABCD Study includes many measures within each domain. We will examine only a small set:\r\n