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Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world

Published online by Cambridge University Press:  15 October 2012

Scott I. Vrieze*
Affiliation:
University of Minnesota
William G. Iacono
Affiliation:
University of Southern Denmark
Matt McGue
Affiliation:
University of Minnesota University of Southern Denmark
*
Address correspondence and reprint requests to: Scott Vrieze, Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455; E-mail: [email protected].

Abstract

This article serves to outline a research paradigm to investigate main effects and interactions of genes, environment, and development on behavior and psychiatric illness. We provide a historical context for candidate gene studies and genome-wide association studies, including benefits, limitations, and expected payoffs. Using substance use and abuse as our driving example, we then turn to the importance of etiological psychological theory in guiding genetic, environmental, and developmental research, as well as the utility of refined phenotypic measures, such as endophenotypes, in the pursuit of etiological understanding and focused tests of genetic and environmental associations. Phenotypic measurement has received considerable attention in the history of psychology and is informed by psychometrics, whereas the environment remains relatively poorly measured and is often confounded with genetic effects (i.e., gene–environment correlation). Genetically informed designs, which are no longer limited to twin and adoption studies thanks to ever-cheaper genotyping, are required to understand environmental influences. Finally, we outline the vast amount of individual difference in structural genomic variation, most of which remains to be leveraged in genetic association tests. Although the genetic data can be massive and burdensome (tens of millions of variants per person), we argue that improved understanding of genomic structure and function will provide investigators with new tools to test specific a priori hypotheses derived from etiological psychological theory, much like current candidate gene research but with less confusion and more payoff than candidate gene research has to date.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012

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References

Agrawal, A., Balasubramanian, S., Smith, E. K., Madden, P. A., Bucholz, K. K., Heath, A. C., et al. (2010). Peer substance involvement modifies genetic influences on regular substance involvement in young women. Addiction, 105, 18441853.Google Scholar
Allen, H. L., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., et al. (2010). Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 467, 832838.Google Scholar
Altshuler, D. L., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., Clark, A. G., et al. (2010). A map of human genome variation from population-scale sequencing. Nature, 467, 10611073.Google Scholar
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.Google Scholar
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010). Statistical analysis strategies for association studies involving rare variants. Nature Reviews Genetics, 11, 773785.Google Scholar
Begleiter, H., Porjesz, B., Bihari, B., & Kissin, B. (1984). Event-related brain potentials in boys at risk for alcoholism. Science, 225, 14931496.Google Scholar
Bornovalova, M. A., Hicks, B. M., Iacono, W. G., & McGue, M. (2010). Familial transmission and heritability of childhood disruptive disorders. American Journal of Psychiatry, 167, 10661074.Google Scholar
Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., et al. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls [Review]. Nature, 447, 661678.Google Scholar
Button, T. M., Lau, J. Y., Maughan, B., & Eley, T. C. (2008). Parental punitive discipline, negative life events and gene–environment interplay in the development of externalizing behavior. Psychological Medicine, 38, 2939.Google Scholar
Caspi, A., Hariri, A. R., Holmes, A., Uher, R., & Moffitt, T. E. (2010). Genetic sensitivity to the environment: The case of the serotonin transporter gene and its implications for studying complex diseases and traits. American Journal of Psychiatry, 167, 509527.Google Scholar
Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 297, 851854.Google Scholar
Choi, M., Scholl, U. I., Ji, W. Z., Liu, T. W., Tikhonova, I. R., Zumbo, P., et al. (2009). Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proceedings of the National Academy of Sciences, 106, 1909619101.Google Scholar
Cook, E. H., & Scherer, S. W. (2008). Copy-number variations associated with neuropsychiatric conditions. Nature, 455, 919923.Google Scholar
Cooper, G. M., & Shendure, J. (2011). Needles in stacks of needles: Finding disease-causal variants in a wealth of genomic data. Nature Reviews Genetics, 12, 628640.Google Scholar
Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry, 60, 837844.Google Scholar
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281302.Google Scholar
Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., et al. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16, 9961005.Google Scholar
de Bakker, P. I. W., Yelensky, R., Pe'er, I., Gabriel, S. B., Daly, M. J., & Altshuler, D. (2005). Efficiency and power in genetic association studies. Nature Genetics, 37, 12171223.Google Scholar
de Geus, E. J. (2010). From genotype to EEG endophenotype: A route for post-genomic understanding of complex psychiatric disease? Genome Medicine, 2, 63.Google Scholar
de Moor, M. H. M., Boomsma, D. I., de Geus, E. J. C., Willemsen, G., Hottenga, J. J., Distel, M. A., et al. (2009). Meta-analysis of genome-wide association results in > 10,000 individuals for the big five personality traits. Behavior Genetics, 39, 643.Google Scholar
DerSimonian, R., & Kacker, R. (2007). Random-effects model for meta-analysis of clinical trials: An update. Contemporary Clinical Trials, 28, 105114.Google Scholar
Dick, D. M. (2011). Gene–environment interaction in psychological traits and disorders. Annual Review of Clinical Psychology, 7, 383409.Google Scholar
Duffy, D. L., Iles, M. M., Glass, D., Zhu, G., Barrett, J. H., Hoiom, V., et al. (2010). IRF4 variants have age-specific effects on nevus count and predispose to melanoma. American Journal of Human Genetics, 87, 616.Google Scholar
Duncan, L. E., & Keller, M. C. (2011). A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. American Journal of Psychiatry, 168, 10411049.Google Scholar
Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H., et al. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics, 11, 446450.Google Scholar
Elia, J., Gai, X., Xie, H. M., Perin, J. C., Geiger, E., Glessner, J. T., et al. (2010). Rare structural variants found in attention-deficit/hyperactivity disorder are preferentially associated with neurodevelopmental genes. Molecular Psychiatry, 15, 637646.Google Scholar
Elia, J., Glessner, J. T., Wang, K., Takahashi, N., Shtir, C. J., Hadley, D., et al. (2012). Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention-deficit/hyperactivity disorder. Nature Genetics, 44, 7884.Google Scholar
Feinberg, M. E., Button, T. M., Neiderhiser, J. M., Reiss, D., & Hetherington, E. M. (2007). Parenting and adolescent antisocial behavior and depression: Evidence of Genotype × Parenting environment interaction. Archives of General Psychiatry, 64, 457465.Google Scholar
Flint, J., & Munafo, M. R. (2007). The endophenotype concept in psychiatric genetics. Psychological Medicine, 37, 163180.Google Scholar
Furberg, H., Kim, Y., Dackor, J., Boerwinkle, E., Franceschini, N., Ardissino, D., et al. (2010). Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nature Genetics, 42, 441447.Google Scholar
Gershon, E. S., Alliey-Rodriguez, N., & Liu, C. Y. (2011). After GWAS: Searching for genetic risk for schizophrenia and bipolar disorder. American Journal of Psychiatry, 168, 253256.Google Scholar
Goto, Y., Yang, C. R., & Otani, S. (2010). Functional and dysfunctional synaptic plasticity in prefrontal cortex: Roles in psychiatric disorders. Biological Psychiatry, 67, 199207.Google Scholar
Grant, J. D., Lynskey, M. T., Scherrer, J. F., Agrawal, A., Heath, A. C., & Bucholz, K. K. (2010). A co-twin-control analysis of drug use and abuse/dependence risk associated with early-onset cannabis use. Addictive Behaviors, 35, 3541.CrossRefGoogle Scholar
Greenwood, T. A., Lazzeroni, L. C., Murray, S. S., Cadenhead, K. S., Calkins, M. E., Dobie, D. J., et al. (2011). Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. American Journal of Psychiatry, 168, 930946.Google Scholar
Greenwood, T. A., Light, G. A., Swerdlow, N. R., Radant, A. D., & Braff, D. L. (2012). Association analysis of 94 candidate genes and schizophrenia-related endophenotypes. PLoS One, 7, e29630.CrossRefGoogle ScholarPubMed
Grove, W. M. (1991). When is a diagnosis worth making—A statistical comparison of 2 prediction strategies. Psychological Reports, 69, 317.Google Scholar
Grove, W. M., & Vrieze, S. I. (2010). On the substantive grounding and clinical utility of categories versus dimensions. In Millon, T., Kruger, R. F., & Simonsen, E. (Eds.), Contemporary directions in pschopathology towards DSM-V and ICD-11. New York: Guilford Press.Google Scholar
Hamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., et al. (2011). The PhenX Toolkit: Get the most from your measures. American Journal of Epidemiology, 174, 253260.Google Scholar
Han, B., & Eskin, E. (2012). Interpreting meta-analyses of genome-wide association studies. PLoS Genetics, 8.Google Scholar
Harden, K. P., Hill, J. E., Turkheimer, E., & Emery, R. E. (2008). Gene–environment correlation and interaction in peer effects on adolescent alcohol and tobacco use. Behavior Genetics, 38, 339347.CrossRefGoogle ScholarPubMed
Heils, A., Teufel, A., Petri, S., Stober, G., Riederer, P., Bengel, D., et al. (1996). Allelic variation of human serotonin transporter gene expression. Journal of Neurochemistry, 66, 26212624.Google Scholar
Hicks, B. M., Schalet, B. D., Malone, S. M., Iacono, W. G., & McGue, M. (2011). Psychometric and genetic architecture of substance use disorder and behavioral disinhibition measures for gene association studies. Behavior Genetics, 41, 459475.Google Scholar
Hicks, B. M., South, S. C., DiRago, A. C., Iacono, W. G., & McGue, M. (2009). Environmental adversity and increasing genetic risk for externalizing disorders. Archives of General Psychiatry, 66, 640648.CrossRefGoogle ScholarPubMed
Hirschhorn, J. N., & Daly, M. J. (2005). Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics, 6, 95108.Google Scholar
Hodgkinson, C. A., Yuan, Q. P., Xu, K., Shen, P. H., Heinz, E., Lobos, E. A., et al. (2008). Addictions biology: Haplotype-based analysis for 130 candidate genes on a single array. Alcohol and Alcoholism, 43, 505515.Google Scholar
Huibregtse, B. M., Bornovalova, M. A., Hicks, B. M., McGue, M., & Iacono, W. (2011). Testing the role of adolescent sexual initiation in later-life sexual risk behavior: A longitudinal twin design. Psychological Science, 22, 924933.Google Scholar
Iacono, W. G., Carlson, S. R., & Malone, S. M. (2000). Identifying a multivariate endophenotype for substance use disorders using psychophysiological measures. International Journal of Psychophysiology, 38, 8196.Google Scholar
Iacono, W. G., & Malone, S. M. (2011). Developmental endophenotypes: Indexing genetic risk for substance abuse with the P300 brain event-related potential. Child Development Perspectives, 5, 239247.Google Scholar
Iacono, W. G., Malone, S. M., & McGue, M. (2008). Behavioral disinhibition and the development of early-onset addiction: Common and specific influences. Annual Review of Clinical Psychology, 4, 325348.Google Scholar
Iacono, W. G., McGue, M., & Krueger, R. F. (2006). Minnesota Center for Twin and Family Research. Twin Research and Human Genetics, 9, 978984.CrossRefGoogle ScholarPubMed
Ioannidis, J. P. A., Castaldi, P., & Evangelou, E. (2010). A compendium of genome-wide associations for cancer: Critical synopsis and reappraisal [Review]. Journal of the National Cancer Institute, 102, 846858.CrossRefGoogle ScholarPubMed
Irons, D. E., Iacono, W. G., Oetting, W. S., & McGue, M. (in press). Developmental trajectory and environmental moderation of the effect of ALDH2 polymporphism on alcohol use. Alcoholism: Clinical and Experimental Research.Google Scholar
Irons, D. E., McGue, M., Iacono, W. G., & Oetting, W. S. (2007). Mendelian randomization: A novel test of the gateway hypothesis and models of gene–environment interplay. Development and Psychopathology, 19, 11811195.CrossRefGoogle ScholarPubMed
Jaffee, S. R., & Price, T. S. (2007). Gene–environment correlations: A review of the evidence and implications for prevention of mental illness [Review]. Molecular Psychiatry, 12, 432442.Google Scholar
Johnson, W., Kyvik, K. O., Mortensen, E. L., Skytthe, A., Batty, G. D., & Deary, I. J. (2010). Education reduces the effects of genetic susceptibilities to poor physical health. International Journal of Epidemiology, 39, 406414.Google Scholar
Johnson, W., McGue, M., & Iacono, W. G. (2006). Genetic and environmental influences on academic achievement trajectories during adolescence. Developmental Psychology, 42, 514532.Google Scholar
Kaffman, A., & Krystal, J. J. (2012). New frontiers in animal research of psychiatric illness. Methods in Molecular Biology, 829, 330.Google Scholar
Kandel, D. B., & Jessor, R. (2002). The gateway hypothesis revisited. In Kandel, D. B. (Ed.), Stages and pathways of drug involvement. New York: Cambridge University Press.Google Scholar
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S. Y., Freimer, N. B., et al. (2010). Variance component model to account for sample structure in genome-wide association studies. Nature Genetics, 42, 348354.Google Scholar
Karg, K., Burmeister, M., Shedden, K., & Sen, S. (2011). The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Archives of General Psychiatry, 68, 444454.Google Scholar
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review [Review]. Psychological Medicine, 37, 615626.Google Scholar
Kendler, K. S., Bulik, C. M., Silberg, J., Hettema, J. M., Myers, J., & Prescott, C. A. (2000). Childhood sexual abuse and adult psychiatric and substance use disorders in women—an epidemiological and co-twin control analysis. Archives of General Psychiatry, 57, 953959.Google Scholar
Kendler, K. S., Jacobson, K. C., Prescott, C. A., & Neale, M. C. (2003). Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. American Journal of Psychiatry, 160, 687695.Google Scholar
Keyes, M. A., Iacono, W. G., & McGue, M. (2007). Early-onset problem behavior, young adult psychopathology, and contextual risk. Twin Research and Human Genetics.Google Scholar
Keyes, M. A., Legrand, L. N., Iacono, W. G., & McGue, M. (2008). Parental smoking and adolescent problem behavior: An adoption study of general and specific effects. American Journal of Psychiatry, 165, 13381344.Google Scholar
Kilpatrick, D. G., Koenen, K. C., Ruggiero, K. J., Acierno, R., Galea, S., Resnick, H. S., et al. (2007). The serotonin transporter genotype and social support and moderation of posttraumatic stress disorder and depression in hurricane-exposed adults. American Journal of Psychiatry, 164, 16931699.Google Scholar
Krueger, R. F., Eaton, N. R., Clark, L. A., Watson, D., Markon, K. E., Derringer, J., et al. (2011). Deriving an empirical structure of personality pathology for DSM-V. Journal of Personality Disorders, 25, 170191.Google Scholar
Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411424.Google Scholar
Krueger, R. F., Markon, K. E., Patrick, C. J., & Iacono, W. G. (2005). Externalizing psychopathology in adulthood: A dimensional-spectrum conceptualization and its implications for DSM-5. Journal of Abnormal Psychology, 114, 537550.CrossRefGoogle Scholar
Lander, E. S. (2011). Initial impact of the sequencing of the human genome. Nature, 470, 187197.Google Scholar
Lasky-Su, J., Lyon, H. N., Emilsson, V., Heid, I. M., Molony, C., Raby, B. A., et al. (2008). On the replication of genetic associations: Timing can be everything! American Journal of Human Genetics, 82, 849858.Google Scholar
Lee, S. H., Decandia, T. R., Ripke, S., Yang, J., Sullivan, P. F., Goddard, M. E., et al. (2012). Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genetics, 44, 247250.Google Scholar
Legrand, L. N., McGue, M., & Iacono, W. G. (1999). Searching for interactive effects in the etiology of early-onset substance use. Behavior Genetics, 29, 433444.Google Scholar
Levinson, D. F., Duan, J. B., Oh, S., Wang, K., Sanders, A. R., Shi, J. X., et al. (2011). Copy mumber variants in schizophrenia: Confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. American Journal of Psychiatry, 168, 302316.CrossRefGoogle Scholar
Li, Y., Sidore, C., Kang, H. M., Boehnke, M., & Abecasis, G. R. (2011). Low-coverage sequencing: Implications for design of complex trait association studies. Genome Research, 21, 940951.Google Scholar
Lichter, J. B., Barr, C. L., Kennedy, J. L., Vantol, H. H. M., Kidd, K. K., & Livak, K. J. (1993). A hypervariable segment in the human dopamine receptor D4 (DRD4) gene. Human Molecular Genetics, 2, 767773.Google Scholar
Lindblad-Toh, K., Wade, C. M., Mikkelsen, T. S., Karlsson, E. K., Jaffe, D. B., Kamal, M., et al. (2005). Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature, 438, 803819.Google Scholar
Luck, S. J., Mathalon, D. H., O'Donnell, B. F., Hamalainen, M. S., Spencer, K. M., Javitt, D. C., et al. (2011). A roadmap for the development and validation of event-related potential biomarkers in schizophrenia research. Biological Psychiatry, 70, 2834.CrossRefGoogle ScholarPubMed
Luczak, S. E., Glatt, S. J., & Wall, T. L. (2006). Meta-analyses of ALDH2 and ADH1B with alcohol dependence in Asians. Psychological Bulletin, 132, 607621.CrossRefGoogle ScholarPubMed
Lynskey, M. T., Heath, A. C., Bucholz, K. K., Slutske, W. S., Madden, P. A. F., Nelson, E. C., et al. (2003). Escalation of drug use in early-onset cannabis users vs co-twin controls. Journal of the American Medical Association, 289, 427433.Google Scholar
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., et al. (2009). Finding the missing heritability of complex diseases. Nature, 461, 747753.Google Scholar
Marchini, J., Donnelly, P., & Cardon, L. R. (2005). Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature Genetics, 37, 413417.Google Scholar
Markon, K. E., Chmielewski, M., & Miller, C. J. (2011). The reliability and validity of discrete and continuous measures of psychopathology: A quantitative review. Psychological Bulletin, 137, 856879.Google Scholar
McGue, M., & Iacono, W. G. (2005). The association of early adolescent problem behavior with adult psychopathology. American Journal of Psychiatry, 162, 11181124.Google Scholar
McGue, M., Iacono, W. G., Legrand, L. N., & Elkins, I. (2001). The origins and consequences of age at first drink: I. Associations with substance use disorders, disinhibitory behavior and psychopathology, and P3 amplitude. Alcoholism: Clinical and Experimental Research, 25, 11561165.Google ScholarPubMed
McGue, M., Keyes, M., Sharma, A., Elkins, I., Legrand, L., Johnson, W., et al. (2007). The environments of adopted and nonadopted youth: Evidence on range restriction from the Sibling Interaction and Behavior Study (SIBS). Behavior Genetics, 37, 449462.Google Scholar
McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research: The utility of twins. Perspectives on Psychological Science, 5, 546556.Google Scholar
Meyer-Lindenberg, A., Nichols, T., Callicott, J. H., Ding, J., Kolachana, B., Buckholtz, J., et al. (2006). Impact of complex genetic variation in COMT on human brain function. Molecular Psychiatry, 11, 867877.Google Scholar
Miller, G. A. (2010). Mistreating psychology in the decades of the brain. Perspectives on Psychological Science, 5, 716743.Google Scholar
Miller, M. B., Basu, S., Cunningham, J., Oetting, W., Schork, N. J., Iacono, W. G., et al. (2012). The Minnesota Center for Twin and Family Research Genome-Wide Association Study. Manuscript submitted for publication.Google Scholar
Mills, R. E., Walter, K., Stewart, C., Handsaker, R. E., Chen, K., Alkan, C., et al. (2011). Mapping copy number variation by population-scale genome sequencing. Nature, 470, 5965.Google Scholar
Myers, R. M., Stamatoyannopoulos, J., Snyder, M., Dunham, I., Hardison, R. C., Bernstein, B. E., et al. (2011). A user's guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biology, 9.Google Scholar
O'Connor, T. G., Caspi, A., Defries, J. C., & Plomin, R. (2003). Genotype–environment interaction in children's adjustment to parental separation. Journal of Child Psychology and Psychiatry, 44, 849856.Google Scholar
O'Connor, T. G., Deater-Deckard, K., Fulker, D. W., Rutter, M., & Plomin, R. (1998). Genotype–environment correlation in late childhood and early adolscence: Antisocial behavior problems and coercive parenting. Developmental Psychology, 34, 970981.CrossRefGoogle Scholar
Ong, K. K., Elks, C. E., Li, S. X., Zhao, J. H., Luan, J., Andersen, L. B., et al. (2009). Genetic variation in LIN28B is associated with the timing of puberty. Nature Genetics, 41, 729733.Google Scholar
O'Roak, B. J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., et al. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature Genetics, 43, 585589.Google Scholar
Pare, G., Cook, N. R., Ridker, P. M., & Chasman, D. I. (2010). On the use of variance per genotype as a tool to identify quantitative trait interaction effects: A report from the Women's Genome Health Study. PLoS Genetics, 6.Google Scholar
Pennacchio, L. A., Ahituv, N., Moses, A. M., Prabhakar, S., Nobrega, M. A., Shoukry, M., et al. (2006). In vivo enhancer analysis of human conserved noncoding sequences. Nature, 444, 499502.Google Scholar
Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. New York: Springer.Google Scholar
Prescott, C. A., & Kendler, K. S. (1999). Age at first drink and risk for alcoholism: A noncausal association. Alcoholism: Clinical and Experimental Research, 23, 101107.Google Scholar
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904909.CrossRefGoogle ScholarPubMed
Priebe, L., Degenhardt, F. A., Herms, S., Haenisch, B., Mattheisen, M., Nieratschker, V., et al. (2011). Genome-wide survey implicates the influence of copy number variants (CNVs) in the development of early-onset bipolar disorder. Molecular Psychiatry, 17, 421432.Google Scholar
Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., et al. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748752.Google Scholar
Risch, N., Herrell, R., Lehner, T., Liang, K. Y., Eaves, L., Hoh, J., et al. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. Journal of the American Medical Association, 301, 24622471.Google Scholar
Risch, N. J., & Merikangas, K. R. (1996). The future of genetic studies of complex human diseases. Science, 273, 15161517.Google Scholar
Rueter, M. A., Keyes, M. A., Iacono, W. G., & McGue, M. (2009). Family interactions in adoptive compared to nonadoptive families. Journal of Family Psychology, 23, 5866.Google Scholar
Rutter, M. (2007). Proceeding from observed correlation to causal inference: The use of natural experiments. Perspectives on Psychological Science, 2, 377395.CrossRefGoogle ScholarPubMed
Rutter, M., Pickles, A., Murray, R., & Eaves, L. J. (2001). Testing hypotheses on specific causal effects on behavior. Psychological Bulletin, 127, 291324.Google Scholar
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype → environment effects. Child Development, 54, 424435.Google Scholar
Schumann, G., Coin, L. J., Lourdusamy, A., Charoen, P., Berger, K. H., Stacey, D., et al. (2011). Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption (2011). Proceedings of the National Academy of Sciences, 108, 71197124.CrossRefGoogle ScholarPubMed
Simonson, M. A., Wills, A. G., Keller, M. C., & McQueen, M. B. (2011). Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk. BMC Medical Genetics, 12, 146.Google Scholar
Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., et al. (2011). Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nature Genetics, 43, 977983.Google Scholar
Slutske, W. S., Hunt-Carter, E. E., Nabors-Oberg, R. E., Sher, K. J., Bucholz, K. K., Madden, P. A. F., et al. (2004). Do college students drink more than their non-college-attending peers? Evidence from a population-based longitudinal female twin study. Journal of Abnormal Psychology, 113, 530540.Google Scholar
Smith, G. D. (2010). Mendelian randomization for strengthening causal inference in observational studies: Application to Gene × Environment interactions. Perspectives on Psychological Science, 5, 527545.Google Scholar
Smith, G. D., & Ebrahim, S. (2003). “Mendelian randomization”: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32, 122.Google Scholar
Sovio, U., Mook-Kanamori, D. O., Warrington, N. M., Lawrence, R., Briollais, L., Palmer, C. N. A., et al. (2011). Association between common variation at the FTO locus and changes in body mass index from infancy to late childhood: The complex nature of genetic association through growth and development. PLoS Genetics, 7, e1001307.CrossRefGoogle ScholarPubMed
Spear, L. P. (2000). The adolescent brain and age-related behavioral manifestations. Neuroscience and Biobehavioral Reviews, 24, 417463.Google Scholar
Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., et al. (2010). Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genetics, 42, 937953.Google Scholar
Stankiewicz, P., & Lupski, J. R. (2010). Structural variation in the human genome and its role in disease. Annual Review of Medicine, 61, 437455.Google Scholar
Steinberg, L. (2007). Risk taking in adolescence—new perspectives from brain and behavioral science. Current Directions in Psychological Science, 16, 5559.Google Scholar
Tang, H. (2006). Confronting ethnicity-specific disease risk. Nature Genetics, 38, 1315.CrossRefGoogle ScholarPubMed
Taylor, J., Roehrig, A. D., Hensler, B. S., Connor, C. M., & Schatschneider, C. (2010). Teacher quality moderates the genetic effects on early reading. Science, 328, 512514.Google Scholar
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., et al. (2010). Biological, clinical, and population relevance of 95 loci for blood lipids. Nature, 466, 707713.Google Scholar
Thapar, A., Harold, G., Rice, F., Langley, K., & O'Donovan, M. (2007). The contribution of gene–environment interaction to psychopathology. Development and Psychopathology, 19, 9891004.Google Scholar
The International HapMap Consortium. (2003). The International HapMap Project. Nature, 426, 789796.Google Scholar
Thomas, D. C. (2010a). Gene-environment-wide association studies: Emerging approaches. Nature Reviews Genetics, 11, 259272.Google Scholar
Thomas, D. C. (2010b). Methods for investigating gene–environment interactions in candidate pathway and genome-wide association studies. Annual Review of Public Health, 31, 2136.Google Scholar
Thomas, D. C., Lewinger, J. P., Murcray, C. E., & Gauderman, W. J. (2011). Invited commentary: GE-whiz! Ratcheting gene–environment studies up to the whole genome and the whole exposome. American Journal of Epidemiology, 175, 203207.Google Scholar
Timpson, N. J., Harbord, R., Smith, G. D., Zacho, J., Tybjaerg-Hansen, A., & Nordestgaard, B. G. (2009). Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension, 54, 8490.Google Scholar
Torkamani, A., Scott–Van Zeeland, A. A., Topol, E. J., & Schork, N. J. (2011). Annotating individual human genomes. Genomics, 98, 233241.Google Scholar
Turkheimer, E., Haley, A., Waldron, M., D'Onofrio, B., & Gottesman, I. I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14, 623628.Google Scholar
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. American Journal of Human Genetics, 90, 724.Google Scholar
Vissers, L. E. L. M., de Ligt, J., Gilissen, C., Janssen, I., Steehouwer, M., de Vries, P., et al. (2010). A de novo paradigm for mental retardation. Nature Genetics, 42, 11091112.Google Scholar
Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17, 228243.Google Scholar
Vrieze, S. I., Hicks, B. M., McGue, M., & Iacono, W. G. (in press). Genetic influence on the co-occurrence of alcohol, marijuana, and nicotine dependence symptoms declines from age 14 to 29. American Journal of Psychiatry.Google Scholar
Vrieze, S. I., McGue, M., & Iacono, W. G. (2012). The interplay of genes and adolescent development in substance use disorders: Leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption. Human Genetics, 131, 791801.Google Scholar
Vrieze, S. I., McGue, M., Miller, M. B., Legrand, L. N., Schork, N. J., & Iacono, W. G. (2011). An assessment of the individual and collective effects of variants on height using twins and a developmentally informative study design. PLoS Genetics, 7, e1002413.Google Scholar
Vrieze, S. I., Perlman, G., Krueger, R. F., & Iacono, W. G. (2011). Is the continuity of externalizing psychopathology the same in adolescents and middle-aged adults? A test of the externalizing spectrum's developmental coherence. Journal of Abnormal Child Psychology, 40, 459470.CrossRefGoogle Scholar
Widen, E., Ripatti, S., Cousminer, D. L., Surakka, I., Lappalainen, T., Jarvelin, M. R., et al. (2010). Distinct variants at LIN28B influence growth in height from birth to adulthood. American Journal of Human Genetics, 86, 773782.Google Scholar
Woolfe, A., Goodson, M., Goode, D. K., Snell, P., McEwen, G. K., Vavouri, T., et al. (2005). Highly conserved noncoding sequences are associated with vertebrate development. PLoS Biology, 3, 116130.Google Scholar
Xu, B., Roos, J. L., Dexheimer, P., Boone, B., Plummer, B., Levy, S., et al. (2011). Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nature Genetics, 43, 864868.CrossRefGoogle Scholar
Yang, J. A., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42, 565569.Google Scholar
Yang, J. A., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88, 7682.Google Scholar
Yang, J. A., Manolio, T. A., Pasquale, L. R., Boerwinkle, E., Caporaso, N., Cunningham, J. M., et al. (2011). Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genetics, 43, 519525.Google Scholar
Zhu, H., Shah, S., Shyh-Chang, N., Shinoda, G., Einhorn, W. S., Viswanathan, S. R., et al. (2010). Lin28a transgenic mice manifest size and puberty phenotypes identified in human genetic association studies. Nature Genetics, 42, 626630.Google Scholar
Zucker, R. A., Heitzeg, M. M., & Nigg, J. T. (2011). Parsing the undercontrol/disinhibition pathway to substance use disorders: A multilevel developmental problem. Child Development Perspectives, 5, 248255.Google Scholar
Zuk, O., Hechter, E., Sunyaev, S. R., & Lander, E. S. (2012). The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences. Advance online publication. doi:10.1073/pnas.1119675109Google Scholar