Sociology, Genetics, and the Coming of Age of Sociogenomics

Literature Cited

1. Abdellaoui A, Hugh-Jones D, Yengo L, Kemper KE, Nivard MG et al. 2019. Genetic correlates of social stratification in Great Britain. Nat. Hum. Behav. 3:1332–42
   [Google Scholar]
2. Adams PM, Albert MS, Albin RL, Apostolova LG, Arnold SE et al. 2016. Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol. Aging 41:200.e13–e20
   [Google Scholar]
3. Arnau-Soler A, Adams MJ, Clarke T-K, MacIntyre DJ, Milburn K et al. 2019. A validation of the diathesis-stress model for depression in Generation Scotland. Transl. Psychiatry 9:25
   [Google Scholar]
4. Barban N, Jansen R, De Vlaming R, Vaez A, Mandemakers JJ et al. 2016. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48:121462–72
   [Google Scholar]
5. Barr PB, Kuo SI, Aliev F, Latvala A, Viken R et al. 2019. Polygenic risk for alcohol misuse is moderated by romantic partnerships. Addiction 114:101753–62
   [Google Scholar]
6. Baselmans BML, Jansen R, Ip HF, van Dongen J, Abdellaoui A et al. 2019. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51:3445–51
   [Google Scholar]
7. Belsky DW, Caspi A, Arseneault L, Corcoran DL, Domingue BW et al. 2019. Genetics and the geography of health, behaviour and attainment. Nat. Hum. Behav. 3:6576–86
   [Google Scholar]
8. Belsky DW, Domingue BW, Wedow R, Arseneault L, Boardman JD et al. 2018. Genetic analysis of social-class mobility in five longitudinal studies. PNAS 115:31E7275–84
   [Google Scholar]
9. Belsky J, Pluess M. 2009. Beyond diathesis stress: differential susceptibility to environmental influences. Psychol. Bull. 135:885–908
   [Google Scholar]
10. Bliss C. 2018. Social by Nature: The Promise and Peril of Sociogenomics Stanford, CA: Stanford Univ. Press
11. Boardman JD. 2009. State-level moderation of genetic tendencies to smoke. Am. J. Public Health 99:3480–86
    [Google Scholar]
12. Boardman JD, Blalock CL, Pampel FC 2010. Trends in the genetic influences on smoking. J. Health Soc. Behav. 51:1108–23
    [Google Scholar]
13. Boardman JD, Blalock CL, Pampel FC, Hatemi PK, Heath AC, Eaves LJ 2011. Population composition, public policy, and the genetics of smoking. Demography 48:41517–33
    [Google Scholar]
14. Boardman JD, Daw J, Freese J 2013. Defining the environment in gene-environment research: lessons from social epidemiology. Am. J. Public Health 103:1064–72
    [Google Scholar]
15. Bowden J, Davey Smith G, Haycock PC, Burgess S 2016. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40:4304–14
    [Google Scholar]
16. Boyle EA, Li YI, Pritchard JK 2017. An expanded view of complex traits: from polygenic to omnigenic. Cell 169:71177–86
    [Google Scholar]
17. Breen R, Jonsson JO. 2005. Inequality of opportunity in comparative perspective: recent research on educational attainment and social mobility. Annu. Rev. Sociol. 31:223–43
    [Google Scholar]
18. Bronfenbrenner U, Ceci SJ. 1994. Nature-nurture reconceptualized in developmental perspective: a bioecological model. Psychol. Rev. 101:568–86
    [Google Scholar]
19. Burgess S, Thompson SG. 2017. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32:5377–89
    [Google Scholar]
20. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT et al. 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562:7726203–9
    [Google Scholar]
21. Caspi A. 2002. Role of genotype in the cycle of violence in maltreated children. Science 297:5582851–54
    [Google Scholar]
22. Chabris CF, Lee JJ, Benjamin DJ, Beauchamp JP, Glaeser EL et al. 2013. Why it is hard to find genes associated with social science traits: theoretical and empirical considerations. Am. J. Public Health 103:S1S152–66
    [Google Scholar]
23. Cho Y, Haycock PC, Sanderson E, Gaunt TR, Zheng J et al. 2020. Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nat. Comm. 11:1010
    [Google Scholar]
24. Conley D. 2016. Socio-genomic research using genome-wide molecular data. Annu. Rev. Sociol. 42:275–99
    [Google Scholar]
25. Conley D. 2017. The challenges of GxE: commentary on “Genetic endowments, parental resources and adult health: evidence from the Young Finns Study. .” Soc. Sci. Med. 188:201–3
    [Google Scholar]
26. Conley D, Domingue BW, Cesarini D, Dawes C, Rietveld CA, Boardman JD 2015. Is the effect of parental education on offspring biased or moderated by genotype. Sociol. Sci. 2:82–105
    [Google Scholar]
27. Conley D, Fletcher J. 2017. Genome Factor. What the Social Genomics Revolution Reveals About Ourselves, Our History and the Future. Princeton, NJ: Princeton Univ. Press
28. Conley D, Rauscher E, Dawes C, Magnusson PKE, Siegal ML 2013. Heritability and the equal environments assumption: evidence from multiple samples of misclassified twins. Behav. Genet. 43:5415–26
    [Google Scholar]
29. Conley D, Zhang S. 2018. The promise of genes for understanding cause and effect. PNAS 115:225626–28
    [Google Scholar]
30. Davey Smith G. 2005. What can Mendelian randomisation tell us about modifiable behavioural and environmental exposures. BMJ 330:74991076–79
    [Google Scholar]
31. Davies G, Lam M, Harris SE, Trampush JW, Luciano M et al. 2018a. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9:12098
    [Google Scholar]
32. Davies NM, Dickson M, Smith GD, Van Den Berg GJ, Windmeijer F 2018b. The causal effects of education on health outcomes in the UK Biobank. Nat. Hum. Behav. 2:117–25
    [Google Scholar]
33. Day FR, Bulik-Sullivan B, Hinds DA, Finucane HK, Murabito JM et al. 2015. Shared genetic aetiology of puberty timing between sexes and with health-related outcomes. Nat. Commun. 6:18842
    [Google Scholar]
34. de Vlaming R, Okbay A, Rietveld CA, Johannesson M, Magnusson PKE et al. 2017. Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLOS Genet 13:1e1006495
    [Google Scholar]
35. Demange PA, Malanchini M, Mallard TT, Biroli P, Cox SR et al. 2020. Investigating the genetic architecture of non-cognitive skills using GWAS-by-subtraction. bioRxiv 905794. https://doi.org/10.1101/2020.01.14.905794
    [Crossref]
36. Ding X, Barban N, Mills MC 2019. Educational attainment and allostatic load in later life: evidence using genetic markers. Prev. Med. 129:105866
    [Google Scholar]
37. DiPrete TA, Burik CAP, Koellinger PD 2018. Genetic instrumental variable regression: explaining socioeconomic and health outcomes in nonexperimental data. PNAS 115:22E4970–79
    [Google Scholar]
38. Domingue BW, Belsky DW. 2017. The social genome: current findings and implications for the study of human genetics. PLOS Genet 13:3e1006615
    [Google Scholar]
39. Domingue BW, Belsky DW, Fletcher JM, Conley D, Boardman JD, Harris KM 2018. The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health. PNAS 115:4702–7
    [Google Scholar]
40. Domingue BW, Belsky DW, Harrati A, Conley D, Weir DR, Boardman JD 2017a. Mortality selection in a genetic sample and implications for association studies. Int. J. Epidemiol. 46:41285–94
    [Google Scholar]
41. Domingue BW, Conley D, Fletcher J, Boardman JD 2016. Cohort effects in the genetic influence on smoking. Behav. Genet. 46:131–42
    [Google Scholar]
42. Domingue BW, Fletcher J, Conley D, Boardman JD 2014. Genetic and educational assortative mating among US adults. PNAS 111:227996–8000
    [Google Scholar]
43. Domingue BW, Liu H, Okbay A, Belsky DW 2017b. Genetic heterogeneity in depressive symptoms following the death of a spouse: polygenic score analysis of the U.S. Health and Retirement Study. Am. J. Psychiatry 174:10963–70
    [Google Scholar]
44. Dudbridge F. 2013. Power and predictive accuracy of polygenic risk scores. PLOS Genet 9:3e1003348
    [Google Scholar]
45. Duncan LE, Pollastri AR, Smoller JW 2014. Mind the gap: why many geneticists and psychological scientists have discrepant views about gene-environment interaction (G×E) research. Am. Psychol. 69:3249–68
    [Google Scholar]
46. Durkheim E. 1938. 1895. The Rules of Sociological Method Chicago: Univ. Chicago Press
47. Duster T. 2006. Backdoor to Eugenics New York: Routledge
48. Eckland BC. 1967. Genetics and sociology: a reconsideration. Am. Sociol. Rev. 32:3173–94
    [Google Scholar]
49. Elwood JM. 2013. Commentary: on representativeness. Int. J. Epidemiol. 42:41014–15
    [Google Scholar]
50. Engzell P, Tropf FC. 2019. Heritability of education rises with intergenerational mobility. PNAS 116:5125386–88
    [Google Scholar]
51. Erikson R, Goldthorpe JH. 1993. The Constant Flux: Study of Class Mobility in Industrial Societies Oxford, UK: Clarendon Press
52. Erlich Y, Shor T, Pe'er I, Carmi S 2018. Identity inference of genomic data using long-range familial searches. Science 362:6415690–94
    [Google Scholar]
53. Euesden J, Lewis CM, O'Reilly PF 2014. PRSice: Polygenic Risk Score software. Bioinformatics 31:91466–68
    [Google Scholar]
54. Figlio DN, Freese J, Karbownik K, Roth J 2017. Socioeconomic status and genetic influences on cognitive development. PNAS 114:5113441–46
    [Google Scholar]
55. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y et al. 2015. Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet. 47:1228–35
    [Google Scholar]
56. Fletcher JM. 2012. Why have tobacco control policies stalled? Using genetic moderation to examine policy impacts. PLOS ONE 7:12e50576
    [Google Scholar]
57. Freese J. 2008. Genetics and the social science explanation of individual outcomes. Am. J. Sociol. 114:S1S1–35
    [Google Scholar]
58. Freese J. 2018. The arrival of social science genomics. Contemp. Sociol. 47:5524–36
    [Google Scholar]
59. Freese J, Domingue B, Trejo S, Sicinski K, Herd P 2019. Problems with a causal interpretation of polygenic score differences between Jewish and non-Jewish respondents in the Wisconsin longitudinal study. SocArXiv. https://doi.org/10.31235/osf.io/eh9tq
    [Crossref] [Google Scholar]
60. Freese J, Shostak S. 2009. Genetics and social inquiry. Annu. Rev. Sociol. 35:107–28
    [Google Scholar]
61. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L et al. 2017. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186:91026–34
    [Google Scholar]
62. Galton F. 1869. Hereditary Genius: An Inquiry into Its Laws and Consequences New York: Macmillan
63. Ganna A, Verweij KJH, Nivard MG, Maier R, Wedow R et al. 2019. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science 365:6456eaat7693
    [Google Scholar]
64. Ganzeboom HBG, Treiman DJ, Ultee WC 1991. Comparative intergenerational stratification research: three generations and beyond. Annu. Rev. Sociol. 17:277–302
    [Google Scholar]
65. Gaydosh L, Belsky DW, Domingue BW, Boardman JD, Harris KM 2018. Father absence and accelerated reproductive development in non-Hispanic white women in the United States. Demography 55:41245–67
    [Google Scholar]
66. Gillborn D. 2016. Softly, softly: genetics, intelligence and the hidden racism of the new geneism. J. Educ. Policy 31:4365–88
    [Google Scholar]
67. Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT et al. 2019. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3:5513–25
    [Google Scholar]
68. Guo G. 2006. The linking of sociology and biology. Soc. Forces 85:1145–49
    [Google Scholar]
69. Guo G, Li Y, Wang H, Cai T, Duncan GJ 2015. Peer influence, genetic propensity, and binge drinking: a natural experiment and a replication. Am. J. Sociol. 121:3914–54
    [Google Scholar]
70. Guo G, Tong Y, Cai T 2008. Gene by social-context interactions for number of sexual partners among white male youths: genetics-informed sociology. AJS 114:Suppl.S36–66
    [Google Scholar]
71. Gurdasani D, Carstensen T, Fatumo S, Chen G, Franklin CS et al. 2019. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell 179:4984–1002.e36
    [Google Scholar]
72. Hamer D, Sirota L. 2000. Beware the chopsticks gene. Mol. Psychiatry 5:111–13
    [Google Scholar]
73. Harden KP, Domingue BW, Belsky DW, Boardman JD, Crosnoe R et al. 2019. Genetic associations with mathematics tracking and persistence in secondary school. Sci. Learn. 5:1
    [Google Scholar]
74. Harris SE, Hagenaars SP, Davies G, Hill WD, Liewald DCM et al. 2016. Molecular genetic contributions to self-rated health. Int. J. Epidemiol. 46:3994–1009
    [Google Scholar]
75. Heckman JJ. 1979. Sample selection bias as a specification error. Econometrica 47:1153–61
    [Google Scholar]
76. Henrich J, Heine SJ, Norenzayan A 2010. Most people are not WEIRD. Nature 466:29
    [Google Scholar]
77. Hewitt J. 2012. Editorial policy on candidate gene association and candidate gene-by-environment interaction studies of complex traits. Behav. Genet. 41:11–2
    [Google Scholar]
78. Hill WD, Davies NM, Ritchie SJ, Skene NG, Bryois J et al. 2019a. Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nat. Commun. 10:15741
    [Google Scholar]
79. Hill WD, Hagenaars SP, Marioni RE, Harris SE, Liewald DCM et al. 2016. Molecular genetic contributions to social deprivation and household income in UK biobank. Curr. Biol. 26:223083–89
    [Google Scholar]
80. Hill WD, Weiss A, Liewald DC, Davies G, Porteous DJ et al. 2019b. Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life. Mol. Psychiatry. https://doi.org/10.1038/s41380-019-0387-3
    [Crossref] [Google Scholar]
81. Hudson KL, Holohan MK, Collins FS 2008. Keeping pace with the times—the Genetic Information Nondiscrimination Act of 2008. N. Engl. J. Med. 358:252661–63
    [Google Scholar]
82. Jaffee SR, Lombardi CM, Coley RL 2013. Using complementary methods to test whether marriage limits men's antisocial behavior. Dev. Psychopathol. 25:165–77
    [Google Scholar]
83. Jensen AR. 1968. Social class, race, and genetics: implications for education. Am. Educ. Res. J. 5:11–42
    [Google Scholar]
84. Joshi PK, Fischer K, Schraut KE, Campbell H, Esko T, Wilson JF 2016. Variants near CHRNA3/5 and APOE have age- and sex-related effects on human lifespan. Nat. Commun. 7:111174
    [Google Scholar]
85. Karavani E, Zuk O, Zeevi D, Atzmon G, Barzilai N et al. 2019. Screening human embryos for polygenic traits has limited utility. Cell 179:61424–35
    [Google Scholar]
86. Karlsson Linnér R, Biroli P, Kong E, Meddens SFW, Wedow R et al. 2019. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 51:2245–57
    [Google Scholar]
87. Keller MC. 2014. Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol. Psychiatry 75:118–24
    [Google Scholar]
88. Keyes KM, Westreich D. 2019. UK Biobank, big data, and the consequences of non-representativeness. Lancet 393:101781297
    [Google Scholar]
89. Khan R, Mittelman D. 2018. Consumer genomics will change your life, whether you get tested or not. Genome Biol 19:1120
    [Google Scholar]
90. Khramtsova EA, Davis LK, Stranger BE 2019. The role of sex in the genomics of human complex traits. Nat. Rev. Genet. 20:3173–90
    [Google Scholar]
91. Kim J, Edge MD, Algee-Hewitt BFB, Li JZ, Rosenberg NA 2018. Statistical detection of relatives typed with disjoint forensic and biomedical loci. Cell 175:3848–58.e6
    [Google Scholar]
92. Kim MS, Patel KP, Teng AK, Berens AJ, Lachance J 2018. Genetic disease risks can be misestimated across global populations. Genome Biol 19:1179
    [Google Scholar]
93. Kohler H-P, Rodgers JL, Christensen K 1999. Is fertility behavior in our genes? Findings from a Danish twin study. Popul. Dev. Rev. 25:2253–88
    [Google Scholar]
94. Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson B, Young AI et al. 2018. The nature of nurture: effects of parental genotypes. Science 359:424–28
    [Google Scholar]
95. Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC et al. 2019. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat. Commun. 10:11499
    [Google Scholar]
96. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O et al. 2018. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50:81112–21
    [Google Scholar]
97. Lee SH, Wray NR, Goddard ME, Visscher PM 2011. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88:3294–305
    [Google Scholar]
98. Liu H. 2018. Social and genetic pathways in multigenerational transmission of educational attainment. Am. Sociol. Rev. 83:2278–304
    [Google Scholar]
99. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM et al. 2019. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51:2237–44
    [Google Scholar]
100. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH et al. 2015. Genetic studies of body mass index yield new insights for obesity biology. Nature 518:7538197–206
     [Google Scholar]
101. Maney DL. 2017. Polymorphisms in sex steroid receptors: From gene sequence to behavior. Front. Neuroendocrinol. 47:47–65
     [Google Scholar]
102. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al. 2009. Finding the missing heritability of complex diseases. Nature 461:7265747–53
     [Google Scholar]
103. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:4635–49
     [Google Scholar]
104. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
     [Google Scholar]
105. Martschenko D, Trejo S, Domingue BW 2019. Genetics and education: recent developments in the context of an ugly history and an uncertain future. AERA Open 5:1233285841881051
     [Google Scholar]
106. Mathieson I, Day FR, Barban N, Tropf FC, Brazel DM et al. 2020. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus. bioRxiv https://doi.org/10.1101/2020.05.19.104455
     [Crossref] [Google Scholar]
107. McBride CM, Koehly LM, Sanderson SC, Kaphingst KA 2010. The behavioral response to personalized genetic information: Will genetic risk profiles motivate individuals and families to choose more healthful behaviors. Annu. Rev. Public Health 31:89–103
     [Google Scholar]
108. Mills MC. 2019. How do genes affect same-sex behavior. Science 365:6456869–70
     [Google Scholar]
109. Mills MC, Barban N, Tropf F 2018. The sociogenomics of polygenic scores of reproductive behavior and their relationship to other fertility traits. RSF Russell Sage Found. J. Soc. Sci. 4:4122–36
     [Google Scholar]
110. Mills MC, Barban N, Tropf FC 2020a. An Introduction to Statistical Genetic Data Analysis Cambridge, MA: MIT Press
111. Mills MC, Rahal C. 2019. A scientometric review of genome-wide association studies. Commun. Biol. 2:9
     [Google Scholar]
112. Mills MC, Rahal C. 2020. The GWAS Diversity Monitor tracks diversity by disease in real-time. Nat. Genet. 52:242–43
     [Google Scholar]
113. Mills MC, Tropf FC. 2015. The biodemography of fertility: a review and future research frontiers. Kölner Z. Soz. Sozialpsychol. 67:Suppl. 1397–424
     [Google Scholar]
114. Mills MC, Tropf FC, Brazel DM, van Zuydam N, Vaez A et al. 2020b. Identification of 370 loci for age at onset of sexual and reproductive behavior, highlighting common aetiology with reproductive biology, externalizing behavior and longevity. bioRxiv https://doi.org/10.1101/2020.05.06.081273
     [Crossref] [Google Scholar]
115. Monroe SM, Simons AD. 1991. Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychol. Bull. 110:406–25
     [Google Scholar]
116. Moore R, Casale FP, Jan Bonder M, Horta D, Franke L et al. 2019. A linear mixed-model approach to study multivariate gene-environment interactions. Nat. Genet. 51:1180–86
     [Google Scholar]
117. Morris TT, Davies NM, Smith GD 2019. Can education be personalised using pupils’ genetic data?. bioRxiv 645218. https://doi.org/10.1101/645218
     [Crossref]
118. Mostafavi H, Berisa T, Day FR, Perry JRB, Przeworski M, Pickrell JK 2017. Identifying genetic variants that affect viability in large cohorts. PLOS Biol 15:9e2002458
     [Google Scholar]
119. Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M 2020. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9:e48376
     [Google Scholar]
120. Mullins N, Bigdeli TB, Børglum AD, Coleman JRI, Demontis D et al. 2019. GWAS of suicide attempt in psychiatric disorders and association with major depression polygenic risk scores. Am. J. Psychiatry 176:8651–60
     [Google Scholar]
121. Nelson A. 2016. The Social Life of DNA Boston: Beacon Press
122. Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR et al. 2008. Genes mirror geography within Europe. Nature 456:721898–101
     [Google Scholar]
123. Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG et al. 2016a. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48:6624–33
     [Google Scholar]
124. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH et al. 2016b. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533:7604539–42
     [Google Scholar]
125. Panofsky A, Bliss C. 2017. Ambiguity and scientific authority. Am. Sociol. Rev. 82:159–87
     [Google Scholar]
126. Perry JRB, Day FR, Elks CE, Sulem P, Thompson DJ et al. 2014. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514:752092–97
     [Google Scholar]
127. Pilling LC, Kuo C-L, Sicinski K, Tamosauskaite J, Kuchel GA et al. 2017. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging 9:122504–20
     [Google Scholar]
128. Plomin R. 2018. Blueprint: How DNA Makes Us Who We Are Cambridge, MA: MIT Press
129. Plomin R, DeFries JC, Loehlin JC 1977. Genotype-environment interaction and correlation in the analysis of human behavior. Psychol. Bull. 84:2309–22
     [Google Scholar]
130. Plomin R, von Stumm S 2018. The new genetics of intelligence. Nat. Rev. Genet. 19:3148–59
     [Google Scholar]
131. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A et al. 2015. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47:7702–9
     [Google Scholar]
132. Popejoy AB, Fullerton SM. 2016. Genomics is failing on diversity. Nature 538:7624161–64
     [Google Scholar]
133. Reardon J. 2017. The Postgenomic Condition: Ethics, Justice & Knowledge After the Genome Chicago: Univ. Chicago Press
134. Rice F, Lewis G, Harold GT, Thapar A 2013. Examining the role of passive gene-environment correlation in childhood depression using a novel genetically sensitive design. Dev. Psychopathol. 25:137–50
     [Google Scholar]
135. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T et al. 2013. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340:61391467–71
     [Google Scholar]
136. Rimfeld K, Krapohl E, Trzaskowski M, Coleman JRI, Selzam S et al. 2018. Genetic influence on social outcomes during and after the Soviet era in Estonia. Nat. Hum. Behav. 2:4269–75
     [Google Scholar]
137. Robinson GE, Grozinger CM, Whitfield CW 2005. Sociogenomics: social life in molecular terms. Nat. Rev. Genet. 6:4257–70
     [Google Scholar]
138. Robinson MR, Kleinman A, Graff M, Vinkhuyzen AAE, Couper D et al. 2017. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1:0016
     [Google Scholar]
139. Rothman K, Gallacher J, Hatch E 2013. Why representativeness should be avoided. Int. J. Epidemiol. 42:41012–14
     [Google Scholar]
140. Sanchez-Roige S, Fontanillas P, Elson SL, Gray JC, de Wit H et al. 2019. Genome-wide association studies of impulsive personality traits (BIS-11 and UPPSP) and drug experimentation in up to 22,861 adult research participants identify loci in the CACNA1I and CADM2 genes. J. Neurosci. 39:2562–72
     [Google Scholar]
141. Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J et al. 2018. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50:7912–19
     [Google Scholar]
142. Scarr-Salapatek S. 1971. Race, social class, and IQ. Science 174:40161285–95
     [Google Scholar]
143. Schmitz LL, Conley D. 2017. The effect of Vietnam-era conscription and genetic potential for educational attainment on schooling outcomes. Econ. Educ. Rev. 61:85–97
     [Google Scholar]
144. Schwartz CR. 2013. Trends and variation in assortative mating: causes and consequences. Annu. Rev. Sociol. 39:451–70
     [Google Scholar]
145. Selzam S, Ritchie SJ, Pingault J-B, Reynolds CA, O'Reilly PF, Plomin R 2019. Comparing within- and between-family polygenic score prediction. Am. J. Hum. Genet. 105:2351–63
     [Google Scholar]
146. Shanahan M, Hofer S. 2005. Social context in gene-environment interactions: retrospect and prospect. J. Gerontol. B Psychol. Sci. Soc. Sci. 60: Special Issue 1 65–76
     [Google Scholar]
147. Sorokin P. 1927. Social Mobility New York: Harper and Brothers
148. South SC, Krueger RF. 2013. Marital satisfaction and physical health: evidence for an orchid effect. Psychol. Sci. 24:373–78
     [Google Scholar]
149. Stolk L, Perry JRB, Chasman DI, He C, Mangino M et al. 2012. Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways. Nat. Genet. 44:3260–68
     [Google Scholar]
150. Stulp G, Barrett L, Tropf FC, Mills M 2015. Does natural selection favour taller stature among the tallest people on earth. Proc. R. Soc. B Biol. Sci. 282:180620150211
     [Google Scholar]
151. Trejo S, Belsky DW, Boardman JD, Freese J, Harris KM et al. 2018. Schools as moderators of genetic associations with life course attainments: evidence from the WLS and Add Health. Sociol. Sci. 5:513–40
     [Google Scholar]
152. Trejo S, Domingue BW. 2019. Genetic nature or genetic nurture? Quantifying bias in analyses using polygenic scores. bioRxiv 524850. https://doi.org/10.1101/524850
     [Crossref]
153. Tropf FC, Hong Lee S, Verweij RM, Stulp G, van der Most PJ et al. 2017. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1:757–65
     [Google Scholar]
154. Tropf FC, Stulp G, Barban N, Visscher P, Yang J et al. 2015. Human fertility, molecular genetics, and natural selection in modern societies. PLOS ONE 10:6e0126821
     [Google Scholar]
155. Tucker-Drob EM. 2017. Measurement error correction of genome-wide polygenic scores in prediction samples. bioRxiv 165472. https://doi.org/10.1101/165472
     [Crossref]
156. Tucker-Drob EM, Bates TC. 2016. Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychol. Sci. 27:2138–49
     [Google Scholar]
157. Turkheimer E, Haley A, Waldron M, D'Onofrio B, Gottesman II 2003. Socioeconomic status modifies heritability of IQ in young children. Psychol. Sci. 14:6623–28
     [Google Scholar]
158. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ et al. 2018. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50:2229–37
     [Google Scholar]
159. Udry JR. 1995. Sociology and biology: What biology do sociologists need to know. Soc. Forces 73:41267–78
     [Google Scholar]
160. van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR 2019. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20:10567–81
     [Google Scholar]
161. Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S et al. 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97:4576–92
     [Google Scholar]
162. Visscher PM, Hill WG, Wray NR 2008. Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9:4255–66
     [Google Scholar]
163. Ware EB, Schmitz LL, Faul JD, Gard AM, Mitchell C et al. 2018. Heterogeneity in polygenic scores for common human traits. bioRxiv 106062. https://doi.org/10.1101/106062
     [Crossref]
164. Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C et al. 2019. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51:91339–48
     [Google Scholar]
165. Wedow R, Zacher M, Huibregtse BM, Mullan Harris K, Domingue BW, Boardman JD 2018. Education, smoking, and cohort change: forwarding a multidimensional theory of the environmental moderation of genetic effects. Am. Sociol. Rev. 83:4802–32
     [Google Scholar]
166. Wertz J, Belsky J, Moffitt TE, Belsky DW, Harrington HL et al. 2019a. Genetics of nurture: a test of the hypothesis that parents’ genetics predict their observed caregiving. Dev. Psychol. 55:71461–72
     [Google Scholar]
167. Wertz J, Moffitt TE, Agnew‐Blais J, Arseneault L, Belsky DW et al. 2019b. Using DNA from mothers and children to study parental investment in children's educational attainment. Child Dev In press
     [Google Scholar]
168. Wood AR, Esko T, Yang J, Vedantam S, Pers TH et al. 2014. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46:111173–86
     [Google Scholar]
169. Wray NR, Maier R. 2014. Genetic basis of complex genetic disease: the contribution of disease heterogeneity to missing heritability. Curr. Epidemiol. Rep. 1:4220–27
     [Google Scholar]
170. Wright KM, Rand KA, Kermany A, Noto K, Curtis D et al. 2019. A prospective analysis of genetic variants associated with human lifespan. G3 9:92863–78
     [Google Scholar]
171. Yang J, Lee SH, Goddard ME, Visscher PM 2011. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88:176–82
     [Google Scholar]
172. Yengo L, Sidari M, Verweij KJH, Visscher PM, Keller MC, Zietsch BP 2019. No evidence for social genetic effects or genetic similarity among friends beyond that due to population stratification: a reappraisal of Domingue et al. (2018). Behav. Genet. 50:67–71
     [Google Scholar]
173. Young AI, Benonisdottir S, Przeworski M, Kong A 2019. Deconstructing the sources of genotype-phenotype associations in humans. Science 365:64601396–400
     [Google Scholar]
174. Zeng Y, Nie C, Min J, Chen H, Liu X et al. 2018. Sex differences in genetic associations with longevity. JAMA Netw. Open 1:4e181670
     [Google Scholar]
175. Zenin A, Tsepilov Y, Sharapov S, Getmantsev E, Menshikov LI et al. 2019. Identification of 12 genetic loci associated with human healthspan. Commun. Biol. 2:41
     [Google Scholar]