TY - JOUR
T1 - Genetic nature or genetic nurture? Introducing social genetic parameters to quantify bias in polygenic score analyses
AU - Trejo, Sam
AU - Domingue, Benjamin W.
N1 - Funding Information:
We wish to thank Kathleen Mullan Harris and the Add Health staff for access to the restricted-use genetic data. We are grateful to Patrick Turley, Dalton Conley, attendees of the Stanford Genetics & Social Science journal club, seminar participants at the Integrating Genetics and the Social Sciences 2018 Conference, and two anonymous referees for helpful comments. This work has been supported by the Russell Sage Foundation and the Ford Foundation under Grant No. 96-17-04, by the National Science Foundation under Grant No. DGE-1656518, and by the Institute of Education Sciences under Grant No. R305B140009. This research uses Add Health GWAS data funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant R01 HD073342 to Kathleen Mullan Harris and the Eunice Kennedy Shriver National Institute of Child Health and Human Development and National Institutes of Health grant R01 HD060726 to Kathleen Mullan Harris, Jason Boardman, and Matthew McQueen. Add Health is a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill; it is funded by grant P01 HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development with cooperative funding from 23 other federal agencies and foundations. The opinions expressed in this paper are those of the authors alone and should not be construed as representing the opinions of any foundation.
Funding Information:
This work was supported by the National Science Foundation [DGE-1656518]; Institute for Educational Sciences [R305B140009]; Russell Sage Foundation [96-17-04]. We wish to thank Kathleen Mullan Harris and the Add Health staff for access to the restricted-use genetic data. We are grateful to Patrick Turley, Dalton Conley, attendees of the Stanford Genetics & Social Science journal club, seminar participants at the Integrating Genetics and the Social Sciences 2018 Conference, and two anonymous referees for helpful comments. This work has been supported by the Russell Sage Foundation and the Ford Foundation under Grant No. 96-17-04, by the National Science Foundation under Grant No. DGE-1656518, and by the Institute of Education Sciences under Grant No. R305B140009. This research uses Add Health GWAS data funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant R01 HD073342 to Kathleen Mullan Harris and the Eunice Kennedy Shriver National Institute of Child Health and Human Development and National Institutes of Health grant R01 HD060726 to Kathleen Mullan Harris, Jason Boardman, and Matthew McQueen. Add Health is a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill; it is funded by grant P01 HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development with cooperative funding from 23 other federal agencies and foundations. The opinions expressed in this paper are those of the authors alone and should not be construed as representing the opinions of any foundation.
Publisher Copyright:
© 2019, © 2019 Society for Biodemography and Social Biology.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - Results from a genome-wide association study (GWAS) can be used to generate a polygenic score (PGS), an individual-level measure summarizing identified genetic influence on a trait dispersed across the genome. For complex, behavioral traits, the association between an individual’s PGS and their phenotype may contain bias (from geographic, ancestral, and/or socioeconomic confounding) alongside the causal effect of the individual’s genes. We formalize the introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes, known as genetic nurture. GWAS do not discriminate between the various pathways through which genes become associated with outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects. We construct a theoretical model for genetic effects and show that the presence of genetic nurture biases PGS coefficients from both naïve OLS (between-family) and family fixed effects (within-family) regressions. This bias is in opposite directions; while naïve OLS estimates are biased away from zero, family fixed effects estimates are biased toward zero. We quantify this bias using two novel parameters: (1) the genetic correlation between the direct and nurture effects and (2) the ratio of the SNP heritabilities for the direct and nurture effects.
AB - Results from a genome-wide association study (GWAS) can be used to generate a polygenic score (PGS), an individual-level measure summarizing identified genetic influence on a trait dispersed across the genome. For complex, behavioral traits, the association between an individual’s PGS and their phenotype may contain bias (from geographic, ancestral, and/or socioeconomic confounding) alongside the causal effect of the individual’s genes. We formalize the introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes, known as genetic nurture. GWAS do not discriminate between the various pathways through which genes become associated with outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects. We construct a theoretical model for genetic effects and show that the presence of genetic nurture biases PGS coefficients from both naïve OLS (between-family) and family fixed effects (within-family) regressions. This bias is in opposite directions; while naïve OLS estimates are biased away from zero, family fixed effects estimates are biased toward zero. We quantify this bias using two novel parameters: (1) the genetic correlation between the direct and nurture effects and (2) the ratio of the SNP heritabilities for the direct and nurture effects.
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U2 - 10.1080/19485565.2019.1681257
DO - 10.1080/19485565.2019.1681257
M3 - Article
C2 - 31852332
AN - SCOPUS:85076877879
SN - 1948-5565
VL - 64
SP - 187
EP - 215
JO - Social Biology
JF - Social Biology
IS - 3-4
ER -