TY - JOUR
T1 - Genetic analysis of social-class mobility in five longitudinal studies
AU - Belsky, Daniel W.
AU - Domingue, Benjamin W.
AU - Wedow, Robbee
AU - Arseneault, Louise
AU - Boardman, Jason D.
AU - Caspi, Avshalom
AU - Conley, Dalton
AU - Fletcher, Jason M.
AU - Freese, Jeremy
AU - Herd, Pamela
AU - Moffitt, Terrie E.
AU - Poulton, Richie
AU - Sicinski, Kamil
AU - Wertz, Jasmin
AU - Harris, Kathleen Mullan
N1 - Funding Information:
ACKNOWLEDGMENTS. We thank David Corcoran, Joseph Prinz, Karen Sugden, and Benjamin Williams for assistance with E-Risk Study and Dunedin Study genetics data; Christy Avery, Heather Highland, and Joyce Tabor for assistance with the Add Health Study genetics data; David Braudt for assistance with Add Health Study occupational data; and Dan Benjamin and David Cesarini for comments on the article. This study used data from the E-Risk Study, the Add Health Study, the Dunedin Study, the HRS, and the WLS. The E-Risk Study is supported by UK Medical Research Council Grant G1002190 and Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant R01HD077482. The Add Health Study is supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant P01HD31921 and GWAS Grants R01HD073342 and R01HD060726, with cooperative funding from 23 other federal agencies and foundations. The Dunedin Study is supported by the New Zealand Health Research Council, New Zealand Ministry of Business, Innovation, and Employment, National Institute on Aging Grant R01AG032282, and UK Medical Research Council Grant MR/P005918/1. The HRS is supported by National Institute on Aging Grants U01AG009740, RC2AG036495, and RC4AG039029 and is conducted by the University of Michigan. The WLS is supported by National Institute on Aging Grants R01AG041868 and P30AG017266. This research received additional support from National
Funding Information:
We thank David Corcoran, Joseph Prinz, Karen Sugden, and Benjamin Williams for assistance with E-Risk Study and Dunedin Study genetics data; Christy Avery, Heather Highland, and Joyce Tabor for assistance with the Add Health Study genetics data; David Braudt for assistance with Add Health Study occupational data; and Dan Benjamin and David Cesarini for comments on the article. This study used data from the E-Risk Study, the Add Health Study, the Dunedin Study, the HRS, and the WLS. The E-Risk Study is supported by UK Medical Research Council Grant G1002190 and Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant R01HD077482. The Add Health Study is supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant P01HD31921 and GWAS Grants R01HD073342 and R01HD060726, with cooperative funding from 23 other federal agencies and foundations. The Dunedin Study is supported by the New Zealand Health Research Council, New Zealand Ministry of Business, Innovation, and Employment, National Institute on Aging Grant R01AG032282, and UK Medical Research Council Grant MR/P005918/1. The HRS is supported by National Institute on Aging Grants U01AG009740, RC2AG036495, and RC4AG039029 and is conducted by the University of Michigan. The WLS is supported by National Institute on Aging Grants R01AG041868 and P30AG017266. This research received additional support from National Institute on Aging Grant R24AG04506 and Russell Sage and Ford Foundation Grant 961704. D.W.B. is supported by a Jacobs Foundation Early Career Research Fellowship and by National Institute on Aging Grants R01AG032282 and P30AG028716. R.W. is supported by National Science Foundation Grant DGE1144083. L.A. is an Economic and Social Research Council Heath Leadership Fellow. This research benefitted from GWAS results made publicly available by the SSGAC. Some of the work used a high-performance computing facility partially supported by North Carolina Biotechnology Center Grant 2016-IDG-1013.
Funding Information:
Institute on Aging Grant R24AG04506 and Russell Sage and Ford Foundation Grant 961704. D.W.B. is supported by a Jacobs Foundation Early Career Research Fellowship and by National Institute on Aging Grants R01AG032282 and P30AG028716. R.W. is supported by National Science Foundation Grant DGE1144083. L.A. is an Economic and Social Research Council Heath Leadership Fellow. This research benefitted from GWAS results made publicly available by the SSGAC. Some of the work used a high-performance computing facility partially supported by North Carolina Biotechnology Center Grant 2016-IDG-1013.
Publisher Copyright:
© 2018 National Academy of Sciences. All rights reserved.
PY - 2018/7/31
Y1 - 2018/7/31
N2 - A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.
AB - A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.
KW - Genetics
KW - Polygenic score
KW - Social class
KW - Social mobility
KW - Sociogenomics
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UR - http://www.scopus.com/inward/citedby.url?scp=85051748136&partnerID=8YFLogxK
U2 - 10.1073/pnas.1801238115
DO - 10.1073/pnas.1801238115
M3 - Article
C2 - 29987013
AN - SCOPUS:85051748136
SN - 0027-8424
VL - 115
SP - E7275-E7284
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 31
ER -