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Cited by 20Genetic Associations and EpidemiologyGenetics and Physical PerformanceExperimental Behavioral Economics Studies

Social-Science Genomics: Progress, Challenges, and Future Directions

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Benjamin, Daniel, Cesarini, David, Turley, Patrick, Young, Alexander Strudwick·2024·National Bureau of Economic Research
DOI 10.3386/w32404
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Abstract

Rapid progress has been made in identifying links between human genetic variation and social and behavioral phenotypes.Applications in mainstream economics are beginning to emerge.This review aims to provide the background needed to bring the interested economist to the frontier of social-science genomics.Our review is structured around a theoretical framework that nests many of the key methods, concepts and tools found in the literature.We clarify key assumptions and appropriate interpretations.After reviewing several significant applications, we conclude by outlining future advances in genetics that will expand the scope of potential applications, and we discuss the ethical and communication challenges that arise in this area of research.

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  • Because segregation of chromosomes during meiosis is independent of environment, estimates of DGEs are free from confounding due to gene-environment correlation, including from IGEs and population-stratification.

    “Because the segregation of chromosomes during meiosis is independent of environment, estimates of DGEs are free from confounding due to gene-environment correlation, including from IGEs and population-stratification3,9,15,16.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [15]

    “The Natural Experiment of Mendelian Segregation” says allele transmission is random and that controlling for parental genotypes isolates variation that gives the genetic-effect estimate a causal interpretation. “The Challenge of Causal Inference” identifies population stratification and parental genetic effects as sources of gene–environment correlation. The claim omits the need for an appropriate family-based analysis and states freedom from confounding categorically, whereas the paper also notes caveats to the segregation-based approach. The indexed passages have section labels but no page numbers. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Cross-trait assortative mating is expected to have a negligible influence on genetic correlations estimated using direct genetic effects because the vast majority of correlations between causal alleles induced by cross-trait assortative mating are cross-chromosome and therefore do not contribute to direct genetic effect estimates.

    “xAM is expected to have a negligible influence on genetic correlations estimated using DGEs because the vast majority of correlations between causal alleles induced by xAM are cross-chromosome and therefore do not contribute to DGE estimates14–16.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [15]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • If the average coefficient on parental PGIs differs from zero, this shows that the PGI's predictive power derives from factors other than direct genetic effects of the variants used in the PGI and those in local LD with them.

    “If, in FPGI analysis, the average coefficient on the parental PGIs (average NTC of the PGI) differs from zero, this shows that the PGI’s predictive power derives from factors other than DGEs of the variants used in the PGI and those in local LD with them15.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [15]

    In the section labeled “99” (page number not provided), the paper derives a parental-PGI coefficient that depends on both parental and own genetic effects, and notes that assortative mating can make own genetic effects contribute to it. This supports caution against interpreting a nonzero coefficient as solely a parental effect, but the cited passages do not establish the claimed categorical inference that it proves predictive power comes from beyond the direct effects of variants in the PGI and those in local LD. (LLM, conf=0.80)

    Experimental — accuracy not yet measured

  • DGEs include causal effects of the focal variant and correlated variants on the same chromosome.

    “As in GWAS, variants are analyzed one at a time, so DGEs include causal effects of the focal variant and correlated variants on the same chromosome14–16.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [15]

  • These factors include gene-environment correlation due to IGEs and population stratification as well as confounding due to correlations with genetic variants across the genome due to non-random mating.

    “These factors include gene-environment correlation due to IGEs and population stratification as well as confounding due to correlations with genetic variants across the genome due to non-random mating15,16.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [15]

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Authors
Benjamin, D., Cesarini, D., Turley, P., & Young, A. S.
Affiliations
  • National Bureau of Economic Research (US)
  • University of Southern California (US)
Corresponding author
Benjamin, Daniel
ORCID
Venue
National Bureau of Economic Research
Publisher
National Bureau of Economic Research
Published
2024-05
DOI
10.3386/w32404
OpenAlex
W4396664798
Cited by
20
References
235
Citations by year
2026: 6 · 2025: 12 · 2024: 2
Type
report
Language
en
Open access
Locations
  • National Bureau of Economic Research (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Open Philanthropy Project
  • National Institutes of Health (R01-AG081518, AG042568, R00-AG062787)
  • National Institute on Aging (R24AG065184, R01AG042568)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
  • Social Sciences › Safety Research › Experimental Behavioral Economics Studies
Keywords
human genetic variation, behavioral genetics, genetic interpretation, ethical considerations, science communication, theoretical framework

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