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Assessing Orthogonality in Gene-Environment Interaction Studies Using Polygenic Indices.

Eric A W Slob1, Dilnoza Muslimova2, Cornelius A Rietveld2

  • 1Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062PA, Rotterdam, The Netherlands. e.slob@essb.eur.nl.

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Summary
This summary is machine-generated.

Gene-environment interaction studies can be biased by correlations between genetic scores and environments. A new genetic correlation test using bivariate GREML offers a more robust assessment, revealing intertwined genetic and environmental factors for educational attainment.

Keywords:
Gene-environment interactionsOrthogonalityPolygenic index

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Area of Science:

  • Behavioral Genetics
  • Quantitative Genetics
  • Social Epidemiology

Background:

  • Gene-environment interaction (G×E) studies investigate how genetic predispositions and environmental factors influence traits.
  • A potential confounder in G×E studies is gene-environment correlation (rGE), where genetic and environmental factors are associated.
  • Current methods for assessing the independence of genetic propensity and environment may be insufficient.

Purpose of the Study:

  • To evaluate the limitations of correlation-based tests for PGI-environment orthogonality in G×E studies.
  • To propose and validate a novel method using bivariate GREML for assessing genetic correlation between phenotypes and environmental conditions.
  • To investigate the genetic and environmental interplay for educational attainment using UK Biobank data.

Main Methods:

  • Utilized bivariate Genome-wide Complex Trait Analysis (GREML) to estimate genetic correlations.
  • Employed UK Biobank data for empirical illustrations.
  • Assessed orthogonality using both traditional correlation tests and the proposed bivariate GREML method.

Main Results:

  • Confirmed orthogonality for the "Raising of School Leave Age 1972" reform and gender, though gender failed the simpler correlation test.
  • Identified a significant genetic correlation between birth district social class and the genetic propensity for educational attainment.
  • Demonstrated that the proposed bivariate GREML method can detect genetic correlations missed by standard tests.

Conclusions:

  • The standard correlation-based test for PGI-environment orthogonality has limitations.
  • Bivariate GREML provides a more comprehensive assessment of genetic correlation in G×E research.
  • Genetic and environmental factors influencing educational attainment are significantly intertwined, necessitating advanced analytical approaches.