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Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data

Author

Listed:
  • Nicole M. Warrington

    (The University of Queensland
    The University of Queensland
    Medical Research Council Integrative Epidemiology Unit at the University of Bristol
    University of Bristol)

  • Liang-Dar Hwang

    (The University of Queensland
    The University of Queensland)

  • Michel G. Nivard

    (VU University)

  • David M. Evans

    (The University of Queensland
    The University of Queensland
    Medical Research Council Integrative Epidemiology Unit at the University of Bristol)

Abstract

Estimation of direct and indirect (i.e. parental and/or sibling) genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies.

Suggested Citation

  • Nicole M. Warrington & Liang-Dar Hwang & Michel G. Nivard & David M. Evans, 2021. "Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25723-z
    DOI: 10.1038/s41467-021-25723-z
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    Cited by:

    1. Hans Kippersluis & Pietro Biroli & Rita Dias Pereira & Titus J. Galama & Stephanie Hinke & S. Fleur W. Meddens & Dilnoza Muslimova & Eric A. W. Slob & Ronald Vlaming & Cornelius A. Rietveld, 2023. "Overcoming attenuation bias in regressions using polygenic indices," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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