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

Nicole M. Warrington; Liang Dar Hwang; Michel G. Nivard; David M. Evans

doi:10.1038/s41467-021-25723-z

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

Nicole M. Warrington^*, Liang Dar Hwang, Michel G. Nivard, David M. Evans

^*Corresponding author for this work

Biological Psychology

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	5420
Pages (from-to)	1-12
Number of pages	12
Journal	Nature Communications
Volume	12
Issue number	1
Early online date	14 Sept 2021
DOIs	https://doi.org/10.1038/s41467-021-25723-z
Publication status	Published - Dec 2021

Bibliographical note

Funding Information:
This research was carried out at the Translational Research Institute, Woolloongabba, QLD 4102, Australia. The Translational Research Institute is supported by a grant from the Australian Government. This study has been conducted using the UK Biobank Resource under Application Number 53641. D.M.E. is supported by an Australian National Health and Medical Research Council Senior Research Fellowship (1137714) and this work was supported by a Australian National Health and Medical Research Council Project Grant (1157714) and Ideas Grant (1183074). M.G.N. is supported by the Jacobs foundation, ZonMW grants 849200011 and 531003014 from The Netherlands Organisation for Health Research and Development, and a VENI grant awarded by NWO (VI.Veni.191 G.030).

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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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.",

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

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