1. Genetics and Genomics

Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes

  1. Liang-Dar Hwang
  2. Gunn-Helen Moen
  3. David M Evans  Is a corresponding author
  1. The University of Queensland, Australia
  2. University of Queensland, Australia
https://doi.org/10.7554/eLife.73671
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  1. Liang-Dar Hwang
  2. Gunn-Helen Moen
  3. David M Evans
(2022)
Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes
eLife 11:e73671.
https://doi.org/10.7554/eLife.73671
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Abstract

Maternal genetic effects can be defined as the effect of a mother's genotype on the phenotype of her offspring, independent of the offspring's genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We assume that in biological families, offspring phenotypes are influenced prenatally by their mother's genotype and postnatally by both parents' genotypes, whereas adopted individuals' phenotypes are influenced prenatally by their biological mother's genotype and postnatally by their adoptive parents' genotypes. Our SEM framework allows us to model the (potentially) unobserved genotypes of biological and adoptive parents as latent variables, permitting us in principle to leverage the thousands of adopted singleton individuals in the UK Biobank. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.

Data availability

Human genotype and phenotype data on which the results of this study were based were accessed from the UK Biobank (http://www.ukbiobank.ac.uk/) with accession ID 53641. The genotype and phenotype data are available upon application from the UK Biobank (http://www.ukbiobank.ac.uk/).R code for performing the analyses described in this manuscript is available in the Supplementary Materials.

The following previously published data sets were used

Article and author information

Author details

  1. Liang-Dar Hwang

    The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5535-2199

  2. Gunn-Helen Moen

    The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.

  3. David M Evans

    University of Queensland, Brisbane, Australia
    For correspondence
    d.evans1@uq.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0663-4621

Funding

National Health and Medical Research Council (APP1137714)

  • David M Evans

National Health and Medical Research Council (GNT1157714)

  • David M Evans

National Health and Medical Research Council (GNT1183074)

  • David M Evans

Norwegian Research Council (Post doctorial mobility research grant 287198)

  • Gunn-Helen Moen

Nils Normans minnegave

  • Gunn-Helen Moen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The UK Biobank study was approved by the UK National Health Service National Research Ethics Service. Written consent was obtained from both the participants and their parents (for subjects younger than 18 years old). This study was approved by the Human Research Ethics Committee at the University of Queensland (approval number: 2019002705).

Copyright

© 2022, Hwang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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