| Title: | Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes |
| Journal: | eLife |
| Published: | 13 Jul 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35822614/ |
| DOI: | https://doi.org/10.7554/elife.73671 |
| Citations: | 2 (1 in last 2 years) as of 8 Aug 2024 |
Publication 7086
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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 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 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 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.3 Authors
- Liang-Dar Hwang
- Gunn-Helen Moen
- David M Evans
1 Application
| Application ID | Title |
|---|---|
| 53641 | Determining the genetic and environmental aetiology of complex traits and diseases using existing and novel statistical methodologies |
Enabling scientific discoveries that improve human health