Year: 2023
Type: preprint
Abstract: The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM.
Authors: Marta Bilghese, Regina Manansala, Dhruva Jaishankar, Jonathan Jala, Daniel J. Benjamin, Miles Kimball, Paul L. Auer, Michael A. Livermore, Patrick Turley
Institutions: Massachusetts Institute of Technology, University of Antwerp, University of California, Los Angeles, National Bureau of Economic Research, University of Southern California, University of Colorado Boulder, Medical College of Wisconsin, Medical College of Wisconsin Cancer Center, University of Virginia
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Estimating assortative mating, its history, and its future effect on genetic variance for health, behavioral, and ancestry phenotypes using crosssectionaldata, Infrastructure and Core Activities of the Social Science Genetic Association Consortium, G5023765005, G8198795195, Genome-Wide Analyses of Health and Well-Being Phenotypes
OpenAlex ID: W4386449390