Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Alexander I Young; Seyed Moeen Nehzati; Stefania Benonisdottir; Aysu Okbay; Hariharan Jayashankar; Chanwook Lee; David Cesarini; Daniel J Benjamin; Patrick Turley; Augustine Kong

doi:10.1038/s41588-022-01085-0

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Nat Genet. 2022 Jun;54(6):897-905. doi: 10.1038/s41588-022-01085-0. Epub 2022 Jun 9.

Authors

Alexander I Young^{1

2

3

4}, Seyed Moeen Nehzati^{5

6}, Stefania Benonisdottir⁷, Aysu Okbay⁸, Hariharan Jayashankar⁹, Chanwook Lee¹⁰, David Cesarini^{9

11

12}, Daniel J Benjamin^{6

13

9}, Patrick Turley^{5

14}, Augustine Kong¹⁵

Affiliations

¹ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. alextisyoung@gmail.com.
² Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA. alextisyoung@gmail.com.
³ UCLA Anderson School of Management, Los Angeles, CA, USA. alextisyoung@gmail.com.
⁴ Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA. alextisyoung@gmail.com.
⁵ Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.
⁶ UCLA Anderson School of Management, Los Angeles, CA, USA.
⁷ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
⁸ Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
⁹ National Bureau of Economic Research, Cambridge, MA, USA.
¹⁰ Department of Economics, Harvard University, Cambridge, MA, USA.
¹¹ Department of Economics, New York University, New York, NY, USA.
¹² Research Institute of Industrial Economics (IFN), Stockholm, Sweden.
¹³ Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
¹⁴ Department of Economics, University of Southern California, Los Angeles, CA, USA.
¹⁵ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. augustine.kong@bdi.ox.ac.uk.

Abstract

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Genome-Wide Association Study* / methods
Genotype
Humans
Parents
Polymorphism, Single Nucleotide* / genetics
Software

Abstract

Publication types

MeSH terms

Grants and funding