Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects
Open accessPreprint — published as 10.1038/s41588-022-01085-0 · Paper page
Abstract
Associations between genotype and phenotype derive from four sources: direct genetic effects, indirect genetic effects from relatives, population stratification, and correlations with other variants affecting the phenotype through assortative mating. Genome-wide association studies (GWAS) of unrelated individuals have limited ability to distinguish the different sources of genotype-phenotype association, confusing interpretation of results and potentially leading to bias when those results are applied – in genetic prediction of traits, for example. With genetic data on families, the randomisation of genetic material during meiosis can be used to distinguish direct genetic effects from other sources of genotype-phenotype association. Genetic data on siblings is the most common form of genetic data on close relatives. We develop a method that takes advantage of identity-by-descent sharing between siblings to impute missing parental genotypes. Compared to no imputation, this increases the effective sample size for estimation of direct genetic effects and indirect parental effects by up to one third and one half respectively. We develop a related method for imputing missing parental genotypes when a parent-offspring pair is observed. We provide the imputation methods in a software package, SNIPar (single nucleotide imputation of parents), that also estimates genome-wide direct and indirect effects of SNPs. We apply this to a sample of 45,826 White British individuals in the UK Biobank who have at least one genotyped first degree relative. We estimate direct and indirect genetic effects for ∼5 million genome-wide SNPs for five traits. We estimate the correlation between direct genetic effects and effects estimated by standard GWAS to be 0.61 (S.E. 0.09) for years of education, 0.68 (S.E. 0.10) for neuroticism, 0.72 (S.E. 0.09) for smoking initiation, 0.87 (S.E. 0.04) for BMI, and 0.96 (S.E. 0.01) for height. These results suggest that GWAS based on unrelated individuals provides an inaccurate picture of direct genetic effects for certain human traits.
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GWAS associations can be interpreted as estimates of direct individual genetic effects, that is, the effect of inheriting a genetic variant (or a correlated variant) on a phenotype.
“GWAS associations can be interpreted as estimates of direct individual genetic effects, that is, the effect of inheriting a genetic variant (or a correlated variant) on a phenotype4–6.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [6]
The Abstract says GWAS associations include direct genetic effects but can also include indirect effects and confounding. The results section states that a standard GWAS estimates a population effect—the direct effect plus an average nontransmitted coefficient—not the direct effect alone. The Abstract also reports that standard GWAS estimates were inaccurate estimates of direct effects for educational attainment and cognitive ability. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredSibling effects have been estimated to be modest compared with parental effects but could have impacted the GWAS estimates.
“Sibling effects have been estimated to be modest compared with parental effects6,48 but could have impacted our GWAS estimates.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [6]
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“20 only with genetic effects that operate through the inherited family environment, while Factor 10 is also associated with direct genetic effects (65).”
Background
- Authors
- Young, A. I., Nehzati, S. M., Lee, C., Benonisdottir, S., Cesarini, D., Benjamin, D. J., Turley, P., & Kong, A.
- Affiliations
- University of Southern California (US)
- National Bureau of Economic Research (US)
- University of Oxford (GB)
- Corresponding author
- Young, Alexander I.
- Kong, Augustine
- ORCID
- Venue
- Nature Genetics
- Publisher
- openRxiv
- Published
- 2020-07-03
- Volume
- 54
- Issue
- 6
- Pages
- 897-905
- ISSN
- 1061-4036 (ISSN-L)
- PMC
- PMC9197765
- OpenAlex
- W4281917178
- Cited by
- 143
- References
- 30
- Citations by year
- 2026: 31 · 2025: 30 · 2024: 29 · 2023: 19 · 2022: 19 · 2021: 12 · 2020: 3
- FWCI
- 14.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Foundation for the National Institutes of Health (R24-AG065184, R01-AG042568, K99-AG062787-01)
- Massachusetts General Hospital
- University of Southern California
- Li Ka Shing Foundation
- Broad Institute
- Open Philanthropy Project (010623-00001 and 2019-198171, 010623-00001)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (11867, 016.Veni.198.058, 00001, 016.Veni)
- National Institutes of Health (00001, AG042568)
- University of California, Los Angeles
- Medical Research Council (MC_PC_17228)
- Nationaal Regieorgaan Praktijkgericht Onderzoek SIA (016.Veni.198.058)
- National Institute on Aging (R21AG067585, R24AG065184, K99-AG062787-01, R00AG062787, K99AG062787, R01AG042568)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- Mendelian randomization, direct genetic effects, indirect genetic effects, confounding, educational attainment, cognitive ability
- MeSH terms
- Genotype, Humans, Parents, Software, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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