Novel estimators for family-based genome-wide association studies increase power and robustness
Open accessAbstract
A goal of genome-wide association studies (GWASs) is to estimate the causal effects of alleles carried by an individual on that individual (‘direct genetic effects’). Typical GWAS designs, however, are susceptible to confounding due to gene-environment correlation and non-random mating (population stratification and assortative mating). Family-based GWAS, in contrast, is robust to such confounding since it uses random, within-family genetic variation. When both parents are genotyped, a regression controlling for parental genotype provides the most powerful approach. However, parental genotypes are often missing. We have previously shown that imputing the genotypes of missing parent(s) can increase power for estimation of direct genetic effects over using genetic differences between siblings. We extend the imputation method, which previously only applied to samples with at least one genotyped sibling or parent, to ‘singletons’ (individuals without any genotyped relatives). By including singletons, the effective sample size for estimation of direct effects can be increased by up to 50%. We apply this method to 408,254 ‘White British’ individuals from the UK Biobank, obtaining an effective sample size increase of between 25% and 43% (depending upon phenotype) by including 368,629 singletons. While this approach maximizes power, it can be biased when there is strong population structure. We therefore introduce an imputation based estimator that is robust to population structure and more powerful than other robust estimators. We implement our estimators in the software package snipar using an efficient linear-mixed model (LMM) specified by a sparse genetic relatedness matrix. We examine the bias and variance of different family-based and standard GWAS estimators theoretically and in simulations with differing levels of population structure, enabling researchers to choose the appropriate approach depending on their research goals.
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Such confounding may reduce prediction ability in other contexts, such as predicting across ancestries and within families.
“such confounding may reduce prediction ability in other contexts, such as predicting across ancestries and within families8,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [20]
The Discussion (page unavailable) says confounding biases polygenic scores and contributes to reduced predictive accuracy across genetic ancestries. The Introduction discusses using family-based methods to remove confounding, but the passages in our copy do not say that confounding reduces predictive ability within families. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAlthough population structure can introduce bias into imputed parental genotypes, the resulting bias in DGE estimates is negligible for samples of relatively homogeneous genetic ancestry typically used in GWAS.
“Although population structure can introduce bias into imputed parental genotypes, the resulting bias in DGE estimates is negligible for the samples of relatively homogeneous genetic ancestry typically used in GWAS3,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [20]
Young et al. presented an approach that enables sibling pair data, parent-offspring pair data, and complete parental genotype data to be jointly analyzed through imputation of missing parental genotypes.
“Young et al. presented an alternative approach that enables sibling pair data, parent-offspring pair data, and complete parental genotype data to be jointly analyzed in a unified analytical framework through imputation of missing parental genotypes3,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [20]
The imputation approach gives consistent and unbiased estimates of DGEs provided that imputed parental genotypes are unbiased.
“The imputation approach has been proven to give consistent and unbiased estimates of DGEs provided that the imputed parental genotypes are unbiased3.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [20]
FGWAS and related methods present an opportunity to analyze genetically diverse samples in a way that is not susceptible to population stratification confounding.
“FGWAS and related methods present an opportunity to analyze genetically diverse samples in a way that is not susceptible to population stratification confounding20”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [20]
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Family-GWAS reveals effects of environment and mating on genetic associations
Tan, T., Jayashankar, H., Guan, J., Nehzati, S. M., Mir, M., Bennett, M., Agerbo, E., Ahlskog, R., de Andrade Anapaz, V. P., Benonisdottir, S., et al. (2024) · doi:10.1101/2024.10.01.24314703 · Cited by 47 · see citing works
“Although population structure can introduce bias into imputed parental genotypes, the resulting bias in DGE estimates is negligible for the samples of relatively homogeneous genetic ancestry typically used in GWAS 3,20 .”
InfluentialSelf-citationSURFBAT: a surrogate family based association test building on large imputation reference panels
Herzig, A. F., Rubinacci, S., Marenne, G., Perdry, H., Génin, E., Campion, D., Dartigues, J., Deleuze, J., Lambert, J., Redon, R., et al. (2024) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkae287 · Cited by 4 · see citing works
- Authors
- Guan, J., Nehzati, S. M., Benjamin, D. J., & Young, A. I.
- Affiliations
- University of California, Los Angeles (US)
- National Bureau of Economic Research (US)
- Corresponding author
- Guan, Junming
- Young, Alexander I.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-10-26
- OpenAlex
- W4307486522
- Cited by
- 2
- References
- 27
- Citations by year
- 2024: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Funders
- Open Philanthropy Project (010623-00001)
- National Institutes of Health (00001)
- University of California, Los Angeles
- Medical Research Council (MC_PC_17228)
- 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
- direct genetic effects, singletons, population structure, robust estimation, linear mixed models, effective sample size, gene-environment correlation, assortative mating
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