Software package for imputation of missing genotypes in a pedigree and family-based analyses.
snipar (single nucleotide imputation of parents) is a Python package for inferring identity-by-descent (IBD) segments shared between siblings, imputing missing parental genotypes, and for performing family based genome-wide association and polygenic score analyses using observed and/or imputed parental genotypes.
See the guide in the official documentation for more details.
This shows a typical snipar workflow for performing family-based GWAS: inferring IBD segments shared between siblings, imputing missing parental genotypes, then performing family-based GWAS using the observed/imputed parental genotypes.
References
2023
Estimation of indirect genetic effects and heritability under assortative mating
Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects — effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) — can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or ‘genetic nurture’ through analysis of parental and offspring polygenic predictors (called polygenic indices or scores — PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents’ underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.
2022
Mendelian imputation of parental genotypes improves estimates of direct genetic effects
Alexander Strudwick Young, Seyed Moeen Nehzati, Stefania Benonisdottir, and 7 more authors
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.
Novel estimators for family-based genome-wide association studies increase power and robustness
Junming Guan, Seyed Moeen Nehzati, Daniel J Benjamin, and 1 more author
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.
