Population Structure and Cryptic Relatedness in Genetic Association Studies

By William J. Astle, David J. Balding. 2009

476 citations recorded by the source corpus.

We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple "island" model of population structure. We take a broader approach, which views population structure and cryptic relatedness as different aspects of a single confounder: the unobserved pedigree defining the (often distant) relationships among the study subjects. Kinship is therefore a central concept, and we review methods of defining and estimating kinship coefficients, both pedigree-based and marker-based. In this unified framework we review solutions to the problem of population structure, including family-based study designs, genomic control, structured association, regression control, principal components adjustment and linear mixed models. The last solution makes the most explicit use of the kinships among the study subjects, and has an established role in the analysis of animal and plant breeding studies. Recent computational developments mean that analyses of human genetic association data are beginning to benefit from its powerful tests for association, which protect against population structure and cryptic kinship, as well as intermediate levels of confounding by the pedigree.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/154bec20c6cc8509c352b8435af319fd2a0ae5f9

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In Genetics

Citations in the corpus

Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies (2013, methodology): “2 SNPs. We quantile transformed each phenotype to a standard normal distribution to guard against model mis-specication. We used the product of centered genotype matrix as an estimate of relatedness [58, 59, 22]. Note that the sample size used here is smaller than the original study [48], and the ph”
Using Genetic Distance to Infer the Accuracy of Genomic Prediction (2015, methodology): “Kinship Coefficients and FST A common measure of kinship from marker data is average allelic correlation [24, 36], which is defined as K = [kij] with”
Epigenome-Wide Association Studies for common human diseases (2011, background): “Second, confounding can arise because the ancestry of cases differs systematically from that of controls (known as population structure and cryptic relatedness”
Implicit Causal Models for Genome-wide Association Studies (2017, unknown): “Building on previous GWAS methods (Price et al., 2006; Yu et al., 2005; Astle & Balding, 2009), we build a model that jointly captures z n ’s and the mechanisms for X m → Y .”
ProbABEL package for genome-wide association analysis of imputed data (2010, methodology): “It can be demonstrated that unbiased and positive semi-definite estimator of the kin-ship matrix [19] can be obtained by computing the kin-ship coefficients between individuals i and j with where L is the number of loci, p l is the allelic frequency at l -th locus and g l, j is the genotype of j…”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, background): “It has long been known that the existence of relatedness in the data would lead to inflated association test statistics 23,24,56 , confirmed by our simulation ( Figure 1a ).”
Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017, background): “Another common source of sample dependence is family relatedness and population ancestry between individuals [1].”
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations (2012, unknown): “For completeness, we also compared MLMM to other single-locus mixed model implementations, the exact mixed-model 30 and the approximate mixed-model with compression 14 , as they have been shown to perform better than the approximate method used above.”
An overview of STRUCTURE: applications, parameter settings, and supporting software (2013, background): “…can be negligible for well-designed studies in outbred populations but cryptic relatedness is more signiﬁcant in effect for small and isolated populations, when extensive inbreeding occurs or with bias in sample collection toward relatives (Voight and Pritchard, 2005; Astle and Balding, 2009).”