Confounding from Cryptic Relatedness in Case-Control Association Studies

By Benjamin F. Voight, J. K. Pritchard. 2005

232 citations recorded by the source corpus.

Case-control association studies are widely used in the search for genetic variants that contribute to human diseases. It has long been known that such studies may suffer from high rates of false positives if there is unrecognized population structure. It is perhaps less widely appreciated that so-called “cryptic relatedness” (i.e., kinship among the cases or controls that is not known to the investigator) might also potentially inflate the false positive rate. Until now there has been little work to assess how serious this problem is likely to be in practice. In this paper, we develop a formal model of cryptic relatedness, and study its impact on association studies. We provide simple expressions that predict the extent of confounding due to cryptic relatedness. Surprisingly, these expressions are functions of directly observable parameters. Our analytical results show that, for well-designed studies in outbred populations, the degree of confounding due to cryptic relatedness will usually be negligible. However, in contrast, studies where there is a sampling bias toward collecting relatives may indeed suffer from excessive rates of false positives. Furthermore, cryptic relatedness may be a serious concern in founder populations that have grown rapidly and recently from a small size. As an example, we analyze the impact of excess relatedness among cases for six phenotypes measured in the Hutterite population.

Subfields: Genetics

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Citations in the corpus

Variance component model to account for sample structure in genome-wide association studies (2010, unknown): “(1) can be translated to the following regression framework:”
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).”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, background): “In addition to the QC described above, a close examination of results of a test genome-wide association study (GWAS) (discussed in Section 2.5) provided further evidence for the quality of the resulting set of genotype calls.”
New approaches to population stratification in genome-wide association studies (2010, background): “A limitation of this strategy is that it fails to account for other types of sample structure, such as family structure or cryptic relatednes”
STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement (2009, unknown): “If investigators have assessed for relatedness, they should state the method used [150–152] and”
Population Structure and Cryptic Relatedness in Genetic Association Studies (2009, background): “A subsequent theoretical investigation of plausible demographic and sampling scenarios (Voight and Pritchard, 2005) showed that the effect of cryptic relatedness in well-designed studies of outbred populations should be negligible, but it can be noticeable for small and isolated populations.”