Voight BF, Pritchard JK. Confounding from cryptic relatedness in case-control association studies. PLoS Genet 1: e32

Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
PLoS Genetics (Impact Factor: 7.53). 10/2005; 1(3):e32. DOI: 10.1371/journal.pgen.0010032
Source: PubMed


There has long been concern in the human genetics community that case-control association studies may be subject to high rates of false positives if there is unrecognized population structure. After being considered rather suspect in the 1990s for this reason, case-control studies are regaining popularity, and will no doubt be used widely in future genome-wide association studies.
Therefore, it is important to fully understand the types of factors that can lead to excess rates of false positives in case-control studies. Virtually all of the previous discussion in the literature of excess false positives (confounding) in case-control studies has focused on the role of population structure. Yet a widely cited 1999 paper by Devlin and Roeder (that introduced the genomic control concept) argued that, in fact, “cryptic relatedness” (referring to the idea that some members of a case-control sample might actually be close relatives, unbeknownst to the investigator) is likely to be a far more important confounder than population structure. Moreover, one of the two main types of statistical approaches for dealing with confounding in case-control studies (i.e., structured association methods) does not correct for cryptic relatedness.
This work provides the first careful model of cryptic relatedness, and outlines exactly when cryptic relatedness is and is not likely to be a problem. The authors provide simple expressions that predict the extent of confounding due to cryptic relatedness. Surprisingly, these expressions are functions of directly observable parameters. The 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.

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Available from: Benjamin F Voight, Jul 24, 2014
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    • "So identification of true positives in association mapping requires correction for the confounding effects of population structure. Similarly, covariance between individuals because of their relatedness can increase the false positive rate (Voight and Pritchard 2005). To reduce the false positive associations in our study, we used the population structure matrix (Q) to evaluate the effects of population structure and pairwise kinship (K) to evaluate relatedness among individuals. "
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    • "The above analyses suggest that population stratification does not correlate with the trait and does not influence the results of the association study. Cryptic relatedness, i.e., unknown genetic relationships between individuals in a sample, can also confound the association analysis due to nonindependence and larger than expected phenotypic variance (Voight and Pritchard 2005; Cheng et al. 2010). We estimated the GRM from whole-genome SNP data using mixmogam (a Python implementation of EMMAX) (Kang et al. 2010; Segura et al. 2012). "
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    • "Identity by Descent in the DGRP Resource Because association studies rely on the assumption that individuals are unrelated (Voight and Pritchard 2005), we used the SNP calls from Mackay et al. (2012) to scan for regions of extensive identity by descent (IBD) in the DGRP sample; >95% similarity in 1 Mb windows with 100 kb steps. We began with a total of 148 DGRP lines for which we have sequence data. "
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