Abstract
Cryptic relationships such as first-degree relatives often appear in studies that collect population samples such as the case–control genome-wide association studies (GWAS). Cryptic relatedness not only creates increased type 1 error rate but also affects other aspects of GWAS, such as population stratification via principal component analysis. Here we discuss two effective methods, as implemented in PREST and PLINK, to detect and correct for the problem of cryptic relatedness using high-throughput SNP data collected from GWAS or next-generation sequencing (NGS) experiments. We provide the analytical and practical details involved using three application examples.
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Sun, L., Dimitromanolakis, A. (2012). Identifying Cryptic Relationships. In: Elston, R., Satagopan, J., Sun, S. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 850. Humana Press. https://doi.org/10.1007/978-1-61779-555-8_4
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DOI: https://doi.org/10.1007/978-1-61779-555-8_4
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