Sun Lei, Dimitromanolakis Apostolos
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Methods Mol Biol. 2012;850:47-57. doi: 10.1007/978-1-61779-555-8_4.
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.
诸如一级亲属之类的隐秘关系在收集人群样本的研究中经常出现，例如病例对照全基因组关联研究（GWAS）。隐秘相关性不仅会导致I型错误率增加，还会影响GWAS的其他方面，例如通过主成分分析进行的群体分层。在这里，我们讨论两种有效的方法（如PREST和PLINK中所实现的），以使用从GWAS或下一代测序（NGS）实验收集的高通量SNP数据来检测和纠正隐秘相关性问题。我们通过三个应用示例提供了所涉及的分析和实际细节。
