Ouyang Weiwei, Zhu Xiaofeng, Qin Huaizhen
Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 1610, New Orleans, LA, 70112, USA.
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Methods Mol Biol. 2017;1666:527-538. doi: 10.1007/978-1-4939-7274-6_26.
Genome-wide association studies have identified many common genetic variants which are associated with certain diseases. The identified common variants, however, explain only a small portion of the heritability of a complex disease phenotype. The missing heritability motivated researchers to test the hypothesis that rare variants influence common diseases. Next-generation sequencing technologies have made the studies of rare variants practicable. Quite a few statistical tests have been developed for exploiting the cumulative effect of a set of rare variants on a phenotype. The best-known sequence kernel association tests (SKATs) were developed for rare variants analysis of homogeneous genomes. In this chapter, we illustrate applications of the SKATs and offer several caveats regarding them. In particular, we address how to modify the SKATs to integrate local allele ancestries and calibrate the cryptic relatedness and population structure of admixed genomes.
全基因组关联研究已经鉴定出许多与特定疾病相关的常见基因变异。然而,已鉴定出的常见变异仅解释了复杂疾病表型遗传力的一小部分。这种“缺失的遗传力”促使研究人员检验罕见变异影响常见疾病的假说。新一代测序技术使得对罕见变异的研究变得可行。已经开发了不少统计检验方法来利用一组罕见变异对表型的累积效应。最著名的序列核关联检验(SKAT)是为均匀基因组的罕见变异分析而开发的。在本章中,我们阐述了SKAT的应用,并就其提出了几点注意事项。特别是,我们讨论了如何修改SKAT以整合局部等位基因祖先信息,并校准混合基因组的隐秘相关性和群体结构。