Detecting Multiethnic Rare Variants

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 1666; p. 527
• Main Authors: Ouyang, Weiwei, Zhu, Xiaofeng, Qin, Huaizhen
• Format: Journal Article
• Language: English
• Published: United States 01.01.2017
• Subjects: Alleles; Gene Frequency; Genetic Association Studies - methods; Genetic Variation; Genotype; High-Throughput Nucleotide Sequencing; Humans; Linear Models; Models, Genetic; Pedigree; Phenotype; Software; Admixed population; Homogeneous population; Next-generation sequencing; Linear mixed-effect models; Global ancestry; Population structure; Cryptic relatedness; Family designs; Unrelated individuals; Common disease–rare variants hypothesis; Local ancestry; Sib pair designs
• ISSN: 1940-6029
• 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.