[佳学基因]GWAS分析中的结构分析有什么作用

One crucial step in GWAS analysis is to study the population structure (PS). The main reason to perform this study is that, as a consequence of having different population genetic histories, distinct subpopulations could have differences in allele frequencies for many polymorphisms throughout the genome. If the populations have different overall values for the phenotype, any polymorphisms that differ in frequency between the two populations are associated with the phenotype even though they are not casual or in strong linkage disequilibrium with casual polymorphisms [7–9]. Principal component analysis (PCA) on genotypic data is used to visualize the structure of our populations using the function “svd()” in R. Population structure accounted by PCA is limited to correcting for spurious associations on a global level of genetic variation. Thereby, PS does not adequately capture the relatedness between individuals, and this relationship between genotypes (K, kinship matrix) needs also be taking into account on the analysis. Not taking into account of PS, K as well as a potential confounding between the phenotype and the genotype effects, could lead to unrealistic assessments in GWAS analysis.

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