Year: 2017
Type: article
Abstract: The success of genome-wide association study (GWAS) depends on genotyping a large number of SNPs and determining which of these SNPs are significantly associated with the outcome of disease.While studying for these associations, it is important to take into account the effects caused by differences of ethnicities and population groups.The study of human population genetic structure focused on analyzing the human genetic variations between different populations and on assigning individuals to subpopulations based on the degree of human genetic variations.Currently the leading statistical method for uncovering population structure in GWAS is Principal Component Analysis (PCA).However one major problem of using PCA on SNPs data is that the principal components that are defined do not correspond to actual SNP variables, we need to find ways that can map the principal components to measure the importance of actual SNP variables in terms of ancestry information.To overcome these limitations, Sparse Principal Component Analysis (SPCA) has been proposed to identify a small set of structure informative markers more efficiently by modifying the alternating regression equation for PCA with including a penalty term during optimization
Author: Zhouyang Weng
Institution: University of Cincinnati
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2804751550