Statistical methods to detect novel genetic variants using publicly available GWAS summary data. (June 2018)

• Record Type: Journal Article
• Authors: Guo, Bin; Wu, Baolin
• Is Part Of: Computational biology and chemistry. Volume 74(2018)
• Page Start: 76
• Page End: 79
• Publication Date: 2018-06
• Subjects: GWAS -- SNP-set association test -- Summary statistics; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.02.016
• Languages: English
• ISSNs: 1476-9271

Abstract

Highlights: Propose efficient GWAS summary data based SNP-set association test methods. Implement the proposed methods in a publicly available R package. Find several novel loci related to fasting glucose worth further study. Abstract: We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.