a knowledge-based weighting framework to boost the power of genome-wide association studies以知识为基础的加权框架来提高全基因组关联研究的力量.pdfVIP

A Knowledge-Based Weighting Framework to Boost the Power of Genome-Wide Association Studies Miao-Xin Li1,2,3, Pak C. Sham2,3,4, Stacey S. Cherny2,4, You-Qiang Song1,3* 1 Department of Biochemistry, The University of Hong Kong, Hong Kong, China, 2 Department of Psychiatry, The University of Hong Kong, Hong Kong, China, 3 The Centre for Reproduction, Development and Growth, The University of Hong Kong, Hong Kong, China, 4 The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China Abstract Background: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. Methodology/Principal Findings: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power.