TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data

摘要

Even though genome-wide association studies (GWASs) have been remarkably successful in identifying a large number of genetic variants associated with complex traits and diseases, these identified variants can only explain a small to modest fraction of heritability [ ]. Larger sample sizes and more powerful statistical tests are needed to boost power to identify novel genetic variants, especially for weakly associated variants with small effect sizes or low frequency variants. Due to various reasons, it is often difficult for researchers to obtain access to individual level data, and thus difficult to obtain a sufficient sample size to obtain reliable results. The increase in public availability of genome-wide association study (GWAS) summary statistics, e.g. minor allele frequency (MAF), estimated effect size, odds ratio, or p-values, for individual single nucleotide polymorphisms (SNPs) motivated us to develop novel powerful methods for analyzing GWAS summary data. Methods based on summary statistics can also be viewed as a complementary approach to the traditional single variant single trait association test.