An Optimally Weighted Combination Method to Detect Novel Disease Associated Genes Using Publicly Available GWAS Summary Data

Abstract

Gene-based analyses offer a useful alternative and complement to the usual single nucleotide polymorphism (SNP) based analysis for genome-wide association studies (GWASs). Using appropriate weights (pre-specified or eQTL-derived) can boost statistical power, especially for detecting weak associations between a gene and a trait. Because the sparsity level or association directions of the underlying association patterns in real data are often unknown and access to individual-level data is limited, we propose an optimal weighted combination (OWC) test 1 applicable to summary statistics from GWAS. This method includes burden tests, weighted sum of squared score (SSU), weighted sum statistic (WSS), and the score test as its special cases. We analytically prove that aggregating the variants in one gene is the same as using the weighted combination of Z-scores for each variant based on the score test method. We also numerically illustrate that our proposed test outperforms several existing comparable methods via simulation studies. Lastly, we utilize schizophrenia GWAS data and a fasting glucose GWAS meta-analysis data to demonstrate that our method outperforms the existing methods in real data analyses. Our proposed test is implemented in the R program OWC, which is freely and publicly available.

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

“…Glutamate receptor dysfunction has been long studied for its role in schizophrenia development [25,26], and GRM7 in particular was recently investigated for its potential as a biomarker for risperidone response [27]. GRM7 was also previously identified by another proposed method, OWC [7], indicating TS is equivalently adapted for identifying weakly associated signals.…”

“…The burden test and SKAT can be considered as special cases of SKAT-O. The SKAT-O statistic can also be rewritten as a special combination of Z statistics [7]. In addition to the aforementioned gene based association tests, there are several other pvalue based methods which are not based on Z statistics, such as the minimum p-value, a general gene-based p-value adaptive combination approach (GPA) [8] or the gene-based association test, which uses extended Simes procedure (GATES) [9].…”