Abstract
BackgroundIn the last decade, a large number of common variants underlying complex diseases have been identified through genome-wide association studies (GWASs). Summary data of the GWASs are freely and publicly available. The summary data is usually obtained through single marker analysis. Gene-based analysis offers a useful alternative and complement to single marker analysis. Results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigations. Most existing gene-based methods fall into two categories: burden tests and quadratic tests. Burden tests are usually powerful when the directions of effects of causal variants are the same. However, they may suffer loss of statistical power when different directions of effects exist at the causal variants. The power of quadratic tests is not affected by the directions of effects but could be less powerful due to issues such as the large number of degree of freedoms. These drawbacks of existing gene based methods motivated us to develop a new powerful method to identify disease associated genes using existing GWAS summary data.Methods and ResultsIn this paper, we propose a new truncated statistic method (TS) by utilizing a truncated method to find the genes that have a true contribution to the genetic association. Extensive simulation studies demonstrate that our proposed test outperforms other comparable tests. We applied TS and other comparable methods to the schizophrenia GWAS data and type 2 diabetes (T2D) GWAS meta-analysis summary data. TS identified more disease associated genes than comparable methods. Many of the significant genes identified by TS may have important mechanisms relevant to the associated traits. TS is implemented in C program TS, which is freely and publicly available online.ConclusionsThe proposed truncated statistic outperforms existing methods. It can be employed to detect novel traits associated genes using GWAS summary data.
Highlights
In the last decade, a large number of common variants underlying complex diseases have been identified through genome-wide association studies (GWASs)
Kwak et al [10] have shown that the performance using any reference data from the same ancestry in estimating linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs) is mostly satisfactory with an estimated inflation factor close to 1
We can see that the type I error rates of all of the methods are controlled well, which indicate that all the tests are valid
Summary
A large number of common variants underlying complex diseases have been identified through genome-wide association studies (GWASs). The power of quadratic tests is not affected by the directions of effects but could be less powerful due to issues such as the large number of degree of freedoms These drawbacks of existing gene based methods motivated us to develop a new powerful method to identify disease associated genes using existing GWAS summary data. 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. The sequence kernel association test (SKAT) [4] and the sum of squared score test (SSU) [5] are proposed to test for association between genetic variants and a single trait Both tests can be viewed as the weighted sum of summary statistics, where the summary statistic is a score test. 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]
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