Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

Abstract

Summary: To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene-or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP-and traitlevels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a metaanalysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. Availability and Implementation: The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/.

Pathway-based analysis enables deeper mining of GWAS data on H/L in chickens

“…However, pinpointing the causative variation is more di cult. To reveal the mechanism through which a mutation site affects a phenotype and to perform subsequent functional research, GWAS joint analysis on multiple genomic levels can be used and biological pathway analysis can be applied to detect the superposition of multiple minor genes by examining genes involved in the same biological pathway, thus enabling deeper mining of GWAS data [10][11][12][13]. With the development of genome sequencing technology and the continuous improvement of statistical methods, GWAS is expected to be more e ciently applied to gene identi cation for important traits in livestock and poultry and to play an increasingly important role in animal breeding.…”

Subset testing and analysis of multiple phenotypes

“…Our approach also differs from other recently proposed methods for gene-based testing that require phenotypes to be measured on the same individuals to estimate between phenotype correlations (Kwak & Pan, 2017;Tang & Ferreira, 2012;Van der Sluis et al, 2015). For cancer outcomes, one could simply assume outcomes are uncorrelated, as it is exceedingly unlikely for an individual to be diagnosed with two primary cancers, and apply these methods to the summary statistics from multiple studies to test whether there is at least one study that shows associations.…”

“…We test if the mixture distribution provides a better fit to the region-specific summary statistics for all phenotypes than a single component density, estimate the parameters of the mixture, and for each phenotype compute its posterior probabilities to be associated with the genomic region (Section 2.2). This method thus also expands upon adaptive gene-based approaches that study multiple SNPs simultaneously and accommodate heterogeneous SNP effects, but only give global measures of association and do not identify the subset of associated phenotypes (Kwak & Pan, 2017;Tang & Ferreira, 2012;Van der Sluis et al, 2015). We study our method in simulations and compare its power with existing meta-analytic approaches (Section 3.1).…”

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

“…Then, we use the null scores to calculate the null test statistic T b following the aforementioned procedure for each b, and then the p-value of the test is the proportion of the number of the null test statistic T b with T b ≤ T (Kwak and Pan, 2016). A larger B is needed to estimate a smaller p-value.…”