Association study between gene expression and multiple phenotypes in omics applications of complex diseases

TL;DR

This paper introduces AFp, a novel p-value combination method for gene-phenotype association analysis in complex diseases, effectively handling phenotype heterogeneity and different data types, with superior performance demonstrated through simulations and real data.

Contribution

The paper proposes AFp, a new method that improves gene-phenotype association analysis by considering phenotype heterogeneity and accommodating various data types.

Findings

AFp outperforms traditional global testing methods in simulations.

AFp accurately estimates phenotype-specific association weights.

Application to lung disease data reveals biologically meaningful gene associations.

Abstract

Studying phenotype-gene association can uncover mechanism of diseases and develop efficient treatments. In complex disease where multiple phenotypes are available and correlated, analyzing and interpreting associated genes for each phenotype respectively may decrease statistical power and lose intepretation due to not considering the correlation between phenotypes. The typical approaches are many global testing methods, such as multivariate analysis of variance (MANOVA), which tests the overall association between phenotypes and each gene, without considersing the heterogeneity among phenotypes. In this paper, we extend and evaluate two p-value combination methods, adaptive weighted Fisher's method (AFp) and adaptive Fisher's method (AFz), to tackle this problem, where AFp stands out as our final proposed method, based on extensive simulations and a real application. Our proposed AFp method has three advantages over traditional global testing methods. Firstly, it can consider the heterogeneity of phenotypes and determines which specific phenotypes a gene is associated with, using phenotype specific 0-1 weights. Secondly, AFp takes the p-values from the test of association of each phenotype as input, thus can accommodate different types of phenotypes (continuous, binary and count). Thirdly, we also apply bootstrapping to construct a variability index for the weight estimator of AFp and generate a co-membership matrix to categorize (cluster) genes based on their association-patterns for intuitive biological investigations. Through extensive simulations, AFp shows superior performance over global testing methods in terms of type I error control and statistical power, as well as higher accuracy of 0-1 weights estimation over AFz. A real omics application with transcriptomic and clinical data of complex lung diseases demonstrates insightful biological findings of AFp.