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Studying phenotype-gene association can uncover mechanism of diseases anddevelop efficient treatments. In complex disease where multiple phenotypes areavailable and correlated, analyzing and interpreting associated genes for eachphenotype respectively may decrease statistical power and lose intepretationdue to not considering the correlation between phenotypes. The typicalapproaches are many global testing methods, such as multivariate analysis ofvariance (MANOVA), which tests the overall association between phenotypes andeach gene, without considersing the heterogeneity among phenotypes. In thispaper, we extend and evaluate two p-value combination methods, adaptiveweighted Fisher's method (AFp) and adaptive Fisher's method (AFz), to tacklethis problem, where AFp stands out as our final proposed method, based onextensive simulations and a real application. Our proposed AFp method has threeadvantages over traditional global testing methods. Firstly, it can considerthe heterogeneity of phenotypes and determines which specific phenotypes a geneis associated with, using phenotype specific 0-1 weights. Secondly, AFp takesthe p-values from the test of association of each phenotype as input, thus canaccommodate different types of phenotypes (continuous, binary and count).Thirdly, we also apply bootstrapping to construct a variability index for theweight estimator of AFp and generate a co-membership matrix to categorize(cluster) genes based on their association-patterns for intuitive biologicalinvestigations. Through extensive simulations, AFp shows superior performanceover global testing methods in terms of type I error control and statisticalpower, as well as higher accuracy of 0-1 weights estimation over AFz. A realomics application with transcriptomic and clinical data of complex lungdiseases demonstrates insightful biological findings of AFp.

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