Subset testing and analysis of multiple phenotypes

Abstract

Meta‐analysis of multiple genome‐wide association studies (GWAS) is effective for detecting single‐ or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region‐based meta‐analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between‐study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated. Extensive simulations show that STAMP is more powerful than standard approaches for meta‐analyses when the proportion of truly associated outcomes is between 25% and 50%. For other settings, the power of STAMP is similar to that of existing methods. We illustrate our method on two examples, the association of a region on chromosome 9p21 with the risk of 14 cancers, and the associations of expression of quantitative trait loci from two genetic regions with their cis‐single‐nucleotide polymorphisms measured in 17 tissue types using data from The Cancer Genome Atlas.

Immune‐related conditions and cancer‐specific mortality among older adults with cancer in the United States

“…We therefore used CPBAYES and STAMP, which borrow strength across groups of cancers and/or medical conditions, as complementary methods to discern significant associations that would otherwise have been missed. 21,22 In particular, the global tests of association provided by STAMP indicated that grouped immunosuppressive, autoimmune, and allergic conditions were broadly associated with outcomes for specific cancers. These results suggest that immunocontainment has multifaceted aspects affecting the control of cancers originating from different organ systems.…”

“…Indeed, visual inspection of the volcano plots in Figure 1 reveals clear patterns for associations with P ‐values between these two thresholds. We therefore used CPBAYES and STAMP, which borrow strength across groups of cancers and/or medical conditions, as complementary methods to discern significant associations that would otherwise have been missed 21,22 . In particular, the global tests of association provided by STAMP indicated that grouped immunosuppressive, autoimmune, and allergic conditions were broadly associated with outcomes for specific cancers.…”

“…To identify specific patterns among associations, we applied CPBAYES and STAMP, two Bayesian methods developed for genome‐wide association studies 21,22 . For each medical condition, CPBAYES provides posterior probabilities (PPs) that identify subgroups of cancers for which that particular condition is associated with cancer‐specific survival, based on the aHRs.…”