Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure

Taban Baghfalaki, Pierre-Emmanuel Sugier, Therese Truong, Anthony N. Pettitt, Kerrie Mengersen, Benoit Liquet*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)


    An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

    Original languageEnglish
    Pages (from-to)1498-1518
    Number of pages21
    JournalStatistics in Medicine
    Issue number6
    Early online date27 Dec 2020
    Publication statusPublished - 15 Mar 2021


    • group variable selection
    • Markov chain Monte Carlo
    • pleiotropy
    • sparsity
    • spike and slab priors


    Dive into the research topics of 'Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure'. Together they form a unique fingerprint.

    Cite this