The number of readily available summary level data from genome-wide association studies (GWAS) is increasing continually. In this presentation, we describe a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within groups (e.g., at SNP level). We consider both multivariate continuous and Dirac spike and slab priors for group selection, to facilitate selection of important variables both at the group level and within groups. The approach can be applied to multiple types of phenotypes for studies with overlapping or non-overlapping subjects, and takes into account heterogeneity in the size and direction of the genetic effects across traits.
Simulations show that the proposed method outperforms benchmark approaches such as ASSET and CPBayes with respect to the ability to retrieve pleiotropic associations at both SNP and gene-level.
We will also describe the application of the GCPBayes approach to two datasets to investigate shared genetic effects between thyroid cancer and breast cancer.
This work is joint with T. Baghfalaki, P.E. Sugier, T. Truong A.N., Pettitt and his part of the Cross Cancer Genomic Investigation of Pleiotropy project supported by “La Ligue contre le Cancer”.
Distinguished Professor of Statistics, Queensland University of Technology, Australia
Dr Mengersen is a Distinguished Professor of Statistics, Director of the QUT Centre for Data Science and Deputy Director of the ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS). Her research interests include Bayesian methods, dealing with diverse and high-dimensional data, and the interface between statistics and machine learning, with applications in a range of areas including genetic and genomic modelling and analysis.
Professor of Mathematical and Computational Statistics, Department of Statistics and Mathematics, Macquarie University, NSW Australia
Dr Liquet is Professor of Mathematical and Computational Statistics at Macquarie University in the Department of Mathematics and Statistics. In addition he is affiliated to the Université de Pau et Pays de l’Adour (UPPA) and was previously affiliated with ACEMS (Centre of Excellence for Mathematical and Statistical Frontiers), Queensland University of Technology. He was a senior lecturer in Statistics at The University of Queensland (from 2013-2015), Senior Investigator Statistician at Medical Research Council Biostatistics Unit in Cambridge (from 2012-2013), Associate Professor at Bordeaux University (from 2017-2012). Throughout his career he has extensively worked in developing novel statistical models mainly to provide novel tools to analyse clinical, health and biological data arising from epidemiological studies. More recently (since 2011), he moved to the field of computational biology and generalised some of these methods so that they scale to high throughput (“omic”) data.