Statistics Across Campuses: Benoit Liquet-Weiland -- Leveraging Pleiotropy effect from genome-wide association studies using Sparse Group Models

Leveraging Pleiotropy effect from genome-wide association studies using Sparse Group
Models 

Date: Friday 2 October 2020 

Time: 3 pm 

Speaker: Prof Benoit Liquet-Weiland (Macquarie University) 

Abstract: 

Genome-wide association studies (GWAS) focus on testing association between millions of
genetic markers (or single nucleotide polymorphisms, SNPs) and a phenotype in an
agnostic way, where every SNP is tested independently from the other SNPs for
association with the phenotype.  One major finding from GWAS era is that pleiotropy –
that occurs when one gene influence two or more unrelated traits - is a widespread
phenomenon in human complex traits.  Several methods were proposed to combine results
across studies of different phenotypes in order to improve the power of detecting
pleiotropic associations at SNP level.  It is well established that incorporating prior
biological knowledge as gene or biological pathways structures to consider complex
mechanisms can help to discover additional genetic risk factors.  We propose different
Sparse Group Models considering gene (or pathway) structure We develop methods using
both penalised likelihood methods and Bayesian spike and slab priors to induce
structured sparsity at a pathway, gene or SNP level.  



Zoom Link: 
https://macquarie.zoom.us/j/94357515790?pwd=bXZDVWd5SjNwVXBHRHFBWmxpTGM2UT09