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Project #7607
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#7607 : JASS: an online tool for the joint analysis of GWAS summary statistics
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Name of Applicant : Hugues Aschard
Date of application : 14-12-2016
Unit : Center of Bioinformatics, Biostatistics and Integrative Biology
Location : Laveran (64) – 2 – 01
Phone : 01 44 38 93 35
@ Mail : hugues.aschard@pasteur.fr

Project context and summary :

In recent years, large genome-wide association studies (GWAS) have been successful in identifying thousands of significant genetic associations for multiple traits and diseases1. In the course of this endeavor, sample size has proven to be the key factor for identifying new variants. For example, GWAS of body mass index (BMI), now including up to 350,000 individuals from more than 100 cohorts, have been able to identify genetic variant that explain as low as 0.02% of BMI variance2. While standard approaches for detecting new genetic variants associated with traits and diseases will go on as sample size increases, multivariate analyses have been proposed as an alternative strategy for both improving detection of new variants and exploring the multidimensional components of complex traits and diseases. Intuitively, multivariate analysis can be used to improve detection of variants displaying a pleiotropic effect3 by accumulating moderate evidence of association across multiple traits and diseases. Several recent examples have been published about not only GWAS hit overlap across related traits4, but also of genome-wide shared genetic effect5. Multivariate analyses of GWAS have also proven useful to understand shared genetics between diseases5, and potential causal relationship between phenotypes using Mendelian randomization (MR)6. Importantly, most of existing multivariate methods are based on GWAS summary statistics, while approaches based on individual-level data have been seldom considered because of major practical and ethical issues. In the continuity of ongoing work on multi-phenotype analysis (Aschard et al 20147, Aschard et al 20158), we developed an effective and robust multivariate approach of GWAS summary statistics that addresses the major barriers of existing approaches, i.e. the presence of correlation between studies that would exists when GWAS analyzed share sample9-16. Our approach consists in a robust omnibus multivariate test of GWAS summary statis


Related team publications :
7. Aschard, H. et al. Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am J Hum Genet 94, 662-76 (2014).
8. Aschard, H., Vilhjalmsson, B.J., Joshi, A.D., Price, A.L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet 96, 329-39 (2015).
Service Delivery
Project Manager : pierre.lechat@pasteur.fr
Project Type : Medium
Status : Awaiting Publication
Publication : 10.1002/gepi.22062
Global Satisfaction for this application : Excellent (5/5)


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