Meta-Analysis models with group structure for pleiotropy detection at gene and variant level by using summary statistics from multiple datasets
Résumé
Genome-wide association studies (GWAS) have highlighted the importance of pleiotropy in human diseases, where one gene can impact two or more unrelated traits. Examining shared genetic risk factors across multiple diseases can enhance our understanding of these conditions by pinpointing new genes and biological pathways involved. Furthermore, with an increasing wealth of GWAS summary statistics available to the scientific community, leveraging these findings across multiple phenotypes could unveil novel pleiotropic associations. Existing selection methods examine pleiotropy association one by one at a scale of either the genetic variant or the gene, and though cannot consider all the genetic information at the same time. To address this limitation, we propose a new approach called MPSG (Meta-analysis model adapted for Pleiotropy Selection with Group structure). This method performs a penalised multivariate meta-analysis method adapted for pleiotropy and takes into account the group structure information nested in the data to select relevant variants and genes (or pathways) from all the genetic information. To do so, we implemented an alternating direction method of multipliers (ADMM) algorithm. We compared the performance of the method with other benchmark meta-analysis approaches as GCPBayes, PLACO, and ASSET by considering as inputs different kinds of summary statistics. We provide an application of our method to the identification of potential pleiotropic genes between breast and thyroid cancers.
In this case, V s is in the form of a diagonal matrix.
Traditional meta-analysis methods propose to analyse each covariate separately. Though, a multivariate meta-analysis version called the inverse-variance estimator ( Lin and Zeng, 2010]. Motivated by this estimator, a sparse meta-analysis (SMA) method has been developed to perform variable selection in meta-analysis when raw data are not available [He et al., 2016].
Domaines
Statistiques [stat]Origine | Fichiers produits par l'(les) auteur(s) |
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