PT  - JOURNAL ARTICLE
AU  - Asma Nouira
AU  - Chloé-Agathe Azencott
TI  - Multitask group Lasso for Genome Wide association Studies in admixed populations
AID  - 10.1101/2021.08.02.454499
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.08.02.454499
4099  - http://biorxiv.org/content/early/2021/08/03/2021.08.02.454499.short
4100  - http://biorxiv.org/content/early/2021/08/03/2021.08.02.454499.full
AB  - Genome-Wide Association Studies, or GWAS, aim at finding Single Nucleotide Polymorphisms (SNPs) that are associated with a phenotype of interest. GWAS are known to suffer from the large dimensionality of the data with respect to the number of available samples. Other limiting factors include the dependency between SNPs, due to linkage disequilibrium (LD), and the need to account for population structure, that is to say, confounding due to genetic ancestry.We propose an efficient approach for the multivariate analysis of admixed GWAS data based on a multitask group Lasso formulation. Each task corresponds to a subpopulation of the data, and each group to an LD-block. This formulation alleviates the curse of dimensionality, and makes it possible to identify disease LD-blocks shared across populations/tasks, as well as some that are specific to one population/task. In addition, we use stability selection to increase the robustness of our approach. Finally, gap safe screening rules speed up computations enough that our method can run at a genome-wide scale.To our knowledge, this is the first framework for GWAS on admixed populations combining feature selection at the LD-groups level, a multitask approach to address population structure, stability selection, and safe screening rules. We show that our approach outper-forms state-of-the-art methods on both a simulated and a real-world cancer datasets.Competing Interest StatementThe authors have declared no competing interest.