Leveraging pleiotropic association using sparse group variable selection in genomics data

BMC Medical Research Methodology

Pierre-Emmanuel Sugier¹  Benoit Liquet¹  Matthew Sutton²  Therese Truong³ 

[1] Laboratoire De Mathématiques et de leurs Applications de PAU E2S UPPA, CNRS;Queensland University of Technology Centre for Data Science;University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team “Exposome and Heredity”;

关键词: Genetic epidemiology;    High dimensional data;    Lasso penalization;    Oncology;    Pathway analysis;    Pleiotropy;   

DOI  :  10.1186/s12874-021-01491-8

来源: DOAJ

【 摘 要 】

Abstract Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

【 授权许可】

Unknown