Leveraging pleiotropic association using sparse group variable selection in genomics data

BMC Medical Research Methodology

Benoit Liquet¹  Pierre-Emmanuel Sugier²  Matthew Sutton³  Therese Truong⁴ 

[1]Laboratoire De Mathématiques et de leurs Applications de PAU E2S UPPA, CNRS, Pau, France

[2]Department of Mathematics and Statistics, Macquarie University, Sydney, Australia

[3]Laboratoire De Mathématiques et de leurs Applications de PAU E2S UPPA, CNRS, Pau, France

[4]University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team “Exposome and Heredity”, Villejuif, France

[5]Queensland University of Technology Centre for Data Science, Brisbane, Australia

[6]University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team “Exposome and Heredity”, Villejuif, France

关键词: Genetic epidemiology;    High dimensional data;    Lasso penalization;    Oncology;    Pathway analysis;    Pleiotropy;    Sparse methods;    Variable selection;   

DOI  :  10.1186/s12874-021-01491-8

来源: Springer

【 摘 要 】

BackgroundGenome-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.MethodsWe 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.ResultsOur 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.ConclusionWe 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】

CC BY