Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure
This work addresses the challenge of improving genetic risk factor identification in GWAS for diseases like Alzheimer's, though it appears incremental by building on existing sparse modeling techniques.
The paper tackles the problem of identifying genetic risk factors in genome-wide association studies by proposing a novel two-level structured sparse model, SGLGG, which incorporates gene-level priors and nucleotide-level sparsity, and demonstrates its effectiveness on Alzheimer's disease data with competitive performance against state-of-the-art methods.
Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. Recently, incorporating biological priors together with machine learning methods in GWA studies has attracted increasing attention. However, in real-world, nucleotide-level bio-priors have not been well-studied to date. Alternatively, studies at gene-level, for example, protein--protein interactions and pathways, are more rigorous and legitimate, and it is potentially beneficial to utilize such gene-level priors in GWAS. In this paper, we proposed a novel two-level structured sparse model, called Sparse Group Lasso with Group-level Graph structure (SGLGG), for GWAS. It can be considered as a sparse group Lasso along with a group-level graph Lasso. Essentially, SGLGG penalizes the nucleotide-level sparsity as well as takes advantages of gene-level priors (both gene groups and networks), to identifying phenotype-associated risk SNPs. We employ the alternating direction method of multipliers algorithm to optimize the proposed model. Our experiments on the Alzheimer's Disease Neuroimaging Initiative whole genome sequence data and neuroimage data demonstrate the effectiveness of SGLGG. As a regression model, it is competitive to the state-of-the-arts sparse models; as a variable selection method, SGLGG is promising for identifying Alzheimer's disease-related risk SNPs.