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Genome-wide association studies (GWA studies or GWAS) investigate therelationships between genetic variants such as single-nucleotide polymorphisms(SNPs) and individual traits. Recently, incorporating biological priorstogether with machine learning methods in GWA studies has attracted increasingattention. However, in real-world, nucleotide-level bio-priors have not beenwell-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. Inthis paper, we proposed a novel two-level structured sparse model, calledSparse Group Lasso with Group-level Graph structure (SGLGG), for GWAS. It canbe considered as a sparse group Lasso along with a group-level graph Lasso.Essentially, SGLGG penalizes the nucleotide-level sparsity as well as takesadvantages of gene-level priors (both gene groups and networks), to identifyingphenotype-associated risk SNPs. We employ the alternating direction method ofmultipliers algorithm to optimize the proposed model. Our experiments on theAlzheimer's Disease Neuroimaging Initiative whole genome sequence data andneuroimage data demonstrate the effectiveness of SGLGG. As a regression model,it is competitive to the state-of-the-arts sparse models; as a variableselection method, SGLGG is promising for identifying Alzheimer'sdisease-related risk SNPs.

Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure