Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso

Background: Single nucleotide polymorphism (SNP) based association studies aim at identifying SNPs associated with phenotypes, for example, complex diseases. The associated SNPs may influence the disease risk individually (main effects) or behave jointly (epistatic interactions). For the analysis of high throughput data, the main difficulty is that the number of SNPs far exceeds the number of samples. This difficulty is amplified when identifying interactions. Results: In this paper, we propose an Adaptive Group Lasso (AGL) model for large-scale association studies. Our model enables us to analyze SNPs and their interactions simultaneously. We achieve this by introducing a sparsity constraint in our model based on the fact that only a small fraction of SNPs is disease-associated. In order to reduce the number of false positive findings, we develop an adaptive reweighting scheme to enhance sparsity. In addition, our method treats SNPs and their interactions as factors, and identifies t...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Can Yang, Xiang Wan, Qiang Yang, Hong Xue, Weichuan Yu
• Keywords: Association Studies, BMCBI 2010, Single Nucleotide Polymorphism, SNPs