Variable Selection, Sparse Meta-Analysis and Genetic Risk Prediction for Genome-Wide Association Studies

QIANCHUAN HE: Variable Selection, Sparse Meta-Analysis and Genetic Risk Prediction for Genome-Wide Association Studies (Under the direction of Dr. Danyu Lin and Dr. Hao Helen Zhang) Genome-wide association studies (GWAS) usually involve more than half a million single nucleotide polymorphisms (SNPs). The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Recently developed variable selection methods allow the joint analysis for GWAS data, but they tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs). Genetic risk prediction becomes highly challenging when the number of causal variants is large and many of the effects are weak. Existing methods mostly rely on marginal regression estimates, and their prediction power is quite limited. In meta-analysis, the involvement of multiple studies adds one more layer of complexity to variable selection. While existing variable selection methods can be potentially applied to meta-analysis, they require direct access to raw data, which are often difficult to be obtained. In the first part of this dissertation, we introduce GWASelect, a statistically powerful and computationally efficient variable selection method for analyzing GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false-positive findings.