A0950
Title: Genome-wide iterative fine-mapping for non-Gaussian data
Authors: Shuangshuang Xu - Virginia Tech (United States) [presenting]
Marco Ferreira - Virginia Tech (United States)
Jacob Williams - Virginia Tech (United States)
Allison Tegge - Virginia Tech (United States)
Abstract: Fine-mapping seeks to identify causal variants in genomic regions of interest previously identified by genome-wide association studies (GWAS). However, because fine-mapping is performed separately from GWAS, fine-mapping does not extract as much information as possible from the data. A novel genome-wide iterative fine-mapping method for non-Gaussian data (GINA-X) is presented. GINA-X efficiently extracts information from GWAS data by iterating two steps: a screening step and a model selection step. The screening step provides a list of candidate genetic variants and an estimate of the proportion of null genetic variants. After that, the model selection step searches the model space defined by the list of candidate genetic variants and uses the estimated proportion of null genetic variants to appropriately control for genome-wide multiplicity. A simulation study shows that, when compared to competing fine-mapping methods, GINA-X reduces the false discovery rate and increases recall of true causal genetic variants. The usefulness and flexibility of GINA-X are illustrated with two case studies on alcohol use disorder and breast cancer.
