A1005
Title: Genome-wide iterative fine-mapping for related individuals
Authors: Marco Ferreira - Virginia Tech (United States) [presenting]
Jacob Williams - Virginia Tech (United States)
Shuangshuang Xu - Virginia Tech (United States)
Abstract: Current fine-mapping methods are often implemented on small SNP sets and require the maximum number of causal SNPs to be less than a pre-specified value. Examining an entire genotype array with these methods is often not computationally possible and impractical as determining the maximum number of causal SNPs is non-trivial. However, by not examining the entire array, these methods can miss weaker causal signals and, thus, have diminished statistical power. To address this issue, the method Genome-wide Iterative fiNe-mApping (GINA) is presented. It is shown with an extensive simulation study that, when compared to currently used fine-mapping methods, the method GINA reduces the false discovery rate and increases the discovery rate of true causal variants. The application of GINA is illustrated with case studies on plant science and human health.
