Improvement of missing genotype imputation through bi - directional parsing of large SNP panels Christine Sinoquet

Such difficult analyses as disease association studies, which aim at mappping genetic variants underlying complex human diseases, rely on high-throughput genotyping techniques. However, a shortcoming of these techniques is the generation of missing calls. Computational inference of missing data represents a challenging alternative to genotyping again the missing regions. In this paper, we present SNPShuttle, an algorithm designed to gain accuracy over a former method described by Roberts and co-authors [7] (NPUTE). Given an SNP panel, NPUTE algorithm infers missing data through a single parse, relying on local similarity within sliding windows. Instead, SNPShuttle scans an SNP panel in an iterative bi-directional way, to resolve missing data with more confidence. ha l-0 03 00 59 6, v er si on 3 12 F eb 2 00 9