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

Christine Sinoquet (1)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique

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 (2007), 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.

Mots clés: biomedical data analysis; missing genotype inference; missing call imputation; nearest-neighbor

Domaines: Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Christine Sinoquet. Improvement of missing genotype imputation through bi-directional parsing of large SNP panels. 2008. ⟨hal-00300596v3⟩