Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data.

ABSTRACT: Well-powered genomic studies require genome-wide marker coverage across many individuals. For non-model species with few genomic resources, high-throughput sequencing (HTS) methods, such as Genotyping-By-Sequencing (GBS), offer an inexpensive alternative to array-based genotyping. Although affordable, datasets derived from HTS methods suffer from sequencing error, alignment errors, and missing data, all of which introduce noise and uncertainty to variant discovery and genotype calling. Under such circumstances, meaningful analysis of the data is difficult. Our primary interest lies in the issue of how one can accurately infer or impute missing genotypes in HTS-derived datasets. Many of the existing genotype imputation algorithms and software packages were primarily developed by and optimiz

SUBMITTER: Chan AW 

PROVIDER: S-EPMC4990193 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature