Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data
This study uses GBS data collected by the Next Generation Cassava Breeding Project
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
This work is part of the “Next Generation Cassava Breeding Project” which is supported by the UK Department for International Development, in partnership with the Bill & Melinda Gates Foundation.
Citation
Chan A.W, M. Hamblin and J-L Jannink. Evaluating imputation algorithms for low-depth genotyping-by-sequencing (GBS) data. PLOS One; 18 August 2016 https://doi.org/10.1371/journal.pone.0160733
Links
Evaluating imputation algorithms for low-depth genotyping-by-sequencing (GBS) data