title: Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data.
abstract: Low-coverage next-generation sequencing methodologies are routinely employed to genotype large populations. Missing data in these populations manifest both as missing markers and markers with incomplete allele recovery. False homozygous calls at heterozygous sites resulting from incomplete allele recovery confound many existing imputation algorithms. These types of systematic errors can be minimized by incorporating depth-of-sequencing read coverage into the imputation algorithm. Accordingly, we developed Low-Coverage Biallelic Impute (LB-Impute) to resolve missing data issues. LB-Impute uses a hidden Markov model that incorporates marker read coverage to determine variable emission probabilities. Robust, highly accurate imputation results were reliably obtained with LB-Impute, even at extremely low (<1×) average per-marker coverage. This finding will have implications for the design of genotype imputation algorithms in the future. LB-Impute is publicly available on GitHub at https://github.com/dellaporta-laboratory/LB-Impute.
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Fragoso
authors.affiliation: Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520.
authors.forename: Christopher
authors.initials: C
authors.lastname: Heffelfinger
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520 Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut 06520.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.affiliation: Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520 stephen.dellaporta@yale.edu.
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Dellaporta
mesh_terms: D000465:Algorithms; D000483:Alleles; D005828:Genetics, Population; D018745:Genome, Plant; D055106:Genome-Wide Association Study / Q000379:methods; D023281:Genomics / Q000379:methods; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D008390:Markov Chains; D008957:Models, Genetic; D010944:Plants / Q000235:genetics; D015203:Reproducibility of Results; D000066908:Simulation Training
keywords: hidden Markov models; imputation; next-generation sequencing; plant genomics; population genetics