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.
journal_name
Geneticsjournal_title
Geneticsauthors
Fragoso CA,Heffelfinger C,Zhao H,Dellaporta SLdoi
10.1534/genetics.115.182071subject
Has Abstractpub_date
2016-02-01 00:00:00pages
487-95issue
2eissn
0016-6731issn
1943-2631pii
genetics.115.182071journal_volume
202pub_type
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