Reveel: large-scale population genotyping using low-coverage sequencing data

Lin Huang, Bo Wang, Ruitang Chen, Sivan Bercovici, Serafim Batzoglou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Motivation: Population low-coverage whole-genome sequencing is rapidly emerging as a prominent approach for discovering genomic variation and genotyping a cohort. This approach combines substantially lower cost than full-coverage sequencing with whole-genome discovery of low-allele frequency variants, to an extent that is not possible with array genotyping or exome sequencing. However, a challenging computational problem arises of jointly discovering variants and genotyping the entire cohort. Variant discovery and genotyping are relatively straightforward tasks on a single individual that has been sequenced at high coverage, because the inference decomposes into the independent genotyping of each genomic position for which a sufficient number of confidently mapped reads are available. However, in low-coverage population sequencing, the joint inference requires leveraging the complex linkage disequilibrium (LD) patterns in the cohort to compensate for sparse and missing data in each individual. The potentially massive computation time for such inference, as well as the missing data that confound low-frequency allele discovery, need to be overcome for this approach to become practical. Results: Here, we present Reveel, a novel method for single nucleotide variant calling and genotyping of large cohorts that have been sequenced at low coverage. Reveel introduces a novel technique for leveraging LD that deviates from previous Markov-based models, and which is aimed at computational efficiency as well as accuracy in capturing LD patterns present in rare haplotypes. We evaluate Reveel's performance through extensive simulations as well as real data from the 1000 Genomes Project, and show that it achieves higher accuracy in low-frequency allele discovery and substantially lower computation cost than previous state-of-the-art methods.
Original languageEnglish (US)
Pages (from-to)1686-1696
Number of pages11
Issue number11
StatePublished - Sep 9 2015
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-01
Acknowledgements: This work was supported in part by a grant from the Stanford-KAUST alliance for academic excellence. L.H. was supported in part by a Stanford Graduate Fellowship.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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