Extremely low-coverage whole genome sequencing in South Asians captures population genomics information.

TitleExtremely low-coverage whole genome sequencing in South Asians captures population genomics information.
Publication TypeJournal Article
Year of Publication2017
AuthorsRustagi, N, Zhou, A, W Watkins, S, Gedvilaite, E, Wang, S, Ramesh, N, Muzny, DM, Gibbs, RA, Jorde, LB, Yu, F, Xing, J
JournalBMC Genomics
Volume18
Issue1
Pagination396
Date Published2017 May 22
ISSN1471-2164
Abstract

BACKGROUND: The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined.

RESULTS: South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples.

CONCLUSIONS: Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies.

DOI10.1186/s12864-017-3767-6
Alternate JournalBMC Genomics
PubMed ID28532386
PubMed Central IDPMC5440948
Grant ListR00 HG005846 / HG / NHGRI NIH HHS / United States
R01 GM059290 / GM / NIGMS NIH HHS / United States
R35 GM118335 / GM / NIGMS NIH HHS / United States
U54 HG003273 / HG / NHGRI NIH HHS / United States