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BMC Genomics. 2017 May 22;18(1):396. doi: 10.1186/s12864-017-3767-6.

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

Author information

1
Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA.
2
Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
3
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
4
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA. lbj@genetics.utah.edu.
5
Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA. fyu@bcm.edu.
6
Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. xing@biology.rutgers.edu.

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.

KEYWORDS:

Extremely low coverage; Imputation; Population structure; Single nucleotide variant; South Asian; Whole genome sequencing

PMID:
28532386
PMCID:
PMC5440948
DOI:
10.1186/s12864-017-3767-6
[Indexed for MEDLINE]
Free PMC Article

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