Extremely low-coverage whole genome sequencing in South Asians captures population genomics information.
Extremely low-coverage whole genome sequencing in South Asians captures population genomics information.
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DOI：
10.1186/s12864-017-3767-6
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发表时间：
2017-05-22
期刊：
影响因子：
4.4
通讯作者：
Xing J
中科院分区：
文献类型：
--
作者：
Rustagi N;Zhou A;Watkins WS;Gedvilaite E;Wang S;Ramesh N;Muzny D;Gibbs RA;Jorde LB;Yu F;Xing J
近年来，由于下一代测序技术的进步，全基因组测序（WGS）的成本大幅下降。然而，使用WGS进行大规模队列研究的成本仍然令人望而生畏。过去覆盖率约为2x的模拟研究表明，在集中于变异发现、关联研究重复和群体基因组学表征的研究中，使用低覆盖率WGS是有希望的。然而，低覆盖率WGS在具有复杂历史和没有参考面板的人群中的表现仍有待确定。众所周知，南印度人口具有复杂的人口结构，是缺乏足够参考小组的主要人口群体的一个例子。为了测试极低覆盖率WGS （EXL-WGS）在具有复杂历史的种群中的表现，并为南印度种群提供参考资源，我们对来自8个种群的185个南印度个体进行了EXL-WGS，覆盖范围约为1.6倍。使用两个变异发现管道，SNPTools和GATK，我们生成了一个共识调用集，对识别常见变异（次要等位基因频率≥10%）具有~90%的灵敏度。插值进一步提高了我们的调用集的灵敏度。此外，我们获得了全线粒体基因组的高覆盖率，以推断印度样本的母系进化史。总的来说，我们证明，即使在具有复杂历史和没有可用参考数据的人群中，带有imputation的EXL-WGS也可以作为一种有价值的研究设计，用于变异发现，其成本比标准WGS低得多。此外，本研究生成的南印度EXL-WGS数据将为未来的印度基因组研究提供宝贵的资源。本文的在线版本（doi:10.1186/s12864-017-3767-6）包含补充材料，可供授权用户使用。
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. 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. 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. The online version of this article (doi:10.1186/s12864-017-3767-6) contains supplementary material, which is available to authorized users.