Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland.
Nat Genet. 2021 Jan;53(1):120-126. doi: 10.1038/s41588-020-00756-0. Epub 2021 Jan 7.
Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined because current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. GLIMPSE achieves imputation of a genome for less than US$1 in computational cost, considerably outperforming other methods and improving imputation accuracy over the full allele frequency range. As a proof of concept, we show that 1× coverage enables effective gene expression association studies and outperforms dense SNP arrays in rare variant burden tests. Overall, this study illustrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.
低覆盖度全基因组测序结合 imputation 已被提议作为疾病和人群遗传学研究的一种经济有效的基因分型方法。然而，由于当前的 imputation 方法计算成本高昂，且无法利用大型参考面板，因此其与 SNP 芯片相比竞争力较弱。在这里，我们描述了一种用于现代参考面板中低覆盖度测序数据集相位和 imputation 的方法，GLIMPSE。我们证明了它在不同覆盖度和人群中的出色性能。GLIMPSE 以低于 1 美元的计算成本实现了基因组 imputation，显著优于其他方法，并在整个等位基因频率范围内提高了 imputation 准确性。作为概念验证，我们表明 1×覆盖度可以实现有效的基因表达关联研究，并在稀有变异负担测试中优于密集 SNP 芯片。总体而言，本研究说明了低覆盖度 imputation 的有前途的潜力，并为未来基因组学研究的设计带来了范式转变。
