小柯机器人

瑞士洛桑大学Olivier Delaneau课题组使用大型参考面板对低覆盖率测序数据进行有效的定相和归算。2021年1月7日出版的《自然-遗传学》杂志发表了这项成果。

他们描述了一种用于对来自现代参考面板的低覆盖率测序数据集进行定相和归算的方法-GLIMPSE。他们证明了其在不同覆盖范围和人群中的表现出色。GLIMPSE以不到1美元的计算成本实现了基因组的估算，大大优于其他方法，并在整个等位基因频率范围内提高了估算精度。

作为概念的证明，他们证明了1x覆盖率能够进行有效的基因表达关联研究，并在罕见的变异负荷测试中优于密集的SNP阵列。

总的来说，这项研究说明了低覆盖估算的潜力，并提出了未来基因组研究设计的范式转变。

研究人员表示，低覆盖率全基因组测序后再进行插补已被认为是一种用于疾病和人群遗传学研究的经济有效的基因分型方法。但是，由于当前的插补方法计算量大且无法利用大型参考面板，因此其与SNP阵列的竞争力明显减弱。

附：英文原文

Title: Efficient phasing and imputation of low-coverage sequencing data using large reference panels

Author: Simone Rubinacci, Diogo M. Ribeiro, Robin J. Hofmeister, Olivier Delaneau

Issue&Volume: 2021-01-07

Abstract: 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.

DOI: 10.1038/s41588-020-00756-0

Source: https://www.nature.com/articles/s41588-020-00756-0

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