Département de Phytologie, Université Laval, Québec City, QC, Canada.
Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.
Methods Mol Biol. 2021;2243:271-281. doi: 10.1007/978-1-0716-1103-6_13.
The quality, statistical power, and resolution of genome-wide association studies (GWAS) are largely dependent on the comprehensiveness of genotypic data. Over the last few years, despite the constant decrease in the price of sequencing, whole-genome sequencing (WGS) of association panels comprising a large number of samples remains cost-prohibitive. Therefore, most GWAS populations are still genotyped using low-coverage genotyping methods resulting in incomplete datasets. Imputation of untyped variants is a powerful method to maximize the number of SNPs identified in study samples, it increases the power and resolution of GWAS and allows to integrate genotyping datasets obtained from various sources. Here, we describe the key concepts underlying imputation of untyped variants, including the architecture of reference panels, and review some of the associated challenges and how these can be addressed. We also discuss the need and available methods to rigorously assess the accuracy of imputed data prior to their use in any genetic study.
全基因组关联研究（GWAS）的质量、统计效力和分辨率在很大程度上取决于基因型数据的全面性。尽管过去几年测序价格持续下降，但包含大量样本的关联面板的全基因组测序（WGS）仍然成本过高。因此，大多数 GWAS 群体仍使用低覆盖率的基因分型方法进行基因分型，导致数据集不完整。未分型变体的推断是一种强大的方法，可以最大限度地增加研究样本中鉴定的 SNP 数量，它提高了 GWAS 的效力和分辨率，并允许整合来自各种来源的基因分型数据集。在这里，我们描述了推断未分型变体的关键概念，包括参考面板的架构，并回顾了一些相关的挑战以及如何解决这些挑战。我们还讨论了在任何遗传研究中使用之前，严格评估推断数据准确性的必要性和可用方法。
