Accurate Imputation of Untyped Variants from Deep Sequencing Data.
Accurate Imputation of Untyped Variants from Deep Sequencing Data.
复制标题
根据深度测序数据准确估算非类型变异。
作者：
D. Torkamaneh;F. Belzile
全基因组关联研究（GWAS）的质量、统计功效和分辨率在很大程度上取决于基因型数据的全面性。在过去的几年中，尽管测序价格不断下降，但包含大量样品的关联组的全基因组测序（WGS）仍然成本高昂。因此，大多数GWAS人群仍然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。未分型变异的插补是一种有效的方法，可以最大限度地增加研究样本中识别的SNP数量，它提高了GWAS的功效和分辨率，并允许整合从各种来源获得的基因分型数据集。在这里，我们描述的关键概念，包括参考面板的架构，未分型的变异，并审查了一些相关的挑战，以及如何解决这些问题。我们还讨论了需要和可用的方法，以严格评估插补数据的准确性之前，他们在任何遗传研究中使用。
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.
登录
查看更多内容
影响因子：
3.7
作者：
Ramnarine S;Zhang J;Chen LS;Culverhouse R;Duan W;Hancock DB;Hartz SM;Johnson EO;Olfson E;Schwantes-An TH;Saccone NL
通讯作者：
Saccone NL
影响因子：
0.8
作者：
Zhang B;Zhi D;Zhang K;Gao G;Limdi NN;Liu N
通讯作者：
Liu N
影响因子：
9.8
作者：
Browning, Sharon R.;Browning, Brian L.
通讯作者：
Browning, Brian L.
影响因子：
9.8
作者：
Scheet, P;Stephens, M
通讯作者：
Stephens, M