Iglesias Adriana I, van der Lee Sven J, Bonnemaijer Pieter W M, Höhn René, Nag Abhishek, Gharahkhani Puya, Khawaja Anthony P, Broer Linda, Foster Paul J, Hammond Christopher J, Hysi Pirro G, van Leeuwen Elisabeth M, MacGregor Stuart, Mackey David A, Mazur Johanna, Nickels Stefan, Uitterlinden André G, Klaver Caroline C W, Amin Najaf, van Duijn Cornelia M
Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
Department of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands.
Hum Mutat. 2017 Aug;38(8):1025-1032. doi: 10.1002/humu.23247. Epub 2017 Jun 9.
Recently, the Haplotype Reference Consortium (HRC) released a large imputation panel that allows more accurate imputation of genetic variants. In this study, we compared a set of directly assayed common and rare variants from an exome array to imputed genotypes, that is, 1000 genomes project (1000GP) and HRC. We showed that imputation using the HRC panel improved the concordance between assayed and imputed genotypes at common, and especially, low-frequency variants. Furthermore, we performed a genome-wide association meta-analysis of vertical cup-disc ratio, a highly heritable endophenotype of glaucoma, in four cohorts using 1000GP and HRC imputations. We compared the results of the meta-analysis using 1000GP to the meta-analysis results using HRC. Overall, we found that using HRC imputation significantly improved P values (P = 3.07 × 10 ), particularly for suggestive variants. Both meta-analyses were performed in the same sample size, yet we found eight genome-wide significant loci in the HRC-based meta-analysis versus seven genome-wide significant loci in the 1000GP-based meta-analysis. This study provides supporting evidence of the new avenues for gene discovery and fine mapping that the HRC imputation panel offers.
最近,单倍型参考联盟(HRC)发布了一个大型的归因面板,可实现对基因变异更准确的归因。在本研究中,我们将外显子阵列中一组直接检测的常见和罕见变异与归因基因型进行了比较,即千人基因组计划(1000GP)和HRC。我们发现,使用HRC面板进行归因可提高常见变异尤其是低频变异的检测基因型与归因基因型之间的一致性。此外,我们在四个队列中使用1000GP和HRC归因法对垂直杯盘比(青光眼的一种高度可遗传的内表型)进行了全基因组关联荟萃分析。我们将使用1000GP的荟萃分析结果与使用HRC的荟萃分析结果进行了比较。总体而言,我们发现使用HRC归因法可显著改善P值(P = 3.07×10),尤其是对于提示性变异。两项荟萃分析的样本量相同,但我们发现在基于HRC的荟萃分析中有8个全基因组显著位点,而在基于1000GP的荟萃分析中有7个全基因组显著位点。本研究为HRC归因面板提供的基因发现和精细定位新途径提供了支持证据。