Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, UK.
Nature. 2020 Jul;583(7814):96-102. doi: 10.1038/s41586-020-2434-2. Epub 2020 Jun 24.
Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and causative genes for more than half such disorders remain to be discovered. Here we used whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants in the coding and non-coding regions of the genome. We generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 extensively phenotyped participants. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed to be aetiological. By generating WGS data of UK Biobank participants, we found that rare alleles can explain the presence of some individuals in the tails of a quantitative trait for red blood cells. Finally, we identified four novel non-coding variants that cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.
大多数罕见病患者未得到分子诊断,超过一半的此类疾病的病因变异和致病基因仍有待发现。在这里,我们在国家卫生系统中使用全基因组测序 (WGS) 来简化诊断并发现基因组编码区和非编码区的未知病因变异。我们为 13037 名参与者生成了 WGS 数据,其中 9802 名患有罕见病,并为 7065 名广泛表型参与者中的 1138 名提供了遗传诊断。我们确定了 95 个基因与罕见病之间的孟德尔关联,其中 11 个是自 2015 年以来发现的,至少有 79 个被确认为病因。通过生成 UK Biobank 参与者的 WGS 数据,我们发现稀有等位基因可以解释红细胞数量性状尾部的一些个体的存在。最后,我们确定了四个新的非编码变异,它们通过破坏 ARPC1B、GATA1、LRBA 和 MPL 的转录导致疾病。我们的研究表明,在常规医疗保健中使用 WGS 进行诊断和病因发现具有协同作用。