Using whole genome sequencing to identify non-coding elements associated with diabetes and related traits across ancestries

研究人员今年将获得100多万人的完整基因序列、医疗记录和广泛的健康数据。最近，在理解人类基因组中的调控序列方面取得了重大进展，这些序列充当开关，在细胞中打开和关闭基因。这些DNA开关的变异导致疾病的例子只有少数。我们已经确定了这些开关的变体，导致非常罕见的疾病。我们已经确定了一个短序列的变异，这意味着孩子出生时没有胰腺。我们发现，这个短序列是开启导致胰腺发育的关键基因的主开关。我们还在另一种开关中发现了非常罕见的变异，这种开关会导致儿童产生过多的胰岛素，并出现危险的低血糖水平。在这种情况下，这是因为开关被不适当地打开，胰腺中产生了一种不应该产生的蛋白质。在这个项目中，我们将使用100万个体的全基因组测序数据来确定对普通2型糖尿病很重要的开关。作为初步数据和原理证明，我们已经分析了15万英国生物银行参与者的身高。我们确定了31个以前未知的关联。其中一个例子是一种开关的变体，这种开关可以打开一种名为HMGA1的基因。拥有这些基因变体的人平均要高5厘米。这是特别有趣的，因为改变HMGA1的蛋白质序列并不影响身高。我们已经确认了来自All of Us和TOPMed队列的20万人的这些关联。我们还对糖尿病进行了初步分析。我们已经确定了与HNF1A附近的一种罕见变异的关联，这种变异发生在长链非编码RNA中，这是一种特定类型的开关。我们最近证明了这种长链非编码RNA对于开启HNF1A很重要。分析100万个完整基因组的数据在计算上是极具挑战性的。口译是一个巨大的挑战。该项目将在我们最初工作的基础上，完善我们的WGS分析管道，使其高效、具有成本效益并可公开使用。这个项目很及时，因为英国生物银行将在今年年底公布50万人的全基因组序列数据。我们将使用这些数据对调节开关进行单变量和组测试。分析将在不同的祖先群体中进行，也可以进行组合分析。我们将使用美国队列“All of US”和TOPMed来确认我们的发现，这些队列将有50万不同祖先的个体可供分析。我们将在罕见家族性糖尿病队列和100,000基因组计划中测试已确定的区域。这是一群人，我们认为他们的糖尿病是由单一的遗传原因引起的。这很重要，因为我们在将基因诊断转化为治疗变化方面有着良好的记录。我们还将对开关子集进行功能随访，以提供对胰腺发育和功能的新见解。这个项目将为我们理解非编码变异在人类疾病中的作用提供实质性的进展。它将使我们能够开发出高效、经济的方法来分析全基因组序列数据。我们将为胰腺发育和功能的调控提供新的见解。它还可能显著改善一些罕见糖尿病患者的生活质量。如果我们要利用全基因组测序在理解疾病机制方面取得重大进展，我们的项目就很重要。

The complete genetic sequences, medical records, and extensive health data of over 1 million people will become available for researchers this year. Major progress has recently been made on understanding the regulatory sequences in the human genome that act as switches, turning genes on and off in cells. There are only a few examples of variants in these DNA switches causing disease. We have identified variants of these switches causing very rare disease. We have identified variants of a short sequence that mean children are born without a pancreas. We showed that this short sequence is a master switch that turns on the key gene leading to pancreas development. We have also identified very rare variants in another switch that leads to children producing too much insulin and having dangerously low glucose levels. In this case it is because the switch is inappropriately turned on and a protein is produced in the pancreas that shouldn't be. In this project we will use the >1 million individuals with whole genome sequencing data to identify the switches that are important for common type 2 diabetes.As preliminary data and proof of principle we have already analysed height in 150,000 UK Biobank participants. We identified 31 previously unknown associations. One example is variants of a switch that turns on a gene called HMGA1. People with these switch variants are, on average, 5cm taller. This is particularly interesting because changing the protein sequence of HMGA1 does not affect height. We have confirmed these associations in 200,000 people from the All of Us and TOPMed cohorts. We have also performed preliminary analyses for diabetes. We have identified an association with a rare variant near HNF1A that occurs in a long non-coding RNA, a specific type of switch. We have recently demonstrated this long non-coding RNA is important for turning on HNF1A.It is extremely challenging computationally to analyse data on 1,000,000 complete whole genomes. Interpretation is a substantial challenge. This project will build on our initial work by refining our WGS analysis pipeline to make it efficient, cost-effective and publically available. This project is timely because UK Biobank will release whole genome sequence data on 500,000 people by the end of this year. We will use this data to perform single variant and group testing of regulatory switches. The analyses will be performed in different ancestry groups as well as a combined analysis. We will confirm our findings using the US cohorts All of Us and TOPMed which will have >500,000 individuals of diverse ancestries available for analysis. We will test the identified regions in our rare familial diabetes cohort and in the 100,000 genomes project. These are a collection of people where it is expected that there is a single genetic cause of their diabetes. This is important because we have an excellent track record of translating genetic diagnosis into treatment change. We will also perform functional follow-up of a subset of switches to provide new insights into pancreas development and function.This project will provide a substantial advance in our understanding of the role of non-coding variants in human disease. It will allow us to develop efficient and cost-effective approaches analysing whole genome sequence data. We will provide new insights into the regulation of pancreas development and function. It may also dramatically improve the quality of life for some patients with rare forms of diabetes. Our project is important if we are to make major advances in understanding disease mechanisms using whole genome sequencing.