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

今年，研究人员将获得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，这些序列起着开关的作用，在细胞中开启和关闭基因。这些DNA开关中只有几个变异导致疾病的例子。我们已经确定了这些开关的变体会导致非常罕见的疾病。我们已经确定了一个短序列的变体，这意味着孩子出生时没有胰腺。我们证明，这一短序列是一个主开关，开启了导致胰腺发育的关键基因。我们还在另一个导致儿童产生过多胰岛素和血糖水平低得危险的开关中发现了非常罕见的变异。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100万人的全基因组测序数据来识别对常见的2型糖尿病至关重要的开关。作为初步数据和原理证明，我们已经分析了15万名英国生物库参与者的身高。我们确定了31个以前未知的关联。一个例子是启动一种名为HMGA1的基因的开关的变体。携带这些Switch变种的人平均身高增加了5厘米。这一点特别有趣，因为改变HMGA1的蛋白质序列不会影响身高。我们已经在我们所有人和TOPMed队列中的20万人中证实了这些关联。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，该变异发生在一种特定类型的开关--长的非编码RNA中。我们最近证明了这种长的非编码RNA对于开启HNF1A是重要的。分析1,000,000个完整基因组的数据在计算上是极其困难的。口译是一个巨大的挑战。这个项目将在我们最初工作的基础上，通过完善我们的WGS分析管道，使其高效、成本效益高和公开可用。这个项目是及时的，因为英国生物库将在今年年底之前公布50万人的全基因组序列数据。我们将使用这些数据来执行监管开关的单变量和分组测试。分析将在不同的祖先群体中进行，并进行组合分析。我们将使用我们所有人和TOPMed的美国队列来证实我们的发现，这些队列将有50万个不同祖先的个体可供分析。我们将测试我们罕见的家族性糖尿病队列和100,000基因组计划中已识别的区域。这些人的集合中，人们预计他们的糖尿病是由单一基因引起的。这一点很重要，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。我们还将对开关的一个子集进行功能跟踪，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体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有效和成本效益高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。它还可能极大地提高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.