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

使用全基因组测序来识别与糖尿病相关的非编码元件和跨祖先的相关特征

• 批准号: MR/Y003748/1
• 负责人: Michael Weedon
• 金额: $ 159.46万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY003748%2F1
• 关键词: Using; whole; genome; sequencing; identify

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.

今年，研究人员将可以获得超过 100 万人的完整基因序列、医疗记录和广泛的健康数据。最近在了解人类基因组中充当开关、打开和关闭细胞中基因的调控序列方面取得了重大进展。这些 DNA 开关的变异导致疾病的例子很少。我们已经发现这些开关的变体会导致非常罕见的疾病。我们已经发现了一个短序列的变体，这意味着孩子出生时没有胰腺。我们发现这个短序列是一个主开关，可以打开导致胰腺发育的关键基因。我们还发现了另一个开关中非常罕见的变异，它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，这是因为开关打开不当，胰腺中产生了不应该产生的蛋白质。在这个项目中，我们将使用超过 100 万个体的全基因组测序数据来识别对常见 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 150,000 名英国生物银行参与者的身高。我们发现了 31 个以前未知的关联。一个例子是打开 HMGA1 基因的开关变体。具有这些开关变体的人平均身高高 5 厘米。这是特别有趣的，因为改变 HMGA1 的蛋白质序列不会影响身高。我们已经在 All of Us 和 TOPMed 队列的 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。我们发现与 HNF1A 附近的一种罕见变异存在关联，这种变异发生在长非编码 RNA（一种特定类型的开关）中。我们最近证明了这种长非编码 RNA 对于开启 HNF1A 非常重要。分析 1,000,000 个完整全基因组的数据在计算上极具挑战性。口译是一个巨大的挑战。该项目将以我们最初的工作为基础，完善我们的 WGS 分析流程，使其高效、具有成本效益并可公开使用。这个项目非常及时，因为英国生物银行将在今年年底前发布 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体和组测试。分析将在不同的血统群体中进行以及组合分析。我们将使用美国队列 All of Us 和 TOPMed 来证实我们的发现，该队列将有超过 500,000 名不同血统的个体可供分析。我们将测试罕见家族糖尿病队列和 100,000 基因组计划中已确定的区域。这些人的糖尿病预计只有单一的遗传原因。这很重要，因为我们在将基因诊断转化为治疗改变方面拥有良好的记录。我们还将对开关子集进行功能跟踪，以提供对胰腺发育和功能的新见解。该项目将为我们对非编码变异在人类疾病中的作用的理解提供实质性进展。它将使我们能够开发出高效且具有成本效益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节提供新的见解。它还可能显着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，我们的项目就非常重要。