The Impact of Human Gene Knockouts in Type 2 Diabetes and Related Traits

描述(由申请人提供)：开发治疗2型糖尿病(T2D)的新的有效疗法需要确定新的药物靶点，理想情况下是那些通过人类人群研究的临床益处的强有力证据来验证的药物靶点。遗传性功能丧失(LoF)变异体提供了一种评估体内基因活性降低的影响的方法。通过观察对疾病提供保护而不会产生不良后果的LOF变体，可以获得靶标确认的特别有力的证据(如CCR5和PCSK9)。申请者收集了2,800名个体(T2D病例和对照)DNA样本中的高覆盖率外显子组测序数据，并将使用“外显子”阵列对45,000名个体(T2D病例和对照)中非同义蛋白改变变异的全面集合进行基因分型。为了对LOF变体的这些数据进行系统和有力的分析，必须克服几个挑战：(A)开发和应用算法，稳健地检测插入和缺失变体(LOF变体的一个主要机制，目前的算法表征不佳)，并准确注释所有类别的LOF变体；(B)表征对LOF变体的频谱和特征敏感的统计测试，及其在病例和对照中检测到的LOF变体目录中的应用；(C)在大的、独立的样本中跟踪假定的LOF关联。为了系统地测试罕见的改变蛋白质的LoF变体在T2D风险中的作用，申请人建议：(A)开发算法来检测序列数据中的Indels和使用外显子阵列的数据的更大的缺失；将这些算法应用于总计47,000个DNA样本的序列和基因型数据；并注释整个基因组；(B)评估LOF分析的罕见变体测试的能力，并使用选定的方法对LOF变体与T2D以及与葡萄糖、胰岛素、血脂和BMI的相关代谢性状进行关联分析；以及(C)通过对来自T2D基因项目的多达10,000名个体(T2D病例和对照)的数据进行电子跟踪，并通过对另外20,000名个体(T2D病例和对照)进行靶向测序，来验证LOF变异与T2D的RIK改变(特别是对T2D的保护)之间的假定关联。人类遗传学研究的一个中心目标是提供能够指导预防和治疗的突破性方法的见解。申请者在开发用于基因分析的数据集、实验室方法和算法方面一直处于领先地位，并合作了十多年，以应用这些方法来发现T2D的基因。现在，来自T2D病例和对照的大临床样本、下一代测序技术和算法改进的融合使系统地评估LOF变体对T2D的影响、提名和验证潜在的治疗靶点成为可能。

DESCRIPTION (provided by applicant): The development of new and effective therapies for type 2 diabetes (T2D) requires the identification of novel drug targets, ideally ones that are validated by strong evidence of clinical benefit from studies in human populations. Inherited loss of function (LoF) variants offer one approach to assess the impact of reducing gene activity in humans in vivo. Particularly strong evidence for target validation can be obtained by observing LoF variants that provide protection against disease without undesirable consequences (as in CCR5 and PCSK9). The applicants have collected high coverage exome sequencing data in DNA samples from each of 2,800 individuals (T2D cases and controls), and will have genotyped a comprehensive collection of non-synonymous protein altering variants in >45,000 individuals (T2D cases and controls) using the "exome" array. To perform a systematic and well powered analysis of these data for LoF variants, several challenges must be overcome: (a) development and application of algorithms for robust detection of insertion and deletion variants (a major mechanism for LoF variants which is poorly characterized with today's algorithms), and for the accurate annotation of all classes of LoF variants; (b) characterization of statistical tests that re sensitive for the frequency spectrum and characteristics of LoF variants, and their application to the catalogue of LoF variants detected in cases and controls; and (c) follow-up of putative LoF associations in large, independent samples. To test systematically the role of rare protein-altering LoF variants in risk of T2D, the applicants propose: (a) to develop algorithms to detect indels in sequence data, and larger deletions using data from the exome array; to apply these algorithms to sequence and genotype data totalling >47,000 DNA samples; and to annotate LoF variants across the genome; (b) to evaluate the power of rare variant tests for LoF analysis, and to perform association analyses using chosen methods for LoF variants, both singly and for sets of LoF variants within a gene, with T2D, as well as with the related metabolic traits of glucose, insulin, lipids, and BMI; and (c) to validate putative associations of LoF variants with altered rik of T2D (in particular, protection from T2D) by performing in silico follow-up in data on up to 10,000 individuals (T2D cases and controls) from the T2D-GENES Project, and by targeted sequencing in 20,000 additional individuals (T2D cases and controls). A central goal of human genetics research is to provide insights that can guide breakthrough approaches to prevention and therapy. The applicants have been leaders in the development of datasets, laboratory methods, and algorithms for genetic analysis, and have collaborated for over a decade to apply these methods to discover genes for T2D. Now, the convergence of large clinical samples from T2D cases and controls, of next-generation sequencing technology, and of algorithmic improvements make it possible to evaluate systematically LoF variants for effects on T2D, nominating and validating potential therapeutic targets.