Non-coding genetic variants that impact immune phenotypes and diseases

 描述（申请人提供）：全基因组关联研究（GWAS）揭示了数千个与常见疾病相关的遗传基因座。对于每个基因座，一个“标签”单核苷酸多态性（SNP）已被确定沿着与几十个额外的SNP连锁不平衡。然而，对于这些基因座中的许多基因座，因果SNP和受因果SNP影响的基因是未知的。由于大多数SNP都位于基因组的非编码区，并且对这些区域的功能知之甚少，因此根据其对功能的预测影响来确定因果SNP仍然具有挑战性。尽管非编码基因组注释数据库已经可用，并且缩小因果SNP范围的计算方法也已开发，但大多数因果SNP尚未得到验证，迫切需要对候选SNP进行大规模验证。两项技术进步使得满足这些需求并全面验证数千个候选SNP成为可能。首先，DNA合成现在可以在一个平行和具有成本效益的过程中进行，从而能够快速生成数百万个DNA报告基因构建体，以测试非编码SNP对报告基因表达的影响。其次，基于CRISPR的基因组编辑工具正在快速发展，现在能够以中高通量直接改变基因组中的非编码序列。这两种方法的组合干扰DNA序列并研究其结果提供了一种非常适合于在更大的候选者集合中找到少量因果SNP的强大方法。在我们小组最近的一项研究中，我们使用这两种方法来确定免疫系统中少数基因的因果SNP，为这一提议提供了原理证明。我们现在在计算和实验上扩展这种方法。首先，我们将开发一个贝叶斯分层框架来整合注释数据集，以帮助精细映射和提名基因表达的因果SNP，并基于验证的eQTL SNP开发一种荟萃分析方法来精细映射因果GWAS SNP。其次，使用免疫细胞中的基因表达作为疾病的代表，我们将应用大规模平行报告基因测定（MPRA）和CRISPR的高效基因组工程来测试候选GWAS SNP对基因表达的影响。第三，我们将使用这些数据集来完善计算模型，以更好地预测因果SNP。我们的数据集和模型有望：（i）提高我们预测任何疾病中因果SNP的能力;（ii）引导我们推断免疫系统中遗传和功能变异的原理;（iii）引导我们预测免疫系统中遗传和功能变异的原理。 系统;（iii）揭示常见人类免疫疾病的机制。

 DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have revealed thousands of genetic loci associated with common diseases. For each locus, a 'tag' single nucleotide polymorphism (SNP) has been identified along with dozens of additional SNPs in linkage disequilibrium. However, for many of these loci, the casual SNP(s) and the genes affected by the causal SNPs are not known. Since most SNPs fall in non-coding regions of the genome, and relatively little are known about the functions of these regions, it remains challenging to pinpoint the causal SNP based on its predicted impact on function. Despite the availability of non-coding genome annotation database and the development of computational approaches for narrowing down causal SNPs, most of the causal SNPs have not been validated, and there is an urgent need for large-scale validation of candidate SNPs. Two technological advances make it possible to address these needs and comprehensively validate thousands of candidate SNPs. First, DNA synthesis is now possible to perform in a parallel and cost-effective process, enabling the rapid generation of millions of DNA reporter constructs to test the impact of non-coding SNPs on reporter expression. Second, CRISPR-based genome editing tools are evolving at a fast pace, and are now able to directly alter non-coding sequences in the genome at medium- to high-throughput. The combination of these two methods to perturb DNA sequences and study the consequences provides a powerful approach highly suited to finding a small number of causal SNPs within a larger set of candidates. In a recent study from our group, we used these two approaches to pinpoint causal SNPs for a small number of genes in the immune system, providing a proof-of-principle for this proposal. We now extend this approach computationally and experimentally. First, we will develop a Bayesian hierarchical framework to integrate annotation datasets to help fine map and nominate causal SNPs for gene expression, and a meta-analysis approach to fine map causal GWAS SNPs based on validated eQTL SNPs. Second, using gene expression in immune cells as a proxy for disease, we will apply massively parallel reporter assays (MPRAs) and efficient genome engineering with CRISPR to test the impact of candidate GWAS SNPs on gene expression. Third, we will use these datasets to refine the computational models to better predict causal SNPs. Our datasets and models are expected to: (i) improve our ability to predict causal SNPs in any disease; (ii) lead us to deduce principles of genetic and functional variation in the immune system; (iii) reveal mechanisms underlying common human immune diseases.