Systems Genetics of Adiposity Traits in Outbred Rats

 描述（由申请人提供）：肥胖和超重是多种疾病的主要风险因素，包括心血管疾病、糖尿病、癌症和中风。特别重要的是内脏或腹部脂肪组织，它是代谢健康的已知预测因子。遗传学在调节内脏肥胖和肥胖方面发挥着重要作用，了解潜在的遗传机制将有助于预防和治疗。人类全基因组关联研究（GWAS）和遗传基因组学（将mRNA表达与基因组变异相结合）已经鉴定出40多个拟人化特征基因。尽管取得了这些成功，但绝大多数遗传变异仍然未知。由于其余基因座的效应量较小，因此很难在人类中识别这些基因座。 高度重组的动物模型，如异质性股票（HS）提供的能力，精细定位数量性状基因座（QTL），以小于5 Mb。我的实验室已经使用HS大鼠对内脏肥胖和相关性状的60多个QTL进行了精细定位，平均只有2.2 Mb。我们已经确定了这些位点中的一些候选基因，但还需要其他方法来确定和验证其余的候选基因。使用表达QTL（eQTL）定位和HS创始人序列，我们最近确定Tpcn 2作为可能的致病基因的基础上的3.1 Mb位点的空腹血糖和胰岛素水平。尽管含有80多个基因，HS策略使我们能够在短短几年内鉴定出该基因。在目前的提议中，我们假设HS大鼠携带肥胖相关性状的致病基因和变异体.我们提出了一个遗传和统计工具相结合，以确定这些QTL的致病基因和变异。这项工作的主要影响将是加速发现与肥胖特征相关的基因和网络，从而为改善与内脏肥胖相关的健康奠定基础。在目标1中，我们将检验RNA表达水平的变化是肥胖性状QTL的基础这一假设。RNA-seq将用于确定HS大鼠肝脏和脂肪组织中基因和转录物的丰度水平。将使用eQTL和网络分析来鉴定候选基因和基因网络。我们将确定顺式调节eQTL，属于同一地区的肥胖QTL，并使用因果模型和人类和小鼠GWAS优先考虑候选人。在目标2中，我们将检验肥胖QTL受单个序列变体调节的假设。使用完全测序的HS创始人，我们将估算每个QTL内所有可能的SNP，并进行统计合并分析以鉴定潜在的因果SNP。将使用保守性和蛋白质建模对变体进行优先排序。我们将使用锌指核酸酶（ZFN）或成簇的规则间隔短回文重复序列（CRISPR/Cas9）技术验证五个或六个高优先级候选基因和/或变体，以操纵大鼠的这些基因。我们期望eQTL和网络分析，结合统计工具和大鼠敲除/导入技术，将使我们能够验证许多已经确定的肥胖和相关性状QTL的因果基因。

 DESCRIPTION (provided by applicant): Obesity and overweight are major risk factors for multiple diseases including cardiovascular disease, diabetes, cancer and stroke. Of particular importance is visceral, or abdominal, adipose tissue, which is a known predictor of metabolic health. Genetics play a large role in regulating visceral adiposity and obesity and understanding the underlying genetic mechanisms will help with prevention and treatment. Genome wide association studies (GWAS) and genetical genomics (integrating mRNA expression with genomic variation) in humans has led to identification of over 40 genes for anthropomorphic traits. Despite these successes, the vast majority of genetic variation remains unknown. With the small effect size of the remaining loci, it will be difficult to identify these loci in humans. Highly recombinant animal models such as heterogeneous stocks (HS) provide the ability to fine-map quantitative trait loci (QTL) to less than five Mb. My laboratory has used HS rats to fine-map over 60 QTL for visceral adiposity and related traits to an average of only 2.2 Mb. We have identified candidate genes within some of these loci, but additional methods are needed to identify and verify the remaining candidates. Using expression QTL (eQTL) mapping and HS founder sequence, we recently identified Tpcn2 as the likely causal gene underlying a 3.1 Mb locus for fasting glucose and insulin levels. Despite containing over 80 genes, the HS strategy enabled us to identify this gene within only a few years. In the current proposal, we hypothesize that HS rats harbor causal genes and variants for adiposity- related traits. We propose a combination of genetic and statistical tools to identify the causal genes and variants underlying many of these QTL. The major impact of this work will be to accelerate discovery of genes and networks involved in adiposity traits, thereby laying the foundation for improving health associated with visceral adiposity. In Aim 1 we will test the hypothesis that changes in RNA expression levels underlie QTL for adiposity traits. RNA-seq will be used to determine abundance levels of genes and transcripts in liver and adipose tissue of HS rats. Candidate genes and gene networks will be identified using eQTL and network analyses. We will identify cis-regulating eQTLs that fall within the same region as the adiposity QTL and use causal modeling and human and mouse GWAS to prioritize candidates. In Aim 2 we will test the hypothesis that adiposity QTL are regulated by a single sequence variant. Using the fully sequenced HS founders, we will impute all possible SNPs within each QTL and run a statistical merge analysis to identify potentially causal SNPs. Variants will be prioritized using conservation and protein modeling. We will validate five or six high priority candidate genes and/or variants using zinc-finger nuclease (ZFN) or clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) technology to manipulate these genes the rat. We expect that eQTL and network analysis, when combined with the statistical tools and rat knock-out/in technology, will allow us to validate causal genes underlying many of the already identified QTL for adiposity and related traits.