Integrative Genomic Analysis of Congenital Heart Disease

项目摘要/摘要 我的目标是成为一名独立的人类遗传学研究者，专注于了解分子 心血管疾病的基础，如先天性心脏病(CHD)。先天性心脏病影响了约1%的活产儿， 现在，患有冠心病的成年人比儿童更多。尽管CHD有很强的遗传成分，但 致病机制仍然知之甚少。作为儿科心脏基因组学联盟的一部分 (PCGC)，我们已经对来自13,000名患者的3,443例三重病例进行了完整外显子组测序(WES)。 被招募到研究中。我们发现，新生突变(DNMs)是10%的病例的基础，很少是遗传性的 突变导致约1.8%的病例。加上环境风险因素，拷贝数变化，以及 非整倍体，这些发现只能解释约45%的CHD。我的中心假设是冠心病病例的一个子集 是同一生物途径中稀有和常见变异上位性相互作用的结果 多基因遗传可以解释一些无法解释的冠心病病例。此外，我假设 对新发变异和传播变异的联合分析增强了识别其他CHD的能力 风险基因。我提出了三个目标，这将利用我在统计遗传学方面的背景来研究冠心病遗传学。在AIM 1，我将确定Flt4的遗传修饰物，我们已经证明，功能突变的丧失导致2.3%的 法洛四联症，尽管有惊人的不完全外观。我将应用一种基于假设的候选基因 研究修饰基因中常见变异如何调节驱动突变的表达能力的方法 通过联合分析来自约2,500个欧洲CHD三个组的WES和SNP阵列数据，Flt4。然后我会分析韦斯 来自3,443个CHD三人组的数据以确定是否存在显著的FLT4错义传递不平衡 突变。在目标2中，我将对DNM、罕见的遗传变异和从头开始的CNV进行综合分析 识别在为不同类型的遗传变异建模时无法识别的其他CHD基因 分开的。在目标3中，我将分析约2,500个欧洲CHD三联体的SNP阵列和WES数据，以调查 常见多基因变异体和DNMS的联合效应。此外，我将使用全基因组的多基因风险 评分(Prs)方法识别具有等同于单基因引入风险的高prs患者 致病突变。在K99阶段，我将接受基因组和结构变异分析方面的培训 心脏遗传学和生理学。在我的K99培训之后，我将使用这些技术来开发 用于综合分析心血管疾病常见多基因和罕见变异的生物信息学流水线 向独立过渡。这项提议将确定一定比例的 未解释的病例，使人们能够对疾病发展的机制和机会有新的见解 以减轻这些风险。我将通过开发统计方法将我的研究与我导师的研究区分开来 多组数据和复杂遗传模型在心血管疾病中的整合和扩展对心血管疾病的理解 疾病遗传学来源于罕见的变异，对复杂遗传学有很大的贡献。

Project Summary/Abstract My goal is to become an independent investigator in human genetics focusing on understanding the molecular basis of cardiovascular (CV) diseases such as congenital heart disease (CHD). CHD affects ~1% of live births, and there are now more adults with CHD than children. Although CHD has a strong genetic component, the causative mechanisms remain poorly understood. As part of the Pediatric Cardiac Genomics Consortium (PCGC), we have performed whole exome sequencing (WES) on 3,443 case trios from > 13,000 patients recruited into the study. We found that de novo mutations (DNMs) underlie 10% of cases and rare inherited mutations contribute to ~1.8% of cases. Together with environmental risk factors, copy number variation, and aneuploidy, these findings only explain ~45% of CHD. My central hypothesis is that a subset of CHD cases result from the epistatic interaction of rare and common variants in the same biological pathway and that polygenic inheritance can account for some proportion of unexplained CHD cases. Moreover, I hypothesize that a combined analysis of de novo and transmitted variations has enhanced power to identify additional CHD risk genes. I propose three aims that will utilize my background in statistical genetics to CHD genetics. In Aim 1, I will identify genetic modifiers of FLT4, a gene we have shown that loss of function mutations cause 2.3% of Tetralogy of Fallot, albeit with striking incomplete penetrance. I will apply a hypothesis-based candidate gene approach to study how common variants in modifier genes modulate the expressivity of driver mutations in FLT4 by jointly analyzing WES and SNP array data from ~2,500 European CHD trios. I will then analyze WES data from 3,443 CHD trios to determine if there is significant transmission disequilibrium for FLT4 missense mutations. In Aim 2, I will perform an integrated analysis of DNMs, rare inherited variants, and de novo CNVs to identify additional CHD genes that could not be identified when modeling different types of genetic variants separately. In Aim 3, I will analyze SNP array and WES data in ~2,500 European CHD trios to investigate the combined effects of common polygenic variants and DNMs. Further, I will use a genome-wide polygenic risk score (PRS) method to identify patients with a high PRS equivalent to the risk introduced by a monogenic pathogenic mutation. In the K99 phase, I will receive training in both genome & structural variation analyses and cardiac genetics & physiology. Following my K99 training, I will use these techniques to develop bioinformatics pipelines for the integrated analysis of common polygenic and rare variants in CV diseases as I transition to independence. This proposal will identify the genetic underpinnings of some proportion of unexplained cases, allowing new insight into mechanisms governing disease development, and the opportunity to mitigate these risks. I will distinguish my research from my mentors’ by developing statistical methods for the integration of multi-omic data and complex genetic models in CV disease and extend the understanding of CV disease genetics from rare variants with a large effect to the contribution of complex genetics.