Integrative Genomic Analysis of Congenital Heart Disease

项目总结/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 心血管（CV）疾病的基础，如先天性心脏病（CHD）。CHD影响约1%的活产婴儿， 现在患冠心病的成年人比儿童多。虽然冠心病有很强的遗传成分， 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， 在PCGC（PCGC）中，我们对来自> 13，000名患者的3，443例病例进行了全外显子组测序（WES） 招募到研究中。我们发现，10%的病例和罕见的遗传性突变（DNMs）是新生突变的基础。 突变导致约1.8%的病例。与环境风险因素、拷贝数变异和 非整倍体，这些发现只能解释约45%的CHD。我的中心假设是， 由相同生物学途径中罕见和常见变体的上位相互作用引起， 多基因遗传可解释部分原因不明的CHD病例。另外，我假设 对新发和传播变异的综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，将利用我的背景，统计遗传学CHD遗传学。在Aim中 1，我将确定FLT 4的遗传修饰剂，我们已经证明，FLT 4基因的功能丧失突变导致2.3%的 法洛四联症，尽管有明显的不完全昏迷。我会将基于假设的候选基因 一种研究修饰基因中常见变异如何调节驱动突变表达的方法， FLT 4通过联合分析来自约2,500个欧洲CHD三人组的WES和SNP阵列数据。然后我将分析WES 来自3，443例CHD三人组的数据，以确定FLT 4错义是否存在显著的传递不平衡 突变。在目标2中，我将对DNM、罕见遗传变异和新生CNV进行综合分析 识别在建模不同类型的遗传变异时无法识别的其他CHD基因 分开在目标3中，我将分析约2,500例欧洲CHD三人组的SNP阵列和WES数据，以研究 常见的多基因变异和DNM的组合效应。此外，我将使用全基因组多基因风险 评分（PRS）方法，以识别具有高PRS的患者，其等同于由单基因 致病突变在K99阶段，我将接受基因组和结构变异分析的培训 心脏遗传学和生理学。在K99培训之后，我将使用这些技术来开发 用于CV疾病中常见多基因和罕见变异综合分析的生物信息学管道， 过渡到独立。这项建议将确定某些比例的遗传基础， 无法解释的病例，使人们对疾病发展的机制有了新的认识， 来降低这些风险。我将通过发展统计方法来区分我的研究和我的导师的研究。 整合CV疾病中的多组学数据和复杂遗传模型，并扩展对CV的理解 疾病遗传学从罕见变异到影响大的复杂遗传学贡献。

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.