Bridging the Gap between Genomics and Clinical Outcomes in CHD

 描述（由申请人提供）：尽管分配了大量资源，但先天性心脏病（CHD）的基因组基础在很大程度上仍然未知，因为罕见和从头单核苷酸和拷贝数变异占CHD的一小部分。我们假设：（1）罕见的不完全显性、简单隐性和复合杂合子疾病模型占CHD“缺失遗传性”的非常重要的部分，以及（2）有害变体的基因组负荷，以及与一些CHD致病等位基因（但不是其他等位基因）相关的多态性，以超越其对原发性CHD的直接贡献的方式影响临床结果。我们的主要目标是建立犹他州中心作为儿科心脏基因组学联盟（PCGC）的一个不可或缺的合作伙伴，并与其他PCGC中心合作，以便（1）为PCGC提供患者，专业知识和软件，以识别CHD的缺失遗传性;和（2）通过定义与CHD相关的多形性，将基因组变异与相关临床结果相关联。导致等位基因和通过确定有害变体的基因组负荷。 目前，PCGC缺乏两种关键的诊断方法，阻碍了其定义CHD及其结果的基因组基础的能力：（1）强大的生物信息学管道，能够计算不完全显性，简单隐性和复合杂合子疾病模型，以及（2）基于家族的全基因组测序方法，能够识别编码和非编码区中的新CHD等位基因。我们认为，在PCGC先证者-父母三人组中发现的新生变异只是冰山一角，还有许多基因和等位基因尚未发现。 该提案包括创新的方法，将以非常实用的方式使PCGC受益。该建议利用了最近验证的和新兴的生物信息学技术，该技术提供了四个基本功能：（1）评估变体的功能影响的能力，无论它们位于基因组编码、基因间、剪接位点等的何处; (2)不仅针对显性从头等位基因，而且针对不完全外显显性、简单隐性和复合杂合子疾病模型计算询问患者基因型的能力;（3）在来自患者特异性诱导多能干细胞衍生的心肌细胞的谱系、表型信息和表达数据的背景下进行这些分析的能力;以及（4）共享患者管理和联盟范围的协作分析的结果的能力。 总之，我们的建议旨在填补知识空白的“缺失遗传性”周围的冠心病，利用新的工具设计的犹他州大学，使个人基因组/外显子序列，患者表型描述和谱系，和患者特异性表达数据的综合计算，所有在一个强大的统计框架。

 DESCRIPTION (provided by applicant): Despite the allocation of significant resources, the genomic basis of congenital heart disease (CHD) remains largely unknown in that rare and de novo single nucleotide and copy-number variants account for a small fraction of CHD. We hypothesize that (1) rare incompletely dominant, simple recessive, and compound- heterozygote disease models account for a very significant fraction of the "missing heritability" o CHD and (2) the genomic load of deleterious variants, together with pleomorphies associated with some CHD-causing alleles (but not others), influence clinical outcomes in a manner that transcends their immediate contribution to the primary CHD. Our principal goal is to establish the Utah Center as an integral partner of the Pediatric Cardiac Genomics Consortium (PCGC) and to work collaboratively with other PCGC centers in order to (1) provide the PCGC with the patients, expertise and software that will identify the missing heritability of CHD; and (2) associate genomic variants with relevant clinical outcomes by defining the pleomorphies associated with CHD-causing alleles and by determining the genomic load of deleterious variants. Currently, the PCGC is lacking two key diagnostic approaches that hinder its ability to define the genomic basis for CHD and its outcomes: (1) a robust bioinformatics pipeline that is capable of computing on incompletely dominant, simple recessive, and compound-heterozygote disease models, and (2) a family-based whole-genome sequencing approach that is powered to identify novel CHD alleles in coding and non-coding regions. We argue that the identification of de novo variants in PCGC proband-parent trios represents only the tip of the iceberg, with many genes and alleles still undiscovered. This proposal encompasses innovative methodologies that will benefit the PCGC in a very practical manner. This proposal capitalizes on a recently validated and emerging bioinformatics technology that provides four basic functionalities: (1) the ability to estimate the functional impact of variants no matter where they lie in the genome coding, intergenic, splice sites, etc.; (2) the ability to computationally interrogate patient genotypes not only for dominant de novo alleles, but also for incompletely penetrant dominant, simple recessive, and compound heterozygote disease models; (3) the ability to carry out these analyses in the context of pedigrees, phenotype information, and expression data from patient-specific induced pluripotent stem cell-derived cardiomyocytes; and (4) the capability to share results for patient management and consortium-wide collaborative analyses. In summary, our proposal aims to fill the knowledge gap of "missing heritability" surrounding CHD by leveraging novel tools designed at the University of Utah that enable integrated computation on personal genome/exome sequences, patient phenotype descriptions and pedigrees, and patient-specific expression data, all in a robust statistical framework.