Bridging the Gap between Genomics and Clinical Outcomes in CHD

 描述(申请人提供)：尽管分配了大量的资源，先天性心脏病(CHD)的基因组基础仍然很大程度上是未知的，因为罕见的从头开始的单核苷酸和拷贝数变异只占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在CHD的“缺失遗传性”中占非常重要的比例，(2)有害变异的基因组负荷，以及与一些导致CHD的等位基因(但不是其他)相关的多态，以一种超越其对原发CHD的直接贡献的方式影响临床结果。我们的主要目标是将犹他州中心建立为儿科心脏基因组联盟(PCGC)不可或缺的合作伙伴，并与其他PCGC中心合作，以便(1)向PCGC提供患者、专业知识和软件，以确定CHD缺失的遗传性；以及(2)通过定义与导致CHD的等位基因相关的多态和确定有害变量的基因组负载，将基因组变体与相关临床结果联系起来。目前，PCGC缺乏两种关键的诊断方法，这两种方法阻碍了其定义CHD及其结果的基因组基础的能力：(1)能够计算不完全显性、简单隐性和复合杂合子疾病模型的强大的生物信息学管道；(2)能够在编码区和非编码区识别新的CHD等位基因的基于家族的全基因组测序方法。我们认为，在PCGC先证者-亲代三人组中识别从头变异只是冰山一角，许多基因和等位基因仍未被发现。这项建议包含了创新的方法，这些方法将以非常实际的方式使PCGC受益。这一建议利用了一种最近得到验证的新兴生物信息学技术，该技术提供了四种基本功能：(1)无论变异位于基因组编码、基因间、剪接位点等什么位置，都能够估计它们的功能影响；(2)不仅能够计算询问患者的新等位基因，而且还能够对不完全穿透性显性、简单隐性和复合杂合子疾病模型进行计算；(3)能够在患者特定诱导的多能干细胞来源的心肌细胞的家系、表型信息和表达数据的背景下进行这些分析；以及(4)共享患者管理和联盟范围内协作分析的结果的能力。总之，我们的建议旨在通过利用犹他大学设计的新工具来填补围绕CHD的“缺失遗传性”的知识空白，这些工具能够在一个强大的统计框架中对个人基因组/外显子组序列、患者表型描述和系谱以及患者特定的表达数据进行集成计算。

 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.