Exploring the genetic architecture of structural birth defects

摘要 结构性出生缺陷（SBD）每年影响美国和世界各地数百万儿童， 年加布里埃拉米勒儿童第一（GMKF）计划和其他举措已承诺 重要的资源，以了解这些条件的遗传基础，然而， 对于大多数儿童，遗传病因学和潜在的致病机制仍然未知， 与SBD一起出现。该应用程序将集中在两类基因组变异， 结构变异（SV）和非编码突变是导致SBD的原因之一。 监管变化。广义上定义为大小> 50 bp的变体，已经发现SV是 在每个人类基因组中的普遍性和多样性远远超过以前的认识。在这 建议，我们将使用我们最近开发的SV检测算法从全基因组 在GMKF SBD队列中进行测序，并将这些结果与大规模人群进行比较 基因组聚合数据库（gnomAD-SV）中的数据集。除了变异检测 和表征，我们将在SBD队列中进行疾病相关性分析。鉴于 在儿童中发生的大多数新的（从头）突变位于编码序列之外， 基因，疾病关联模型必须同时考虑编码和 非编码变异在这里，我们将利用最近开发的分析框架， “全类别关联研究”（CWAS）和从头风险评分方法进行 所有编码和非编码单核苷酸变体、插入缺失和 GMKF中的结构变体。最后，我们将整合病例的基因组和外显子组发现 神经发育障碍（NDD）研究基因和途径的共性 与SBD和NDD相关。综合起来，我们希望这些分析能够发展出一种 询问SV和非编码关联的遗传景观的综合资源 在GMKF家族和人群对照中的SBD之间。

ABSTRACT Structural birth defects (SBDs) impact millions of children in the US and around the world every year. The Gabriella Miller Kids First (GMKF) program and other initiatives have committed significant resources toward understanding the genetic basis of these conditions, yet the genetic etiology and underlying pathogenic mechanisms remain unknown for most children that present with SBDs. This application will focus on two classes of genomic variation that likely contribute to this unexplained etiology in SBDs: structural variation (SV) and noncoding regulatory variation. Broadly defined as variants >50bp in size, SVs have been discovered to be far more prevalent and diverse in each human genome than previously appreciated. In this proposal, we will use our recently developed SV detection algorithms from whole-genome sequencing across GMKF SBD cohorts, and compare these findings to large population-scale datasets in the genome aggregation database (gnomAD-SV). In addition to variant detection and characterization, we will pursue disease association analyses in SBD cohorts. Given that most new (de novo) mutations that occur in children reside outside of the coding sequence of genes, it is imperative that models of disease association account for both coding and noncoding variation. Here, we will leverage recently developed analytic frameworks of ‘category-wide association study’ (CWAS) and de novo risk score approaches to perform association tests across all coding and noncoding single nucleotide variants, indels and structural variants in GMKF. Finally, we will integrate genome and exome findings from cases with neurodevelopmental disorders (NDD) to investigate commonalities in genes and pathways associated with SBDs and NDDs. Taken together, we expect these analyses to develop a comprehensive resource to interrogate the genetic landscape of SV and noncoding associations across SBDs in GMKF families and population controls.