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·米勒儿童优先(GMKF)计划和其他倡议已承诺 了解这些疾病的遗传基础的重要资源，然而 对于大多数儿童来说，遗传病因和潜在的致病机制尚不清楚 带着SBDS出席。本申请将重点研究两类可能的基因组变异 导致SBDS的原因不明：结构变异(SV)和非编码 监管变异。SVS被广泛定义为大小为50个基点的变体，已发现 在每个人类基因组中比以前认识到的更普遍和更多样化。在这 提案中，我们将使用我们最近开发的全基因组SV检测算法 对GMKF SBD队列进行测序，并将这些发现与大规模人口规模进行比较 基因组聚合数据库(gnomAD-SV)中的数据集。除了变异检测之外 和特征，我们将在SBD队列中进行疾病关联分析。考虑到 大多数发生在儿童身上的新(从头)突变存在于编码序列之外 基因，疾病关联模型必须同时考虑编码和 非编码变体。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执行 对所有编码和非编码单核苷酸变体、INDELs和 GMKF中的结构变体。最后，我们将整合基因组和外显子组的研究结果。 与神经发育障碍(NDD)一起研究基因和途径的共性 与SBDS和NDDS相关。综上所述，我们预计这些分析将开发出 全面的资源来询问SV和非编码关联的遗传格局 在GMKF家庭和人口控制中跨越SBD。

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.