Functional Dissection of CNVs in Neurodevelopmental Traits

 描述（由申请人提供）：尽管基因组技术取得了重大进展，但遗传数据与临床管理的整合仍然具有挑战性。这个问题对于拷贝数变异（CNV）来说尤其尖锐，CNV通常会扰乱数十个基因的剂量，并且正如最近的数据所揭示的那样，是遗传疾病的主要贡献者，最值得注意的是神经发育特征。在基因组病变检测方面提供上级分辨率的最新进展加速了患者中CNV的发现;然而，除了CNV内离散基因的点突变的罕见病例之外，从CNV检测到指定特定基因的表型贡献的过渡在很大程度上仍然是棘手的。我们已经采取了正交的方法来解决这个问题，基于两个关键的观察：a）一些与神经发育性状相关的CNV表现出定量解剖表型替代物;和B）一些CNV表现出相互关系（相同或重叠基因组片段的缺失和重复），具有相似或镜像的临床表型。基于这些观察结果，我们在斑马鱼胚胎中进行了系统的过表达和抑制研究，并将KCTD 13和CHD 1 L分别确定为与16p11.2和1q21.1 CNV相关的神经解剖学表型的主要驱动因素，这些结果已经通过随后在自闭症谱系障碍患者中鉴定出这两个基因中罕见的非典型从头缺失而得到证实（ASD）。在这里，我们建议进一步发展我们的工具，系统地剖析其他互惠CNVs与神经认知性状。在一系列筛选蛋白质编码基因数量的过滤器下，并要求在斑马鱼胚胎中重现易于处理的解剖特征，我们将首先关注12个CNV，并在体内测定这些病变中包含的每个基因的可能贡献。此外，我们将询问是否伴随的剂量失调的主要CNV驱动基因座和CNV内的每个额外的基因可能会影响相关的解剖，定量表型的表达。对于所得的候选或贡献CNV驱动基因组，我们将询问这些基因是否也涉及通过高分辨率阵列比较基因组杂交（aCGH）数据集鉴定的非典型、罕见缺失，所述数据集从具有丰富神经认知表型的群组产生;和B）在ASD外显子组中携带点突变，其将使用斑马鱼模型进行功能测试以测定致病性和作用方向。最后，我们会问是否检测所有等位基因检测 对于病例和对照中的每个转录本，可能揭示了这些基因座处的突变负担，这仅通过统计手段是不可见的。总之，这些数据将确定许多驱动人类神经认知效应的基因，并为系统解剖患者外显子组和基因组中发现的CNV提供有效的平台。

 DESCRIPTION (provided by applicant): Despite major advances in genomic technologies, the integration of genetic data with clinical management remains challenging. This problem is particularly poignant for copy number variants (CNVs), lesions that typically perturb the dosage of dozens of genes and, as recent data have revealed, are major contributors to genetic disorders, most notably neurodevelopmental traits. Recent advances that afford superior resolution in the detection of genomic lesions have accelerated the discovery of CNVs in patients; however, with the exception of rare cases with point mutations of discrete genes within a CNV, the transition from CNV detection to assigning phenotypic contribution of specific genes remains largely intractable. We have taken an orthogonal approach to the problem, grounded on two key observations: a) that some CNVs associated with neurodevelopmental traits exhibit quantitative anatomical phenotypic surrogates; and b) that some CNVs manifest in reciprocal relationships (deletions and duplications of the same or overlapping genomic segment) with either similar or mirroring clinical phenotypes. Grounded on these observations, we have performed systematic overexpression and suppression studies in zebrafish embryos and have identified KCTD13 and CHD1L as the primary drivers of the neuroanatomical phenotypes associated with the 16p11.2 and 1q21.1 CNVs respectively, results that have been substantiated by the subsequent identification of rare atypical de novo deletions in these two genes in patients with autism spectrum disorders (ASD). Here we propose to develop further our tools to systematically dissect other reciprocal CNVs associated with neurocognitive traits. Under a series of filters that select for number of protein encoding genes, and mandate tractable anatomical features that can be recapitulated in zebrafish embryos, we will focus initially on 12 CNVs and assay in vivo the possible contribution of each gene contained within these lesions. Additionally, we will ask whether concomitant dosage misregulation of the primary CNV driver locus and each additional gene within the CNV might influence the expressivity of relevant anatomical, quantitative phenotypes. For the resulting group of candidate or contributing CNV driver genes, we will ask whether these genes a) are also involved in atypical, rare deletions identified by high resolution array comparative genomic hybridization (aCGH) datasets generated from cohorts with an enrichment of neurocognitive phenotypes; and b) bear point mutations in ASD exomes that will be functionally tested using a zebrafish model to assay pathogenicity and direction of effect. Finally, we will ask whether testing of all alleles detected for each transcript in cases and controls might reveal a mutational burden at these loci that is invisible by statistical means alone. Together, these data will identify a number of genes that drive neurocognitive effects in humans and provide an efficient platform for the systematic dissection of CNVs discovered in patient exomes and genomes.