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，但基因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)来说尤其令人痛苦，CNV是一种通常会扰乱数十个基因的剂量的病变，最近的数据显示，它是遗传疾病的主要贡献者，最显著的是神经发育特征。最近在检测基因组损伤方面取得的进展加速了患者中CNV的发现；然而，除了罕见的CNV内离散基因点突变的病例外，从CNV检测到指定特定基因的表型贡献的过渡仍然在很大程度上是棘手的。我们对这个问题采取了正交法，基于两个关键的观察：a)一些与神经发育特征相关的CNV表现出定量的解剖表型替代；b)一些CNV表现出与临床表型相似或相似的相互关系(相同或重叠基因组片段的缺失和重复)。基于这些观察，我们在斑马鱼胚胎中进行了系统的过表达和抑制研究，并确定KCTD13和CHD1L分别是与16p11.2和1q21.1 CNV相关的神经解剖学表型的主要驱动因素，这一结果已被随后在自闭症谱系障碍(ASD)患者中发现的这两个基因罕见的非典型从头缺失所证实。在这里，我们建议进一步开发我们的工具，系统地剖析与神经认知特征相关的其他相互作用的CNV。在一系列筛选蛋白质编码基因的过滤器下，我们将首先关注12个CNV，并在体内分析这些损伤中包含的每个基因的可能贡献。此外，我们将询问CNV主要驱动基因和CNV内每个额外基因的伴随剂量失调是否会影响相关解剖、数量表型的表达。对于最终得到的一组候选或促成CNV驱动基因，我们将询问这些基因是否也参与了通过高分辨率阵列比较基因组杂交(ACGH)数据集确定的非典型、罕见的缺失，这些数据集是从具有丰富神经认知表型的队列生成的；以及b)ASD外显体中带有点突变，这些突变将使用斑马鱼模型进行功能测试，以分析致病性和影响方向。最后，我们将询问是否检测到所有等位基因 对于病例和对照中的每一份转录本，都可能揭示出这些基因座上的突变负担，这是仅通过统计手段看不到的。总之，这些数据将确定一些驱动人类神经认知效应的基因，并为系统剖析在患者外显体和基因组中发现的CNV提供一个有效的平台。

 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.