Mapping Nonsyndromic Cleft Lip and Palate Genetic Loci

描述(申请人提供)：非综合征性唇裂伴或不伴腭裂(NSCLP)是一种常见的复杂的口腔面部出生缺陷，与显著的发病率和增加的死亡率有关。手术矫正总是需要的，多学科的健康干预每年花费大约5亿美元。候选基因研究和全球遗传研究已经确定了一些可能的NSCLP基因和位点，估计它们只占遗传变异的20%。虽然这是一个重要的起点，但非小细胞肺癌的大部分遗传风险仍未被发现，是一个有待探索的前沿。我们已经使用候选基因和GWA法来定义NSCLP的遗传变异；一个以前没有被怀疑的基因CRISPLD2被发现与NSCLP有关，当在斑马鱼中被敲除时，会导致腭部和颌部的异常。该项目的目标是继续识别和了解对NSCLP遗传结构有贡献的未被发现的变异。为了实现这一目标，我们将应用最新的技术，整个外显子组下一代测序(WES)和染色体微阵列分析(CMA)，我们的特点良好的广泛的家族为基础的NSCLP数据集。我们将使用检测编码和非编码变体的WES(Agilent 50Mb v.4外显子内容+Utr)，因为我们已经证明了这两种类型的变体都会导致NSCLP。此外，CMA将检测WES可能遗漏的拷贝数变体(CNV)。这将提供最完整的覆盖范围，以家庭为基础的设计将允许检测非小细胞肺癌的家族性原因。候选基因将被优先排序，在斑马鱼中进行功能测试，以确定生物学意义，并在我们的案例中分析光谱变异和风险建模的对照。结果将提供有关风险变量的重要信息，分别和 总而言之。在整个研究期间，我们将继续扩大我们的NSCLP数据集，用于本次和未来的基因研究。将最新技术应用于我们广泛的基于家庭的数据集是揭示导致这种常见出生缺陷的基因变异的有力方法。这一方法是向前迈出的重要一步；这一结果将为发展中的NSCLP基因变异知识库增加重要的新信息，这将转化为高危家庭的遗传咨询。

DESCRIPTION (provided by applicant): Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common complex orofacial birth defect associated with significant morbidity and increased mortality. Surgical correction is always required and the multidisciplinary health interventions cost approximately a half billion dollars each year. Candidate gene studies and GWAS have identified a number of putative NSCLP genes and loci that are estimated to account for only ~20% of the genetic variation. While this represents an important starting point, the majority of genetic risk for NSCLP remains undiscovered and represents a frontier to be explored. We have used both candidate gene and GWAS approaches to define genetic variation in NSCLP; a previously unsuspected gene, CRISPLD2, was found to be associated with NSCLP and, when knocked down in zebrafish, causes palatal and jaw abnormalities. The goal of this project is to continue identifying and understanding the undiscovered variation that contributes to the genetic architecture of NSCLP. To accomplish this goal, we will apply the newest technologies, whole exome next generation sequencing (WES) and chromosomal microarray analysis (CMA) to our well-characterized extensive family-based NSCLP dataset. We will use the WES that detects coding and noncoding variants (Agilent 50Mb v.4 exon content +UTR) because we have shown that both types of variation contribute to NSCLP. In addition, CMA will detect copy number variants (CNVs) that would be missed by WES. This will provide the most complete coverage and the family-based design will allow for detection familial causes of NSCLP. Candidate genes will be prioritized, functionally tested in zebrafish to determine biological significance and analyzed in our case controls for spectrum variation and risk modeling. The results will provide important information about risk variants, individually and in aggregate. During the entire study period, we will continue to expand our NSCLP dataset for this and future genetic studies. Application of the newest technology to our extensive family-based dataset is a powerful method for uncovering the genetic variation contributing to this common birth defect. This approach is a significant step forward; the results will add important new information to the developing knowledge base of genetic variation responsible for NSCLP, which will translate into genetic counseling for at-risk families.