Computational analysis of whole genome sequence data for discovering novel risk genes of structural birth defects

项目摘要 我们的目标是提高我们对结构性出生缺陷的遗传基础的理解。为了实现这一目标，我们 建议发展和改进稀有变种解释的计算方法，并进行综合 对蛋白质编码和非编码变异进行统计分析，以识别新的风险基因。 聚集的结构性出生缺陷在活产中很常见。虽然患者的存活率 由于严重的出生缺陷在最近几十年有了显著的改善，许多幸存的患者仍然有 晚年的重大临床问题，包括生长、神经发育障碍、儿童期癌症和 其他健康问题。更好地理解结构性出生缺陷的遗传基础将带来新的见解 调查这些临床问题的原因，并将为医疗干预和治疗提供目标。近期 出生缺陷的大规模基因组测序研究，包括由加布里埃拉·米勒儿童资助的项目 第一个(GMKF)计划，已经发现了新的危险基因，特别是通过蛋白质编码的从头开始变异 地区。然而，出生缺陷的遗传学是复杂的。到目前为止，已知的风险基因只解释了5%到30%的 常见的出生缺陷，如先天性心脏病。大多数风险基因都是未知的。所做的贡献 对于这种疾病，人们对罕见的遗传变异或非编码变异的风险知之甚少。为了调查这些 有效识别变异类型和识别新的风险基因，我们需要更大的样本量和更好的计算能力 改进稀有变异对功能影响预测的工具。在这项研究中，我们提出了两个目标 通过跨队列利用不断增长的GMKF全基因组测序(WGS)数据集来解决这些问题 机器学习和其他基因组数据集的最新发展：具体目标1.发展和改进 在遗传学研究中优先处理破坏性、稀有错义和非编码变异的计算方法。特定的 目的2.对罕见的编码和非编码变异进行综合分析，以确定新的结构性出生风险基因 缺陷。 我们建议的研究将通过将GMKF WGS数据集与其他外显子组或 同样出生缺陷的WGS数据，反过来又提高了我们对多效性效应和组织的理解 出生缺陷风险基因和变异的特异性。口译的新计算和统计工具 罕见的变异将广泛适用于出生缺陷和其他疾病的遗传学研究。

Project Summary We aim to improve our understanding of the genetic basis of structural birth defects. To achieve that, we propose to develop and improve computational methods for interpretation of rare variants and perform integrative statistical analysis of both protein-coding and noncoding variants to identify new risk genes. Structural birth defects in aggregation are common in live births. Although the survival rate of patients with severe birth defects has been dramatically improved in recent decades, many survived patients still have significant clinical problems later in life, including growth, neurodevelopmental disorders, childhood cancer, and other health issues. Better understanding of the genetic basis of structural birth defects will lead to new insights into the cause of these clinical issues and will provide targets for medical intervention and treatment. Recent large-scale genomic sequencing studies of birth defects, including projects funded by the Gabriella Miller Kids First (GMKF) program, have identified new risk genes, especially through de novo variants in protein coding regions. However, the genetics of birth defects is complex. By far, known risk genes only explain 5 to 30% of common birth defects such as congenital heart disease. The majority of risk genes are unknown. The contribution to the disease risk from rare inherited variants or noncoding variants is much less known. To investigate these types of variants effectively and identify new risk genes, we need larger sample size and better computational tools that improve the prediction of functional impact of rare variants. In this study, we propose two aims to address these questions by leverage growing GMKF whole genome sequencing (WGS) data sets across cohorts and latest development in machine learning and other genomic data sets: Specific Aim 1. Develop and improve computational methods to prioritize damaging rare missense and noncoding variants in genetic studies. Specific Aim 2. Integrative analysis of rare coding and noncoding variants to identify new risk genes of structural birth defects. Our proposed study will identify new risk genes by combining GMKF WGS data sets with other exome or WGS data of the same birth defects, and in turn improve our understanding of the pleiotropic effects and tissue specificity of risk genes and variants in birth defects. The new computational and statistical tools for interpreting rare variants will be broadly applicable to genetic studies of birth defects and other conditions.