Illuminating the distribution of extreme evolutionary constraint in the human genome from fetal demise to severe developmental disorders

摘要 自然选择从影响生存或死亡的基因组区域的群体中清除有害突变。 生殖能力最近对大规模人群队列的遗传学研究揭示了一种持续的 这种对有害突变的限制在整个人类基因组中的分布。大型协会 研究发现，在许多儿童中，基因组区域的突变是主要的危险因素。 发育障碍（DDs），这表明高度限制的序列可能在发育障碍中发挥关键作用。 发展然而，对前三个月（FD）后胎儿死亡的遗传研究，这是DD的一个极端结果， 到目前为止，在范围和规模上都是有限的。我的导师们建立了一个国际胎儿 基因组学联盟将对10,500个样本（3,500个FD病例及其家庭成员）进行测序， 疑似遗传病因的FD现在提供了一个前所未有的机会来阐明最极端的 个体基因突变的后果和对人类至关重要的剂量敏感基因组片段 发展在这个奖学金中，我将整合来自这个队列的全基因组测序和尸检数据， 与来自先前建立的DD队列的数据一起，以发现和功能性表征突变 人类基因组中的不耐受位点。我将首先定义FD中跨约束度量的遗传变异模式 （功能丧失、错义和非编码）和基因组变异类别（点突变、插入缺失、结构突变）。 变异和重复扩增），并调查这些模式中关于胎儿性别和突变的偏倚。 原始父母然后，我将采用一个能够整合证据的疾病关联统计框架 从所有编码和非编码变体类与约束区域的优先级，我将适用于 在FD中进行新基因发现（目标1）。我将利用这些发现来产生功能预测， 通过定义生物活性网络和它们可能存在的细胞类型， 在开发早期运行（目标2）。最后，我将在不具备这些功能的DD中测试这些功能假设。 导致FD，包括活产胎儿结构异常，神经发育障碍和先天性 异常（目标3）。在这些研究目标的同时，一个由六位导师和顾问组成的特殊团队， 多学科，职业阶段和机构将提供教学培训，动手研究支持， 定期在研讨会和会议上发言的机会，以及各种软技能的发展 这些课程与我在博士培训期间的职业目标直接相关。总的来说， 这项提案将利用独特的工具和资源，对糖尿病的病因学产生新的见解。 沿着发展异常和进化限制的连续性的极端，并将作为一个 在计算、统计和功能性疾病基因组学方面给了我一个极好的培训机会。

Abstract Natural selection purges deleterious mutations from populations in genomic regions impacting survival or reproductive capacity. Recent genetic studies of massive population cohorts have revealed a continuous distribution across the human genome of this constraint on deleterious mutations. Large-scale association studies have found mutations in strongly constrained genomic regions to be major risk factors in many childhood developmental disorders (DDs), suggesting that highly constrained sequences are likely to play key roles in development. However, genetic studies of fetal demise after the first trimester (FD), an extreme outcome of DDs, have thus far been limited in scope and size. The establishment by my mentors of an international Fetal Genomics Consortium to sequence 10,500 samples (3,500 FD cases and their family members) ascertained for FD of suspected genetic etiology now offers an unprecedented opportunity to illuminate the most extreme consequences of mutation across individual genes and dosage sensitive genomic segments critical for human development. In this fellowship, I will integrate whole-genome sequencing and autopsy data from this cohort together with data from previously established DD cohorts to discover and functionally characterize mutationally intolerant loci in the human genome. I will first define patterns of genetic variation in FD across constraint metrics (loss-of-function, missense, and noncoding) and genomic variation classes (point mutations, indels, structural variants, and repeat expansions), and investigate biases in these patterns with respect to fetal sex and mutational parent-of-origin. I will then adapt a statistical framework for disease association capable of integrating evidence from all coding and noncoding variant classes with prioritization of constrained regions, which I will apply to perform novel gene discovery in FD (Aim 1). I will leverage these findings to generate functional predictions of mutationally intolerant loci by defining the biological networks of activity and the cell types in which they are likely to operate early in development (Aim 2). Finally, I will test these functional hypotheses across DDs that do not result in FD, including liveborn fetal structural abnormalities, neurodevelopmental disorders, and congenital anomalies (Aim 3). In parallel with these research aims, an exceptional team of six mentors and advisors across multiple disciplines, career stages, and institutions will provide didactic training, hands-on research support, regular opportunities for presentation in seminars and conferences, and a variety of soft skill development sessions that directly align with my career objectives during my PhD training. Collectively, the aims outlined in this proposal will take advantage of unique tools and resources to yield novel insights into the etiology of the extremes along the continuum of developmental anomalies and evolutionary constraint, and will serve as an outstanding training opportunity for me in computational, statistical, and functional disease genomics.