Computational approaches to advance genomic, biological and clinical understandings of human disease

促进对人类疾病的基因组、生物学和临床理解的计算方法

• 批准号: 10552389
• 负责人: Ron Do
• 金额: $ 45.31万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552389
• 关键词: Address; Area; Award; Biological; Clinical; Clinical Data; Code; Complex; Data; Data Set; Development; Diagnosis; Disease; Drug Side Effects; Drug Targeting; Electronic Health Record; Evaluation; Genes; Genetic; Genomics; Heterogeneity; Human; Individual; Knowledge; Laboratories; Link; Medical; Mendelian randomization; National Institute of General Medical Sciences; Natural Selections; Penetrance; Pharmaceutical Preparations; Phenotype; Prevention; Publishing; Research; Research Design; Research Personnel; Risk Factors; Science; Series; Statistical Methods; Translating; Variant; clinical risk; data resource; disorder risk; drug development; exome; exome sequencing; fitness; genetic architecture; genetic variant; human disease; human genomics; improved; indexing; insight; method development; novel; population based; programs; therapeutic candidate; tool; trait; translational medicine

PROJECT SUMMARY / ABSTRACT The NIGMS Established Investigator (EI) R35 Maximizing Investigators’ Research Award (MIRA) proposal aims to use computational approaches to advance genomic, biological, and clinical understandings of human disease. The research program is broadly focused on three areas of human genomics: genomic association, biological mechanism and translational medicine. Within these areas, research in the laboratory has focused on: 1) evaluation of disease risk of genetic variants; 2) development and application of Mendelian randomization to infer causal relationships between complex traits and diseases; 3) evaluation of the complex interplay between natural selection and human diseases; and 4) using human genomics to inform drug side effect prediction. The proposed research program leverages large-scale genetic and clinical data resources, combined with statistical methods development, building directly on our prior published research in each of the research areas. Importantly, we have highlighted critical unmet needs, key knowledge gaps in our understanding and important challenges to be addressed pertaining to general medical sciences research. Over the next five years, we plan to embark on a series of studies designed to address these unmet needs and overcome associated challenges. First, the disease risk of clinical variants at the variant level is uncertain. We will quantify disease risk of clinical variants for human diseases by quantifying population-based penetrance in the exome sequences of 510,000 individuals with linked electronic health record data. This research area can refine variant interpretation. Second, little is known about the full spectrum of causal risk factors contributing to complex diseases. We will dissect the phenotypic heterogeneity of complex diseases using a novel Mendelian randomization framework. This research area can provide new insights into the heterogenous causes of complex diseases. Third, little is known about the contribution of rare coding variants on deleterious load, and its effect on human phenotypes. We will examine the interplay between fitness via the load, its constituents and human phenotypes in a very large exome sequencing dataset (e.g. 510,000 individuals) that enables capture of rare coding variants. This research area will provide insights into the bidirectional relationship between deleterious load and human phenotypes, which can inform about the genetic architecture of human phenotypes. Fourth, studies have shown that the side effects of drugs targeting genes are enriched for certain human genomic features; however, these studies have not yet translated to useful prediction of drug side effects. We will build a human genomics-guided priority index for drug side effect prediction using a drug side effect dataset and a wide array of genetic features. This research area can potentially improve selection of drug therapeutic candidates in drug development. Taken together, the research program will deliver new biological insights and useful tools that can inform prevention, diagnosis and treatment of human diseases.

项目总结/摘要 NIGMS建立研究者（EI）R35最大化研究者研究奖（MIRA） 该提案旨在使用计算方法来推进基因组，生物学和临床理解， 人类疾病该研究计划主要集中在人类基因组学的三个领域： 关联、生物机制和转化医学。在这些领域，实验室研究 主要集中在：1）遗传变异的疾病风险评估; 2）孟德尔遗传学的发展和应用 随机化以推断复杂性状与疾病之间的因果关系; 3）评估复杂性状与疾病之间的因果关系。 自然选择和人类疾病之间相互作用; 4）利用人类基因组学为药物方面提供信息 效果预测拟议的研究计划利用大规模的遗传和临床数据资源， 结合统计方法的发展，直接建立在我们以前发表的研究中， 研究领域。重要的是，我们强调了关键的未满足的需求， 理解和重要的挑战，以解决有关一般医学科学研究。 在未来五年，我们计划开展一系列研究，以解决这些未得到满足的需求， 克服相关挑战。首先，临床变异在变异水平上的疾病风险是不确定的。我们 将量化人类疾病的临床变异的疾病风险， 510，000个与电子健康记录数据相关的个体的外显子组序列。该研究领域可以 完善变体解释。第二，对导致糖尿病的全部因果风险因素知之甚少。 复杂的疾病。我们将使用一种新的孟德尔模型来剖析复杂疾病的表型异质性。 随机化框架这一研究领域可以提供新的见解异质性的原因， 复杂的疾病。第三，关于罕见编码变体对有害负荷的贡献知之甚少， 它对人类表型的影响。我们将通过负载，其组成部分， 和人类表型在一个非常大的外显子组测序数据集（例如510，000个个体）， 捕获罕见的编码变体。这一研究领域将提供对双向关系的见解 有害负荷和人类表型之间的关系，这可以为人类的遗传结构提供信息。 表型第四，研究表明，针对基因的药物的副作用肯定会增加， 人类基因组特征;然而，这些研究尚未转化为有用的预测药物副作用 方面的影响.我们将建立一个人类基因组学指导的优先指数，用于药物副作用预测， 影响数据集和广泛的遗传特征。这一研究领域可以潜在地改善选择 药物开发中的药物治疗候选物。总的来说，该研究计划将提供新的 生物学见解和有用的工具，可以为预防、诊断和治疗人类疾病提供信息。