Functional and population genetic architectures of complex disease

复杂疾病的功能和群体遗传结构

• 批准号: 10675744
• 负责人: Benjamin Michael Neale
• 金额: $ 80.48万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675744
• 关键词: Address; Alleles; Architecture; Automobile Driving; Binding; Biological; Biological Process; Biology; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Set; Development; Disease; Disease model; Drug Targeting; Epigenetic Process; Funding; Genes; Genetic; Genetic Diseases; Genetic Models; Genetic Risk; Genetic Variation; Genotype; Heritability; Human Resources; Individual; Knowledge; Link; Maintenance; Maps; Measurable; Methodology; Methods; Molecular; Natural Selections; Pathway interactions; Phenotype; Population; Population Genetics; Publications; Publishing; Quantitative Trait Loci; RNA Splicing; Rare Diseases; Regulation; Regulatory Element; Research; Sampling; Shapes; Signal Transduction; Stable Disease; Statistical Data Interpretation; Statistical Methods; Techniques; Testing; Tissues; Twin Multiple Birth; Untranslated RNA; Variant; causal variant; cell type; computerized tools; design; disease phenotype; disorder risk; experimental study; fitness; functional genomics; genetic architecture; genetic association; genome wide association study; genome-wide; genomic data; improved; insight; method development; molecular phenotype; neuropsychiatric disorder; open source; phenotypic data; programs; rare variant; simulation; trait; transcription factor; transcriptomics

Project Summary Recent progress in the genetics of complex diseases, including neuropsychiatric diseases, has revealed that the bulk of disease heritability is explained by the action of many common non-coding variants. Most of these variants exert individually tiny effects on disease risk, but collectively they contribute most of the measurable genetic risk for most diseases and typically account for between a third and half of twin estimates of heritability. These observations motivate three important questions concerning the biology underlying phenotypic variation: First, how do individual non-coding variants associated to disease impact biological function? A growing number of studies, including those published by the Key Personnel of this renewal application, provide statistical analyses that link genetic and epigenetic data to identify regulatory elements active in cell types that are key to disease biology. However, the mechanism of regulatory action is understood just for a few examples of genetic associations to disease. Second, how do the causative variants conspire together to perturb genes, biological pathways and networks and induce a disease phenotype? Despite the body of knowledge on genes and functional modules accumulated by decades of experimental research, we lack understanding of specific gene programs and networks through which the thousands of causative variants act to impact disease phenotypes. Third, how is genetic variation associated to common neuropsychiatric diseases stably maintained in the population given the loss of fitness associated to these disorders? Established population genetics models applicable to rare diseases are inconsistent with recent data on common disease genetics. We propose to develop new statistical and computational methods to generate biological insights from genomic data and to apply these methods to genome-scale genotype-phenotype datasets. We will design new strategies to combine functional genomic and molecular phenotype data with disease association results to shed light on the proximal regulatory function of non-coding variants. We propose new statistical methods to interpret genetic association data at different levels of biological organization ranging from individual regulatory interactions to genes, pathways and networks. We will use population genetics models to address conceptual issues of the origin of allelic architecture of common disease and, in particular, neuropsychiatric diseases. Our collaboration has an extensive publication record and a record of producing widely-used open-source software in the previous funding cycle. We have extensive computational and statistical expertise, but our approach is always rooted in data. All proposed method development will be guided by available large-scale genetics datasets and functional genomics datasets spanning over 2 million samples.

项目摘要 包括神经精神疾病在内的复杂疾病遗传学的最新进展表明， 疾病遗传性的大部分是由许多常见的非编码变异体的作用来解释的。大多数这些 变异个体对疾病风险的影响很小，但它们共同贡献了大部分可测量的风险。 大多数疾病的遗传风险，通常占双胞胎遗传力估计的三分之一到一半。 这些观察结果激发了有关表型变异背后的生物学的三个重要问题： 首先，与疾病相关的个体非编码变异如何影响生物功能？越来越 许多研究，包括本续期申请的关键人员发表的研究， 统计分析，将遗传和表观遗传数据联系起来，以识别细胞类型中的调节元件， 是疾病生物学的关键然而，仅举几个例子， 基因与疾病的联系。 第二，致病性变异如何共同扰乱基因、生物通路和网络 并诱导疾病表型吗尽管基因和功能模块的知识体系 经过几十年的实验研究积累，我们缺乏对特定基因程序的了解， 成千上万的致病变异体通过网络影响疾病表型。 第三，与常见神经精神疾病相关的遗传变异是如何在神经系统中稳定维持的？ 考虑到与这些疾病相关的健康损失，人口？建立群体遗传学模型 适用于罕见疾病的遗传学数据与最近关于常见疾病遗传学的数据不一致。 我们建议开发新的统计和计算方法，以产生生物学的见解， 基因组数据，并将这些方法应用于基因组规模的基因型-表型数据集。 我们将设计新的策略，将联合收割机功能基因组和分子表型数据与疾病相结合 关联结果揭示了非编码变体的近端调控功能。我们提出了新的 解释不同生物组织水平遗传关联数据的统计方法 从个体调控相互作用到基因、通路和网络。我们将使用群体遗传学 解决常见疾病等位基因结构起源的概念问题的模型，特别是， 神经精神疾病我们的合作有着广泛的出版记录和生产记录 在上一个供资周期中广泛使用的开源软件。我们有大量的计算和 统计专业知识，但我们的方法始终植根于数据。所有拟议的方法开发都将 在现有的大规模遗传学数据集和功能基因组学数据集的指导下， 样品

项目成果

Discovery of rare variants for complex phenotypes.

• DOI: 10.1007/s00439-016-1679-1
• 发表时间: 2016-06
• 期刊: Human genetics
• 影响因子: 5.3
• 作者: Kosmicki JA;Churchhouse CL;Rivas MA;Neale BM
• 通讯作者: Neale BM

Distinct and shared genetic architectures of Gestational diabetes mellitus and Type 2 Diabetes Mellitus.

妊娠期糖尿病和 2 型糖尿病具有不同和共同的遗传结构。

• DOI: 10.1101/2023.02.16.23286014
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Elliott,A;Walters,RK;Pirinen,M;Kurki,M;Junna,N;Goldstein,J;Reeve,MP;Siirtola,H;Lemmelä,S;Turley,P;FinnGen;Palotie,A;Daly,M;Widén,E
• 通讯作者: Widén,E