The origin, the function and the phenotypic impact of human alleles

人类等位基因的起源、功能和表型影响

• 批准号: 10441144
• 负责人: SHAMIL SUNYAEV
• 金额: $ 89.67万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-11 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10441144
• 关键词: Alleles; Architecture; Biology; Biophysics; Code; Complex; Computer Analysis; Computing Methodologies; Data; Data Set; Disease; Evolution; Genetic; Genetic Models; Genetic Variation; Genotype; Germ Lines; Human; Human Genetics; Large-Scale Sequencing; Light; Maintenance; Malignant Neoplasms; Medical Genetics; Methods; Molecular; Mutation; Phenotype; Population; Population Dynamics; Population Genetics; Process; Property; Research; Shapes; Source; Statistical Data Interpretation; Statistical Models; Theoretical model; Untranslated RNA; Variant; cancer genetics; cancer genomics; comparative; de novo mutation; design; disorder risk; fascinate; genetic architecture; genetic predictors; improved; innovation; neuropsychiatric disorder; novel strategies; trait

Genetic variation is the primary source of evolutionary innovation and a major factor responsible for phenotypic variation. Consequently, understanding such variation has great importance in both basic biology and evolution, and ultimately Mendelian and complex disease. We will study the origin of genetic variation through spontaneous mutational processes. Computational analysis of sequencing datasets will shed light on the mechanistic forces underlying germ-line and somatic cancer mutations in human. We will design new statistical models of de novo mutation that will have applications in population genetics, cancer genomics and genetics of neuropsychiatric disease. Next, we will improve computational methods for interpreting and predicting the effect of mutation on molecular function, including both coding and non-coding variation. Our methods integrate data from evolutionary genetics and biophysics and rely on comparative, functional and structural data. The newly developed methods will have applications in both medical and population genetics. We will study the population dynamics of alleles to estimate the forces that shape genetic variation within populations. We will rely on population genetics models to analyze evolutionary maintenance and genetic architecture of human phenotypes. Fascinated by the relationship between genotype and phenotype, we will combine theoretical models and statistical analysis of large-scale sequencing datasets to infer properties of the allelic architecture of complex traits. We will design new approaches to characterize and predict the genetic component of common disease risk. !

遗传变异是进化创新的主要来源，也是负责的主要因素 用于表型变异。因此，理解这种差异非常重要 基本生物学和进化，以及最终的孟德尔和复杂疾病。 我们将通过自发突变过程研究遗传变异的起源。 测序数据集的计算分析将阐明机械力 人类的基本种系和体细胞癌突变。我们将设计新的统计 从头突变的模型将在人群遗传学，癌症基因组中应用 和神经精神病的遗传学。 接下来，我们将改善解释和预测效果的计算方法 分子功能的突变，包括编码和非编码变化。我们的方法 整合来自进化遗传学和生物物理学的数据，并依靠比较，功能 和结构数据。新开发的方法将在医疗和 人口遗传学。 我们将研究等位基因的种群动态，以估计塑造遗传的力 人群中的差异。我们将依靠种群遗传学模型来分析 人类表型的进化维持和遗传结构。着迷于 基因型与表型之间的关系，我们将结合理论模型和 大规模测序数据集的统计分析以推断等位基因的性质 复杂特征的结构。我们将设计新方法来表征和预测 普通疾病风险的遗传成分。呢