Statistical Genetics and Genomics for Epidemiologic Research

流行病学研究的统计遗传学和基因组学

• 批准号: 10426287
• 负责人: Wei Sun
• 金额: $ 42.65万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-17 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426287
• 关键词: Alcohol consumption; American; Biological Markers; Cancer Etiology; Cancer Prognosis; Cessation of life; Chemoprevention; Chemopreventive Agent; Clinical; Coffee; Complex; DNA Methylation; Development; Diagnosis; Disease; Disease Outcome; Ensure; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Epigenetic Process; Etiology; Exercise; Gene Expression; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic Risk; Genetic study; Genome; Genomics; Genotype; Gleason Grade for Prostate Cancer; Guide prevention; Incidence; Individual; Indolent; Insulin-Like Growth-Factor-Binding Proteins; International; Intervention; Life Style; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Mediation; Mendelian randomization; Methodology; Methods; Methylation; Modeling; Modernization; Molecular; Neoplasm Metastasis; Outcome; PSA screening; Patients; Population; Population Study; Precision therapeutics; Prevention; Primary Neoplasm; Prognostic Marker; Prostate Cancer Prevention Trial; Prostate Cancer therapy; Public Health; Research; Research Methodology; Risk Factors; Role; Sampling; Selenium and Vitamin E Efficacy Trial; Smoking; Southwest Oncology Group; Statistical Methods; Stratification; Subgroup; The Sun; Transcription Alteration; Translating; Translations; Treatment outcome; Tumor Tissue; Variant; alcohol effect; anticancer research; base; blood lipid; cancer therapy; cohort; diagnostic tool; disorder risk; drinking; epidemiology study; epigenetic marker; gene discovery; genetic epidemiology; genetic variant; genome wide association study; genome-wide; high dimensionality; high risk; individualized prevention; innovation; intervention effect; lifestyle factors; men; methylation biomarker; modifiable risk; molecular marker; mortality; neglect; novel; overtreatment; pleiotropism; population based; predictive marker; predictive modeling; prostate cancer prevention; prostate cancer risk; risk variant; statistical learning; success; treatment effect; tumor; Alcohol consumption; American; Biological Markers; Cancer Etiology; Cancer Prognosis; Cessation of life; Chemoprevention; Chemopreventive Agent; Clinical; Coffee; Complex; DNA Methylation; Development; Diagnosis; Disease; Disease Outcome; Ensure; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Epigenetic Process; Etiology; Exercise; Gene Expression; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic Risk; Genetic study; Genome; Genomics; Genotype; Gleason Grade for Prostate Cancer; Guide prevention; Incidence; Individual; Indolent; Insulin-Like Growth-Factor-Binding Proteins; International; Intervention; Life Style; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Mediation; Mendelian randomization; Methodology; Methods; Methylation; Modeling; Modernization; Molecular; Neoplasm Metastasis; Outcome; PSA screening; Patients; Population; Population Study; Precision therapeutics; Prevention; Primary Neoplasm; Prognostic Marker; Prostate Cancer Prevention Trial; Prostate Cancer therapy; Public Health; Research; Research Methodology; Risk Factors; Role; Sampling; Selenium and Vitamin E Efficacy Trial; Smoking; Southwest Oncology Group; Statistical Methods; Stratification; Subgroup; The Sun; Transcription Alteration; Translating; Translations; Treatment outcome; Tumor Tissue; Variant; alcohol effect; anticancer research; base; blood lipid; cancer therapy; cohort; diagnostic tool; disorder risk; drinking; epidemiology study; epigenetic marker; gene discovery; genetic epidemiology; genetic variant; genome wide association study; genome-wide; high dimensionality; high risk; individualized prevention; innovation; intervention effect; lifestyle factors; men; methylation biomarker; modifiable risk; molecular marker; mortality; neglect; novel; overtreatment; pleiotropism; population based; predictive marker; predictive modeling; prostate cancer prevention; prostate cancer risk; risk variant; statistical learning; success; treatment effect; tumor

Project Summary/Abstract Genome-wide association studies have identified an unprecedented number of genetic variants associated with disease risk, yet molecular mechanisms and clinical implications of genetic risk alleles are largely unknown. Epidemiologic research is extending its reach to more translational and mechanistic studies, integrating genetic risk variants with environmental exposures, interventions, gene expression and epigenetics. Motivated by population-based studies for prostate cancer research, we will develop statistical methods for emerging translational topics: how to search for genotypes that predict individual and subgroup intervention effects? how to identify epigenetic alterations that may be an interface of the environment and the genome? how to assess causal mediation effect of a modifiable risk factor or a molecular alteration in relation to disease outcomes? These topics present unmet statistical challenges because of high dimensionality and complex modeling. Specific statistical methods to be developed include high-dimensional gene- treatment interaction, multi-locus regional association, mediation analyses, instrumental variable analyses, Mendelian randomization, and shrinkage and regularization. The methodological research in this project is driven primarily by prostate cancer, the most common noncutaneous cancer and the second leading cause of cancer death in American men, a ecting one in six in his lifetime. This project nests in highly-accomplished consortium studies (PCPT/SELECT/PRACTICAL/PCPS), all of which have generated far-reaching impact on prostate cancer research. The unique feature of this project is that methodological development will be seamlessly integrated with ongoing analyses, ensuring immediate translation. Our transdisciplinary research team has been actively engaged in statistical genetics and genomics, and conducting molecular epidemiological studies. The PI brings a wealth of expertise in high-dimensional methods, molecular biomarkers, and genetic epidemiology. This project will have a far-reaching impact on methodologies in cancer etiology, prevention, and treatment outcomes.

项目摘要/摘要 全基因组关联研究已经确定了与疾病相关的遗传变异的数量 风险，但遗传风险等位基因的分子机制和临床意义在很大程度上尚不清楚。流行病学 研究将其影响范围扩展到了更多的转化和机械研究，将遗传风险变体与 环境暴露，干预措施，基因表达和表观遗传学。以人群为基础的研究进行 前列腺癌研究，我们将开发新兴翻译主题的统计方法：如何搜索 预测个体和亚组干预效果的基因型？如何识别可能是的表观遗传改变 环境和基因组的界面？如何评估可修改风险因素或 与疾病结局有关的分子改变？这些主题出现了未满足的统计挑战，因为 高维度和复杂的建模。要开发的特定统计方法包括高维基因 治疗相互作用，多层次区域关联，中介分析，工具变量分析，门德利安 随机化，收缩和正则化。 该项目中的方法论研究主要由前列腺癌（前列腺癌）驱动，前列腺癌是最常见的非外观癌症 癌症和美国男性癌症死亡的第二大原因，这是他一生中六分之一的阶段。这个项目 筑巢中的巢中的筑巢联盟研究（PCPT/SELECT/实用/PCP），所有这些都已经产生 对前列腺癌研究的影响深远。该项目的独特特征是方法论发展 将与正在进行的分析无缝集成，以确保立即翻译。我们的跨学科研究 团队一直积极从事统计遗传学和基因组学，并进行了分子流行病学研究。 PI在高维方法，分子生物标志物和遗传流行病学方面具有丰富的专业知识。这 项目将对癌症病因，预防和治疗结果的方法论产生深远影响。