Methods for Mendelian randomization and mediation analysis using integrative genetic and genomic data for breast cancer

使用乳腺癌综合遗传和基因组数据进行孟德尔随机化和中介分析的方法

• 批准号: 10115425
• 负责人: Haoyu Zhang
• 金额: $ 10万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115425
• 关键词: Address; African; Applications Grants; Asians; Automobile Driving; Biological; Biometry; Breast; Breast Cancer Risk Factor; Confidence Intervals; Data; Data Set; Development; Development Plans; Disease; Doctor of Philosophy; Environment; Epidemiology; Ethnic Origin; Etiology; European; Event; Genetic; Genetic Research; Genome; Genomics; Goals; Hormones; Institution; Investigation; Knowledge; Latino; Lead; Leadership; Life; Link; Malignant Neoplasms; Mediating; Mediation; Mendelian randomization; Mentors; Methodology; Methods; Molecular Biology; Outcome; Paper; Pathway interactions; Performance; Population; Postdoctoral Fellow; Prevention strategy; Public Health Schools; Research; Research Personnel; Resource Development; Risk; Risk Factors; Techniques; Testing; Training; Universities; Variant; Woman; Work; Writing; analytical tool; anticancer research; biobank; biological research; cancer subtypes; career; career development; clinical decision-making; course development; disease phenotype; experience; functional genomics; genetic association; genetic variant; genome wide association study; genomic data; genomic locus; individualized medicine; individualized prevention; insight; malignant breast neoplasm; meetings; molecular marker; molecular subtypes; multi-ethnic; multidisciplinary; novel; novel strategies; novel therapeutics; personalized therapeutic; polygenic risk score; reproductive; simulation; skills; statistics; tool; treatment strategy; user-friendly

Project summary Haoyu Zhang, PhD is a statistician whose ultimate career goal is incorporating advanced causal inference techniques into genetics and biological research in order to make impactful advances in epidemiological and clinical decision-making. The research he proposes will develop powerful causal inference approaches to identify causal risk factors and underlying genetic pathways leading to the risk of breast cancer. Candidate: Dr. Zhang is a postdoctoral fellow in the Department of Biostatistics at Harvard T.H. Chan School of Public Health (HSPH). He completed a Ph.D. in Biostatistics at Johns Hopkins University. His previous work in breast cancer genome-wide association studies (GWAS) focusing on identifying genetic associations and building polygenic risk has prepared him to conduct the proposed research. The proposed career development plan will build upon his previous training with three training goals to enhance trajectory toward becoming an independent investigator: 1) acquire and apply cutting edge causal inference methodologies to apply on large genetic datasets; 2) gain knowledge in molecular biology and cancer; 3) develop leadership and professional skills to conduct multidisciplinary analysis. Mentors/Environment: Dr. Zhang has assembled a strong mentoring committee with complementary expertise in the required fields for the proposed research. All the mentors have committed to meet with him in a regular basis and participate the advisory meeting to oversight his training and research progress every six months. As an institution, HSPH is committed to help young researchers. Dr. Zhang will have access to professional and career development resources, which include professional development courses, writing and editing support for papers and grant applications, etc. Research: Risk factors for the breast cancer include reproductive and life events (collectively classic risk factors) and genetic factors; however, the causal associations and pathways linking these risk factors with breast cancer are unclear. To solve these two issues, He will develop a robust and powerful approach for Mendelian randomization analysis to estimate the causal effects between classic risk factors and breast cancer risk (Aim 1). He will also develop a causal mediation approach integrating functional annotation datasets to identify the underlying pathways for breast cancer risk (Aim 2). In Aim 3, he will apply both the standard approaches and novel approaches developed in Aim 1 and 2 on the largest breast cancer GWAS dataset from the multi-ethnic Confluence Project. The results of this proposal will provide advanced statistical tools to identify causal effect, elucidate the underlying genetic pathways and guide developments of personalized therapeutics and prevention strategies. The proposal will also provide him the required training and research experience to become an independent research with casual inference and breast cancer expertise.

项目摘要 Haoyu Zhang博士是一名统计学家，其最终职业目标是整合高级因果推理 技术用于遗传学和生物学研究，以便在流行病学和 临床决策。他提出的研究将发展强大的因果推理方法， 确定导致乳腺癌风险的因果风险因素和潜在遗传途径。 候选人：张博士是哈佛大学生物统计学系的博士后研究员。阐教 公共卫生（HSPH）。他获得了博士学位在约翰霍普金斯大学的生物统计学。他以前的工作 在乳腺癌全基因组关联研究（GWAS）中，重点是识别遗传关联， 构建多基因风险为他进行拟议的研究做好了准备。拟议的职业发展 该计划将建立在他以前的训练基础上，有三个训练目标，以提高他成为一名 独立调查员：1）获得并应用最先进的因果推理方法，以广泛应用于 遗传数据集; 2）获得分子生物学和癌症知识; 3）培养领导力和专业知识 进行多学科分析的能力。 导师/环境：张博士组建了一个强大的导师委员会， 在所需领域的专业知识为拟议的研究。所有的导师都承诺在一个 定期参加顾问会议，监督培训和研究进展 个月作为一个机构，HSPH致力于帮助年轻的研究人员。张博士将有机会 专业和职业发展资源，其中包括专业发展课程，写作和 为论文和资助申请等提供编辑支持。 研究：乳腺癌的风险因素包括生殖和生活事件（统称为经典风险） 因素）和遗传因素;然而，将这些风险因素与 乳腺癌是不明确的。为了解决这两个问题，他将开发一个强大而强大的方法， 经典危险因素与乳腺癌因果关系的孟德尔随机化分析 风险（目标1）。他还将开发一种因果调解方法，将功能注释数据集集成到 确定乳腺癌风险的潜在途径（目标2）。在目标3中，他将应用标准 Aim 1和Aim 2中开发的最大乳腺癌GWAS数据集的方法和新方法， 多民族融合项目。这项建议的结果将提供先进的统计工具， 确定因果关系，阐明潜在的遗传途径，并指导个性化的发展 治疗和预防策略。该提案还将为他提供所需的培训和研究 经验，成为一个独立的研究与因果推理和乳腺癌的专业知识。