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，PhD是一位统计学家，其最终职业目标是结合高级因果推论 遗传学和生物学研究的技术，以便在流行病学和 临床决策。他提出的研究将开发出强大的因果推论方法 确定因果风险因素和潜在的遗传途径，导致乳腺癌的风险。 候选人：张博士是哈佛T.H.生物统计学系的博士后研究员。陈学校 公共卫生（HSPH）。他完成了博士学位。约翰霍普金斯大学生物统计学专业。他以前的作品 在乳腺癌全基因组关联研究（GWAS）中，重点是鉴定遗传关联和 建立多基因风险使他准备进行拟议的研究。拟议的职业发展 计划将以他以前的培训为基础，并通过三个培训目标来增强轨迹 独立研究者：1）获取并应用最尖端的因果推理方法，以申请大型 遗传数据集； 2）获得分子生物学和癌症知识； 3）发展领导力和专业 进行多学科分析的技能。 导师/环境：张博士已与补充的强大指导委员会组成了强大的指导委员会 拟议研究所需领域的专业知识。所有导师都承诺在 定期并参加咨询会议，以监督他的培训和研究一次 月份。作为一个机构，HSPH致力于帮助年轻的研究人员。张博士将可以使用 专业和职业发展资源，包括专业发展课程，写作和 编辑对论文和赠款申请等的支持，等等。 研究：乳腺癌的危险因素包括生殖和生活事件（共同经典风险 因素）和遗传因素；但是，将这些风险因素与 乳腺癌尚不清楚。为了解决这两个问题，他将为 孟德尔随机分析以估计经典危险因素与乳腺癌之间的因果关系 风险（目标1）。他还将开发一种因果中介方法，将功能注释数据集整合到 确定乳腺癌风险的潜在途径（AIM 2）。在AIM 3中，他将同时应用标准 AIM 1和2在最大的乳腺癌GWAS数据集中开发的方法和新方法 多种族汇合项目。该提案的结果将为高级统计工具提供 确定因果效应，阐明基本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 具有随意推理和乳腺癌专业知识的独立研究的经验。