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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结