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

> **NIH NIH K99** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2022 · $92,679

## Abstract

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.

## Key facts

- **NIH application ID:** 10319172
- **Project number:** 5K99CA256513-02
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Haoyu Zhang
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $92,679
- **Award type:** 5
- **Project period:** 2021-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10319172

## Citation

> US National Institutes of Health, RePORTER application 10319172, Methods for Mendelian randomization and mediation analysis using integrative genetic and genomic data for breast cancer (5K99CA256513-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10319172. Licensed CC0.

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