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- Haoyu Zhang
My name is Haoyu Zhang, and I am currently an Earl Stadtman tenure-track investigator at National Cancer Institute, Division of Cancer Epidemiology and Genetics
- Researches - Haoyu Zhang
Breast cancer represents a heterogeneous group of diseases with different molecular and clinical features Thus, clarifying potential heterogeneous associations between genes and disease subtypes offers a tremendous opportunity to characterize distinct etiological pathways I proposed several new computational and statistical approaches to develop powerful genetic associations tests
- Mendelian Randomization | Haoyu Zhang
Earl Stadtman tenure-track investigator Reassessing the causal role of obesity in breast cancer susceptibility: a comprehensive multivariable Mendelian randomization investigating the distribution and timing of exposure
- Haoyu Zhang, Ph. D.
Haoyu Zhang, Ph D Division of Cancer Epidemiology and Genetics, National Cancer Institute 9609 Medical Center Drive Rockville, MD 20850, USA Q haoyu zhang2@nih gov
- Collaborations | Haoyu Zhang
Earl Stadtman tenure-track investigator Scientific collaborations As a statistician and data scientist, I sincerely believe collaborations across disciplines are critical to scientific discovery and methodology development I participated in broad research collaborations, including genetic association analysis for gall bladder cancer, effect-size distribution analysis for fourteen cancers
- Publications - Haoyu Zhang
Haoyu Zhang, Ni Zhao, Thomas U Ahearn, William Wheeler, Montserrat García-Closas, and Nilanjan Chatterjee (2020) A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics
- Xia Jiang - Haoyu Zhang
Haoyu Zhang Home Research Publications Software Talks Teaching Grants Fellowship Honors Awards Hobby
- Causal Inference | Haoyu Zhang
Earl Stadtman tenure-track investigator Robust Mendelian randomization methods incorporating weak and correlated instruments Mendelian randomization (MR) is a major tool to test the causal association between risk factors and disease using genetic variants as instrumental variables MR makes several strong assumptions, which can be violated in practice and lead to biased estimates and
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