Haoyu Zhang, Ph.D.

As an Earl Stadtman investigator, Haoyu Zhang, Ph.D. will collaborate across the Division to apply Mendelian Randomization (MR) and polygenic risk score (PRS) methods to existing DCEG research studies of the genetic architecture of breast cancer subtypes. Within the Confluence Project—to date the largest and most diverse study of the genetic factors associated with breast cancer by subtypes—he will help design a robust trans-ethnic MR method to uncover genetic contributions to risk. Dr. Zhang aims to improve statistical power and the ability to identify causal variants for common traits and diseases in underrepresented populations, as well as inform prevention and therapeutic strategies. Data from the Breast Cancer Association Consortium will allow him to develop methods to test for genetic variants associated with breast cancer subtypes.

He is planning to evaluate these methods using the 23andMe database of more than 3.7 million individuals and the Global Lipids Genetics Consortium, a worldwide collaboration to investigate the genetic etiology of quantitative lipid traits. In accordance with DCEG’s commitment to FAIR principles, the software Dr. Zhang develops for these projects will be open-access, user-friendly, and suitable for high-performance computing clusters and cloud platforms of the NIH Data Commons.

In complement to his research program, Dr. Zhang serves as the co-chair of the Simulation and Benchmarking Working Group in PRIMED, an NIH-funded consortium aiming to develop and evaluate methods to improve the use of PRS to predict disease and health outcomes in diverse ancestry populations.

Dr. Zhang was appointed Earl Stadtman tenure-track investigator in the Biostatistics Branch (BB) in August 2022. He received his Ph.D. in biostatistics at Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland in 2019, and his B.S. in statistics from Zhejiang University in Hangzhou, China. His Ph.D. dissertation focused on testing for genetic association and building risk prediction models for cancer incorporating tumor characteristics. In addition, he led several analyses through multidisciplinary, international collaborations using the largest breast cancer genome-wide association study (GWAS) dataset from the Breast Cancer Association Consortium (BCAC).

Prior to joining DCEG, Dr. Zhang took postdoctoral training at the Harvard T.H. Chan School of Public Health in Boston, Massachusetts, where he used Mendelian Randomization (MR) to improve a set of tools to test the causal effect between epidemiological risk factors and complex traits and built a trans-ethnic polygenic risk score (PRS) method to improve predictions in under-represented populations.