Haoyu Zhang Appointed Earl Stadtman Investigator

October 21, 2022, by Maura Kate Costello, M.A.

Haoyu Zhang, Ph.D., was appointed Earl Stadtman tenure-track investigator in the Biostatistics Branch (BB)  in August 2022. Dr. Zhang develops scalable statistical methods and software to analyze large-scale multi-ancestry genetic data to address questions related to health disparities and to advance genetic research in diverse populations.   

During his post-doctoral training at Harvard T.H. Chan School of Public Health in Boston, Massachusetts, Dr. Zhang developed novel Mendelian Randomization (MR) methods to improve the statistical power to test for the causal effect between epidemiological risk factors and complex traits. He also developed methods using multi-ancestry polygenic risk scores (PRS) to improve predictions in under-represented populations.  He evaluated the multi-ancestry PRS approaches in 4.9 million individuals from diverse ancestries through large-scale collaborations with 23andMe, the Global Lipids Consortium, All of Us, and UK Biobank. During his postdoctoral training, he received the K99/R00 Pathway to Independence Award from the National Cancer Institute.  

As an Earl Stadtman investigator, his primary research focus will be to develop and apply scalable statistical methods for analyzing multi-ancestry genetic and biobank data, and translate the genetic findings to clinical settings to inform prevention and therapeutic strategies. In addition, Dr. Zhang will collaborate across branches to develop and apply cutting-edge statistical methods to ongoing and upcoming DCEG research studies to investigate the genetic architecture of complex traits and diseases, lead analyses on multi-ancestry association testing, develop multi-ancestry PRS, and estimate heritability for breast cancer within the Confluence Project.  

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 received his Ph.D. in biostatistics from Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, and his Bachelor of Science in statistics from Zhejiang University in Hangzhou, China.