科学研究

Dr. Yang Can is now servicing as an associate professor at the Department of Mathematics, HKUST. He obtained BS. and M.Phil. Degrees at Zhejiang University 2003, and 2006, and Ph.D. Degree at HKUST in 2011. He was a postdoc (2011-2012) and associate scientist (2012-2014) at Yale. His research area focuses on the development of statistical methods and the application of computational tools to large-scale genomic data, including BOOST and GPA. His research papers have appeared in high impact journals, such as Annals of Statistics, Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Communications, PLoS Genetics, Proceedings of the National Academy of Sciences, and The American Journal of Human Genetics. Based on his contribution to data analytic methods and tools, Dr. Yang won the 2012 Hong Kong Young Scientist Award in Engineering Science. As of September 2021, Dr. Yang’s work has been cited 3,685 times, with h-index 27, and i10-index 46. Dr. Yang has also established industrial collaboration with WeGene (a direct-to-consumer DNA ancestry testing platform and personalized healthcare testing provider), as supported by the Innovation and Technology Fund of Hong Kong Government.

Inferring the causal relationship between a risk factor (exposure) and a complex trait of interest (outcome) is essential in biomedical research and social science. Mendelian Randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWAS). Existing MR methods often rely on strong assumptions, resulting in many false positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap.  We propose a unified MR approach, MR-APSS, which (i) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; (ii) allows to include more genetic instruments with moderate effects to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls, and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, such as psychiatric disorders and social traits, where the strengths of IVs tend to be relatively weak and existing methods for causal inference are vulnerable to confounding effects. This is a joint work with HU Xianghong, Zhao Jia, Wang Yang, Peng Heng, Wan Xiang and Zhao Hongyu.