Prof. Maggie Haitian WANG
王海天教授
Research Links
Academic Appointments
- Editor-in-Chief, Human Genetics (Springer Nature)
- Director, Master of Science Program in Epidemiology and Biostatistics, CUHK
- Director, Center for Clinical Research and Biostatistics (CCRB), CUHK
Bio
Dr. Maggie Wan is an Associate Professor (Tenured) at the JC School of Public Health and Primary Care, The Chinese University of Hong Kong (CUHK).
Dr. Wang’s research focuses on bioinformatics and statistical genetics, with an emphasis on deciphering genomic patterns in viruses and humans to advance understanding of disease mechanisms and immunology. Her methodological innovations bridge theoretical biology and translational applications, with significant contributions to vaccine development and precision medicine. Her work has been widely adopted by the pharmaceutical and biotechnology industries.
Dr. Wang obtained her BSc (Honors) in Physics and PhD in Statistics from the Hong Kong University of Science and Technology (HKUST). She was a visiting scholar at Columbia University (2009) and Stanford University (2016). She joined CUHK in 2011 as a Research Assistant Professor, was promoted to Assistant Professor in 2015, and attained tenure as Associate Professor in 2021. She currently serves as Director of the Master Program in Epidemiology and Biostatistics and Director of the Center for Clinical Research and Biostatistics (CCRB). Additionally, she is an External Advisory Board Member for the Centre for Personalized Medicine (CPM) at the University of Oxford.
As a Principal Investigator, she has secured competitive grants from the Research Grants Council (GRF), Innovation and Technology Commission (ITC), Health and Medical Research Fund (HMRF), and the National Natural Science Foundation of China (NSFC). She was awarded the NSFC Excellent Young Scientists Fund (Hong Kong & Macau) in 2023 and received CUHK’s Research Excellence Award (2022-23) and Young Researcher Award (2022).
Dr. Wang holds key editorial and leadership roles, including Editor-in-Chief of Human Genetics (Springer Nature) and Council Member of the Chinese Bioinformatics Society. She was elected to the Board of Directors of the International Genetic Epidemiology Society (IGES, 2021-2023) and serves as the Faculty of Medicine’s Representative for Associate Professors at CUHK.
Research Interest
- Bioinformatics/ Statistical Genetics/ Computational biology: Developing statistical genetic methods for accurate genetic association tests and complex disease classification towards generating clinical impact. Working data types include: whole genome and exome sequencing data, GWAS, transcriptome, methylation genome, and microbiome. Bioinformatics 2017; Nucleic Acids Research 2016
- Infectious diseases genetics epidemiology: modelling the pattern of infectious disease epidemics at population level and genetic level, modelling genetic sequence evolution. Nature Medicine 2022, Nature Communications 2024
- Statistical collaborations: collaborative research with biomedical and clinical scientists on big data analysis. Cancer Research 2016, Microbiome 2020
Recent Funded Research Projects
- PI, RGC-GRF, Profiling high-risk genomic loci of the mpox virus and assessment of mpox vaccine protection against novel genetic variants, 2026/01/01 – 2028/12/31
- PI, NSFC Excellent Young Scientists Fund (HK & Macau) Vaccine antigen optimization through innovative bioinformatics methods. 2024/01/01 – 2026/12/31
- PI, Health and Medical Research Fund (HMRF) commissioned research on the Novel Coronavirus Diseases (COVID-19): Characterizing whole genome stability of the novel coronavirus SARS-CoV-2. 2020/04/01 – 2023/03/31
- PI of sub-project, Health and Medical Research Fund (HMRF) 2018/19, Sub-project title: Real-time surveillance of influenza viral evolution and antigenic mutation estimation, 01/09/2019 – 31/08/2022
- PI, National Natural Science Foundation of China (NSFC) – General Project, Improving prediction accuracy of Alzheimer’s Disease by Incorporating a novel feature selection method, 01/01/2019/ – 31/12/2022
- PI, National Natural Science Foundation of China (NSFC) – General Project, Tracking pattern of amino acids underlying seasonal influenza, 01/01/2015 – 31/12/2018
Selected Publications
- Lou J+, Liang W+, Cao L+, Hu I, Zhao S, Chen Z, Chan RW, Cheung PPH, Zheng H, Liu C, Li Q, Chong MKC, Zhang Y, Yeoh EK, Chan PKS, Zee BCY, Mok CKP*, Wang MH*. Predictive evolutionary modelling for influenza virus by site-based dynamics of mutation. Nature Communications. 2024. (The most-accurate computational method to forecast influenza evolution through a novel angle of modelling site-wise mutation dynamics)
- Xia X, Zhang Y, Sun R, Wei Y, Li Q, Chong MKC, Wu WKK, Zee BCY, Tang H, Wang MH* (2022) A Prism Vote Method for Individualized Risk Prediction of Traits in Genotype Data of Multi-population, PLOS Genetics. Accepted. (A whole new view-point of disease risk is introduced in this study, enabling individualized disease risk prediction and simultaneous inclusion of multiple populations)
- Cao L, Lou J, Chan SY, Zheng H, Liu C, Zhao S, Li Q, Mok KP, Chan SY, Chan RWY, Chong MKC, Wu WKK, Chen Z, Wong ELY, Chan PKY, Zee BCY, Yeoh EK, Wang MH* Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance. Nature Medicine. 2022 Jun 16:1-8. (A first-of-the-kind method to predict COVID-19 vaccine effectiveness by genome analysis, enabling rapid estimation of vaccine protection prior to mass vaccination and infection.)
- Wang MH*, Lou J, Cao L, Zhao S, Chan RW, Chan PK, Chan MC, Chong MK, Wu WK, Wei Y, Zhang H, Zee BC*, Yeoh EK* (2021) Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin. Journal of Infection. 2021 Oct 8. (The first genetic epidemiology framework for statistical association testing of key mutations influencing influenza epidemics.)
- Cao L, Lou J, Zhao S, Chan RW, Chan M, Wu WK, Chong MK, Zee BC, Yeoh EK, Wong SY, Chan PK, Wang MH*. In silico prediction of influenza vaccine effectiveness by sequence analysis. Vaccine. 2021 Feb 12;39(7):1030-4. (A novel method to predict vaccine effectiveness by virus and vaccine genetic sequence analysis, allowing the estimation of influenza VE prior to vaccination or infection.)
- Xia X, Wu WK, Wong SH, Liu D, Kwong TN, Nakatsu G, Yan PS, Chuang Y, Chan MW, Coker OO, Chen Z, Yeoh YK, Zhao L, Wang X, Cheng WY, Chan MT, Chan PK, Sung JJ, Wang MH*, Yu J*. Bacteria pathogens drive host colonic epithelial cell promoter hypermethylation of tumor suppressor genes in colorectal cancer. Microbiome. 2020 July 16;8(1):1-13.
- Wang MH*, Cordell HJ, Van Steen K. Statistical methods for genome-wide association studies. Seminars in cancer biology 2019 Apr 1 (Vol. 55, pp. 53-60). Academic Press.
- Chan MC, Wang MH, Chen Z, Hui DS, Kwok AK, Yeung AC, Liu KM, Yeoh YK, Lee N, Chan PK. Frequent Genetic Mismatch between Vaccine Strains and Circulating Seasonal Influenza Viruses, Hong Kong, China, 1996–2012. Emerging infectious diseases. 2018 Oct;24(10):1825.
- Wang MH*, Weng H, Sun R, Lee J, Wu WK, Chong KC, Zee BC. A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics. 2017 Aug 1;33(15):2330-6.
- Wang MH*, Sun R, Guo J, Weng H, Lee J, Hu I, Sham PC, Zee BC. A fast and powerful W-test for pairwise epistasis testing. Nucleic acids research. 2016 Jul 8;44(12):e115-. (A new statistical method for epistasis testing in GWAS data)
- Wang MH*, Chang B, Sun R, Hu I, Xia X, Wu WK, Chong KC, Zee BC. Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data. Human mutation. 2017 Sep;38(9):1235-9.
- Sun R, Weng H, Hu I, Guo J, Wu WK, Zee BC, Wang MH*. AW‐test collapsing method for rare‐variant association testing in exome sequencing data. Genetic Epidemiology. 2016 Nov;40(7):591-6.
- Wang H, Lo SH, Zheng T, Hu I. Interaction-based feature selection and classification for high-dimensional biological data. Bioinformatics. 2012 Nov 1;28(21):2834-42. (A daring attempt of performing classification/prediction using high-order statistic interactions)