Prof. Maggie Haitian WANG

王海天教授

Associate Professor

Dr. Maggie Wang obtained Bachelor degree (honor) in Physics (2005) from the Hong Kong University of Science and Technology (HKUST) and PhD in Statistics (2011) from ISOM department of HKUST. She joint the JC School of Public Health and Primary Care (SPHPC) of the Chinese University of Hong Kong (CUHK) as a Research Assistant Professor in 2011, and was appointed as Assistant Professor in 2015 and Associate Professor in 2021. Dr. Wang was a visiting scholar in Columbia University in 2009 and Stanford University in 2016. She is the Principle Investigator of projects of RGC-GRF, ITC, HMRF and NSFC.  Dr. Wang serves as the board member of the International Genetic Epidemiology Society (IGES) since 2021, council member of the Chinese Bioinformatics Society (preparatory) since 2019, and review editor of Human Genetics since 2018.

• Human genome statistical genetics: prediction methods for complex human diseases using omics genetic and genomic data, statistical methods for genetic interactions, epistasis, and association testing

• Virus evolution: statistical methods for predicting evolutionary trend, estimation of substitution rate, evaluation of key mutations for infectious disease pathogens, codon optimization algorithm, epitope screening, influenza virus and SARS-CoV-2

• Genetic Epidemiology: GWAS of Alzheimer’s disease in Chinese population, virus genetic epidemiology studies

• 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. PI: Maggie Wang, 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

1. 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)

2. 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.)

3. 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.) 

4. 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.)

5. 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.

6. 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.

7. 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.

8. 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.

9. 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)

10. 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.

11. 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.

12. 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)