Prof SO Hon Cheong - School of Biomedical Sciences, CUHK

Prof SO Hon Cheong

Associate Professor

MBBS, PhD

39439255

hcso@cuhk.edu.hk

https://github.com/lab-hcso

https://labsocuhk.ddns.net:8890/covid19/

Rm 520A, 5/F., Lo Kwee-Seong Integrated Biomedical Sci. Bldg, Area 39, CUHK

Publons

ORCID

Biography

Prof. SO Hon Cheong (蘇漢昌) received his Bachelor of Medicine and Bachelor of Surgery (MBBS) degree together with a PhD degree in 2012 from The University of Hong Kong (HKU). His PhD research focused on statistical and psychiatric genomics. He has received numerous awards for his academic achievement, including the Croucher Foundation Scholarship and the Dr. Stephen K.P. Chang Gold Medal for the best PhD thesis in the Faculty of Medicine, HKU. Prior to taking up the current academic post, he worked as a resident psychiatrist in Queen Mary Hospital and Castle Peak Hospital. He joined the School of Biomedical Sciences of The Chinese University of Hong Kong as an Assistant Professor in Jan 2016 and is also currently assistant professor (by courtesy) of the Department of Psychiatry, CUHK.

His main research interests include the development and application of novel statistical and computational methodologies to “omics” and clinical data in general. In particular, he is interested in uncovering the genetic architecture of complex diseases and predicting disease risk and phenotypes based on bioinformatics and clinical data. He has developed methodologies for evaluating the heritability explained by individual genetic variants and the entire set of markers on a genome-wide association study (GWAS) panel. He has also developed novel methods to combine genetic information with family history in improving risk prediction, as well as new ways to construct polygenic risk scores. He is one of the lead-authors in a schizophrenia GWAS in Chinese population, leading to discovery of a novel susceptibility loci on the X chromosome. His recent interest also includes bioinformatics approaches to repurposing drugs for new indications, and has developed several repurposing methodologies using GWAS and other “omics” data. His work has been published in major journals in the field, including first or corresponding papers in Nature Neuroscience, American Journal of Human Genetics, Plos genetics, Psychological Medicine, Bioinformatics etc. He was recently awarded the Young Researcher Award 2018 by CUHK for outstanding research achievements.

Research Interests

Bioinformatics; statistical/computational methodologies for “omics” studies; applications to cancer/neuropsychiatric/cardio-metabolic phenotypes.
Disease risk prediction methodologies, particularly with the use of genomic profiles.
Genetic architecture of complex diseases.
Genetic epidemiological studies, especially on neuro-psychiatric disorders.
High dimensional data analysis, multiple testing, machine learning.

Selected Publications

Rao, S., Yin, L., Xiang, Y., & So, H.C.* (2021). Analysis of genetic differences between psychiatric disorders: exploring pathways and cell types/tissues involved and ability to differentiate the disorders by polygenic scores. Translational psychiatry, 11(1), 1-13.
Xiang, Y., Wong, K.C.Y., & So, H.C.* (2021). Exploring Drugs and Vaccines Associated with Altered Risks and Severity of COVID-19: A UK Biobank Cohort Study of All ATC Level-4 Drug Categories Reveals Repositioning Opportunities. Pharmaceutics, 13(9), 1514.
Wong, K.C.Y., Xiang, Y., & So, H.C.* (2021). Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR Public Health and Surveillance, In press.
Yin, L., Chau, C.K.L., Lin, Y.P., Rao, S., Xiang, Y., Sham, P.C., & So, H.C.* (2021). A framework to decipher the genetic architecture of combinations of complex diseases: Applications in cardiovascular medicine. Bioinformatics, Epub ahead of print.
Rao, S., Lau, A., & So, H.C.* (2020). Exploring diseases/traits and blood proteins causally related to expression of ACE2, the putative receptor of SARS-CoV-2: A Mendelian Randomization analysis highlights tentative relevance of diabetes-related traits. Diabetes Care, 43(7), 1416-1426.
Rao, S., Shi, M., Han, X., Lam, M. H. B., Liu, G., Wing, Y.K., So, H.C.* & Waye, M. M. Y.* (2020). Genome-wid e copy number variation-, validation-and screening study implicates a novel copy number polymorphism associated with suicide attempts in major depressive disorder. Gene, 755, 144901. (*co-corresponding authors).
Lau, A.L.C., & So,H.C.* (2020). Turning genome-wide association study findings into opportunities for drug repositioning. Computational and Structural Biotechnology Journal, (in press).
Yin, L.Y., Chau, C.K.L., Sham, P.C. & So, H.C.* (2019). Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology. American Journal of Human Genetics, 105(6), 1193-1212.
So, H.C.*, Chau, C.K.L., Cheng, Y.Y., & Sham, P.C. (2020). Causal relationships between blood lipids and depression phenotypes: A Mendelian randomization analysis. Psychological Medicine, (Epub ahead of print).
So, H.C.*, Chau, C.K.L., Ao, F.K., Mo, C.H., & Sham, P.C. (2019). Exploring shared genetic bases and causal relationships of schizophrenia and bipolar disorder with 28 cardiovascular and metabolic traits. Psychological Medicine, 49(8), 1286-1298. doi: 10.1017/S0033291718001812.
Wong, C.F., Chau, C.K.L., Ao, F.K., Mo, C.H., Wong, S.Y., Wong, Y.H., & So, H.C.* (2019). Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants. Depression and Anxiety, 36(4), 330-344. doi: 10.1002/da.22861.
Zhao, K., & So, H.C.* (2019). Using drug expression profiles and machine learning approach for drug repurposing. Methods in Molecular Biology, 1903, 219-237. doi: 10.1007/978-1-4939-8955-3_13.
Zhao, K., & So, H.C.* (2019). Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data. IEEE Journal of Biomedical and Health Informatics, 23(3), 1304-1315. doi: 10.1109/JBHI.2018.2856535.
So, H.C.*, & Wong, Y.H. (2019). Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders. Journal of Psychiatric Research, 110, 83-92. doi: 10.1016/j.jpsychires.2018.12.015.
Yin, L., Cheung, E.F.C., Chen, R.Y.L., Wong, E.H.M., Sham, P.C., & So, H.C.* (2018). Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups. Journal of Psychiatric Research, 106, 106-117. doi: 10.1016/j.jpsychires.2018.09.010.
So, H.C.,* Lau, A., Chau, C.K.L., & Wong, S.Y. (2017). Translating GWAS Findings Into Therapies For Depression And Anxiety Disorders: Drug Repositioning Using Gene-Set Analyses Reveals Enrichment Of Psychiatric Drug Classes. Psychological Medicine. In press.
So, H.C.*, Chau, C.K., Chiu, W.T., Ho, K.S., Lo, C.P., Yim, S.H., & Sham, P.C. (2017). Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nature Neuroscience, 20, 1342-1349.
Anttila, V., Bulik-Sullivan, B., Finucane, H.K., Walters, R.K., Bras, J., Duncan, L., …So, H.C. …. Patsopoulos, N.A. (2018). Analysis of shared heritability in common disorders of the brain. Science, 360(6395), eaap8757. (534 out of 578 authors)
Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2018). Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell, 173(7), 1705-1715.
So, H.C.*, & Sham, P.C.* (2017). Exploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits. Bioinformatics, 33(6), 886-892.
So, H.C.*, & Sham, P.C. (2017). Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Sci Rep, 7, 41262.
Lee, E.H., So, H.C., & Chen, E.Y. (2016). Admission Rates and Psychiatric Beds in Hong Kong, 1999-2014: A Population-Based Study. Psychiatric Services, 67, 579.
Chan, S.K., So, H.C., Hui, C.L., Chang, W.C., Lee, E.H., Chung, D.W., Tso, S., Hung, S.F., Yip, K.C., Dunn, E., Chen E.Y. (2015). 10-year outcome study of an early intervention program for psychosis compared with standard care service. Psychol Med, 45(6), 1181-1193.
Wong, E.H.^#, So, H.C.^#, Li, M., Wang, Q., Butler, A.W., Paul, B., Wu, H.M., Hui, T.C., Choi, S.C., So, M.T., Garcia-Barcelo, M.M., McAlonan, G.M., Chen, E.Y., Cheung, E.F., Chan, R.C., Purcell, S.M., Cherny, S.S., Chen, R.R., Li, T., & Sham, P.C. (2014). Common variants on Xq28 conferring risk of schizophrenia in Han Chinese. Schizophr Bull, 40, 777-786.
Falchi, M., Moustafa, J.S.E., Takousis, P., Pesce, F., Bonnefond, A., Andersson-Assarsson, J.C., Sudmant, P.H., Al-Shafai, M.N., Bottolo, L., So, H.C. et al. (2014). Low number of copies of the salivary amylase gene predisposes to obesity. Nat Genet, 46(5), 492-497.
So, H.C., Gui, A.H., Cherny, S.S., & Sham, P.C. (2011). Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet Epidemiol, 35, 310-317. (cited 201 times as at 5/10/2017 according to Google Scholar)
So, H.C., & Sham, P.C. (2011). Robust association tests under different genetic models, allowing for binary or quantitative traits and covariates. Behav Genet, 41, 768-775.
So, H.C., Li, M., & Sham, P.C. (2011). Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet Epidemiol, 35, 447-456.
So, H.C., Kwan, J.S., Cherny, S.S., & Sham, P.C. (2011). Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening. Am J Hum Genet, 88, 548-565.
So, H.C., Yip, B.H., & Sham, P.C. (2010). Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies. PLoS One, 5, e13898.
So, H.C., & Sham, P.C. (2010). Effect size measures in genetic association studies and age-conditional risk prediction. Hum Hered, 70, 205-218
So, H.C., & Sham, P.C. (2010). A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained. PLoS Genet, 6, e1001230.
So, H.C., Fong, P.Y., Chen, R.Y., Hui, T.C., Ng, M.Y., Cherny, S.S., Mak, W.W., Cheung, E.F., Chan, R.C., Chen, E.Y., & Sham P.C. (2010). Identification of neuroglycan C and interacting partners as potential susceptibility genes for schizophrenia in a Southern Chinese population. Am J Med Genet B Neuropsychiatr Genet, 153B, 103-113.
So, H.C., Chen, E.Y., & Sham, P.C. (2009). Genetics of schizophrenia spectrum disorders: looking back and peering ahead. Ann Acad Med Singapore, 38, 436-434.
So, H.C., Chen, R.Y., Chen, E.Y., Cheung, E.F., Li, T., & Sham, P.C. (2008). An association study of RGS4 polymorphisms with clinical phenotypes of schizophrenia in a Chinese population. Am J Med Genet B Neuropsychiatr Genet, 147B, 77-85.* Corresponding / Co-corresponding author
^# Co-first author

Grants

Health and Medical Research Fund [PI; 08-May-19]: “A Study on the Epiemiology and Genetic Basis of Metabolic Abnormalities in Hong Kong Chinese Patients with Schizophrenia” (HK$1,341,680).
RGC Collaborative Research Fund [Co-PI, 1/5/2018 – 30/4/2021]: “Reading, Writing, and Mathematics: Behavioral Genetics, Molecular Genetics, and Neuro Markers of Early Academic Achievement in Hong Kong Chinese Children” (Total amount awarded from RGC: $6,716,587; CUHK matching: $40,950).
NSFC Grant (面上項目) [PI – 2020 Jan 1 – 2023 Dec 31]: “Mining genomics data and electronic health records to uncover drug repositioning candidates” (RMB 550,000).
NSFC Grant for Young Scientist [PI 2020 Jan 1– 2023 Dec 31]. “Deciphering the genetic architecture of a combination of multiple diseases: A novel framework with applications in cardiovascular medicine and psychiatry” (RMB 240,000).