• Human Molecular Genetics
• Leucocyte Single cell-type Transcript Abundance biomarkers
• Inherited Metabolic Diseases

• Clinical Impact: Pioneered metabolic medicine in Hong Kong and solved the genetic mystery of Primary Carnitine Deficiency.
• Global Recognition: Top-tier researcher in the genetic susceptibility of complex traits like Scoliosis and bone phenotypes.
• Technological Advancement: Developed the BOOST algorithm and award-winning single-cell analysis methods.
• Biomarker Innovation: Patented methods for cell-mixture analysis that are transforming modern diagnostic testing.

Nelson Tang’s research represents a sophisticated convergence of clinical pathology, molecular discovery, and computational innovation. At the heart of his work is a deep commitment to identifying the genetic drivers of human disease, ranging from rare metabolic disorders to common polygenic conditions. A defining achievement in inherited metabolic diseases was his identification of the genetic cause of Primary Carnitine Deficiency. This breakthrough led to the establishment of Hong Kong’s first specialized metabolic clinic, directly enabling life-saving molecular diagnoses and treatments for over 1,000 patients across Asia.

Beyond rare diseases, Nelson is a global authority on the Genetics of Disease Susceptibility. His extensive research into the polygenic architecture of conditions such as Scoliosis and Diabetes has placed him among the top 10 most-cited authors in these fields worldwide. To facilitate this complex research, he has pioneered vital tools in Bioinformatics, most notably the BOOST algorithm for gene-gene interaction, which remains a standard in genome-wide association studies.

His spirit of Innovation is further evidenced by his 2021 Gold Medal at the Geneva Invention Expo for developing patented algorithms that extract single-cell gene expression from bulk tissue data. This technology is instrumental in creating high-precision blood biomarkers, seamlessly bridging the gap between theoretical genetics and real-world clinical application.

1. Geneva Invention Expo Gold Medal for Cell-mixture Analysis Algorithms, Geneva Invention Expo (2021)

1. Huang J, Yam SCP, Leung KS, Deng M, Tang NLS. Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches. J Transl Med. 2025;23:1143. doi:10.1186/s12967-025-07157-z
2. Huang J, Chow ACM, Tang NLS, Yam SCP. An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF. Clin Transl Med. 2025;15(4):e70283. doi:10.1002/ctm2.70283
3. Huang B, Huang J, Chiang NH, Chen Z, Lui G, Ling L, Kwan MYW, Wong JSC, Mak PQ, Ling JWH, Lam ICS, Ng RWY, Wang X, Gao R, Hui DS-C, Ma SL, Chan PKS, Tang NLS. Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients. Front Immunol. 2024;14:1315602. doi:10.3389/fimmu.2023.1315602
4. Xiao FH, Wang HT, Chen XQ, Ge MX, Yan D, Yang XL, Yang LQ, Lin R, Guo RH, Zhang W, Tang NLS, He Y, Zhou J, Cai WW, Kong QP. Hypermethylation in H3K9me3 regions characterizes the centenarian methylomes in healthy aging. Natl Sci Rev. 2023;10(6):nwad067. doi:10.1093/nsr/nwad067
5. Yeo W, Lau TK, Kwok CC, et al. NEPA efficacy and tolerability during (neo)adjuvant breast cancer chemotherapy with cyclophosphamide and doxorubicin. BMJ Support Palliat Care. 2022;12:e264-e270. doi:10.1136/bmjspcare-2019-002037
6. Lee AWT, Ng JKW, Liao J, Luk AC, Suen AHC, Chan TTH, Cheung MY, Chu HT, Tang NLS, Zhao MP, Lian Q, Chan WY, Chan DYL, Leung TY, Chow KL, Wang W, Wang LH, Chen NCH, Yang WJ, Huang JY, Li TC, Lee TL. Single-cell RNA sequencing identifies molecular targets associated with poor in vitro maturation performance of oocytes collected from ovarian stimulation. Hum Reprod. 2021;36(7):1907-1921. doi:10.1093/humrep/deab100
7. Ji L, Wu D, Xie H, Yao B, Chen Y, Irwin DM, Huang D, Xu J, Tang NLS, Zhang Y. Ambient temperature is a strong selective factor influencing human development and immunity. Genomics Proteomics Bioinformatics. 2020;18(5):489-500. doi:10.1016/j.gpb.2019.11.009
8. Tang N, Zheng YT, Zhao H, Chan WY, Yao YG. Editorial: A brief review of the long-term partnership between Kunming Institute of Zoology (KIZ) and the Chinese University of Hong Kong (CUHK) in bioresources and molecular research. Zool Res. 2023;44(3):556-558. doi:10.24272/j.issn.2095-8137.2023.091
9. Tang NL, Hui J, Huang D, Tang MF, Wang X, Wu J, Chan IHS, Leung TF. Reference intervals of spot urine copper excretion in preschool children and potential application in pre-symptomatic screening of Wilson disease. Pathology. 2020;52(4):439-446. doi:10.1016/j.pathol.2020.02.003
10. Styrkarsdottir U, Stefansson OA, Gunnarsdottir K, et al. GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures. Nat Commun. 2019;10(1):2054. doi:10.1038/s41467-019-09860-0
11. Styrkarsdottir U, Helgason H, Sigurdsson A, et al. Whole-genome sequencing identifies rare genotypes in COMP and CHADL associated with high risk of hip osteoarthritis. Nat Genet. 2017;49(5):801-805. doi:10.1038/ng.3816
12. Styrkarsdottir U, Thorleifsson G, Gudjonsson SA, et al. Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures. Nat Commun. 2016;7:10129. doi:10.1038/ncomms10129
13. Zhu Z, Tang NLS, Xu L, et al. Genome-wide association study identifies new susceptibility loci for adolescent idiopathic scoliosis in Chinese girls. Nat Commun. 2015;6:8355. doi:10.1038/ncomms9355
14. Estrada K, Styrkarsdottir U, Evangelou E, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44(5):491-501. doi:10.1038/ng.2249
15. Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NLS, Yu W. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010;87(3):325-340. doi:10.1016/j.ajhg.2010.07.021
16. Khor CC, Vannberg FO, Chapman SJ, et al. CISH and susceptibility to infectious diseases. N Engl J Med. 2010;362(22):2092-2101. doi:10.1056/NEJMoa0905606
17. Woo J, Tang N, Suen E, Leung J, Wong M. Green space, psychological restoration, and telomere length. Lancet. 2009;373(9660):299-300. doi:10.1016/S0140-6736(09)60094-5
18. Tang NLS, Chan PKS, Hui DSC, et al. Lack of support for an association between CLEC4M homozygosity and protection against SARS coronavirus infection. Nat Genet. 2007;39(6):691-692. doi:10.1038/ng0607-691