Clinical Data Engagement


Our group specializes in integrated omics and clinical data science. We use this expertise to study human variation, and develop molecular insights into disease risk prediction, surveillance and intervention. Such an approach is highly useful in targeting complex diseases like obesity and mental illness that are currently affecting millions across the globe. Our projects cover molecular phenotyping of chronic diseases in different stages of life-course (pediatric to adults to elderly), with a special focus on Asian populations as they are underrepresented across many biomedical databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. We work in collaboration with eminent Singaporean cohorts (GUSTO, S-PRESTO, ATTRaCT & PRISM), health clusters, public sector and national platforms (eg. National Precision Medicine SG10K-Health study) to enhance the translational potential of our findings. In addition to the local research landscape, we work closely with the R&D of top nutrition and probiotics companies.

Combining our learnings from omics and clinical data science, we have been using the multi-dimensional data to develop mobile applications that can enhance personalized healthcare experience. These apps can be powered by next generation diagnostic tests developed through precision medicine research and electronic health records to provide a holistic and an up-to-date assessment of an individual’s health journey. These tools can not only provide an actionable intervention roadmap for clinicians but can also increase the scientific literacy of individuals to make better health choices. Beyond health apps, our experience in multi-omics data analytics has led to development of open-source analytics tools (eg. Gene Environment Methylation tool - GEM), cell type reference panels for infant epigenetic studies, and omics databases such as iMOM-db and iDAD_db that cover molecular phenotypes of ethnic variation (eQTLs and meQTLs) and diet-exposure (sperm sncRNA).

Clinical Data Engagement-Figure1

Link to Karnani lab tools, databases and resources.


 Head of Clinical Data Engagement &
 Senior Principal Investigator
 KARNANI  Neerja   |    [View Bio]  
 BII Team
 Senior Research Scientist  CHOO Matthew
 Senior Research Fellow LIM Ives Yubin 
 Full Stack Developer HUAN Jason
 Research Officer CHAN Penny
 Data analysts under joint appointment @SICS
 Research Scientist VAZ Candida
 Senior Research Fellow XU Jia
 Research Fellow MISHRA Priti
 Project Manager ONG Kelly

Selected Publications

  • Chen L, Ling KTM, …, Gluckman PD, Eriksson JG, Karnani N. Variability in newborn telomere length is explained by inheritance and intrauterine environment. BMC Med. 2022 Jan 25;20(1):20.

  • Lim IY, Lin X, …, Gluckman PD, Chong YS, Karnani N. Dichotomy in the Impact of Elevated Maternal Glucose Levels on Neonatal Epigenome. J Clin Endocrinol Metab. 2021 Oct 11.Online ahead of print.

  • Sampathkumar A, Tan KM, … Gluckman PD, Ramasamy A, Karnani N. Genetic Link Determining the Maternal-Fetal Circulation of Vitamin D. Front Genet. 2021 Sep 21;12:721488.

  • Vaz C, Kermack AJ, … Godfrey KM, Kumar P, Lillycrop KA, Karnani N. Short-term diet intervention alters the small non-coding RNA (sncRNA) landscape of human sperm. BioRxiv July 9, 2021

  • Mir SA, Chen L, …Meikle PJ, Wenk MR and Karnani N. Developmental and intergenerational landscape of human circulatory lipidome. (*Co-corresponding authors). BioRxiv. April 24, 2021

  • Wong G, Weir JM, … Gluckman PD, Meaney MJ, Meikle PJ, Karnani N. The placental lipidome of maternal antenatal depression predicts socio-emotional problems in the offspring. Transl Psychiatry. 2021 Feb 4;11(1):1072020.

  • Xu J, Lawley B, …, Tannock GW, Karnani N.  Ethnic diversity in infant gut microbiota is apparent before the introduction of complimentary diets. Gut Microbes. 2020 Sep 2;11(5):1362-137. Journal cover page

  • Chen LW, Xu J, … Gilbert JA, Karnani N*, Lee YS*. Implication of gut microbiota in the association between infant antibiotic exposure and childhood obesity and adiposity accumulation. Int J Obesity. 2020. Jul;44(7):1508-1520. [*Co-Corresponding authors]

  • Migliavacca E, Tay SKH, … Godfrey KM *, Lillycrop KA*, Karnani N*,Feige JN*. Mitochondrial dysfunction is the major molecular signature of human sarcopenia across ethnicities. [*Co-Corresponding authors]. Nat Commun. 2019, Dec 20;10(1):5808.

  • Wu D, Dou J, …, Karnani N, … Liu J, and Wang C on behalf of the SG10KConsortium Large-scale whole-genome sequencing of three diverse Asian populations in Singapore.  Cell. 2019 Oct 17;179(3):736-749.

  • Michael N, Gupta V,…, Gluckman PD, Karnani N*, Velan SS*. Determinants of intramyocellular lipid accumulation in early childhood. [*Co-Corresponding Authors]. Int J Obes.2019 May;44(5):1141-1151.

  • Wu Y, Lin X, … Chong YS, Gluckman PD, Karnani N. Analysis of two birth tissues provides new insights into the epigenetic landscape of neonates born preterm. Clin Epigenetics. 2019 Feb 11(1):26.

  • Lin X, … Cheong CL, Karnani N. Cell-type specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell-types. Epigenetics. 2018 13(9):941-958.

  • Lin X, Lim YI, …, Lee YS, Gluckman PD, Karnani N. Developmental pathways to adiposity begin before birth and are influenced by genotype, prenatal environment and epigenome. BMC Medicine. 2017 Mar7,15(1):50. Editorial highlight.

  • Lim YI, Lin X, Karnani N. Implications of genotype and environment on variation in DNA methylation. Handbook of Nutrition, Diet, and Epigenetics. 20 June 2017 (invited book chapter, Springer Nature).

  • Karnani, N., Taylor, C., Malhotra, A. and Dutta, A. Pan-S replication patterns and chromosomal domains defined by genome-tiling arrays of ENCODE genomic areas. Genome Res. 2007 Jun, 17(6):865-76.

  • ENCODE Project Consortium, Birney E, Stamatoyannopoulos …, Karnani N, …, Luna R, et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007 Jun 14,447(7146):799-816 (contributed the DNA replication timing data).