Clinical Data Engagement

Our team specializes in utilizing cutting-edge technology to study human variation, with a focus on developing molecular insights into disease risk prediction, surveillance, and intervention. By combining integrated omics and clinical data science, we are able to target complex diseases like obesity and mental illness that affect millions globally. Our research covers chronic diseases across the entire lifespan - from pediatric to elderly populations - with a special emphasis on the underrepresented Asian population. Our collaborations with renowned Singaporean cohorts, health clusters, and public sector entities like MOH and HPB, as well as national platforms such as the National Precision Medicine SG10K-Health study and PREPARE, enhance the translational potential of our findings. Additionally, we have formed strategic partnerships with top nutrition and probiotics companies to advance the development of innovative solutions for disease prevention and intervention.

We also leverage our expertise in omics and clinical data science to develop mobile applications that provide a comprehensive and personalized healthcare experience. Our approach integrates next-generation diagnostic tests from precision medicine research and electronic health records to provide up-to-date and actionable insights into an individual's health journey. By empowering clinicians and increasing scientific literacy, our apps facilitate informed decision-making for better health outcomes. In addition to developing health apps, our expertise in multi-omics data analytics has led us to create open-source analytics tools such as GEM, cell type reference panels for infant epigenetic studies, and omics databases such as iMOM-db and iDAD-db. These resources cover molecular phenotypes of ethnic variation and diet-exposure, including complex molecular traits (eg. eQTLs and meQTLs) and contribute to advancing our understanding of personalized medicine. Link to Karnani lab tools, databases and resources -  https://karnanilab.com

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).

• Wong E, Bertin N, …. .. Davila S, Karnani N, Leong KP, Liu JJ, Prabhakar S, Maurer-Stroh S, Verma CS, .., Koh M, Chambers JC, Tai ES, Tan P. Decoding Asian Genomic Diversity - Singapore's National Precision Medicine Strategy. Nat Genet 2023 Feb;55(2):178-186.


• Chan SH, Bylstra Y, .. SG10K_Health Consortium, Prabhakar S, Liu JJ, Cheng CY, Eisenhaber B, Karnani N, Leong KP, Sim X, Yeo KK, Chambers JC, Tai ES, Tan P, Jamuar SS, Ngeow J, Lim WK. Analysis of human disease variants from ancestrally diverse Asian genomes. Nat Commun. 2022 Nov 5;13(1):6694.


• Michael N, Gupta V, ..Lee YS, Eriksson JG, Karnani N. Longitudinal characterization of etiological factors associated with obesogenic growth patterns in early childhood. Int J. Epidemiol. (Ahead of print) 2022. 4. Mir SA, Chen L, .., Wenk MR and Karnani N. Developmental and intergenerational landscape of human circulatory lipidome. BMC Medicine. 2022. 2022 Jul 25;20(1):242.


• Hong P, Tan PF, Lim IY, Huan J, …Gluckman PD and Karnani N. Integrative Multi-Omics database (iMOMdb) of Asian Pregnant Women. Hum Mol Genet. 2022. Sep 10;31(18):3051-3067. Journal cover highlight.


• Kumar M, Ang LT, .. Eriksson JG, Feng M, Karnani N. Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study. JMIR Diabetes. 2022 Jul 5;7(3): e32366.


• 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.


• Tobi EW, Juvinao-Quintero DL, Ronkainen J, Ott R, Karnani N, Jarvelin MR, Steegers-Theunissen RPM, Howe CG, Hivert MF, Sebert S. Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies. Diabetes Care. 2022 Feb 1: dc211701.


• McGill MG, Pokhvisneva I, …. Karnani N, Silveira PP, Kobor MS, de Weerth C, Meaney MJ, O'Donnell KJ. Maternal Prenatal Anxiety and the Fetal Origins of Epigenetic Aging. Biol Psychiatry. 2022 Feb 1;91(3):303-312.


• 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.


• 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.


• 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


• 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).