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Marie Loh

Loh Marie 2 (Custom).JPG




Assistant Professor Marie Loh 
Assistant Professor of Molecular Epidemiology​ ​



Team
  • Jacqueline Tai Fei Li, Research Assistant

Introduction 

Assistant Professor Marie Loh is an Assistant Professor in the Lee Kong Chian School of Medicine at Nanyang Technological University, Singapore and Honorary Senior Lecturer at Imperial College London. She holds a BSc (Hons) in Statistics, as well as a Masters in Bioinformatics and a Masters in Statistics. She obtained her PhD in Pharmacogenetics from the School of Surgery, University of Western Australia in 2012, where her work was focused on genetic variations between East Asians and Caucasians that influence disease risk and treatment outcomes.

Prior to joining LKCMedicine, she was a Principal Investigator at the Translational Laboratories in Genetic Medicine (TLGM) at the Agency for Science, Technology and Research (A*STAR) and a Research Assistant Professor at the Yong Loo Lin School of Medicine, National University of Singapore. She led the Integrative Omics group which focuses on the identification of biomarkers and mechanisms underlying cardiometabolic diseases and associated disturbances, with a specific interest in epigenetics and transethnic studies.

Asst Prof Loh is a molecular epidemiologist with a long-standing interest in the role of genetics and epigenetics underlying ethnic differences in risk and outcome observed in complex diseases. She has received several awards for her work in transethnic studies including the ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research (Semifinalist) and the Young Investigator Award by the International Association for the Study of Lung Cancer (IASLC). She has contributed to more than 80 publications in journals such as Nature Genetics and Nature and is involved in multiple local and international collaborations.

Research Focus

Cardiometabolic diseases are a leading cause of morbidity and mortality worldwide. Our group aims to combine population health with molecular phenotyping and laboratory-based approaches to advance understanding of susceptibility to cardiometabolic diseases, with a specific focus in Asian populations. This is achieved via a combination of molecular epidemiology, functional genomics and method development.

Molecular epidemiology

We will utilise omics datasets generated from extensively phenotyped large-scale population studies to investigate and elucidate the relationship between molecular markers and disease phenotypes. This will include array/NGS-based genetics and epigenetics datasets including high throughput methylation arrays, whole genome/exome sequencing (WGS/WES), whole genome bisulfite sequencing (WGBS) and RNA sequencing, as well as from metabolomics analyses.


Functional genomics

Here, we aim to investigate the mechanism linking genetic variation to DNA methylation and phenotypic variation, as well as to establish platforms for in vitro modelling to provide experimental validation of biological pathways inferred from statistical models. This will involve both in silico analyses such as the systematic evaluation of relationships between DNA sequence variation, DNA methylation and gene expression (quantitative trait loci [QTLs]), as well as gene-editing experiments such as the CRISPR-Cas9 system.

Method development

We will develop and optimise both computational and experimental methods/platforms to refine our understanding of mechanisms underlying DNA regulatory mechanisms and the development of cardiometabolic disturbances. This will involve optimisation of experimental design, systematic evaluation of laboratory methods and bioinformatics pipelines, as well as development of statistical and bioinformatics tools where necessary. 


Selected Publications 

Loh M, Zhou L, Ng HK and Chambers JC. (2019). Epigenetic disturbances in obesity and diabetes: epidemiological and functional insightsMolecular Metabolism, Sep; 27S:S33-S41. https://doi.org/10.1016/j.molmet.2019.06.011

Zhou L, Ng HK, Drautz-Moses DI, Schuster SC, Beck S, Kim C, Chambers JC, Loh M. (2019). Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing. Scientific Reports. Jul 17; 9: 10383. 

Mahajan A, Wessel J, Willems SM, …, Loh M, et al. (2018). Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nature Genetics, Apr 9; 50(4): 559-572. doi: 10.1038/s41588-018-0084-1. doi: 10.1038/s41588-018-0084-1​

Wahl S, Drong A, Lehne B, Loh M, et al. (2017). Epigenome-wide association reveals extensive perturbations in DNA methylation associated with adiposity and its adverse metabolic consequences. Nature, Jan 5;541(7635):81-86. doi: 10.1038/nature20784

Kato N, Loh M, Takeuchi F, …, et al. (2018). Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nature Genetics, Nov;47(11):1282-1293. doi: 10.1038/ng.3405.​

Chambers JC*, Loh M*, Lehne B*, et al. (2015). Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinology, Jul;3(7):526-34. doi: 10.1016/S2213-8587(15)00127-8

Lehne B, Drong A, Loh M, et al. (2015). A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biology, Feb 15;16(1):37. doi: 10.1186/s13059-015-0600-x     ​ 

Loh M, Liem N, Vaithilingam A, et al. (2014). DNA Methylation Subgroups and the CpG Island Methylator Phenotype in Gastric Cancer: A Comprehensive Profiling Approach. BMC Gastroenterology, Mar 28;14:55. doi: 10.1186/1471-230X-14-55​

Subramaniam MM*, Loh M*, Chan JY, et al. (2014). The topography of DNA methylation in the non-neoplastic colonic mucosa surrounding colorectal cancers. Molecular Carcinogenesis, Feb; 53(2):98-108. doi: 10.1002/mc.21951

Loh M, Chua D, Yao Y, et al. (2013). Can population differences in chemotherapy outcomes be inferred from differences in pharmacogenetic frequencies? The Pharmacogenomics Journal, Oct;13(5):423-9. doi: 10.1038/tpj.2012.26​

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