Systems Genetics Group

Duke-NUS Medical School

What we do.

Understanding how genetic information is decoded to produce the complex regulatory systems driving disease remains a great challenge in biomedical sciences. However, the increasing availability of high-dimensional molecular, cellular and phenotypic data now allows a comprehensive investigation of the complex genetic and regulatory mechanisms that underlie the disease process.

our plan

How we do it.

Our research lab focuses on the systems-level integration of genetic, functional genomic and phenotypic data to identify causal determinants and pathways of complex traits and disease, with a focus on cardio-metabolic, inflammatory and neuropsychiatric disorders. To this aim, we have developed an integrated genetic and gene-network approach, called Systems-Genetics, to determine the consequences of key genetic variants ('master genetic regulators') on functional gene-networks in disease.

Who we are.

Enrico

Associate Professor

ENRICO PETRETTO
enrico.petretto@duke-nus.edu.sg
Owen

Senior Research Fellow

OWEN RACKHAM
owen.rackham@duke-nus.edu.sg
Sarah

Senior Research Fellow

SARAH LANGLEY
sarah.r.langley@duke-nus.edu.sg
Nathan

Senior Research Fellow

NATHAN HARMSTON
nathan.harmston@duke-nus.edu.sg
Amelia

PhD Student

AMELIA TAN LI MIN
amelia.tlm@u.nus.edu
Uma

PhD Student

UMA SANGUMATHI KAMARAJ
e0011403@nus.edu.sg
Shiyang

MD/PhD Student

SHIYANG LIU
shiyang_liu@u.duke.nus.edu

Some of our work.

To date, using Systems-Genetics we have uncovered several genes regulating functional gene-networks underling disease processes, including EBI2 regulating an anti-viral expression network and type 1 diabetes risk, KCNN4 and its co-regulatory network underlying cell multinucleation in inflammatory disease and SESN3 as a master genetic regulator of a proconvulsant gene network in human epileptic hippocampus. Our Systems-Genetics strategy will be further developed to study cardio-metabolic traits and disease, with the aim of deciphering the primary genetic factors and regulatory networks underlying hypertrophy, remodeling and fibrosis in the human heart.

Software
  • EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization
Read the paper Use the tool
Software
  • Web-based Gene Pathogenicity Analysis (WGPA): A web platform to interpret gene pathogenicity from personal genomes data
Read the paper Use the tool
Software
  • WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools.
Read the paper Use the tool

Bibliography

Retreiving papers from PubMed.

Centre for Computational Biology, 8 College Road, Singapore, 169857