Project Description

Dr Siew-Kee Amanda Low


Lecturer, Faculty of Pharmacy
The University of Sydney

The role of common genetic variations in complex diseases and pharmacogenomics studies

Bioinformatics methods, models and applications to disease

Wednesday 6 July 2016

Dr Siew-Kee Amanda Low is a lecturer from the Faculty of Pharmacy, University of Sydney. She completed her PhD from the University of Tokyo in 2011. Her main research interest is to study the contributions of genetic variations in complex diseases and drug response (pharmacogenomics studies). Her expertise is in comprehensive genomic analyses by utilising genome-wide association studies (GWAS) and next generation sequencing (NGS) with big data obtained from the Biobank Japan. Thus far, she has identified a handful of common genetic variations associated with complex diseases that include intracranial aneurysm, breast cancer, gastric cancer, endometriosis, pancreatic cancer, uterine fibroids and primary open-angle glaucoma. She also carried out a large-scale pharmacogenomics study that consist of approximately 13,000 cancer patients from the Biobank Japan, which aimed to identify common variations that are associated conventional chemotherapy-induced toxicity.  She also collaborates with various universities and institutions including Yale University, University of Chicago, Harvard Medical School, NIH, University of Cambridge and QIMR Berghofer Medical Research Institute, and participates in the Asia Breast Cancer Consortium. She has published in Nature Genetics, PNAS, and Human Molecular Genetics.

In this seminar, I will demonstrate that genome-wide association study (GWAS) is not just a useful approach to identify common genetic variations that are associated with disease susceptibility but it also suggested additional genes involvement that could improve the understanding of disease pathogenesis by using intracranial aneurysm and breast cancer as examples. I will also discuss about the applicability of genetic variants identified from GWAS and the possibility of developing prediction algorithm to evaluate disease susceptibility in general population. The second part of the talk will cover the applications of GWAS, a hypothesis-free approach which facilitates the identification of novel genetic loci associated with drug response in pharmacogenomics studies.  Further challenges utilising GWAS in pharmacogenomics studies will be addressed.