Sang Hong L
Queensland Institute of Medical Research, PO Royal Brisbane Hospital
In 2002, I completed a Master degree in animal breeding and genetics at the University of New England with an average grade of distinction which was a course work only. In the next year, I started an advanced Master degree that required 2 years of pure research (MSc). In the first year, I could transfer to PhD degree, satisfying the criteria for upgrading from MSc to PhD, with approval of the committee. I completed my PhD degree in animal breeding and genetics at the University of New England in 2006. I was employed as a post-doc at the the University of New England until 2008, spent 1 year in Korea as a reserch officer, and have been at Queensland Institute of Medical Research since 2009.
Dr. Sang Hong Lee is interested in better understanding the genetic architecture of complex traits by using advanced statistical models and methods based on the quantitative genetic theory and molecular information. Dr. Lee has experience in developing advanced statistical methods to estimate genetic variance and individual genetic effects at quantitative trait loci (QTL) based on phenotype-genotype association analyses. Dr. Lee published a number of method papers including developments of a residual maximum likelihood approach, and full and empirical Bayesian approaches. Recently, genome-wide SNP data have been available for many traits in many species. Dr. Lee has developed an empirical Bayesian approach to use genome-wide SNP data to dissect the genetic architecture of complex traits, e.g. estimating genetic variances and individual genetic effects and predicting unobserved future phenotypes based on genome-wide SNP information. The developed statistical approaches were used to analyse a heterogeneous mouse line. Currently, Dr. Lee is focused on understanding the genetic architecture of liability for important human diseases. Dr. Lee developed appropriate statistical approaches to obtain unbiased genetic variances in liability for complex diseases using population-based case-control studies with genome-wide SNP data.