Centre for Population Health Research
School of Health Sciences and Sansom Institute of Health Research
University of South Australia
Adelaide, SA 5000, Australia
hong.lee@unisa.edu.au
I am 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. I have developed advanced statistical approaches 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 number of species including human, livestock and mice population, leading to the papers published in high profile journals. Currently, I focus on understanding the genetic architecture of complex traits by tackling G x E interaction. I have developed an efficient statistical tool that will be applied to economically important traits in livestock, health-related traits in human and many other traits of interest in many species.
Statistical Genetics
Quantitative Genetics
Population Genetics
Genomics
Medical Research
Selected publication
1. Lee, S. H.; Van der Werf, J. H. J. An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree. Genetics Selection Evolution 38: 25-43 (2006). [43 citations (Google Scholar)]
In this work, I developed an efficient and robust residual maximum likelihood (REML) method that has been used to tackle important problems in complex traits over 10 years.
2. Lee, S.H., van der Werf, J., Hayes, B., Goddard, M. & Visscher, P. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLOS Genet 4, e1000231 (2008). [166 citations]
A pioneering study that predicts future phenotypes using a cutting-edge method, featured in the Nov 2008 issue of Nature Review Genetics.
3. Yang, J., Lee, S.H., Goddard, M. & Visscher, P. GCTA: A tool for genome-wide complex trait analysis. The American Journal of Human Genetics 88, 76-82 (2011). [1644 citations]
One of most widely used software in complex traits analysis that has implemented the REML method developed in #1.
4. Lee, S.H., Wray, N., Goddard, M. & Visscher, P. Estimating Missing Heritability for Disease from Genomewide Association Studies. The American Journal of Human Genetics 88, 294-305 (2011). [558 citations]
A breakthrough theory study that generalised Robertson transformation in the liability threshold model. This method has been implemented in GCTA software developed in #3.
5. Lee, S.H., DeCandia, T.R., Ripke, S., Yang, J., PGC-SCZ, ISC, MGS, Sullivan, P.F., Goddard, M.E., Keller, M.C. et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genetics 44, 247-250 (2012). [424 citations]
This paper is the first to dissect the genetic architecture of schizophrenia based on genome-wide SNPs using our method developed in #1, #3 and #4.
6. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540-2542 (2012). [245 citations]
A cutting-edge method was developed to estimate genetic corelation between two independent groups. This method has contributed to a breakthorough study to show evidence of shared genetic architecture of complex tratis (#8).
7. Ripke, S., O'Dushlaine, C., Chambert, K., Moran, J.L., Kahler, A.K., Akterin, S., Bergen, S.E., Collins, A.L., Crowley, J.J., Fromer, M., Kim, Y., Lee, S.H. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genetics 45, 1150-1159 (2013). [842 citations]
A comprehensive study that find novel causal variatns for schizophrenia. We contributed substantially to this study using the statistical models for complex traits developed in #1, #3 and #4.
8. Lee, S.H., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., Perlis, R.H., Mowry, B.J., Thapar, A., Goddard, M.E., Witte, J.S. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics 45, 984-994 (2013). [871 citations]
A breakthrough study that dissects shared genetic architecture between complex traits using the statistical methods developed in #1, #3, #4, #6, which has attracted outstanding media attention (Altmetric score 313 ranked 1st out of 66 tracked articles of a similar age in Nature Genetics).
9. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-427 (2014). [1708 citations]
A landmark study in psychiatric genetics to which we contributed substantailly, e.g. analyses of prediction measures in validation data sets, using the statistical methods developed in #4 and in this study.
10. Maier, R., Moser, G., Chen, G.-B., Ripke, S., Cross disorder Working group of the Psychiatric Genomics Consortium, Coryell, W., Potash, J.B., Scheftner, W.A., Shi, J., Weissman, M.M., …, Lee, S.H. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder and major depression disorder. The American Journal of Human Genetics 96, 283-294 (2015). [Last author, 61 citations]
In this study we developed an efficient multivariate model for estimation and prediction that has been implemented in software, MTG, a multivariate version of GCTA (#3).
For more detail, please see my full CV.
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