Associate Professor Hong Lee

Research focus

Highlights

I have contributed to the dissection of the (shared) genetic architecture of complex traits and diseases using advanced statistical models and methods in computational biology based on genomic information, aiming for personalised genomic medicine. One of key elements in my research is to link two individuals who are not related in the conventional sense but who can be compared experimentally because they share part of their genome by descent over many generations. Genomic data create links between individuals in the population so that traditional experimental design is not essential anymore, which is a paradigm-shifting concept.

Another key element in my research is the development of advanced algorithms that are most suitable for... Read more

Highlights

As evidence of my engagement and national and international standing, I am regularly invited to provide peer review for high quality journals in the discipline and for esteemed funding agencies, and to join key professional bodies and to organise significant events in the field. These include:

Editorial Board

Associate Editor in Frontiers in Genetics, Frontiers in Plant Science and Frontiers in Ecology and Evolution

Referee for peer-reviewed journals

Nature Genetics, Nature Communications, American Journal of Human Genetics, PLoS Genetics, Human Molecular, Genetics, Genetics, Current Genomics, PLoS One, BMC Genetics, Genetics Selection Evolution, Gene and many others.

Referee for funding agencies

NHMRC, ARC and Wellcome Trust/DBT India Alliance.

Organising conference and workshop

Organiser for a workshop of Quantitative, Population and Statistical Genetics (lecture and practicals weekly delivered from Mar/2016 to Jul/2017), sole organiser for the Brisbane Statistical Human Genetics Forum (October 2013) and member of the organizing committee for the summer school in animal genetics and breeding in Armidale in Feb/2005, Feb/2006 and Feb/2007.  

Consulting and other forms of knowledge exchange

I regularly participate scientific discussions via email, media and online forum, e.g. a few hundreds time per year. I have released software that helps students and researchers solve their problems in their data analyses, addressing their (numerous) demands to implement functions and methods that are necessary for them. In the process, I have also disseminated novel knowledge about specific algorithms and models. I have documented some essential components from these discussions and made available to public (MTG2 manual including 13 chapters).

Resource managements

In the genomic era, there are a number of genotype and phenotype data publicly available as a form of “big data”. For example, I have managed to have access to multiple data sets from Wellcome Trust Case Control Consortium (WTCCC), therosclerosis Risk in Communities (ARIC), Framingham Heart Study (FHS), Genetic Epidemiology Research on Adult Health and Aging (GERA), Database of Genotype and Phenotype (dbGaP), UK10K and UK Biobank. I have made these data sets available to other researchers within the centre and university (following the data agreement policy, which is a part of data management). This will help enhance research capacity of the centre and university.       

I also regularly contribute computing facility to the institute so that other researchers in the institute can use the facility for free. For example, I contributed a computer server (~ $30,000) in 2013, two blade servers (~ $50,000) in 2015 and plan to purchase a blade server (~ $50,000) in this year.

Teaching strengths

• Introducing novel and timely knowledge
• Deep (intrinsic) approaches to learning
• Dissecting specific algorithms
• Using software enhancing understanding
• Linking between learning and research

Highlights

I teach novel concepts, designs and tools that can essentially improve students’ capacity to analyse and characterise complex traits in the population based on genomic information in the context of Statistical Genetics.

I have coordinated and taught a number of regular university courses including Statistical Learning, Biomedical Science, Quantitative, Population and Statistical Genetics and Health Data Sources and Management (Big Genomic data). I have been regularly invited to deliver lectures and tutorials in a number of international intensive courses, e.g.

1.     Regional Training Course on Selective Breeding Gene Technologies, Daejon, Korea (3-7 April 2006)

2.     Summer Institute in Statistical Genetics, Seattle, USA (22-29 July 2010)

3.     Summer Institute in Statistical Genetics, Seattle, USA (17-24 July 2011)

4.     Quantitative, Population and Statistical Genetics. The 1st Winter Workshop, Chung-nam National University (Deajeon, South Korea, 22-24 February 2016)

5.     Armidale Summer course (Armidale, Australia, 2-9 February 2016)

6.     Armidale Summer course (Armidale, Australia, 15-22 February 2017)

7.     Population analysis using statistical genetics tool. TBA, Chung-nam National University (Deajeon, South Korea, 5-7 July 2018)

Experience in supervision

From the supervisions of 6 research students and 6 junior researchers, there were 12 publications in high-profile journals (including JAMA Psych (impact factor (IF) 15), Mol Psych (IF 13), Am J Hum Genet (IF 9) and PLoS Genet (IF 6)). There are currently 8 manuscripts in preparation. 

Reference (publications from my supervisions)

1.         Chen, G.-B., Lee, S.H., Montgomery, G.W. et al. 2017. Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. BMC Medical Genetics 18(1) 94.

2.         Lee, S., Cho, Y., Lee, S. et al. 2008. Identification of marbling-related candidate genes in M-longissimus dorsi of high- and low marbled Hanwoo (Korean Native Cattle) steers. Bmb Reports 41(12) 846-851.

3.         Lee, S., van der Werf, J., Lee, S. et al. 2010. Genetic polymorphisms of the bovine Fatty acid binding protein 4 gene are significantly associated with marbling and carcass weight in Hanwoo (Korean Cattle). Animal Genetics 41(4) 442-444.

4.         Lee, S.H., van der Werf, J.H.J., Kim, N.K. et al. 2011. QTL and gene expression analyses identify genes affecting carcass weight and marbling on BTA14 in Hanwoo (Korean Cattle). Mammalian Genome 22(9-10) 589-601.

5.         Lee, S.H., Weerasinghe, W.M.S.P.  and van der Werf, J.H.J. 2017. Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth. Scientific Reports 7 6087.

6.         Lee, S.H., Weerasinghe, W.M.S.P., Wray, N. et al. 2017. Using information of relatives in genomic prediction to apply effective stratified medicine. Scientific Reports 7 42091.

7.         Maier, R., Moser, G., Chen, G.-B. et al. 2015. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder and major depression disorder. Am J Hum Genet 96 283-294.

8.         Maier, R.M., Zhu, Z., Lee, S.H. et al. 2018. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat Commun 9(1) 989.

9.         Mehta, D., Tropf, F.C., Graten, J. et al. 2016. Evidence for genetic overlap between schizophrenia and age at first birth in women. JAMA Psych 73 497-505.

10.       Moser, G., Lee, S.H., Hayes, B.J. et al. 2015. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet 11 e1004969.

11.       Ni, G., Moser, G., Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. 2018. Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood. Am J Hum Genet In Press.

12.       Peyrot, W.J., Lee, S.H., Milaneschi, Y. et al. 2015. The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects. Mol Psychiatry 20(6) 735-743.