Associate Professor Lachlan Coin
During the first decade of the 21st century, sequencing of a single reference genome from multiple animal and plant species provided a tremendous amount of information about eukaryotic genome structure, function and evolution. Now, in the second decade, technological developments allow affordable population sequencing of thousands of genomes per species. Moreover, it is now possible to sequence not just the genome, but also the epigenome and transcriptome of different cell populations at different points in time, enabling use of this technology to profile important physiological processes.
Our research focuses on integrative population genomics, where we develop and apply statistical approaches to extract information from high-throughput population sequence data. In particular, we are interested in mapping the impact of structural variation on disease risk. We have developed population modelling approaches to improve detection and genotyping of indels, copy number variation (CNV), and tandem repeat variation.
We apply these tools to understand the genetic basis of common diseases including: metabolic disease, such as obesity and type 2 diabetes; autoimmune diseases, such as psoriasis, rheumatoid arthritis, and systemic lupus erythematosus; and susceptibility to infectious disease. We have previously identified rare deletions and duplications associated with extreme obesity and also common deletions associated with obesity and variation in lipid levels.
Our team uses integrative genomics approaches to profile the genome, transcriptome and proteome from the acute to recovery stage of disease in order to identify rapid, cheap biomarkers for both early-stage disease diagnosis and prognosis and also to understand biological pathways that are active during disease.
Using this approach, we have recently identified transcriptomic and proteomic signatures that can distinguish active tuberculosis infection from other disease in HIV-positive adults in Africa. Moreover, we have developed an accurate test for diagnosing tuberculosis in children, which we hope to translate into an affordable, point-of-care diagnostic. Finally, we have also used transcriptional profiling of T cells—white blood cells that play a role in immunity—and their response to allergens to identify new pathways involved in allergies, and determine how we can potentially control these pathways to treat allergies.
Research in the news
18 September 2013 - Awards recognise UQ's rising research stars, IMB News
Research training opportunities
Please see IMB's postgraduate website for more information.
Anderson S.T., Kaforou M., Brent A.J., Wright V.J., Banwell C.M., Chagaluka G., Crampin A.C., Dockrell H.M., French N., Hamilton M.S., Hibberd M.L., Kern F., Langford P.R., Ling L., Mlotha R., Ottenhoff T.H.M., Pienaar S., Pillay V., Scott J.A.G., Twahir H., Wilkinson R.J., Coin L.J., Heyderman R.S., Levin M. and Eley B. (2014) Diagnosis of childhood tuberculosis and host RNA expression in Africa. New England Journal of Medicine 18: 1712-1723.
Zhang, Fan, Chen, Ruoyan, Liu, Dongbing, Yao, Xiaotian, Li, Guoqing, Jin, Yabin, Yu, Chang, Li, Yingrui and Coin, Lachlan J. M. (2013) YHap: A population model for probabilistic assignment of Y haplogroups from re-sequencing data. BMC Bioinformatics 1: 331.1-331.4.
Shao H., Bellos E., Yin H., Liu X., Zou J., Li Y., Wang J. and Coin L.J.M. (2013) A population model for genotyping indels from next-generation sequence data. Nucleic Acids Research 3: e46.1-e46.6.
The 1000 Genomes Project Consortium and Coin, L. J. M. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 7422: 56-65.
Coin, Lachlan J. M., Cao, Dandan, Ren, Jingjing, Zuo, Xianbo, Sun, Liangdan, Yang, Sen, Zhang, Xuejun, Cui, Yong, Li, Yingrui, Jin, Xin and Wang, Jun (2012) An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis. Bioinformatics 18: I370-I374.
|Mr Evangelos Bellos
Research higher degree student
+61 7 334 62178
|A/Prof Lachlan Coin
+61 7 334 62649
|Dr Sarah Song
+61 7 334 62178