Inouye Laboratory - Medical Systems Biology
|Contact:||Dr Michael Inouye|
|Phone:||+61 3 9035 8659|
|Fax:||+61 3 8344 4004|
Mike grew up in the Seattle area before beginning undergraduate study in 1999 at the University of Washington, where he later graduated with BSc's in biochemistry and economics. During this time, he was also introduced to computational genomics as the initial draft Human Genome was being finished, spending several years doing part-time research in gene finding and protein structure prediction. He continued studying biochemistry as a graduate student at UCLA but elected to return to genomics when he moved to the Wellcome Trust Sanger Institute (Cambridge, UK) in 2005. While at WTSI, Mike completed his PhD with Prof Leena Peltonen (WTSI) and Prof Gert-Jan van Ommen (Leiden University) and was heavily involved in the first wave of genome-wide association studies, especially the statistical methods thereof. He also led large-scale efforts for the integrative analysis of molecular systems, identifying a gene co-expression network underlying metabolic traits. In 2010, Mike came to the Walter and Eliza Hall Institute in Melbourne on an NHMRC postdoctoral fellowship to continue pursuing interests in genomics and systems biology. In 2012, he joined the faculty at the University of Melbourne as a joint appointment between the Department of Pathology and the Department of Microbiology and Immunology.
Outside of research, Mike plays soccer, brews/drinks beer, hangs out in cafes and goes to (indie) movies.
The group is always looking for talented postdocs and PhD/honours students. If, in reading about the group and browsing our publications, you are interested please do get in touch.
The group is primarily a 'dry' laboratory (i.e. computational, no reagents or chemicals). We draw on many fields, which in practice means each member brings a unique mixture of skills and all are encouraged to work together. We have flexible working hours and are quite goal-oriented; when/where research is done is less important than, say, developing a new method to solve a problem or uncovering a disease gene. Mike is committed to training independent multi-disciplinary researchers who want to use genomics/systems biology/bioinformatics/biostatistics to alleviate disease.
NOTE: The project list below is only an incomplete snapshot. We also encourage young researchers to develop ideas and projects of their own (how else will one become a scientist!), so we are happy to consider any research plans.
“Think of biology as a system for managing information.”
-- Paul Nurse, PRS
Overall aim: To use genomic and molecular profiling technologies to uncover the genetic basis of complex disease, understand pathogenesis at a systems-level, and build predictive models for patient stratification and precision medicine.
In the last decade, technological advances have driven the study of biology towards the statistical and computational sciences. We are now able to differentiate and quantify biomolecules at levels previously unimaginable, allowing us to study their interactions and relationships to health and disease in an unbiased, systems-level manner.
A corollary of this transformation is that sophisticated quantitative models can now be used to tease out underlying biological insights and pathogeneses of molecular systems. In our view, an "organism" can be thought of as a system whose components are derived from its genome(s) and which interact with each other and the environment in a spatial and temporal manner. We think of these components (e.g. RNAs, proteins, mobilized DNA) as operating as part of networks with the other elements (e.g. metabolites, sunlight, micro-organisms). We therefore apply and develop concepts in graph theory, bioinformatics, epidemiology and biostatistics to understand how networks interact and what role they play in human diseases and traits.
Figure 1: Increasing complexity of systems profiling
Our research spans a broad range of diseases and organisms but it is primarily focused on cardiovascular/metabolic and autoimmune disease in humans (we also work in bacteria and mouse models). We are highly collaborative and have strong links with researchers around Melbourne, Australia, UK and Europe as well as the USA.
Molecular networks for cardiovascular and metabolic disease
Hundreds of genomic loci associated with cardiovascular and metabolic disease (CMD) and corresponding risk factors have been discovered and widely replicated, and, while these loci offer tantalizing clues into the pathogenesis of CMD, the downstream functional effects of causal variants and candidate genes need to be investigated empirically. As part of a worldwide network of collaborators, we aim to develop and apply novel approaches to characterize the genetic basis of gene and metabolic networks, to detect genetic and network interactions with CMD, to infer causality for those interactions, and to design powerful replication and meta-analysis strategies.
Building predictive genetic models
One of the goals of medical genetics is to accurately quantify the risk of disease given a genetic profile. With advances in genome-wide genotyping and whole genome sequencing of large-scale disease and control cohorts, there have been recent efforts to assess the performance of genetic prediction. We have previously explored the utility of lasso-penalized regression models for this purpose, with promising findings for autoimmune diseases like celiac disease and type 1 diabetes. For those diseases and traits with sufficient quantities of genetic variance, we aim to build models of genetic variation (SNPs, CNVs, micro-insertions/deletions, etc) which predict disease, trait or outcome at a level relevant for clinical screening and to refine these models so that particular genetic sub-groups at high risk can be identified.
Gene flow in microbial networks and the human microbiome
Together with Dr Kathryn Holt (UoM Microbiology & Immunology), we are investigating how genetic elements (including drug resistance genes, virulence factors, etc) move within and between bacterial species. This includes the development of novel approaches and computational tools to perform microbial genome surveillance with high-throughput DNA sequencing and to model networks of genetic elements across species. A second collaboration with Dr Holt, which also includes researchers from TICHR and UQ, is investigating the interactions between bacterial community structure (the 'microbiome'), viruses, and host genetics in the pathogenesis of childhood asthma.
Understanding immune regulation through gene networks
Many immune cell phenotypes (differentiation, isotype switching, etc) are regulated by gene networks and their dysfunction can have major implications for autoimmune disease. Various so-called master regulatory transcription factors have been identified but how they operate and what their direct/indirect interacting partners are remains largely unknown for many cell types. We are collaborating with researchers at WEHI to perform and analyse high-throughput RNA sequencing and other whole genome profiles (ChIPseq, etc) to address the question of how the immune system is regulated at the genomic level.
Genetics of rheumatic heart disease in Indigenous Australians
Rheumatic heart disease (RHD) is a disease of long-term cardiac valvular damage resulting from host infection with Group A Streptococcus and host autoimmune response. It is almost eradicated from prosperous populations but maintains a prevalence of ~2% in some impoverished populations, including Indigenous Australian communities. The pathogenesis of RHD is not well understood, however previous studies have suggested that there may be substantial genetic susceptibility. We are collaborating with the Menzies School of Health Research, TICHR and others to perform a genomic screen and fine-mapping for loci associated with RHD.
Supercomputers for complex analysis of genetic data
Current statistical approaches have only scratched the surface of what is possible with the large-scale genomic data now available for many disease studies. The use of supercomputers can open new avenues of investigation to researchers, however there remains a gap between those with the genetic/statistical expertise and the computational expertise needed to fully realize the potential of supercomputing. We are collaborating with researchers at UoM (MEGA), NICTA, and IBM to develop analytical and computational approaches for large-scale genomic and ancillary data which leverage massively-parallel, distributed memory supercomputers and to apply these methods to provide new insights into the genetic and environmental causes, aetiology and biology of breast cancer, colorectal cancer and other diseases.
- Illuminus – a genotype calling algorithm which has enabled high-throughput determination of >100,000 human samples worldwide.
- SRST – a tool to quickly and accurately retrieve multi-locus sequence type (MLST) information from ‘next-gen’ short read sets.
- SparSNP – a tool for genetic prediction of disease/phenotype that quickly and efficient applies lasso-penalized linear models to genome-wide or whole-genome SNP data. .
- RHH – an algorithm to identify ethnic outliers and chromosomal mosaicism from genome-wide SNP data.
- FMPR - a fast and efficient statistical learning approach for modelling multiple phenotypes simultaneously
- Michael Inouye (Head)
- Gad Abraham (Postdoctoral Fellow)
- David Savage (Postdoctoral Fellow)
- Shu Mei Teo (Postdoctoral Fellow; joint with Kathryn Holt, Bio21)
- Scott Ritchie (PhD student)
- Michael Walker (PhD student)
- Artika Nath (PhD student)
- Oneil Bhalala (Honorary)
- National Health and Medical Research Council (Australia)
- University of Melbourne
*indicates joint first authors
- Abraham G, Tye-Din JA, Kowalczyk A, Zobel J, Inouye M. Accurate and robust genomic prediction of celiac disease using statistical learning. pre-print
- Abraham G, Kowalczyk A, Zobel J, Inouye M. Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease. Genetic Epidemiology. 2013 Feb;37(2):184-95
- Inouye M, Conway T, Zobel J, and Holt KE. Short Read Sequence Typing (SRST): multi-locus sequence types from short reads. BMC Genomics. 2012 Jul 24;13:338.
- Inouye M, Ripatti S, Kettunen J, Lyytikainen LP, Oksala N, Laurila PP, Kangas AJ, Soininen P, Savolainen MJ, Viikari J, Kahonen M, Perola M, Salomaa V, Raitakari O, Lehtimaki T, Taskinen MR, Jarvelin MR, Ala-Korpela M, Palotie A, de Bakker PIW. Novel loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genetics. 2012 Aug;8(8):e1002907.
- Abraham G, Kowalczyk A, Zobel J,Inouye M. SparSNP: Fast and memory efficient analysis of all SNPs for phenotype prediction. BMC Bioinformatics2012 May 10;13:88.
- Inouye M and Teo YY. Genotype calling. Analysis of Complex Disease Association Studies. Editors: Zeggini E and Morris A. Published by Elsevier (2011).
- Ala-Korpela M, Kangas AJ, Inouye M. Genome-wide association studies and systems biology: together at last. Trends in Genetics. 2011 Dec;27(12):493-8.
- Inouye M*, Kettunen J*, Soininen P, Silander K, Ripatti S, Kumpula LS, Hämäläinen E, Jousilahti P, Kangas AJ, Männistö S, Savolainen MJ, Jula A, Leiviskä J, Palotie A, Salomaa V, Perola M, Ala-Korpela M, Peltonen L. Metabonomic, transcriptomic, and genomic variation of a population cohort. Molecular Systems Biology. 2010 Dec 21;6:441.
- International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature. 2010 Sep 2;467(7311):52-8.
- Inouye M, Silander K, Hamalainen E, Salomaa V, Harald K, Jousilahti P, Männistö S, Eriksson JG, Saarela J, Ripatti S, Perola M, van Ommen GJ, Taskinen MR, Palotie A, Dermitzakis ET, Peltonen L. An immune response network associated with blood lipid levels. PLoS Genetics. 2010 Sep 9;6(9). pii: e1001113.
- McGinnis RE, Deloukas P, McLaren WM, Inouye M. Visualizing chromosome mosaicism and detecting ethnic outliers by the method of "rare" heterozygotes and homozygotes (RHH). Human Molecular Genetics. 2010 Jul 1;19(13):2539-53.
- Richards JB*, Rivadeneira F*, Inouye M*, Pastinen TM, Soranzo N, Wilson SG, Andrew T, Falchi M, Gwilliam R, Ahmadi KR, Valdes AM, Arp P, Whittaker P, Verlaan DJ, Jhamai M, Kumanduri V, Moorhouse M, van Meurs JB, Hofman A, Pols HA, Hart D, Zhai G, Kato BS, Mullin BH, Zhang F, Deloukas P, Uitterlinden AG, Spector TD. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008 May 3;371(9623):1505-12.
- Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, Inouye M, Freathy RM, Attwood AP, Beckmann JS, Berndt SI; Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Jacobs KB, Chanock SJ, Hayes RB, Bergmann S, Bennett AJ, Bingham SA, Bochud M, Brown M, Cauchi S, Connell JM, Cooper C, Smith GD, Day I, Dina C, De S, Dermitzakis ET, Doney AS, Elliott KS, Elliott P, Evans DM, Sadaf Farooqi I, Froguel P, Ghori J, Groves CJ, Gwilliam R, Hadley D, Hall AS, Hattersley AT, Hebebrand J, Heid IM; KORA, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Herrera B, Hinney A, Hunt SE, Jarvelin MR, Johnson T, Jolley JD, Karpe F, Keniry A, Khaw KT, Luben RN, Mangino M, Marchini J, McArdle WL, McGinnis R, Meyre D, Munroe PB, Morris AD, Ness AR, Neville MJ, Nica AC, Ong KK, O'Rahilly S, Owen KR, Palmer CN, Papadakis K, Potter S, Pouta A, Qi L; Nurses' Health Study, Randall JC, Rayner NW, Ring SM, Sandhu MS, Scherag A, Sims MA, Song K, Soranzo N, Speliotes EK; Diabetes Genetics Initiative, Syddall HE, Teichmann SA, Timpson NJ, Tobias JH, Uda M; SardiNIA Study, Vogel CI, Wallace C, Waterworth DM, Weedon MN; Wellcome Trust Case Control Consortium, Willer CJ; FUSION, Wraight, Yuan X, Zeggini E, Hirschhorn JN, Strachan DP, Ouwehand WH, Caulfield MJ, Samani NJ, Frayling TM, Vollenweider P, Waeber G, Mooser V, Deloukas P, McCarthy MI, Wareham NJ, Barroso I, Jacobs KB, Chanock SJ, Hayes RB, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Kraft P, Hankinson SE, Hunter DJ, Hu FB, Lyon HN, Voight BF, Ridderstrale M, Groop L, Scheet P, Sanna S, Abecasis GR, Albai G, Nagaraja R, Schlessinger D, Jackson AU, Tuomilehto J, Collins FS, Boehnke M, Mohlke KL. Association studies involving over 90,000 samples demonstrate that common variants near to MC4R influence fat mass, weight and risk of obesity. Nature Genetics 2008; 40(6): 768-75.
- Teo YY*, Inouye M*, Small KS, Fry AE, Potter SC, Dunstan SJ, Seielstad M, Barroso I, Wareham NJ, Rockett KA, Kwiatkowski DP, Deloukas P. Whole genome-amplified DNA: insights and imputation. Nature Methods. 2008 Apr;5(4):279-80.
- van Heel DA, Franke L, Hunt KA, Gwilliam R, Zhernakova A, Inouye M, Wapenaar MC, Barnardo MC, Bethel G, Holmes GK, Feighery C, Jewell D, Kelleher D, Kumar P, Travis S, Walters JR, Sanders DS, Howdle P, Swift J, Playford RJ, McLaren WM, Mearin ML, Mulder CJ, McManus R, McGinnis R, Cardon LR, Deloukas P, Wijmenga C. A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nature Genetics. 2007 Jul;39(7):827-9.
- Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007 Jun 7;447(7145):661-78.
- Teo YY*, Inouye M*, Small KS, Gwilliam R, Deloukas P, Kwiatkowski DP, Clark TG. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics. 2007 Oct 15;23(20):2741-6.