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Baker Bioinformatics Symposium

Baker Bioinformatics Program, Baker Institute

Thursday, 7 September 2017 from 1:00 pm to 4:00 pm (AEST)

Baker Bioinformatics Symposium

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RSVP 56 Tickets 01/09/2017 Free  

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Baker Bioinformatics Program presents:

 

Bioinformatics Symposium

 

Thursday, September 7, 2017
1:00 – 4:00PM
Baker Tower, Level 7 Tea Area, 75 Commercial Rd, Melbourne

 

This is your opportunity to hear from the experts from Baker/AMREP on a wide variety of topics in bioinformatics, computational biology and genomics. 

 



PROGRAM

 

1:00 – 1:25PM            

Gad Abraham, PhD

Genomic prediction of coronary heart disease in population-scale biobanks

1:25 – 1:50PM

Scott Ritchie, PhD

Integrative omics elucidates biological processes underlying the disease and mortality risks of the biomarker GlycA

1:50 – 2:15PM

Shu Mei Teo, PhD

Dynamics of the early childhood respiratory microbiome and its implications in asthma development

2:15 – 2:45PM

Afternoon Tea

2:45 – 3:10PM

Paul Lacaze, PhD

Genome sequencing of 14,000 healthy elderly Australians

3:10 – 3:35PM

Francine Marques, PhD

Using bioinformatics to identify early mechanisms leading to heart failure: the lipocalin-2 story

3:35 – 4:00PM

Kaushala Jayawardana, PhD

Statistical methods in lipidomics data analysis

 


 

ABSTRACTS

 

Gad Abraham, PhD

Genomic prediction of coronary heart disease in population-scale biobanks

Genetic factors play an important role in determining a person's risk of  coronary heart disease, and large-scale genome-wide association studies have  successfully uncovered a large number of disease-associated loci. However, these  results have largely not translated into clinical utility, in stark contrast  with the widely-accepted use of traditional risk factors such as blood pressure  and cholesterol levels, which form part of clinical risk scores such as the  Framingham Risk Score.   We have previously generated a genomic risk score (GRS) based on the  CARDIoGRAMplusC4D meta-analysis. This GRS is based on 49,000 single nucleotide  polymorphisms (SNPs). We externally validated the GRS in five prospective  population cohorts (three FINRISK cohorts, combined n=12,676 with 757 incident  CHD events; two Framingham Heart Study cohorts, combined n=3,406 with 587  incident CHD events). The GRS captured large differences in lifetime risk  trajectories, particularly in men, where men in the top 20% attained 10%  cumulative CHD risk 12-18y earlier than those in the bottom 20%. However, high  genomic risk could be partially compensated for by low systolic blood pressure,  low total cholesterol levels, and non-smoking, highlighting that genomic risk  can be modified by lifestyle factors.   We have now further validated this approach in the 1st release of the UK Biobank  (UKB, n=148,000), showing consistent results with previous analyses. Further, we  investigate how the GRS can be combined with traditional risk factors, by taking  advantage of the fact that germline variation is essentially fixed for life  whereas other risk factors such as blood pressure may show substantial temporal  variation. Our results suggest a two-stage strategy for coronary heart disease  risk prediction, whereby early in life individuals may be screened for high  genomic risk, and later in life genomic scores can be integrated with  traditional risk factors to provide a more accurate picture of a person's risk  thus allowing for better targeted early interventions to reduce morbidity and  mortality. 

 

Scott Ritchie, PhD

Integrative omics elucidates biological processes underlying the disease and mortality risks of the biomarker GlycA

Integration of electronic health records with systems-level biomolecular information has led to the discovery of robust blood-based biomarkers that predict future health and disease. The GlycA biomarker predicts long-term risk of diverse outcomes, including cardiovascular diseases, type II diabetes, and all-cause mortality. It is an agglomeration of five circulating glycoprotein concentrations: alpha-1-acid glycoprotein (AGP), alpha-1 antitrypsin (AAT), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT), each of which dynamically responds over different times scales, directions, and magnitudes as part of the inflammatory response.
In two recent studies we characterised biological processes associated with elevated GlycA and fine-mapped the GlycA biomarker to determine the contributions of each glycoprotein to disease risk using omics-data from >15,000 individuals from three independent population-based cohorts. We found elevation of GlycA persisted for up to a decade within individuals, correlated with elevation of 29 inflammatory cytokines and correlated with a reproducible gene coexpression network indicative of increased neutrophil activity. Accordingly, analysis of infection-related hospitalization and death records in 7,599 adults showed that increased GlycA increased 14-year risk of severe non-localized and respiratory infections, particularly septicaemia and pneumonia.
Using a machine learning approach, we developed accurate imputation models for predicting the concentrations of AGP, AAT, and HP from serum NMR data and cohort metadata. Estimation of glycoprotein levels in 12,418 adults across two independent population-based cohorts revealed AAT had the strongest and broadest effects on 8-year disease incidence and mortality risk. Transcriptional analyses revealed elevated AAT corresponded to elevation of both innate and adaptive immune response pathways.
In total, our work shows that elevated GlycA levels likely reflect a state of sub-clinical chronic inflammation and elevated immune response.

 

Shu Mei Teo, PhD

Dynamics of the early childhood respiratory microbiome and its implications in asthma development

The bacterial microbiome is increasingly recognized as playing important roles in the susceptibility and severity of acute respiratory illnesses (ARIs), as well as complex respiratory diseases such as asthma, whose origins is believed to arise in early childhood. This study is by far the largest longitudinal collection of nasopharyngeal samples- more than 3000 samples from 244 infants in the first five years of life, collected as part of the prospective Childhood Asthma Study (CAS) in Western Australia. Samples were collected routinely every half-yearly during periods of health as well as at the onset of symptoms of an ARI. We performed deep sequencing of the V4 region of the 16S rRNA gene to comprehensively characterize the nasopharyngeal microbiome (NPM). In this talk, I will describe the relationships and dynamics of the NPM and the variability of health and illness associations over the first five years of life. We also found that the domination of healthy-state NPMs by risk bacteria in the first two years of life was associated with illness frequency, and independently associated with subsequent persistent wheeze at five years of age, especially in the subset of atopic infants, suggesting interaction with mechanisms of allergy. Findings from this study have potential relevance for the prediction and prevention of asthma and other related conditions. 

 

Paul Lacaze, PhD

Genome sequencing of 14,000 healthy elderly Australians

The ASPREE Healthy Ageing Biobank contains ~14,000 consented samples from individuals aged 70 years or older participating in the ASPirin in Reducing Events in the Elderly (ASPREE) study - Australia’s largest clinical trial and longitudinal study of healthy ageing. ASPREE represents a randomly ascertained biobank population depleted of typical monogenetic disease, meaning it can act as a reference population for helping assign pathogenicity and calculating penetrance for predictive risk variants in unaffected healthy elderly individuals. All ASPREE biobank samples are being sequenced using a targeted ‘super-panel’ of 750 genes used commonly in clinical testing, including actionable familial cancer genes, cardiovascular and neurological disease genes. Over 6,000 samples have been sequenced (July 2017), already identifying actionable pathogenic variants in individuals lacking any apparent signs and symptoms of genetic disease beyond the age of 70 years. Results will be presented on these findings, with implications for our understanding of penetrance and clinical actionability for genes used in routine testing. These results will also help inform the Resilience Project, a global effort to identify individuals with highly penetrant pathogenic variants who do not appear to develop typical signs and symptoms of disease well beyond the expected age of onset.

 

 

Francine Marques, PhD

Using bioinformatics to identify early mechanisms leading to heart failure: the lipocalin-2 story

Cardiac hypertrophy increases risks of heart failure and cardiovascular death. The neutrophil inflammatory protein lipocalin-2 (LCN2/NGAL) is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks and genomic determinants. We employed 3 experimental models: a polygenic model of cardiac hypertrophy and failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type-2 diabetes patients and 2,064 healthy subjects of the Young Finns Study (YFS). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly over-expressed before, during and after the development of hypertrophy and failure. Lcn2 expression was increased in hypertrophic hearts in a model of intra-uterine growth restriction, while Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes. In the YFS, LCN2 expression was associated with body mass index and cardiac mass, and with levels of inflammatory markers. The SNP rs13297295 located near LCN2 defined a significant cis-eQTL for LCN2 expression. Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy.

 

Kaushala Jayawardana, PhD

Statistical methods in lipidomics data analysis

Traditional risk factors that include clinical lipid measures have limited capability in predicting cardiovascular disease outcomes in secondary prevention and in elucidating the dysregulation of lipid metabolism in cardiovascular disease. The recent advances in high-throughput “omics” technologies and the generation large datasets including plasma lipidomic measures, present us with the opportunity to improve upon the prediction of disease outcomes. Furthermore, multi-layered data hold the potential to provide complementary information to the biology underlying disease states.
To this end, we used a sub-cohort of the LIPID (Long-Term Intervention with Pravastatin in Ischaemic disease) study to identify plasma lipids associated with future cardiovascular events and assess their potential to predict such events. We further investigate the relationship between changes in individual plasma lipid species through pravastatin treatment and future cardiovascular events in this high-risk population.
Plasma lipid species improved the prediction of cardiovascular outcomes, above traditional risk factors, demonstrating the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification. We identified lipid species whose change can explain a large proportion of the pravastatin treatment effect independent of traditional risk factors. Furthermore, we demonstrate the potential clinical utility of a lipid ratio in stratifying the population into subgroups according to the relative risk reduction received from pravastatin treatment. Statistical approaches in these analyses will be discussed.

 


 

SPEAKERS

 

Gad Abraham, PhD

Research Fellow and Group Leader

Systems Genomics Lab, Baker Heart and Diabetes Institute

Dr Gad Abraham received the BAppSci(Hons) in computer science from RMIT University in 2005, and a PhD at the University of Melbourne in 2012. He then began a postdoctoral fellowship at the Department of Pathology at the University of Melbourne (2012–2015) and later became a group leader and Core Member at the Centre for Systems Genomics, School of BioSciences, University of Melbourne (2015–2017). In mid 2017, he joined the Baker Institute as a Group Leader in the Systems Genomics Laboratory.

His main research interest has been the development of genomic (polygenic) risk scores for complex human disease, including coeliac disease and more recently coronary heart disease. Such scores have the potential to stratify individuals by disease risk early in life, better tailoring treatment or lifestyle modifications to individuals, years or decades before disease manifests. He also has an interest in development of computational tools and methods for practical analysis of large genomic and multi-omic datasets.

  

Scott Ritchie, PhD

Research Officer

Systems Genomics Lab, Baker Heart and Diabetes Institute

Scott completed an undergraduate in Computer Science where he became interested in data analysis and bioinformatics. He then completed a master of science in bioinformatics with distinction in 2012 during which he joined the Inouye lab writing a thesis on gene coexpression networks. After his masters, he returned to do a PhD in the Systems Genomics Laboratory, submitting his thesis in 2017. His PhD thesis explored the use of network methods for integrating multi-omic data to identify and characterise biological processes associated with biomarkers for future disease and mortality risk. He investigated the biomarker GlycA, a recently discovered biomarker for long-term risk of diverse diseases in apparently healthy adults including cardiovascular diseases, type II diabetes, and all-cause mortality.

Scott is currently a postdoctoral research fellow whose research projects include exploring the interactions between the human microbiome and common disease, network analysis method development, and exploration of etiopathogenesis of genomic risk scores for cardiovascular disease.

 

Shu Mei Teo, PhD

Senior Research Officer

Systems Genomics Lab, Baker Heart and Diabetes Institute 

Dr Shu Mei Teo completed her BSc(Honours) in Statistics at the University of Singapore. During her Honours year, she became interested in techniques and algorithms used for analysing data from SNP microarrays. She went on to pursue a PhD focusing on statistical methods for the improved detection and analyses of structural variants, under a joint Genetic and Molecular Epidemiology PhD program from the National University of Singapore in Singapore and Karolinska Institutet in Stockholm, Sweden.

After her PhD, Shu Mei did a brief postdoctoral placement at the National University of Singapore before moving to Melbourne in 2013. Since then, she has been analysing microbiome data and linking them to diseases such as atopy, childhood asthma, cystic fibrosis and Crohn’s disease.

  

Paul Lacaze, PhD

Head – Public Health Genomics Program

Department of Epidemiology and Preventive Medicine

School of Public Health and Preventive Medicine

Monash University, The Alfred Centre                                                                          

Paul Lacaze is the inaugural Head of the Public Health Genomics Program at Monash University School of Public Health and Preventive Medicine. He specialises in large-scale genetic analyses of cohort studies, biobanks, clinical trials and clinical registries. He conducts research in genetics of healthy ageing and chronic disease and leads the genomic analyses of the ASPREE Healthy Ageing Biobank. This involves identifying rare cases of non-penetrance or ‘resilience’ against known pathogenic variants in the healthy elderly. Dr Lacaze also conducts research into the ethical, legal and social implications of genetic information.

 

Francine Marques, PhD

National Heart Foundation Future Leader Fellow and Baker Fellow, Baker Heart and Diabetes Institute

Dr Francine Marques is a National Heart Foundation (NHF) Future Leader and a Baker Fellow at the Baker Heart and Diabetes Institute, Melbourne, Australia, and a former National Health and Medical Research Council and NHF early career fellow (2013-2016). Dr Marques was awarded her PhD in 2012, in the field of the molecular genomics of hypertension at the University of Sydney. She is a member of the executive committee for the High Blood Pressure Research Council of Australia (HBPRCA) and Science and Technology Australia (STA), and a member of the mentoring committee for the International Society of Hypertension (ISH). Her research interests include finding new therapies and early markers to prevent cardiovascular disease. 

 

Kaushala Jayawardana, PhD

Bioinformatician, Metabolomics Lab, Baker Heart and Diabetes Institute

Kaushala Jayawardana is a post-doctoral bioinformatician in the Metabolomics lab at the Baker Institute. She was awarded PhD in Statistics from The Sydney University in 2016, where she studied the integration of multiple high-throughput datasets and clinical data. The title of her thesis is ‘Prognostic methods for integrating data from complex diseases’. She joined Baker Institute in June 2016, and has been working on statistical methods for analyzing clinical and lipidomic data.

Have questions about Baker Bioinformatics Symposium? Contact Baker Bioinformatics Program, Baker Institute

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Baker Heart and Diabetes Institute
75 Commercial Rd
Melbourne, VIC 3004
Australia

Thursday, 7 September 2017 from 1:00 pm to 4:00 pm (AEST)


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