Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Background
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.

Results
Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.

Conclusion
The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Genome - wide association study identifies novel breast cancer susceptibility loci

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r² > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10^-7). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

Analysis of genome-wide association study data using the protein knowledge base

Article 98

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Novel therapeutics for cononary artery disease from genome-wide association study data

Abstract
Background: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.
Methods: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatics modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.
Results: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).
Conclusions: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Suppression by ABA of salicylic acid and lignin accumulation and the expression of multiple genes, in Arabidopsis infected with Pseudomonas syringae pv. tomato

Abscisic acid (ABA) has been implicated in determining the outcome of interactions between many plants and their pathogens. We had previously shown that increased concentrations of ABA within leaves of Arabidopsis induced susceptibility towards an avirulent strain of Pseudomonas syringae pathovar (pv.) tomato. We now show that ABA induces susceptibility via suppression of the accumulation of components crucial for a resistance response. Lignin and salicylic acid concentrations in leaves were increased during a resistant interaction but reduced when plants were treated with ABA. The reduction in lignin and salicylic acid production was independent of the development of the hypersensitive response (HR), indicating that, in this host-pathogen system, HR is not required for resistance. Genome-wide gene expression analysis using microarrays showed that treatment with ABA suppressed the expression of many defence-related genes, including those important for phenylpropanoid biosynthesis and those encoding resistance-related proteins. Together, these results show that resistance induction in Arabidopsis to an avirulent strain of P. syringae pv. tomato is regulated by ABA.

Disease fingerprinting with cDNA microarrays reveals distinct gene expression profiles in lethal type 1 and type 2 cytokine-mediated inflammatory reactions

Development of polarized immune responses controls resistance and susceptibility to many microorganisms. However, studies of several infectious, allergic, and autoimmune diseases have shown that chronic type-1 and type-2 cytokine responses can also cause significant morbidity and mortality if left unchecked. We used mouse cDNA microarrays to molecularly phenotype the gene expression patterns that characterize two disparate but equally lethal forms of liver pathology that develop in Schistosoma mansoni infected mice polarized for type-1 and type-2 cytokine responses. Hierarchical clustering analysis identified at least three groups of genes associated with a polarized type-2 response and two linked with an extreme type-1 cytokine phenotype. Predictions about liver fibrosis,  apoptosis, and granulocyte recruitment and activation generated by the microarray studies were confirmed later by traditional biological assays. The data show that cDNA microarrays are useful not only for determining  coordinated gene expression profiles but are also highly effective for molecularly “fingerprinting” diseased tissues. Moreover, they illustrate the potential of genome-wide approaches for generating comprehensive views on the molecular and biochemical mechanisms regulating infectious  disease pathogenesis.

Investigation of plasmodiophora brassicae (clubroot disease) in vegetable brassica using arabidopsis thaliana as a model system

Clubroot, caused by Plasmodiophora brassicae, is the most devastating soil-borne disease of vegetable brassicas. It occurs all over the world and is responsible for crop losses of up to 10% every year. In Australia, the disease is being managed effectively with chemicals and cultural practices, but ideally control can be improved in the long term by the introduction of resistant cultivars. The life cycle ofP. brassicae and mode of action of plant resistance has not been fully elucidated because of the technical difficulties of working with an obligate, soil-borne plant pathogen. However, Arabidopsis thaliana, which is a host ofP. brassicae, has great potential as a model system for studying the life cycle, the infection process and development of resistance. We have developed a sand-liquid-culture system for growing Arabidopsis that allows easy observation of all life stages and, most importantly, the primary plasmodial stages within the root hair. The method was first optimised for observations of the lifecycle of the pathogen in a susceptible Arabidopsis ecotype (Col-3) where all stages of the lifecycle have now been observed and characterised. Further screening of Arabidopsis ecotypes for disease resistance has utilised one of the most virulent Australian pathotypes of brassica (ECD number 16/19/31). To date, Arabidopsis ecotype Ta-0 has shown a level of tolerance to the disease even though the roots get infected. It has been reported earlier that resistance toP. brassicae in Arabidopsis is due to one or a small number of genes. To examine changes in gene expression during the early, critical stages of infection, RNA was extracted from the susceptible and resistant ecotypes at two time points, 4 days and 17 days after inoculation. Microarray analysis will be used to investigate genome wide changes in gene expression during infection but also to identify candidate genes that may confer resistance to Australian isolates of the pathogen.

Abscisic acid regulation of plant defence responses during pathogen attack

The plant hormone, abscisic acid (ABA), has previously been shown to have an impact on the resistance or susceptibility of plants to pathogens. In this thesis, it was shown that ABA had a regulatory effect on an extensive array of plant defence responses in three different plant and pathogen interaction combinations as well as following the application of an abiotic elicitor. In unique studies using ABA deficient mutants of Arabidopsis, exogenous ABA addition or ABA biosynthesis inhibitor application and simulated drought stress, ABA was shown to have a profound effect on the outcome of interactions between plants and pathogens of differing lifestyles and from different kingdoms. The systems used included a model plant and an important agricultural species: Arabidopsis thaliana (Arabidopsis) and Peronospora parasitica (a biotrophic Oomycete pathogen), Arabidopsis and Pseudomonas syringae pathovar tomato (a biotrophic bacterial pathogen) and an unrelated plant species, soybean (Glycine max) and Phytophthora sojae (a hemibiotrophic Oomycete pathogen), Generally, a higher than basal endogenous ABA concentration within plant tissues at the time of avirulent pathogen inoculation, caused an interaction shift towards what phenotypically resembled susceptibility. Conversely, a lower than basal endogenous ABA concentration in plants inoculated with a virulent pathogen caused a shift towards resistance. An extensive suppressive effect of ABA on defence responses was revealed by a range of techniques that included histochemical, biochemical and molecular approaches. A universal effect of ABA on suppression or induction of the phenylpropanoid pathway via regulation of the key entry point gene, phenylalanine ammonia-lyase (PAL), when stimulated by biotic or abiotic elicitors was shown. ABA also influenced a wide variety of other defence-related components such as: the development of a hypersensitive response (HR), the accumulation of the reactive oxyden species, hydrogen peroxide and the cell wall strengthening compounds lignin and callose, accumulation of SA and the phytoalexin, glyceollin and the transcription of the SA-dependent pathogenesis- related gene (PR-1). The near genome-wide microarray gene expression analysis of an ABA induced susceptible interaction also revealed an yet unprecedented insight into the great diversity of defence responses that were influenced by ABA that included: disease resistance like proteins, antimicrobial proteins as well as phenylpropanoid and tryptophan pathway enzymes. Subtle differences were found in the number and type of defence responses that were regulated by ABA in each type of plant and pathogen interaction that was studied. This thesis has clearly identified in plant/pathogen interactions previously unknown and important roles for ABA in the regulation of many defence responses.

Familial and genomic analyses of postural changes in systolic and diastolic blood pressure

The physiological adaptation to the erect posture involves integrated neural and cardiovascular responses that might be determined by genetic factors. We examined the familial- and individual-specific components of variance for postural changes in systolic and diastolic blood pressure in 767 volunteer nuclear adult families from the Victorian Family Heart Study. In 274 adult sibling pairs, we made a genome-wide scan using 400 markers for quantitative trait loci linked with the postural changes in systolic and diastolic pressures. Overall, systolic pressure did not change on standing, but there was considerable variation in this phenotype (SD=8.1 mm Hg). Familial analyses revealed that 25% of the variance of change in systolic pressure was attributable to genetic factors. In contrast, diastolic pressure increased by 6.3 mm Hg (SD=7.0 mm Hg) on standing and there was no evidence of contributory genetic factors. Multipoint quantitative genome linkage mapping suggested evidence (Z=3.2) of linkage of the postural change in systolic pressure to chromosome 12 but found no genome-wide evidence of linkage for the change in diastolic pressure. These findings suggest that genetic factors determine whether systolic pressure decreases or increases when one stands, possibly as the result of unidentified alleles on chromosome 12. The genetics of postural changes in systolic blood pressure might reflect the general buffering function of the baroreflex; thereby, the predisposition to sudden decreases or increases in systolic pressure might cause postural hypotension or vessel wall disruption, respectively.