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

BackgroundAutomated 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.ResultsUsing 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.ConclusionThe 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.<br /

Gentrepid V2.0: a web server for candidate disease gene prediction

Contains fulltext : 124935.pdf (publisher's version ) (Open Access)BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/

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

Article 9

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Integrating bioinformatics and physiology to describe genetic effects in complex polygenic diseases

Type 2 diabetes mellitus (T2DM) results from interaction between genetic and environmental factors. The worldwide prevalence of T2DM is increasing rapidly due to reduction in physical activity, increase in dietary intake, and the aging of the population. This thesis has focused on dissecting the genetic contribution in T2DM using largescale genomic approaches with a particular emphasis on analysis of gene transcripts in different tissues, predominantly muscle. In paper I, we identified TXNIP as a gene whose expression is powerfully suppressed by insulin yet stimulated by glucose. In healthy individuals, its expression was inversely correlated to total body measures of glucose uptake. Forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming that the gene product is also a regulator of glucose uptake. TXNIP expression is consistently elevated in the muscle of pre-diabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM. TXNIP regulates both insulindependent and insulin-independent pathways of glucose uptake in human skeletal muscle. Combined with recent studies that have implicated TXNIP in pancreatic β-cell glucose toxicity, our data suggest that TXNIP might play a key role in defective glucose homeostasis preceding overt T2DM. In paper II, we investigated molecular mechanisms associated with insulin sensitivity in skeletal muscle by relating global skeletal muscle gene expression to physiological measures of the insulin sensitivity. We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle. Most notably, genes involved in a mammalian target-of-rapamycin signaling pathway were positively whereas genes encoding extracellular matrix structural constituent such as extracellular matrix-receptor, cell communication, and focal adhesion pathways were inversely correlated with insulin sensitivity. More specifically, expression of CPT1B was positively and that of LEO1 inversely correlated with insulin sensitivity, a finding which was replicated in an independent study of 9 non-diabetic men. These data suggest that a high capacity of fat oxidation in mitochondria is reflected by a high expression of CPT1B which is a marker of insulin sensitivity. In paper III, we investigated molecular mechanisms associated with maximal oxygen uptake (VO2max) and type 1 fibers in human skeletal muscle. We identified 66 genes positively and 83 genes inversely correlated with VO2max and 171 genes positively and 217 genes inversely correlated with percentage of type 1 fibers in human skeletal muscle. Genes involved in oxidative phosphorylation (OXPHOS) showed high expression in individuals with high VO2max, whereas the opposite was not the case in individuals with low VO2max. Instead, genes such as AHNAK and BCL6 were associated with low VO2max. Also, expression of the OXPHOS genes, NDUFB5 and ATP5C1, increased with exercise training and decreased with aging. In contrast, expression of AHNAK in skeletal muscle decreased with exercise training and increased with aging. These findings indicate that VO2max closely reflects expression of OXPHOS genes, particularly that of NDUFB5 and ATP5C1 in skeletal muscle and high expression of these genes suggest good muscle fitness. In contrast, a high expression of AHNAK was associated with a low VO2max and poor muscle fitness. In paper IV, we combined results from the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC) genome-wide association (GWA) studies with genome-wide expression profiling in pancreas, adipose tissue, liver, and skeletal muscle in patients with or without T2DM or animal models thereof to identify novel T2DM susceptibility loci. We identified 453 single nucleotide polymorphisms (SNPs) associated with T2DM with P < 0.01 in at least one of the GWA studies and 150 genes that were located in vicinity of these SNPs. Out of these 150 genes, we identified 41 genes differentially expressed using publicly available gene expression profiling data. Most notably, we were able to identify four genes namely IGF2BP2, CDKAL1, TSPAN8, and NOTCH2 for which SNPs located in vicinity of these genes have shown association with T2DM in different populations. In addition, we identified a SNP (rs27582) in the CAST gene which was associated with future risk of T2DM (odds ratio (OR) = 1.10, 95% CI: 1.00-1.20, P < 0.05) in a prospective study of 16,061 Swedish individuals followed for more than 25 years; this association was stronger in lean individuals (OR = 1.19, 95% CI: 1.03-1.36, P = 0.024). Moreover in the Botnia Prospective Study (BPS) involving 2,770 individuals followed for more than 7 years, carriers of the A-allele were more insulin resistant than carriers of the G-allele as indicated by higher fasting insulin concentrations (regression coefficient (β) = 0.048, P = 0.017) and higher HOMA-IR index (β = 0.044, P = 0.025) as well as lower insulin sensitivity index during OGTT (β = -0.039, P = 0.039) at follow-up. In conclusion, using gene expression in different tissues from patients with T2DM and animal models is a powerful tool for prioritizing SNPs from GWA studies for replication studies. We thereby identified association of a variant (rs27582) in the CAST gene with T2DM and insulin resistance