Dataset Information

Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach.

ABSTRACT: Arachnomelia syndrome (AS), mainly found in Brown Swiss and Simmental cattle, is a congenital lethal genetic malformation of the skeletal system. In this study, a network-based disease gene prioritization approach was implemented to rank genes in the previously reported ?7 Mb region on chromosome 23 associated with AS in Simmental cattle. The top 6 ranked candidate genes were sequenced in four German Simmental bulls, one known AS-carrier ROMEL and a pooled sample of three known non-carriers (BOSSAG, RIFURT and HIRMER). Two suspicious mutations located in coding regions, a mis-sense mutation c.1303G>A in the bystin-like (BYSL) gene and a 2-bp deletion mutation c.1224_1225delCA in the molybdenum cofactor synthesis step 1 (MOCS1) gene were detected. Bioinformatic analysis revealed that the mutation in MOCS1 was more likely to be the causative mutation. Screening the c.1224_1225delCA site in 383 individuals from 12 cattle breeds/lines, we found that only the bull ROMEL and his 12 confirmed progeny carried the mutation. Thus, our results confirm the conclusion of Buitkamp et al. that the 2-bp deletion mutation c.1224_1225delCA in exon 11 of the MOCS1 gene is causative for AS in Simmental cattle. Furthermore, a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was developed to detect the causative mutation.

SUBMITTER: Jiao S

PROVIDER: S-EPMC3655968 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach.

Jiao Shihui S Chu Qin Q Wang Yachun Y Xie Zhenquan Z Hou Shiyu S Liu Airong A Wu Hongjun H Liu Lin L Geng Fanjun F Wang Congyong C Qin Chunhua C Tan Rui R Huang Xixia X Tan Shixin S Wu Meng M Xu Xianzhou X Liu Xuan X Yu Ying Y Zhang Yuan Y

PloS one 20130516 5

Arachnomelia syndrome (AS), mainly found in Brown Swiss and Simmental cattle, is a congenital lethal genetic malformation of the skeletal system. In this study, a network-based disease gene prioritization approach was implemented to rank genes in the previously reported ∼7 Mb region on chromosome 23 associated with AS in Simmental cattle. The top 6 ranked candidate genes were sequenced in four German Simmental bulls, one known AS-carrier ROMEL and a pooled sample of three known non-carriers (BOS ...[more]

PMID: 23696895

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Project description:BackgroundAccurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. However, different diseases tend to manifest in different tissues, and the molecular networks in different tissues are usually different. An ideal method should be able to incorporate tissue-specific molecular networks for different diseases.ResultsIn this paper, we develop a robust and flexible method to integrate tissue-specific molecular networks for disease gene prioritization. Our method allows each disease to have its own tissue-specific network(s). We formulate the problem of candidate gene prioritization as an optimization problem based on network propagation. When there are multiple tissue-specific networks available for a disease, our method can automatically infer the relative importance of each tissue-specific network. Thus it is robust to the noisy and incomplete network data. To solve the optimization problem, we develop fast algorithms which have linear time complexities in the number of nodes in the molecular networks. We also provide rigorous theoretical foundations for our algorithms in terms of their optimality and convergence properties. Extensive experimental results show that our method can significantly improve the accuracy of candidate gene prioritization compared with the state-of-the-art methods.ConclusionsIn our experiments, we compare our methods with 7 popular network-based disease gene prioritization algorithms on diseases from Online Mendelian Inheritance in Man (OMIM) database. The experimental results demonstrate that our methods recover true associations more accurately than other methods in terms of AUC values, and the performance differences are significant (with paired t-test p-values less than 0.05). This validates the importance to integrate tissue-specific molecular networks for studying disease gene prioritization and show the superiority of our network models and ranking algorithms toward this purpose. The source code and datasets are available at http://nijingchao.github.io/CRstar/ .

Project description:BackgroundIndoor residual spraying (IRS) is an effective method to control malaria-transmitting Anopheles mosquitoes and often complements insecticide-treated mosquito nets, the predominant malaria vector control intervention. With insufficient funds to cover every household, malaria control programs must balance the malaria risk to a particular human community against the financial cost of spraying that community. This study creates a framework for modelling the distance to households for targeting IRS implementation, and applies it to potential risk prioritization strategies in four provinces (Luapula, Muchinga, Eastern, and Northern) in Zambia.MethodsOptimal network models were used to assess the travel distance of routes between operations bases and human communities identified through remote sensing. Network travel distances were compared to Euclidean distances, to demonstrate the importance of accounting for road routes. The distance to reaching communities for different risk prioritization strategies were then compared assuming sufficient funds to spray 50% of households, using four underlying malarial risk maps: (a) predicted Plasmodium falciparum parasite rate in 2-10 years olds (PfPR), or (b) predicted probability of the presence of each of three main malaria transmitting anopheline vectors (Anopheles arabiensis, Anopheles funestus, Anopheles gambiae).ResultsThe estimated one-way network route distance to reach communities to deliver IRS ranged from 0.05 to 115.69 km. Euclidean distance over and under-estimated these routes by - 101.21 to 41.79 km per trip, as compared to the network route method. There was little overlap between risk map prioritization strategies, both at a district-by-district scale, and across all four provinces. At both scales, agreement for inclusion or exclusion from IRS across all four prioritization strategies occurred in less than 10% of houses. The distances to reaching prioritized communities were either lower, or not statistically different from non-prioritized communities, at both scales of strategy.ConclusionVariation in distance to targeted communities differed depending on risk prioritization strategy used, and higher risk prioritization did not necessarily translate into greater distances in reaching a human community. These findings from Zambia suggest that areas with higher malaria burden may not necessarily be more remote than areas with lower malaria burden.

Project description:ObjectiveRecent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.Approach and resultsWe devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks.ConclusionsOur data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

Dataset Information

Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach.

Publications

Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach.

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