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Pinpointing disease genes through phenomic and genomic data fusion.

ABSTRACT: Pinpointing genes involved in inherited human diseases remains a great challenge in the post-genomics era. Although approaches have been proposed either based on the guilt-by-association principle or making use of disease phenotype similarities, the low coverage of both diseases and genes in existing methods has been preventing the scan of causative genes for a significant proportion of diseases at the whole-genome level.To overcome this limitation, we proposed a rigorous statistical method called pgFusion to prioritize candidate genes by integrating one type of disease phenotype similarity derived from the Unified Medical Language System (UMLS) and seven types of gene functional similarities calculated from gene expression, gene ontology, pathway membership, protein sequence, protein domain, protein-protein interaction and regulation pattern, respectively. Our method covered a total of 7,719 diseases and 20,327 genes, achieving the highest coverage thus far for both diseases and genes. We performed leave-one-out cross-validation experiments to demonstrate the superior performance of our method and applied it to a real exome sequencing dataset of epileptic encephalopathies, showing the capability of this approach in finding causative genes for complex diseases. We further provided the standalone software and online services of pgFusion at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgfusion.pgFusion not only provided an effective way for prioritizing candidate genes, but also demonstrated feasible solutions to two fundamental questions in the analysis of big genomic data: the comparability of heterogeneous data and the integration of multiple types of data. Applications of this method in exome or whole genome sequencing studies would accelerate the finding of causative genes for human diseases. Other research fields in genomics could also benefit from the incorporation of our data fusion methodology.

SUBMITTER: Jiang R

PROVIDER: S-EPMC4331717 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Pinpointing disease genes through phenomic and genomic data fusion.

Jiang Rui R Wu Mengmeng M Li Lianshuo L

BMC genomics 20150121

<h4>Background</h4>Pinpointing genes involved in inherited human diseases remains a great challenge in the post-genomics era. Although approaches have been proposed either based on the guilt-by-association principle or making use of disease phenotype similarities, the low coverage of both diseases and genes in existing methods has been preventing the scan of causative genes for a significant proportion of diseases at the whole-genome level.<h4>Results</h4>To overcome this limitation, we proposed ...[more]

PMID: 25708473

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Project description:Escherichia coli is a major etiological agent of intra-mammary infections (IMI) in cows, leading to acute mastitis and causing great economic losses in dairy production worldwide. Particular strains cause persistent IMI, leading to recurrent mastitis. Virulence factors of mammary pathogenic E. coli (MPEC) involved pathogenesis of mastitis as well as those differentiating strains causing acute or persistent mastitis are largely unknown. This study aimed to identify virulence markers in MPEC through whole genome and phenome comparative analysis. MPEC strains causing acute (VL2874 and P4) or persistent (VL2732) mastitis were compared to an environmental strain (K71) and to the genomes of strains representing different E. coli pathotypes. Intra-mammary challenge in mice confirmed experimentally that the strains studied here have different pathogenic potential, and that the environmental strain K71 is non-pathogenic in the mammary gland. Analysis of whole genome sequences and predicted proteomes revealed high similarity among MPEC, whereas MPEC significantly differed from the non-mammary pathogenic strain K71, and from E. coli genomes from other pathotypes. Functional features identified in MPEC genomes and lacking in the non-mammary pathogenic strain were associated with synthesis of lipopolysaccharide and other membrane antigens, ferric-dicitrate iron acquisition and sugars metabolism. Features associated with cytotoxicity or intra-cellular survival were found specifically in the genomes of strains from severe and acute (VL2874) or persistent (VL2732) mastitis, respectively. MPEC genomes were relatively similar to strain K-12, which was subsequently shown here to be possibly pathogenic in the mammary gland. Phenome analysis showed that the persistent MPEC was the most versatile in terms of nutrients metabolized and acute MPEC the least. Among phenotypes unique to MPEC compared to the non-mammary pathogenic strain were uric acid and D-serine metabolism. This study reveals virulence factors and phenotypic characteristics of MPEC that may play a role in pathogenesis of E. coli mastitis.

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Pinpointing disease genes through phenomic and genomic data fusion.

Publications

Pinpointing disease genes through phenomic and genomic data fusion.

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