Dataset Information

Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

ABSTRACT: BACKGROUND: High-throughput gene expression data can predict gene function through the "guilt by association" principle: coexpressed genes are likely to be functionally associated. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. CONCLUSIONS/SIGNIFICANCE: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis.

SUBMITTER: Miozzi L

PROVIDER: S-EPMC2409962 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

Miozzi Laura L Piro Rosario Michael RM Rosa Fabio F Ala Ugo U Silengo Lorenzo L Di Cunto Ferdinando F Provero Paolo P

PloS one 20080618 6

<h4>Background</h4>High-throughput gene expression data can predict gene function through the "guilt by association" principle: coexpressed genes are likely to be functionally associated.<h4>Methodology/principal findings</h4>We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks ...[more]

PMID: 18560577

Similar Datasets

Project description:BACKGROUND:An unfavorable genetic correlation between milk production and fertility makes simultaneous improvement of milk production and fertility difficult in cattle breeding. Rapid genetic improvement in milk production traits in dairy cattle has been accompanied by decline in cow fertility. The genetic basis of this correlation remains poorly understood. Expanded reference populations and large sets of sequenced animals make genome-wide association studies (GWAS) with imputed markers possible for large populations and thereby studying genetic architecture of complex traits. RESULTS:In this study, we associated 15,551,021 SNPs with female fertility index in 5038 Nordic Holstein cattle. We have identified seven quantitative trait loci (QTL) on six chromosomes in cattle. Along with nearest genes to GWAS hits, we used gene-based analysis and spread of linkage disequilibrium (LD) information to generate a list of potential candidate genes affecting fertility in cattle. Subsequently, we used prior knowledge on gene related to fertility from Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, mammalian phenotype database, and public available RNA-seq data to refine the list of candidate genes for fertility. We used variant annotations to investigate candidate mutations within the prioritized candidate genes. Using multiple source of information, we proposed candidate genes with biological relevance underlying each of these seven QTL. On chromosome 1, we have identified ten candidate genes for two QTL. For the rest of chromosomes, we proposed one candidate gene for each QTL. In the candidate genes list, differentially expressed genes from different studies support FRAS1, ITGB5, ADCY5, and SEMA5B as candidate genes for cow fertility. CONCLUSION:The GWAS result not only confirmed previously mapped QTL, but also made new findings. Our findings contributes towards dissecting the genetics for female fertility in cattle. Moreover, this study shows the usefulness of adding independent information to pick candidate genes during post-GWAS analysis.

Dataset Information

Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

Publications

Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets