Dataset Information

STRING data mining of GWAS data in canine hereditary pigment-associated deafness.

ABSTRACT: Most canine deafness is linked to white pigmentation caused by the piebald locus, shown to be the gene MITF (melanocyte inducing transcription factor), but studies have failed to identify a deafness cause. The coding regions of MITF have not been shown to be mutated in deaf dogs, leading us to pursue genes acting on or controlled by MITF. We have genotyped DNA from 502 deaf and hearing Australian cattle dogs, Dalmatians, and English setters, breeds with a high deafness prevalence. Genome-wide significance was not attained in any of our analyses, but we did identify several suggestive associations. Genome-wide association studies (GWAS) in complex hereditary disorders frequently fail to identify causative gene variants, so advanced bioinformatics data mining techniques are needed to extract information to guide future studies. STRING diagrams are graphical representations of known and predicted networks of protein-protein interactions, identifying documented relationships between gene proteins based on the scientific literature, to identify functional gene groupings to pursue for further scrutiny. The STRING program predicts associations at a preset confidence level and suggests biological functions based on the identified genes. Starting with (1) genes within 500 kb of GWAS-suggested SNPs, (2) known pigmentation genes, (3) known human deafness genes, and (4) genes identified from proteomic analysis of the cochlea, we generated STRING diagrams that included these genes. We then reduced the number of genes by excluding genes with no relationship to auditory function, pigmentation, or relevant structures, and identified clusters of genes that warrant further investigation.

SUBMITTER: Kelly-Smith M

PROVIDER: S-EPMC7386748 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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STRING data mining of GWAS data in canine hereditary pigment-associated deafness.

Kelly-Smith Maria M Strain George M GM

Veterinary and animal science 20200512

Most canine deafness is linked to white pigmentation caused by the piebald locus, shown to be the gene MITF (melanocyte inducing transcription factor), but studies have failed to identify a deafness cause. The coding regions of MITF have not been shown to be mutated in deaf dogs, leading us to pursue genes acting on or controlled by MITF. We have genotyped DNA from 502 deaf and hearing Australian cattle dogs, Dalmatians, and English setters, breeds with a high deafnes ...[more]

PMID: 32734119

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Project description:The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

Dataset Information

STRING data mining of GWAS data in canine hereditary pigment-associated deafness.

Publications

STRING data mining of GWAS data in canine hereditary pigment-associated deafness.

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