Kelly-Smith M, Strain GM. STRING data mining of GWAS data in canine hereditary pigment-associated deafness.
Vet Anim Sci 2020;
9:100118. [PMID:
32734119 PMCID:
PMC7386748 DOI:
10.1016/j.vas.2020.100118]
[Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/06/2020] [Indexed: 02/06/2023] Open
Abstract
Genome-wide association studies may fail to identify significant associations between a disorder and causative genes in complex hereditary disorders.
STRING software is a bioinformatics data mining tool that identifies known and predicted physical and functional relationship networks among the proteins of candidate genes.
STRING analysis provides a mechanism to identify gene-gene interactions that might not otherwise have been recognized.
Relationships identified from STRING analysis can uncover function-based gene-gene relationships that may not be easily extracted from literature, thereby providing genes for pursuit as a cause of a complex hereditary disorder.
In this study STRING analysis was applied to identification of candidate genes to pursue as the cause of pigment-associated hereditary deafness in dogs.
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.
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