Bioinformatic approaches facilitated new disease gene and genomic content discovery in dogs

A new study found causal variants in three new disease genes in dogs. Based on the results of the study, novel genetic tests for diagnostics were developed. The findings have implications also to human medicine with novel candidate genes in same rare disorders.

Purebred breeding has enriched many disease-causing variants in dog breeds. It is important to find the causal genes and variants to better understand the diseases, assist veterinary diagnostics, revision of breeding programs, and to translate results to the human medicine.

PhD student Meharji Arumilli used a range of high-throughput sequencing approaches, deep sequencing data and bioinformatic mining tools in his study to successfully investigate and contribute to disease gene discovery and genomics in dogs. The research targets included three canine forms of diseases namely Caffey disease, van den Ende-Gupta and Raine syndromes with clinical similarities to human form of diseases. In addition, the research included development of bioinformatic resources and tools to facilitate disease variant allele discovery.

The first study identified causal variants in three new disease genes (SLC37A2, SCARF2 and FAM20C) of relevance to Caffey disease, van den Ende-Gupta syndrome (VDEGS) and Raine syndrome in human, respectively. Based on the results, novel genetic tests for diagnostics and dog breeding were developed. The findings have implications also to human medicine with novel candidate genes in three rare disorders.

"The identification of the causal variants in the underlying disorders has made it possible to indicate new physiological functions and establishes preclinical models to corresponding three human diseases", explains Arumilli.

In the second study, new sequences missing in the reference genome were identified through de novo assembly of unmapped short read sequences. These included partially missing gene sequences in the reference genome providing a valuable resource of data for upcoming reference genomes.

"Many short-read sequences that we found to be unmapped to the current genome reference, are earlier thought to be "junk" sequences. We found that such short-reads when assembled to longer contigs to have missing gene sequences associated with genetic diseases. Thus such reads contribute to the improvement of upcoming canine genome reference for variant allele discovery in candidate genes", tells Arumilli.

Third study describes a novel webtool to mine causal variant from billions of variants identified using high-throughput sequencing approaches under different hypothesized modes of genetic inheritance models.

"This tool functions for finding causal variants like finding a needle in a haysack on a desktop computer within short time. The web tool has been successfully utilized in disease gene discovery projects within the research group and can be applied to any model species including human genome", rejoice Arumilli.

Arumilli's doctoral dissertation is part of Professor Hannes Lohi's

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