Association of Common Genetic Variants in the CPSF7 and SDHAF2 Genes with Canine Idiopathic Pulmonary Fibrosis in the West Highland White Terrier

Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture

BACKGROUND

The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 × data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function.

RESULTS

We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection.

CONCLUSIONS

We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.

Dog-human translational genomics: state of the art and genomic resources

Innovative models for medical research are strongly required nowadays. Convincing evidence supports dog as the most suitable spontaneous model for several human genetic diseases. Decades of studies on dog genome allowed the identification of hundreds of mutations causing genetic disorders, many of which are proposed as counterparts responsible for human diseases. Traditionally, the murine model is the most extensively used in human translational research. However, this species shows large physiological differences from humans, and it is kept under a controlled artificial environment. Conversely, canine genetic disorders often show pathophysiological and clinical features highly resembling the human counterpart. In addition, dogs share the same environment with humans; therefore, they are naturally exposed to many risk factors. Thus, different branches of translational medicine aim to study spontaneously occurring diseases in dogs to provide a more reliable model for human disorders. This review offers a comprehensive overview of the knowledge and resources available today for all the researchers involved in the field of dog-human translational medicine. Some of the main successful examples from dog-human translational genomics are reported, such as the canine association studies which helped to identify the causal mutation in the human counterpart. We also illustrated the ongoing projects aiming to create public canine big datasets. Finally, specific online databases are discussed along with several information resources that can speed up clinical translational research.

Best practices for analyzing imputed genotypes from low-pass sequencing in dogs

Although DNA array-based approaches for genome-wide association studies (GWAS) permit the collection of thousands of low-cost genotypes, it is often at the expense of resolution and completeness, as SNP chip technologies are ultimately limited by SNPs chosen during array development. An alternative low-cost approach is low-pass whole genome sequencing (WGS) followed by imputation. Rather than relying on high levels of genotype confidence at a set of select loci, low-pass WGS and imputation rely on the combined information from millions of randomly sampled low-confidence genotypes. To investigate low-pass WGS and imputation in the dog, we assessed accuracy and performance by downsampling 97 high-coverage (> 15×) WGS datasets from 51 different breeds to approximately 1× coverage, simulating low-pass WGS. Using a reference panel of 676 dogs from 91 breeds, genotypes were imputed from the downsampled data and compared to a truth set of genotypes generated from high-coverage WGS. Using our truth set, we optimized a variant quality filtering strategy that retained approximately 80% of 14 M imputed sites and lowered the imputation error rate from 3.0% to 1.5%. Seven million sites remained with a MAF > 5% and an average imputation quality score of 0.95. Finally, we simulated the impact of imputation errors on outcomes for case-control GWAS, where small effect sizes were most impacted and medium-to-large effect sizes were minorly impacted. These analyses provide best practice guidelines for study design and data post-processing of low-pass WGS-imputed genotypes in dogs.