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

Genome biology · 2023 · 122 citations

• Biology
• Evolutionary biology
• Genetics

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.

Natural and human-driven selection of a single non-coding body size variant in ancient and modern canids

Current Biology · 2022 · 56 citations

• Artificial Intelligence
• Biology
• Computer Science

Identification of a naturally-occurring canine model for early detection and intervention research in high grade urothelial carcinoma

Frontiers in Oncology · 2022 · 19 citations

• Medicine
• Oncology
• Internal medicine

Background: Early detection and intervention research is expected to improve the outcomes for patients with high grade muscle invasive urothelial carcinoma (InvUC). With limited patients in suitable high-risk study cohorts, relevant animal model research is critical. Experimental animal models often fail to adequately represent human cancer. The purpose of this study was to determine the suitability of dogs with high breed-associated risk for naturally-occurring InvUC to serve as relevant models for early detection and intervention research. The feasibility of screening and early intervention, and similarities and differences between canine and human tumors, and early and later canine tumors were determined. Methods: STs (n=120) ≥ 6 years old with no outward evidence of urinary disease were screened at 6-month intervals for 3 years with physical exam, ultrasonography, and urinalysis with sediment exam. Cystoscopic biopsy was performed in dogs with positive screening tests. The pathological, clinical, and molecular characteristics of the "early" cancer detected by screening were determined. Transcriptomic signatures were compared between the early tumors and published findings in human InvUC, and to more advanced "later" canine tumors from STs who had the typical presentation of hematuria and urinary dysfunction. An early intervention trial of an oral cyclooxygenase inhibitor, deracoxib, was conducted in dogs with cancer detected through screening. Results: (n=1). Transcriptomic signatures including druggable targets such as EGFR and the PI3K-AKT-mTOR pathway, were very similar between canine and human InvUC, especially within luminal and basal molecular subtypes. Marked transcriptomic differences were noted between early and later canine tumors, particularly within luminal subtype tumors. The deracoxib remission rate (42% CR+PR) compared very favorably to that with single-agent cyclooxygenase inhibitors in more advanced canine InvUC (17-25%), supporting the value of early intervention. Conclusions: The study defined a novel naturally-occurring animal model to complement experimental models for early detection and intervention research in InvUC. Research incorporating the canine model is expected to lead to improved outcomes for humans, as well as pet dogs, facing bladder cancer.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

Human Genetics · 2021 · 39 citations

• Computer Science
• Machine Learning
• Biology

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.