Abstract
The domestic dog is becoming an increasingly valuable model species in medical genetics, showing particular promise to advance our understanding of cancer and orthopaedic disease. Here we undertake the largest canine genome-wide association study to date, with a panel of over 4,200 dogs genotyped at 180,000 markers, to accelerate mapping efforts. For complex diseases, we identify loci significantly associated with hip dysplasia, elbow dysplasia, idiopathic epilepsy, lymphoma, mast cell tumour and granulomatous colitis; for morphological traits, we report three novel quantitative trait loci that influence body size and one that influences fur length and shedding. Using simulation studies, we show that modestly larger sample sizes and denser marker sets will be sufficient to identify most moderate- to large-effect complex disease loci. This proposed design will enable efficient mapping of canine complex diseases, most of which have human homologues, using far fewer samples than required in human studies.
| Original language | English (US) |
|---|---|
| Article number | 10460 |
| Journal | Nature communications |
| Volume | 7 |
| DOIs | |
| State | Published - Jan 22 2016 |
Bibliographical note
Funding Information:This study was made possible by funding support from Zoetis Animal Health, the Cornell University Center for Advanced Technology in Life Science Enterprise, the National Geographic Society, NIH R01 GM103961, NIH R24 GM082910-A1 and R24 GM082910-S1, The American Kennel Club (Grant #1445) and the Cornell University College of Veterinary Medicine. We especially thank the faculty and staff of the Cornell University Hospital for Animals. We would like to acknowledge Gregory Acland and John Schimenti for instigation of the Cornell Veterinary Biobank, and thank Julie Jordan, and Rebecca Cameron and Peter Schweitzer at the Cornell University Genomics Core Facility for technical help, and Dr Brian Collins, Dr William Hornbuckle, Dr Tracy Stokol and Dr Elizabeth Wilcox for assistance with phenotyping. We thank Dr Michael Boyle for assistance with Python scripts and the linear model, Dr Gabe Hoffman and Dr Jason Mezey for discussions, and Ryan Boyko, Cori McLean, Dr Jorge Calero, and numerous pet owners and collaborators for sample collection and phenotyping.
