REASONS FOR PERFORMING STUDY: The wild progenitors of the domestic horse were subject to natural selection for speed and stamina for millennia. Uniquely, this process has been augmented in Thoroughbreds, which have undergone at least 3 centuries of intense artificial selection for athletic phenotypes. While the phenotypic adaptations to exercise are well described, only a small number of the underlying genetic variants contributing to these phenotypes have been reported. OBJECTIVES: A panel of candidate performance-related genes was examined for DNA sequence variation in Thoroughbreds and the association with racecourse performance investigated. MATERIALS AND METHODS: Eighteen candidate genes were chosen for their putative roles in exercise. Re-sequencing in Thoroughbred samples was successful for primer sets in 13 of these genes. SNPs identified in this study and from the EquCab2.0 SNP database were genotyped in 2 sets of Thoroughbred samples (n = 150 and 148) and a series of population-based case-control investigations were performed by separating the samples into discrete cohorts on the basis of retrospective racecourse performance. RESULTS: Twenty novel SNPs were detected in 3 genes: ACTN3, CKM and COX4I2. Genotype frequency distributions for 3 SNPs in CKM and COX4I2 were significantly (P < 0.05) different between elite Thoroughbreds and racehorses that had never won a race. These associations were not validated when an additional (n = 130) independent set of samples was genotyped, but when analyses included all samples (n = 278) the significance of association at COX4I2 g.22684390C > T was confirmed (P < 0.02). CONCLUSIONS: While molecular genetic information has the potential to become a powerful tool to make improved decisions in horse industries, it is vital that rigour is applied to studies generating these data and that adequate and appropriate sample sets, particularly for independent replication, are used.
REASONS FOR PERFORMING STUDY: The wild progenitors of the domestic horse were subject to natural selection for speed and stamina for millennia. Uniquely, this process has been augmented in Thoroughbreds, which have undergone at least 3 centuries of intense artificial selection for athletic phenotypes. While the phenotypic adaptations to exercise are well described, only a small number of the underlying genetic variants contributing to these phenotypes have been reported. OBJECTIVES: A panel of candidate performance-related genes was examined for DNA sequence variation in Thoroughbreds and the association with racecourse performance investigated. MATERIALS AND METHODS: Eighteen candidate genes were chosen for their putative roles in exercise. Re-sequencing in Thoroughbred samples was successful for primer sets in 13 of these genes. SNPs identified in this study and from the EquCab2.0 SNP database were genotyped in 2 sets of Thoroughbred samples (n = 150 and 148) and a series of population-based case-control investigations were performed by separating the samples into discrete cohorts on the basis of retrospective racecourse performance. RESULTS: Twenty novel SNPs were detected in 3 genes: ACTN3, CKM and COX4I2. Genotype frequency distributions for 3 SNPs in CKM and COX4I2 were significantly (P < 0.05) different between elite Thoroughbreds and racehorses that had never won a race. These associations were not validated when an additional (n = 130) independent set of samples was genotyped, but when analyses included all samples (n = 278) the significance of association at COX4I2 g.22684390C > T was confirmed (P < 0.02). CONCLUSIONS: While molecular genetic information has the potential to become a powerful tool to make improved decisions in horse industries, it is vital that rigour is applied to studies generating these data and that adequate and appropriate sample sets, particularly for independent replication, are used.
Authors: Mim A Bower; Beatrice A McGivney; Michael G Campana; Jingjing Gu; Lisa S Andersson; Elizabeth Barrett; Catherine R Davis; Sofia Mikko; Frauke Stock; Valery Voronkova; Daniel G Bradley; Alan G Fahey; Gabriella Lindgren; David E MacHugh; Galina Sulimova; Emmeline W Hill Journal: Nat Commun Date: 2012-01-24 Impact factor: 14.919
Authors: Mikkel Schubert; Hákon Jónsson; Dan Chang; Clio Der Sarkissian; Luca Ermini; Aurélien Ginolhac; Anders Albrechtsen; Isabelle Dupanloup; Adrien Foucal; Bent Petersen; Matteo Fumagalli; Maanasa Raghavan; Andaine Seguin-Orlando; Thorfinn S Korneliussen; Amhed M V Velazquez; Jesper Stenderup; Cindi A Hoover; Carl-Johan Rubin; Ahmed H Alfarhan; Saleh A Alquraishi; Khaled A S Al-Rasheid; David E MacHugh; Ted Kalbfleisch; James N MacLeod; Edward M Rubin; Thomas Sicheritz-Ponten; Leif Andersson; Michael Hofreiter; Tomas Marques-Bonet; M Thomas P Gilbert; Rasmus Nielsen; Laurent Excoffier; Eske Willerslev; Beth Shapiro; Ludovic Orlando Journal: Proc Natl Acad Sci U S A Date: 2014-12-15 Impact factor: 11.205
Authors: Beatrice A McGivney; Paul A McGettigan; John A Browne; Alexander C O Evans; Rita G Fonseca; Brendan J Loftus; Amanda Lohan; David E MacHugh; Barbara A Murphy; Lisa M Katz; Emmeline W Hill Journal: BMC Genomics Date: 2010-06-23 Impact factor: 3.969
Authors: Ryan Doan; Noah D Cohen; Jason Sawyer; Noushin Ghaffari; Charlie D Johnson; Scott V Dindot Journal: BMC Genomics Date: 2012-02-17 Impact factor: 3.969
Authors: Jessica L Petersen; James R Mickelson; Aaron K Rendahl; Stephanie J Valberg; Lisa S Andersson; Jeanette Axelsson; Ernie Bailey; Danika Bannasch; Matthew M Binns; Alexandre S Borges; Pieter Brama; Artur da Câmara Machado; Stefano Capomaccio; Katia Cappelli; E Gus Cothran; Ottmar Distl; Laura Fox-Clipsham; Kathryn T Graves; Gérard Guérin; Bianca Haase; Telhisa Hasegawa; Karin Hemmann; Emmeline W Hill; Tosso Leeb; Gabriella Lindgren; Hannes Lohi; Maria Susana Lopes; Beatrice A McGivney; Sofia Mikko; Nicholas Orr; M Cecilia T Penedo; Richard J Piercy; Marja Raekallio; Stefan Rieder; Knut H Røed; June Swinburne; Teruaki Tozaki; Mark Vaudin; Claire M Wade; Molly E McCue Journal: PLoS Genet Date: 2013-01-17 Impact factor: 5.917