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Background: For centuries, morphology has been the most commonly selected trait in horses. A 3D video recording enabled us to obtain the coordinates of 43 anatomical landmarks of 2089 jumping horses. Generalized Procrustes analysis provided centered and scaled coordinates that were independent of volume, i.e., centroid size. Genetic analysis of these coordinates (mixed model; 17,994 horses in the pedigree) allowed us to estimate a variance-covariance matrix. New phenotypes were then defined: the "summarized shapes". They were obtained by linear combinations of Procrustes coordinates with, as coefficients, the eigenvectors of the genetic variance-covariance matrix. These new phenotypes were used in genome-wide association analyses (GWAS) and multitrait genetic analysis that included judges' scores and competition results of the horses., Results: We defined ten shapes that represented 86% of the variance, with heritabilities ranging from 0.14 to 0.42. Only one of the shapes was found to be genetically correlated with competition success (r g  = - 0.12, standard error = 0.07). Positive and negative genetic correlations between judges' scores and shapes were found. This means that the breeding objective defined by judges involves improvement of anatomical parts of the body that are negatively correlated with each other. Known single nucleotide polymorphisms (SNPs) on chromosomes 1 and 3 for height at withers were significant for centroid size but not for any of the shapes. As these SNPs were not associated with the shape that distinguished rectangular horses from square horses (with height at withers greater than body length), we hypothesize that these SNPs play a role in the overall development of horses, i.e. in height, width, and length but not in height at withers when standardized to unit centroid size. Several other SNPs were found significant for other shapes., Conclusions: The main application of 3D morphometric analysis is the ability to define the estimated breeding value (EBV) of a sire based on the shape of its potential progeny, which is easier for breeders to visualize in a single synthetic image than a full description based on linear profiling. However, the acceptance of these new phenotypes by breeders and the complex nature of summarized shapes may be challenging. Due to the low genetic correlations of the summarized shapes with jumping performance, the methodology did not allow indirect performance selection criteria to be defined., (© 2023. ’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE).)

Functional longevity is essential for the well-being of horses and the satisfaction of riders. Conventional selection using longevity breeding values calculated from competition results is not efficient because it takes too long to obtain reliable information. Therefore, the objective was to identify early criteria for selection. We assessed two types of early criteria: gait traits of young horses and QTLs. Thus, our aim was to estimate the genetic correlation between gait traits and longevity and to perform a genome-wide association study (GWAS) for longevity. Measurements of gaits by accelerometry were recorded on 1,477 show jumping horses that were 4 to 5 years old. Gait analysis provided 9 principal components describing trot, canter, and walk. Longevity estimated breeding values (EBVs) for stallions were calculated using a survival analysis of more than 900,000 years of performances by 179,448 show jumping horses born from 1981 onwards. Longevity was measured as the number of years spent in competition. Model included region and month of birth, age at first competition, year, and performance level. Longevity EBVs were deregressed to obtain weighted pseudo-performances for 1,968 stallions. Genomic data were available for 3,658 jumping horses. Seventy-eight percent of the horses measured for gaits and twenty-five percent of those measured for longevity were genotyped. A GWAS of longevity revealed no significant QTLs. Genetic parameters between each of the 9 principal components of the gait variables and longevity were evaluated with a bi-trait animal linear mixed model using single-step GBLUP analysis with the relationship matrix constructed from genomic data and genealogy (24,448 ancestors over four generations). The heritability of the gait traits varied from 0.11 to 0.44. The third principal component for trot (high lateral activity) and the first principal component for canter (high dorsoventral activity and low stride frequency) were moderately genetically correlated with higher longevity: r g = 0.38 (0.15) and 0.28 (0.13), respectively. Our study revealed that functional longevity is a polygenic trait with no major genes. We found new correlations between longevity and gait traits. Before using gait characteristics in a selection plan, these correlations need to be understood better at the biomechanical level., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dugué, Dumont Saint Priest, Crichan, Danvy and Ricard.)

The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event. A GWAS was performed on residuals with the fixed effect of each SNP. The GWAS did not reveal other QTLs for gait traits than the one related to the height at withers. The accelerometric principal components were highly heritable for the one linked to stride frequency and dorsoventral displacement at trot (0.53) and canter (0.41) and moderately for the one linked to longitudinal activities (0.33 for trot, 0.19 for canter). Low heritabilities were found for the walk traits. The genetic correlations of the accelerometric principal components with the jumping competition were essentially nil, except for a negative correlation with longitudinal activity at canter (-0.19). The genetic correlation between the judges' scores and the jumping competition reached 0.45 for canter (0.31 for trot and 0.17 for walk). But these correlations turned negative when the scores were corrected for the known parental breeding value for competition at the time of the judging. In conclusion, gait traits were not helpful to select for jumping performances. Different gaits may be suitable for a good jumping horse., Competing Interests: The authors have declared that no competing interests exist.

The aim of this study was to evaluate the genetic component of the locomotor jumping ability, via a wearable accelerometer sensor, and to estimate the genetic correlation with performance in competition, to introduce such criteria in selection schema. A sample of 1,056 young 3-year-old horses were equipped with a 3-dimensional accelerometer during a free jumping test, in regular breeding shows from 2015 to 2017. Seven variables were extracted from the dorso-ventral acceleration curve for the last three jumps over a double bar obstacle of 1.15 m for the front pole and 1.20 m for the back pole with a 1.20 m spread. Variables were the peaks of forelimbs, hindlimbs, and landing acceleration, the duration between peaks at take-off, the peak of forelimb acceleration and start of jump, jump duration and duration between the beginning of the impact of forelimbs and the peak at landing. During breeding shows, judges scored balance, strength, style, and reactivity for free jumping and jumping tests under saddle. Jumping competition results were recorded by logarithm of the sum of points earned in each competition. All horses in official competitions were included, i.e., 160,257 horses born in 1997 with a total of 649,491 annual performances. An animal mixed model with complete pedigree over four generations (353,236 horses) were used with fixed effects of jumping test location and date, morning/afternoon, gender, month of birth, rank of jump for accelerometric data, effect of year of competition, combined with age and gender for competition results. As a result, jump duration was the most heritable and repeatable for jump variables: h 2 = 0.16 (0.06), r = 0.52 (0.02), while accelerations were moderately heritable ( h 2 = 0.05-0.09, r = 0.39-0.51). Judgement scores were heritable: 0.21 (0.07)-0.33 (0.09) and were highly correlated. Scores during free jumping were genetically correlated to jump duration: 0.71 (0.15)-0.88 (0.16). Both jump duration and judgement scores were genetically correlated to competition performance: 0.59 (0.13) for jump duration, from 0.60 (0.11) to 0.77 (0.12) for scores. Jump duration and judgement scores can be used as early selection criteria. The advantage of the accelerometric measurement is its objectivity and the ease of recording., (Copyright © 2020 Ricard, Dumont Saint Priest, Danvy and Barrey.)

Genotype imputation is now a key component of genomic analyses as it increases the density of available genotypes within a population. However, many factors can influence imputation accuracy. The aim of this study was to assess and compare the accuracy of imputation of high-density genotypes (Affymetrix Axiom Equine genotyping array, 670,806 SNPs) from two moderate-density genotypes (Illumina Equine SNP50 BeadChip, 54,602 SNPs and Illumina Equine SNP70 BeadChip, 65,157 SNPs), using single-breed or multiple-breed reference sets. Genotypes were available from five groups of horse breeds: Arab (AR, 1,207 horses), Trotteur Français (TF, 979 horses), Selle Français (SF, 1,979 horses), Anglo-Arab (AA, 229 horses) and various foreign sport horses (FH, 209 horses). The proportions of horses genotyped with the high-density (HD) chip in each breed group were 10% in AA, 15% in AR and FH, 30% in TF and 57% in SF. A validation set consisting of one-third of the horses genotyped with the HD chip was formed and their genotypes deleted. Two imputation strategies were compared, one in which the reference population consisted only of horses from the same breed group as in the validation set, and another with horses from all breed groups. For the first strategy, concordance rates (CRs) ranged from 97.8% (AR) to 99.0% (TF) and correlations (r²) from 0.94 (AR) to 0.99 (TF). For the second strategy, CR ranged from 97.4% (AR) to 98.9% (TF) and r² from 0.93 (AR) to 0.99 (TF). Overall, the results show a small advantage of within-breed imputation compared with multi-breed imputation. Adding horses from different breed groups to the reference population does not improve the accuracy of imputation. Imputation provides an accurate means of combining data sets from different genotyping platforms, now necessary with the increasing use of the recently developed Affymetrix Axiom Equine genotyping array., (© 2018 The Authors. Journal of Animal Breeding and Genetics Published by Blackwell Verlag GmbH.)

Since horse breeds constitute populations submitted to variable and multiple outcrossing events, we analyzed the genetic structure and gene flows considering horses raised in France. We used genealogical data, with a reference population of 547,620 horses born in France between 2002 and 2011, grouped according to 55 breed origins. On average, individuals had 6.3 equivalent generations known. Considering different population levels, fixation index decreased from an overall species FIT of 1.37%, to an average [Formula: see text] of -0.07% when considering the 55 origins, showing that most horse breeds constitute populations without genetic structure. We illustrate the complexity of gene flows existing among horse breeds, a few populations being closed to foreign influence, most, however, being submitted to various levels of introgression. In particular, Thoroughbred and Arab breeds are largely used as introgression sources, since those two populations explain together 26% of founder origins within the overall horse population. When compared with molecular data, breeds with a small level of coancestry also showed low genetic distance; the gene pool of the breeds was probably impacted by their reproducer exchanges.

Background: Effective population sizes of 140 populations (including 60 dog breeds, 40 sheep breeds, 20 cattle breeds and 20 horse breeds) were computed using pedigree information and six different computation methods. Simple demographical information (number of breeding males and females), variance of progeny size, or evolution of identity by descent probabilities based on coancestry or inbreeding were used as well as identity by descent rate between two successive generations or individual identity by descent rate., Results: Depending on breed and method, effective population sizes ranged from 15 to 133 056, computation method and interaction between computation method and species showing a significant effect on effective population size (P < 0.0001). On average, methods based on number of breeding males and females and variance of progeny size produced larger values (4425 and 356, respectively), than those based on identity by descent probabilities (average values between 93 and 203). Since breeding practices and genetic substructure within dog breeds increased inbreeding, methods taking into account the evolution of inbreeding produced lower effective population sizes than those taking into account evolution of coancestry. The correlation level between the simplest method (number of breeding males and females, requiring no genealogical information) and the most sophisticated one ranged from 0.44 to 0.60 according to species., Conclusions: When choosing a method to compute effective population size, particular attention should be paid to the species and the specific genetic structure of the population studied.