Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism

Abstract

Endurance horses are able to run at more than 20 km/h for 160 km (in bouts of 30–40 km). This level of performance is based on intense aerobic metabolism, effective body heat dissipation and the ability to endure painful exercise. The known heritabilities of endurance performance and exercise-related physiological traits in Arabian horses suggest that adaptation to extreme endurance exercise is influenced by genetic factors. The objective of the present genome-wide association study (GWAS) was to identify single nucleotide polymorphisms (SNPs) related to endurance racing performance in 597 Arabian horses. The performance traits studied were the total race distance, average race speed and finishing status (qualified, eliminated or retired). We used three mixed models that included a fixed allele or genotype effect and a random, polygenic effect. Quantile-quantile plots were acceptable, and the regression coefficients for actual vs. expected log10 p-values ranged from 0.865 to 1.055. The GWAS revealed five significant quantitative trait loci (QTL) corresponding to 6 SNPs on chromosomes 6, 1, 7, 16, and 29 (two SNPs) with corrected p-values from 1.7 × 10−6 to 1.8 × 10−5. Annotation of these 5 QTL revealed two genes: sortilin-related VPS10-domain-containing receptor 3 (SORCS3) on chromosome 1 is involved in protein trafficking, and solute carrier family 39 member 12 (SLC39A12) on chromosome 29 is active in zinc transport and cell homeostasis. These two coding genes could be involved in neuronal tissues (CNS). The other QTL on chromosomes 6, 7, and 16 may be involved in the regulation of the gene expression through non-coding RNAs, CpG islands and transcription factor binding sites. On chromosome 6, a new candidate equine long non-coding RNA (KCNQ1OT1 ortholog: opposite antisense transcript 1 of potassium voltage-gated channel subfamily Q member 1 gene) was predicted in silico and validated by RT-qPCR in primary cultures of equine myoblasts and fibroblasts. This lncRNA could be one element of the cardiac rhythm regulation. Our GWAS revealed that equine performance during endurance races is a complex polygenic trait, and is partially governed by at least 5 QTL: two coding genes involved in neuronal tissues and three other loci with many regulatory functions such as slowing down heart rate.

A genome-wide association study for harness racing success in the Norwegian-Swedish coldblooded trotter reveals genes for learning and energy metabolism

“…Regions on ECA1, ECA7, and ECA16 have also previously been described as important for endurance performance traits, while regions on ECA14 and ECA18 associated with gallop racing in other studies do not appear to play a significant role in harness racing [ 4 – 8 , 10 – 15 , 19 – 21 , 39 , 66 ]. This likely suggest a greater demand for endurance in harness racing compared to gallop racing and is perhaps a sign of the different physiological demands for speed in trot versus speed in gallop.…”

“…Despite the fact that harness racing success is of high economic importance to the global equine industry, genome scans for performance appear to predominantly target Thoroughbreds and sport horses (e.g. Warmbloods) [ 4 – 6 , 8 – 14 , 39 – 41 ]. Most studies involving harness racing breeds tend to focus on detecting genes underlying certain conditions and diseases [ 42 – 47 ].…”

A genome-wide scan for diversifying selection signatures in selected horse breeds

“…Moreover, our results obtained for the Arabian horses which dominate in endurance riding and racing [50] pinpointed genes classified as involved in vascular smooth muscle contraction (ADCY1 ), taurine and hypotaurine metabolism ( GAD1 ) as well as in oxidative phosphorylation ( COX4I1 ), that is processes with clear implications for racing and athletic performance. It is well known that blood flow through the muscle (conditioned inter alia by vascular smooth muscle contraction) can change over time and highly increase during maximal exercise [51–53].…”

Significance testing and genomic inflation factor using high‐density genotypes or whole‐genome sequence data

“…The family‐wise type 1 error rate is usually controlled by applying a Bonferroni correction that adjusts the significance threshold by the number of independent tests. In livestock, the number of independent tests is usually defined as the total number of SNPs or as the number of independent chromosome fragments, defined as regions of the genome that explain unique genetic variation (Duggal, Gillanders, Holmes, & Bailey‐Wilson, ; Ricard et al, ). Using the total number of SNPs can result in too conservative thresholds because it violates the assumption of independency between tests (Duggal et al, ; Nicodemus, Liu, Chase, Tsai, & Fallin, ).…”