Genetic Factors Underlying Disease and Performance Traits in Standardbreds

Abstract

Many diseases and performance characteristics of the horse are considered to be "complex" traits because they are influenced by both genetic and environmental factors. Furthermore, many are polygenic in nature, reflecting the combined effects of multiple genes. Traditional methodological approaches, such as family linkage analysis and candidate gene sequencing are not ideal for identifying the multiple interacting alleles underlying complex/polygenic traits. An alternative investigational approach is needed that can account for environmental risk factors, issues related to population structure in large study cohorts, and epistatic interactions. In the work presented here, whole-genome approaches, including genome-wide association (GWA) analysis, whole-genome sequencing (WGS), and high-throughput genotyping, were used to investigate the genetic factors underlying three complex traits in Standardbred horses, a breed primarily used for harness racing. These were 1) osteochondrosis (OC; a disease of young horses in which the cartilage at the end of long bones does not form normally); 2) pacing (an alternative pattern of locomotion); and 3) performance (using speed as the phenotype). GWA analysis identified chromosomal regions of association for all three traits of interest, although the significance of the findings for speed was marginal, reflecting the challenge of appropriately phenotyping a complex trait such as performance. WGS performed in eighteen horses identified thousands of variants within chromosomal regions of association identified for OC and pacing, of which a small fraction were predicted to have functional effect. These variants were prioritized and a subset was selected for high-throughput genotyping in the study cohorts (180 horse phenotyped for OC, 500 phenotyped for gait). A few of the markers selected for OC were moderately associated with disease status, while the majority of the markers selected for gait were highly associated with this trait. A crucial next step for interpreting these data will be trying to understand the potential interactions between markers, using a combination of pathway analysis and random forest analysis. Knowledge of gene variants that affect complex traits in the horse - and how they interact with each other - may help reduce the incidence of disease and assist selection for desirable characteristics.