Heritability estimates for racing performance in Japanese Thoroughbred racehorses using linear and non-linear model analyses.

This study evaluated the differences between linear and non-linear modelled heritability estimates of racing performance based on lifetime earnings (LE) and lifetime ranking (LR) in Japanese Thoroughbred racehorses. The heritability estimate (h(2) = 0.25) obtained from a non-linear model based on formal Japan Racing Association ranking was much higher than that obtained from a linear model based on the original trait phenotype (h(2) = 0.11). The linear models showed slightly higher heritability estimates under the trait categorizations than under the original phenotypes, while the non-linear categorical trait models showed much higher heritability estimates than the linear models, especially for binary trait categorizations (h(2) = 0.34) with non-winning and winning horses. The binary trait categorizations were consistent with the case and control classifications in the previous genome-wide association study (GWAS), which identified possible sequence variants on ECA18 that affect racing performance in Japanese Thoroughbred racehorses. Those findings suggested that the different heritability estimates obtained from several trait categorizations would reflect the possible presence of susceptibility gene segregations in the analyzed population, indicating that heritability estimates from non-linear models are useful for the selection of case and control populations in GWAS.