Abstract
Sole ulcers (SUs) and white line disease (WLD) are two common noninfectious claw lesions (NICL) that arise due to a compromised horn production and are frequent causes of lameness in dairy cattle, imposing welfare and profitability concerns. Low to moderate heritability estimates of SU and WLD susceptibility indicate that genetic selection could reduce their prevalence. To identify the susceptibility loci for SU, WLD, SU and/or WLD, and any type of noninfectious claw lesion, genome-wide association studies (GWAS) were performed using generalized linear mixed model (GLMM) regression, chunk-based association testing (CBAT), and a random forest (RF) approach. Cows from five commercial dairies in California were classified as controls having no lameness records and ≥6 years old (n = 102) or cases having SU (n = 152), WLD (n = 117), SU and/or WLD (SU + WLD, n = 198), or any type of noninfectious claw lesion (n = 217). The top single nucleotide polymorphisms (SNPs) were defined as those passing the Bonferroni-corrected suggestive and significance thresholds in the GLMM analysis or those that a validated RF model considered important. Effects of the top SNPs were quantified using Bayesian estimation. Linkage disequilibrium (LD) blocks defined by the top SNPs were explored for candidate genes and previously identified, functionally relevant quantitative trait loci. The GLMM and CBAT approaches revealed the same regions of association on BTA8 for SU and BTA13 common to WLD, SU + WLD, and NICL. These SNPs had effects significantly different from zero, and the LD blocks they defined explained a significant amount of phenotypic variance for each dataset (6.1-8.1%, p < 0.05), indicating the small but notable contribution of these regions to susceptibility. These regions contained candidate genes involved in wound healing, skin lesions, bone growth and mineralization, adipose tissue, and keratinization. The LD block defined by the most significant SNP on BTA8 for SU included a SNP previously associated with SU. The RF models were overfitted, indicating that the SNP effects were very small, thereby preventing meaningful interpretation of SNPs and any downstream analyses. These findings suggested that variants associated with various physiological systems may contribute to susceptibility for NICL, demonstrating the complexity of genetic predisposition.