Lameness is an animal welfare issue that incurs substantial financial and environmental costs. This condition is commonly caused by digital dermatitis (DD), sole ulcers (SU), and white line disease (WLD). Susceptibility to these three foot disorders is due in part to genetics, indicating that genomic selection against these foot lesions can be used to reduce lameness prevalence. It is unclear whether selection against foot lesions will lead to increased susceptibility to other common diseases such as mastitis and metritis. Thus, the aim of this study was to determine the genetic correlation between causes of lameness and other common health disorders to identify loci contributing to the correlation. Genetic correlation estimates between SU and DD and between SU and WLD were significantly different from zero (P < 0.05), whereas estimates between DD and mastitis, DD and milk fever, and SU and metritis were suggestive (P < 0.1). All five of these genetic correlation estimates were positive. Two-trait genome-wide association studies (GWAS) for each of these five pairs of traits revealed common regions of association on BTA1 and BTA8 for pairs that included DD or SU as one of the traits, respectively. Other regions of association were unique to the pair of traits and not observed in GWAS for other pairs of traits. The positive genetic correlation estimates between foot disorders and other health disorders imply that selection against foot disorders may also decrease susceptibility to other health disorders. Linkage disequilibrium blocks defined around significant and suggestive SNPs from the two-trait GWAS included genes and QTL that were functionally relevant, supporting that these regions included pleiotropic loci.
Overall design
Whole blood was collected from 417 cows from the coccygeal vein in the tail. Blood samples were centrifuged, and genomic DNA was extracted from the resulting buffy coat layer using the QIAGEN QIAamp DNA Blood Mini Kit . DNA samples were genotyped on the Illumina BovineHD BeadChip, and Illumina’s GenCall algorithm was used to call genotypes. Of the 417 samples, eight were removed during quality filtering, leaving 409 cows for analysis.
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