Rationale: Lameness affects 36% of dairy cows in the US (USDA, 2016) and is associated with
reduced milk production and premature culling, raising welfare and economic concerns.
Lameness is often caused by claw lesions that manifest as open wounds in the soft tissue behind
the hoof, commonly foot warts (FW) and sole ulcers (SU). To enable genomic selection against
these conditions, the genomic regions that govern susceptibility to FW and SU must first be
Research question: What are the genomic regions associated with increased susceptibility to
FW and SU in dairy cattle?
Significance: Genomic regions identified in this study can be incorporated into selection indices,
allowing producers to select for cows genetically protected against FW and SU. These future
generations of healthier and more efficient cows will produce milk with a smaller environmental
Methods: DNA from healthy and affected cows will be used in a genome wide association
(GWA) analysis for identification of chromosomal regions linked to case/control status. We will
use hoof scoring phenotypes and genotypes from the high-density single nucleotide
polymorphism (SNP) panel (800K SNPs) using FW n = 30, SU n = 30, controls n = 60. The
GWA will be performed using stratified allelic and pedigree-based association.
Expected outcomes: Because FW and SU are complex traits dictated by many loci (van der
Spek et al., 2015), stratified allelic and pedigree-based GWA analyses will likely reveal multiple
chromosomal regions associated with the affected status. Despite the different analyses, we
expect some of the chromosomal regions from the different analyses will agree, validating those
Outreach: We will communicate our findings directly with local producers at conferences and
vicariously through the extension team in the Animal Science Department at the University of
California, Davis. We will also share our findings through trade publications.
The broad, long-term goal of this proposal is to reduce the incidence of lameness and
improve hoof health of Holstein dairy cattle. Thus, the objectives for this proposal are to:
- Perform a genome wide association analysis to identify QTL regions contributing to
genetic susceptibility to FW and SU
- Utilize the the SNPs of largest effect to develop a predictive model to reduce
susceptibility to FW or SU that can be used in conjunction with the current selection tools
for milk production attributes to reduce the prevalence of FW and SU within a herd while
maintaining overall milk quantity and quality
We are currently working to complete our first objective: expanding the genome-wide association study (GWAS) to identify quantitative trait loci (QTL) contributing to susceptibility to foot warts (FW) and sole ulcers (SU) in Holstein cattle. We are concurrently collecting additional blood samples with the generous help of producers and veterinarians, extracting DNA from blood samples, and analyzing the SNP genotyping data as we receive it.
Summary of progress to date
After dairy producers granted us access to hoof trimming records, we used these records to select cases and controls: cases were defined as cows that had at least two episodes of FW or SU, while controls were cows that have never had any lameness event (FW, SU, laminitis, sole hemorrhage, white line abscess, sole abscess, sole fracture, wall abscess, or foot rot) and were at least seven years old. We had initially proposed to sample 30 FW cases, 30 SU cases, and 60 controls for a total of 120 cows. Since then, we were able to leverage additional samples, allowing us to increase the number of control cows sampled. With the invaluable help and expertise of the dairy producers, we have collected 96 additional whole blood samples (SU n = 7, controls n = 89) from one dairy, will receive 35 samples (FW n = 37, SU n = 5; 2 cows have both FW and SU) from a second dairy next week, and are in contact with a third dairy affected with SU to begin sample collection in the near future. All of three dairies reside in the Central Valley of California, which minimizes inconsistencies in phenotyping due to environmental variance.
For the 96 samples we have received thus far, we centrifuged the whole blood and extracted DNA from the resulting buffy coat using the QIAGEN QIAamp® DNA Blood Mini Kit (QIAGEN Inc., Valencia, CA). We then checked the quality and quantity of DNA using the NanoDrop® (ND-1000 v3.2.1) spectrophotometer (Thermo Scientific, Wilmington, DE) and submitted the DNA samples for SNP genotyping using the BovineHD BeadChip (Illumina Inc., San Diego, CA) at GeneSeek (Lincoln, NE). Illumina’s GenCall algorithm was used to call SNP genotypes.
Upon receiving SNP genotype data, we merged the 96 samples with our existing sample pool for a total of 161 cows (FW n = 46, SU n = 28, controls n = 95; 7 cows had both FW and SU) and performed quality filtering using PLINK 1.9 (Chang et al., 2015, Purcell and Chang, 2015) to remove SNPs and individuals with genotyping rates less than 95%. SNPs with minor allele frequencies less than 5% were also removed to exclude rare variants. SNPs that yielded genotypic frequencies deviating from Hardy-Weinberg equilibrium in controls (p < 10-6) were also removed to exclude SNPs with homozygote frequencies that were higher than expected due to genotyping errors. To identify SNPs that segregate strongly with case/control phenotype, genome-wide association analysis was performed using two methods of correcting for population stratification: stratified allelic case-control association and a linear mixed model. Stratified allelic case control association was performed in PLINK 1.9, using the Cochran-Mantel-Haenszel (CMH) p-value to quantify significance of association. Analysis using a linear mixed model was performed using Genome-wide Efficient Mixed Model Association (GEMMA) (Zhou and Stephens, 2012) to test for association of SNP genotypes with FW and SU status. GEMMA fitted a LMM to associate SNP genotypes with phenotype while accounting for population stratification. The LMM included a genetic relatedness matrix as a covariate to correct for population stratification during association testing.
Chang, C. C., C. C. Chow, L. C. Tellier, S. Vattikuti, S. M. Purcell, and J. J. Lee. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4(1):7.
Purcell, S. and C. Chang. 2015. PLINK 1.9. https://www.cog-genomics.org/plink2.
Zhou, X. and M. Stephens. 2012. Genome-wide efficient mixed-model analysis for association studies. Nature genetics 44(7):821-824.
Preliminary GWA analyses using stratified allelic association and a linear mixed model revealed multiple isolated SNPs and peaks comprised of SNPs that were significantly associated with case/control status for both FW and SU. This was expected given the complexity of these traits (i.e. multiple loci contribute to the case/control phenotype). Despite using two different methods of population stratification correction, we expected some significantly associated SNPs to agree between the two approaches, validating those SNPs.
For FW, three SNPs on Bos taurus autosomes (BTA) 1, 2, and 6 were significantly associated with case/control status using stratified allelic association (Fig. 1a) and the linear mixed model approach (Fig. 1b). The significant SNP on BTA1 was downstream of a component of the NFκB signaling pathway that is essential for regulating the immune response to infection. Furthermore, NFκB is downregulated in macrophages exposed to treponemes, a causative factor in the condition (Zuerner et al., 2007, Wilson-Welder et al., 2015). The SNP on BTA2 fell in an intron of a protein involved in regulating cell proliferation, differentiation, and survival which may play a role in the excessive proliferation of keratinocytes during treponeme infection, which eventually develops into a FW. The significant SNP on BTA6 fell in an intron of a protein involved in processing the oxytocin precursor found to be under strong selection in Holsteins likely because this gene is associated with milk production traits (Xu et al., 2015).
Although these three SNPs seemed promising for FW, the analyses did not indicate that neighboring SNPs were significantly segregating with phenotype. The isolation of these SNPs could be due to random chance (i.e. because the SNP was randomly segregating with the phenotype, neighboring SNPs are not expected to follow the same segregation pattern) or the nature of these two GWA analyses. Both methods test for significance of association one marker at a time and assume each marker is independent of all other markers, which is not necessarily true for neighboring markers in linkage disequilibrium with a given marker. To take advantage of and account for this linkage disequilibrium, we are currently performing haplotype analysis in Haploview (Barrett et al., 2004) and fastPHASE (Scheet and Stephens, 2006) because haplotypes can be more strongly associated with phenotype than any single marker. Haplotype analysis will discern whether these three significant SNPs were random associations or true associations whose significance was dampened by the polygenic nature of FW.
For SU, significant SNPs detected using stratified allelic (Fig. 2a) and linear mixed model association (Fig. 2b) were not as concordant between the two methods, likely due to the smaller number of SU cases sampled thus far. Stratified allelic association detected a 63 kb region on BTA13 approaching genome-wide significance containing genes of various functions, including RNA processing, translation, and cell proliferation. The linear mixed model method revealed a 1.8 kb genome-wide significant region on BTA2 containing a gene encoding a protein involved in insulin resistance. Insulin resistance during the peripartum period impedes proper horn growth and predisposes cows to developing sole ulcers (Tomlinson et al., 2004).
Although these SNPs tag promising candidate genes, these results are preliminary; therefore we need to expand our GWAS with a stronger emphasis on collecting SU cases to refine regions of association. If stratified allelic and linear mixed model association yield discordant results despite the larger sample size, we will continue with the linear mixed model approach because it has been shown to more effectively account for population stratification due to familial structure compared to stratified association analysis (Price et al., 2010). In the dairy industry, familial structure is extremely prevalent due to the widespread use of elite sires across thousands of cows through artificial insemination.
Once we are confident in our identification of associated markers, we will disseminate our findings through outreach and extension as we have previously proposed, with the end goal of giving producers the tools to select against FW and SU in their herds.
Barrett, J. C., B. Fry, J. Maller, and M. J. Daly. 2004. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21(2):263-265.
Price, A. L., N. A. Zaitlen, D. Reich, and N. Patterson. 2010. New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics 11(7):459-463.
Scheet, P. and M. Stephens. 2006. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. The American Journal of Human Genetics 78(4):629-644.
Tomlinson, D., C. Mülling, and T. J. J. o. d. s. Fakler. 2004. Invited review: formation of keratins in the bovine claw: roles of hormones, minerals, and vitamins in functional claw integrity. 87(4):797-809.
Wilson-Welder, J. H., D. P. Alt, and J. E. Nally. 2015. Digital Dermatitis in Cattle: Current Bacterial and Immunological Findings. Animals (Basel) 5(4):1114-1135.
Xu, L., D. M. Bickhart, J. B. Cole, S. G. Schroeder, J. Song, C. P. Tassell, T. S. Sonstegard, and G. E. Liu. 2015. Genomic signatures reveal new evidences for selection of important traits in domestic cattle. Mol Biol Evol 32(3):711-725.
Zuerner, R. L., M. Heidari, M. K. Elliott, D. P. Alt, and J. D. Neill. 2007. Papillomatous digital dermatitis spirochetes suppress the bovine macrophage innate immune response. Vet Microbiol 125(3-4):256-264.
Educational & Outreach Activities
We have yet to finish sample collection and genome wide association analyses before we can present our findings to producers and other stakeholders, hence the lack of education and outreach thus far.