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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Cell Wall Biology and Utilization Research » Research » Publications at this Location » Publication #376330
Research Project: Investigating Microbial, Digestive, and Animal Factors to Increase Dairy Cow Performance and Nutrient Use Efficiency

Location: Cell Wall Biology and Utilization Research

Title: Reassembly of cattle immune gene clusters for quantitative analysis

Author
item Bickhart, Derek
item HEIMEIER, DOROTHEA - The Pirbright Institute
item SCHWARTZ, JOHN - The Pirbright Institute
item Heaton, Michael - Mike
item Cole, John
item HAMMOND, JOHN - The Pirbright Institute
item BAKSHI, KIRANMAYEE - Fujifilm/cellular Dynamics
item YOUNG, JULIANA - Former ARS Employee
item Smith, Timothy - Tim

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Objective Animal health is a critical component of dairy cattle productivity; however, current genomic selection genotyping tools have a paucity of genetic markers within key immune gene clusters (IGC) involved in the cattle innate and adaptive immune systems. We sought to assemble IGC haplotypes, identify single nucleotide polymorphisms (SNP) that distinguish each haplotype, and quantify their effect on animal health phenotypes. Methods Using de novo assemblies of unique IGC haplotypes and the newly released long-read cattle genome assembly (ARS-UCDv1.2) as our reference, we aligned whole genome shotgun reads from 125 Holstein bulls and identified candidate SNP markers. These variants were then used to create custom genotyping arrays to genotype a population of 1,800 Holstein cows with bovine tuberculosis resistance phenotypes and 90 beef calves persistently infected (PI) with bovine viral diarrhea virus, and 96 diverse beef cattle from 19 breeds. Phenotypic data was associated with custom genotype status using chi-square analysis of genotype contingency tables so as to assess relative risk of alleles. Results Alignment of whole genome shotgun data from 125 Holstein bulls to these alternative haplotypes revealed 55,410 SNPs; however, many of these variant sites were unsuitable for use on custom genotyping arrays. Using model-based and machine-learning approaches, we selected 124 of these markers for custom genotyping. We found that 105 (~85%) of our markers had genotype call-rates greater than 80% in the Holstein and beef cohorts. We identified two markers from a preliminary analysis of the BVD PI cohort with significant effects when one or two copies were present (relative risks of 4.26 and 0.13, respectively). Conclusions We demonstrate that our approach is suitable for identifying genetic markers in highly polymorphic regions of the cattle genome.