• Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk

      McParland, Sinead; Kennedy, Emer; Lewis, Eva; Moore, Stephen; McCarthy, Brian; O'Donovan, Michael; Berry, Donagh; Department of Agriculture, Food and the Marine, Ireland; European Commission; Marie Curie project International Research Staff Exchange Scheme SEQSEL; et al. (Elsevier for American Dairy Science Association, 2014-12)
      Energy balance (EB) and energy intake (EI) are heritable traits of economic importance. Despite this, neither trait is explicitly included in national dairy cow breeding goals due to a lack of routinely available data from which to compute reliable breeding values. Mid-infrared (MIR) spectrometry, which is performed during routine milk recording, is an accurate predictor of both EB and EI. The objective of this study was to estimate genetic parameters of EB and EI predicted using MIR spectrometry. Measured EI and EB were available for 1,102 Irish Holstein-Friesian cows based on actual feed intake and energy sink data. A subset of these data (1,270 test-day records) was used to develop equations to predict EI, EB, and daily change in body condition score (ΔBCS) and body weight (ΔBW) using the MIR spectrum with or without milk yield also as a predictor variable. Accuracy of cross-validation of the prediction equations was 0.75, 0.73, 0.77, and 0.70 for EI, EB, ΔBCS, and ΔBW, respectively. Prediction equations were applied to additional spectral data, yielding up to 94,653 records of MIR-predicted EI, EB, ΔBCS, and ΔBW available for variance component estimation. Variance components were estimated using repeatability animal linear mixed models. Heritabilities of MIR-predicted EI, EB, ΔBCS, and ΔBW were 0.20, 0.10, 0.07, and 0.06, respectively; heritability estimates of the respective measured traits were 0.35, 0.16, 0.07, and 0.08, respectively. The genetic correlation between measured and MIR-predicted EI was 0.84 and between measured and MIR-predicted EB was 0.54, indicating that selection based on MIR-predicted EI or EB would improve true EI or EB. Genetic and phenotypic associations between EI and both the milk production and body-change traits were generally in agreement, regardless of whether measured EI or MIR-predicted EI was considered. Higher-yielding animals of higher body weight had greater EI. Predicted EB was negatively genetically correlated with milk yield (genetic correlation = −0.29) and positively genetically correlated with both milk fat and protein percent (genetic correlation = 0.17 and 0.16, respectively). Least squares means phenotypic EI of 198 animals stratified as low, average, and high estimated breeding values for MIR-predicted EI (animal phenotypes were not included in the genetic evaluation) were 154.3, 156.0, and 163.3 MJ/d, corroborating that selection on MIR-predicted EI will, on average, result in differences in phenotypic true EI.
    • Genomic prediction of crown rust resistance in Lolium perenne

      Arojju, Sai Krishna; Conaghan, Patrick; Barth, Susanne; Milbourne, Dan; Casler, M.D.; Hodkinson, Trevor R; Michel, Thibauld; Byrne, Stephen L.; Department of Agriculture, Food and the Marine, Ireland; Marie Sklodowska-Curie; et al. (Biomed Central, 29/05/2018)
      Background Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability. Results Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set. Conclusion Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.
    • Grazing of dairy cows on pasture versus indoor feeding on total mixed ration: Effects on low-moisture part-skim Mozzarella cheese yield and quality characteristics in mid and late lactation

      Gulati, Arunima; Galvin, Norann; Hennessy, Deirdre; McAuliffe, Stephen; O'Donovan, Michael; McManus, Jennifer J.; Fenelon, Mark; Guinee, Timothy P.; Department of Agriculture, Food and the Marine, Ireland; Dairy Levy Research Trust; et al. (Elsevier for American Dairy Science Association, 2018-08-16)
      This study investigated the effects of 3 dairy cow feeding systems on the composition, yield, and biochemical and physical properties of low-moisture part-skim Mozzarella cheese in mid (ML; May–June) and late (LL; October–November) lactation. Sixty spring-calving cows were assigned to 3 herds, each consisting of 20 cows, and balanced on parity, calving date, and pre-experimental milk yield and milk solids yield. Each herd was allocated to 1 of the following feeding systems: grazing on perennial ryegrass (Lolium perenne L.) pasture (GRO), grazing on perennial ryegrass and white clover (Trifolium repens L.) pasture (GRC), or housed indoors and offered total mixed ration (TMR). Mozzarella cheese was manufactured on 3 separate occasions in ML and 4 in LL in 2016. Feeding system had significant effects on milk composition, cheese yield, the elemental composition of cheese, cheese color (green to red and blue to yellow color coordinates), the extent of flow on heating, and the fluidity of the melted cheese. Compared with TMR milk, GRO and GRC milks had higher concentrations of protein and casein and lower concentrations of I, Cu, and Se, higher cheese-yielding capacity, and produced cheese with lower concentrations of the trace elements I, Cu, and Se and higher yellowness value. Cheese from GRO milk had higher heat-induced flow and fluidity than cheese from TMR milk. These effects were observed over the entire lactation period (ML + LL), but varied somewhat in ML and LL. Feeding system had little, or no, effect on gross composition of the cheese, the proportions of milk protein or fat lost to cheese whey, the texture of the unheated cheese, or the energy required to extend the molten cheese. The differences in color and melt characteristics of cheeses obtained from milks with the different feeding systems may provide a basis for creating points of differentiation suited to different markets.
    • Markers associated with heading and aftermath heading in perennial ryegrass full-sib families

      Arojju, Sai Krishna; Barth, Susanne; Milbourne, Dan; Conaghan, Patrick; Velmurugan, Janaki; Hodkinson, Trevor R; Byrne, Stephen L.; Department of Agriculture, Food and the Marine, Ireland; Teagasc Walsh Fellowship Programme; EU Marie-Sklodowska-Curie Fellowship; et al. (Biomed Central, 16/07/2016)
      Background Heading and aftermath heading are important traits in perennial ryegrass because they impact forage quality. So far, genome-wide association analyses in this major forage species have only identified a small number of genetic variants associated with heading date that overall explained little of the variation. Some possible reasons include rare alleles with large phenotypic affects, allelic heterogeneity, or insufficient marker density. We established a genome-wide association panel with multiple genotypes from multiple full-sib families. This ensured alleles were present at the frequency needed to have sufficient statistical power to identify associations. We genotyped the panel via partial genome sequencing and performed genome-wide association analyses with multi-year phenotype data collected for heading date, and aftermath heading. Results Genome wide association using a mixed linear model failed to identify any variants significantly associated with heading date or aftermath heading. Our failure to identify associations for these traits is likely due to the extremely low linkage disequilibrium we observed in this population. However, using single marker analysis within each full-sib family we could identify markers and genomic regions associated with heading and aftermath heading. Using the ryegrass genome we identified putative orthologs of key heading genes, some of which were located in regions of marker-trait associations. Conclusion Given the very low levels of LD, genome wide association studies in perennial ryegrass populations are going to require very high SNP densities. Single marker analysis within full-sibs enabled us to identify significant marker-trait associations. One of these markers anchored proximal to a putative ortholog of TFL1, homologues of which have been shown to play a key role in continuous heading of some members of the rose family, Rosaceae.