Fitting covariates representing the number of haplotype alleles rather than single nucleotide polymorphism (SNP) alleles may increase genomic prediction accuracy if linkage disequilibrium between quantitative trait loci and SNPs is inadequate. The objectives of this study were to evaluate the accuracy, bias and computation time of Bayesian genomic prediction methods that fit fixed-length haplotypes or SNPs. Genotypes at 37,740 SNPs that were common to Illumina BovineSNP50 and high-density panels were phased for ~58,000 New Zealand dairy cattle. Females born before 1 June 2008 were used for training, and genomic predictions for milk fat yield (n = 24,823), liveweight (n = 13,283) and somatic cell score (n = 24,864) were validated within breed (predominantly Holstein–Friesian, predominantly Jersey, or admixed KiwiCross) in later-born females. Covariates for haplotype alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated and rare haplotypes were removed at four thresholds (1, 2, 5 or 10%), resulting in 20 scenarios tested. Genomic predictions fitting covariates for either SNPs or haplotypes were calculated by using BayesA, BayesB or BayesN. This is the first study to quantify the accuracy of genomic prediction using haplotypes across the whole genome in an admixed population. A correlation of 0.349 ± 0.016 between yield deviation and genomic breeding values was obtained for milk fat yield in Holstein–Friesians using BayesA fitting covariates. Genomic predictions were more accurate with short haplotypes than with SNPs but less accurate with longer haplotypes than with SNPs. Fitting only the most frequent haplotype alleles reduced computation time with little decrease in prediction accuracy for short haplotypes. Trends were similar for all traits and breeds and there was little difference between Bayesian methods. Fitting covariates for haplotype alleles rather than SNPs can increase prediction accuracy, although it decreased drastically for long (>500 kb) haplotypes. In this population, fitting 250 kb haplotypes with a 1% frequency threshold resulted in the highest genomic prediction accuracy and fitting 125 kb haplotypes with a 10% frequency threshold improved genomic prediction accuracy with comparable computation time to fitting SNPs. This increased accuracy is likely to increase genetic gain by changing the ranking of selection candidates.

Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock, however, these profiles can be difficult and expensive to obtain for thousands of samples. The objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeK I or Pst I, and two bioinformatic pipelines (reference-based and reference-free) were compared to 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n=236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to sequencing. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of host genetics, laboratory and analytical method, and the genetic and phenotypic correlations with methane production. The results suggested that the best method was Pst I RE-RRS analyzed with the reference-free approach via a correspondence analysis, with estimates for repeatability of 0.62±0.06, heritability 0.31±0.29, and genetic and phenotypic correlation with methane emissions of 0.88±0.25 and 0.64±0.05 respectively for the first component of correspondence analysis. The reference-free approach assigned 62.0±5.7% of reads to common 65 bp tags, much higher than the reference-based approach of 6.8±1.8% of reads assigned. Sensitivity studies suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, therefore the current work of 118 samples/lane and future proposed 384 samples/lane are well within that threshold. Our approach is now being used to investigate host factors affecting the rumen and its association with a variety of production and environmental traits. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.