The evolving role of Fourier-transform midinfrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years,significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs.Technological progress has enabled a shift from labour intensive,on-farm collection and processing of samples that assess yield and fat levels in milk,to large-scale processing of samples through centralised laboratories,with the scope extended to include quantification of other traits.Fourier-transform midinfrared(FT-MIR)spectroscopy has had a significant role in the transformation of milk composition phenotyping,with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally.Increasingly,there is interest in analysing the individual FT-MIR wavenumbers,and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs.This includes traits related to the nutritional value of milk,the processability of milk into products such as cheese,and traits relevant to animal health and the environment.The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits,and the genetic correlations between the FT-MIR predicted and actual trait values.Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis.The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes.Additionally,there are other molecular phenotypes such as those related to the metabolome,chromatin accessibility,and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest.Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets,and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Genome-wide association studies of lactation yields of milk, fat, protein and somatic cell score in New Zealand dairy goats

Background: Identifying associations between genetic markers and traits of economic importance will provide practical benefits for the dairy goat industry, enabling genomic prediction of the breeding value of individuals, and facilitating discovery of the underlying genes and mutations. Genome-wide association studies were implemented to detect genetic regions that are significantly associated with effects on lactation yields of milk(MY), fat(FY),protein(PY) and somatic cell score(SCS) in New Zealand dairy goats.Methods: A total of 4,840 goats were genotyped with the Caprine 50 K SNP chip(Illumina Inc., San Diego, CA).After quality filtering, 3,732 animals and 41,989 SNPs were analysed assuming an additive linear model. Four GWAS models were performed, a single-SNP additive linear model and three multi-SNP Bayes C models. For the single-SNP GWAS, SNPs were fitted individually as fixed covariates, while the Bayes C models fit all SNPs simultaneously as random effects. A cluster of significant SNPs were used to define a haplotype block whose alleles were fitted as covariates in a Bayesian model. The corresponding diplotypes of the haplotype block were then fit as class variables in another Bayesian model.Results: Across all four traits, a total of 43 genome-wide significant SNPs were detected from the SNP GWAS. At a genome-wide significance level, the single-SNP analysis identified a cluster of variants on chromosome 19 associated with MY, FY, PY, and another cluster on chromosome 29 associated with SCS. Significant SNPs mapped in introns of candidate genes(45%), in intergenic regions(36%), were 0–5 kb upstream or downstream of the closest gene(14%) or were synonymous substitutions(5%). The most significant genomic window was located on chromosome 19 explaining up to 9.6% of the phenotypic variation for MY, 8.1% for FY, 9.1% for PY and 1% for SCS.Conclusions: The quantitative trait loci for yield traits on chromosome 19 confirms reported findings in other dairy goat populations. There is benefit to be gained from using these results for genomic selection to improve milk production in New Zealand dairy goats.

Genome scan of pigmentation traits in Friesian-Jersey crossbred cattle

Pigmentation traits expressed in animals are visual characteristics that allow us to distinguish between breeds and between strains within breed. The objective of this study was to map quantitative trait loci （QTLs） affecting the pigmentation traits in approximately 800 F2 grand daughter dairy cattle from a Holstein-Friesian and Jersey cross breed cattle. Traits analyzed included pigmentation phenotypes on the body, teat and hoop. The phenoypes were collected from digital photos or visual inspection of live animals. QTL mapping was implemented using half-sib and line-of-descent inheritance models. Our analysis initially detected a number of significant QTLs on chromosomes： 2, 6, 13, 15, 18 and 22. The significant QTLs were divided into two groups： one group influencing the pigmentation color and the other group affecting the absence or level of pigmentation. The most significant QTL peaks were observed on Bovine taurus autosome 18 （BTA18） close to melanocortin 1 receptor （MC1R） for the color traits, on BTA6 close to the receptor tyrosine kinase （K/T） and BTA22 close to microphthalmia-associated transcription factor （M/TF） gene for the spotting traits. Association studies were conducted for candidate regions or genes known to affect pigmentation in dairy cattle.