Identification of candidate novel production variants on the Bos taurus chromosome X.

Chromosome X is often excluded from bovine genetic studies due to complications caused by the sex specific nature of the chromosome. As chromosome X is the second largest cattle chromosome and makes up approximately 6% of the female genome, finding ways to include chromosome X in dairy genetic studies is important. Using female animals and treating chromosome X as an autosome, we performed X chromosome inclusive genome-wide association studies in the selective breeding environment of the New Zealand dairy industry, aiming to identify chromosome X variants associated with milk production traits. We report on the findings of these genome-wide association studies and their potential effect within the dairy industry. We identify missense mutations in the MOSPD1 and CCDC160 genes that are associated with decreased milk volume and protein production and increased fat production. Both of these mutations are exonic SNP that are more prevalent in the Jersey breed than in Holstein-Friesians. Of the 2 candidates proposed it is likely that only one is causal, though we have not been able to identify which is more likely.

The evolving role of Fourier-transform mid-infrared 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 mid-infrared (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.

Short communication: Identification of the pseudoautosomal region in the Hereford bovine reference genome assembly ARS-UCD1.2.

In cattle, the X chromosome accounts for approximately 3 and 6% of the genome in bulls and cows, respectively. In spite of the large size of this chromosome, very few studies report analysis of the X chromosome in genome-wide association studies and genomic selection. This lack of genetic interrogation is likely due to the complexities of undertaking these studies given the hemizygous state of some, but not all, of the X chromosome in males. The first step in facilitating analysis of this gene-rich chromosome is to accurately identify coordinates for the pseudoautosomal boundary (PAB) to split the chromosome into a region that may be treated as autosomal sequence (pseudoautosomal region) and a region that requires more complex statistical models. With the recent release of ARS-UCD1.2, a more complete and accurate assembly of the cattle genome than was previously available, it is timely to fine map the PAB for the first time. Here we report the use of SNP chip genotypes, short-read sequences, and long-read sequences to fine map the PAB (X chromosome:133,300,518) and simultaneously determine the neighboring regions of reduced homology and true pseudoautosomal region. These results greatly facilitate the inclusion of the X chromosome in genome-wide association studies, genomic selection, and other genetic analysis undertaken on this reference genome.

Bovine mammary gland X chromosome inactivation.

X chromosome inactivation (XCI) is a process by which 1 of the 2 copies of the X chromosomes present in female mammals is inactivated. The transcriptional silencing of one X chromosome achieves dosage compensation between XX females and XY males and ensures equal expression of X-linked genes in both sexes. Although all mammals use this form of dosage compensation, the complex mechanisms that regulate XCI vary between species, tissues, and development. These mechanisms include not only varying levels of inactivation, but also the nature of inactivation, which can range from being random in nature to driven by parent of origin. To date, no data describing XCI in calves or adult cattle have been reported and we are reliant on data from mice to infer potential mechanisms and timings for this process. In the context of dairy cattle breeding and genomic prediction, the implications of X chromosome inheritance and XCI in the mammary gland are particularly important where a relatively small number of bulls pass their single X chromosome on to all of their daughters. We describe here the use of RNA-seq, whole genome sequencing and Illumina BovineHD BeadChip (Illumina, San Diego, CA) genotypes to assess XCI in lactating mammary glands of dairy cattle. At a population level, maternally and paternally inherited copies of the X chromosome are expressed equally in the lactating mammary gland consistent with random inactivation of the X chromosome. However, average expression of the paternal chromosome ranged from 10 to 90% depending on the individual animal. These results suggest that either the mammary gland arises from 1 or 2 stem cells, or a nongenetic mechanism that skews XCI exists. Although a considerable amount of future work is required to fully understand XCI in cattle, the data reported here represent an initial step in ensuring that X chromosome variation is captured and used in an appropriate manner for future genomic selection.

Leveraging genetically simple traits to identify small-effect variants for complex phenotypes.

BACKGROUND: Polymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ2P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression). RESULTS: Quantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ2P for milk yield, and 0.11 σ2P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy. CONCLUSIONS: Milk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.