Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows

Abstract

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10−6 were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.

GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle

“…The largest three QTL intervals, on chromosomes 6, 11 and 14, all included well-known QTL associated with milk traits in dairy cattle [ 32 – 35 ]. The most significant QTL interval detected on chromosome 6, between 85,389,494 and 86,548,307 bp, overlaps with a QTL detected for MUN by Ariyarathne et al [ 13 ]. While the most significant variant in this QTL was an intergenic variant, the interval included the casein genes CSN1S1 , CSN1S2 , CSN2 and CSN3 , which are associated with milk production traits [ 33 , 34 ].…”

“…While the most significant variant in this QTL was an intergenic variant, the interval included the casein genes CSN1S1 , CSN1S2 , CSN2 and CSN3 , which are associated with milk production traits [ 33 , 34 ]. The most significant QTL interval on chromosome 11 overlapped with another QTL reported by Ariyarathne et al [ 13 ]. This interval was located between 100,851,568 and 104,180,090 bp, with a synonymous variant located in the GLT6D1 gene as the most significant variant.…”

“…GWAS for MUN may help to identify quantitative trait loci (QTL) in addition to aiding the selection of variants for genomic prediction. Several GWAS have reported QTL associated with MUN [ 13 – 17 ]. However, the size of the datasets used in these GWAS was small, limiting their detection power.…”

Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen

“…When the variant in PAEP was included as a fixed effect in the model, the only remaining significant variant was an intron in the vav guanine nucleotide exchange factor 2 ( VAV2 ) gene located at 104,765,599 bp with a p-value of 9.7 × 10 –7 . Ariyarathne et al [ 10 ] reported a QTL for MUN in the same region on chromosome 11. MUN and MBUN had a genetic correlation of 0.77 in the dataset used in the current study [ 5 ], hence QTL for MBUN and MUN were expected to overlap.…”

“…While the power of a GWAS for BUN may be limited due to the difficulties in obtaining a sufficient number of samples, MBUN may provide an alternative to detect quantitative trait loci (QTL) associated with BUN. While several GWAS have been published for milk urea nitrogen (MUN) [ 10 – 12 ], a trait that is highly related to BUN [ 5 , 13 ], to our knowledge, no GWAS has been published for either BUN or MBUN. Therefore, the objective of our study was to perform GWAS for BUN measured from blood samples and MBUN predicted from milk samples using spectroscopy, compare these two GWAS and detect QTL for both traits, and compare the detected QTL with previously reported QTL for MUN.…”