PC Gene Affects Milk Production Traits in Dairy Cattle.

In previous work, we found that PC was differentially expressed in cows at different lactation stages. Thus, we deemed that PC may be a candidate gene affecting milk production traits in dairy cattle. In this study, we found the polymorphisms of PC by resequencing and verified their genetic associations with milk production traits by using an animal model in a cattle population. In total, we detected six single-nucleotide polymorphisms (SNPs) in PC. The single marker association analysis showed that all SNPs were significantly associated with the five milk production traits (p < 0.05). Additionally, we predicted that allele G of 29:g.44965658 in the 5' regulatory region created binding sites for TF GATA1 and verified that this allele inhibited the transcriptional activity of PC by the dual-luciferase reporter assay. In conclusion, we proved that PC had a prominent genetic effect on milk production traits, and six SNPs with prominent genetic effects could be used as markers for genomic selection (GS) in dairy cattle, which is beneficial for accelerating the improvement in milk yield and quality in Chinese Holstein cows.

Whole-genome resequencing reveals candidate genes associated with milk production trait in Guanzhong dairy goats.

Milk production is one of the most important economic utility of goats. Guanzhong dairy goat is a local dairy goat in Shaanxi Province of China and has high milk yield and quality. However, there are relatively few studies on molecular markers of milk production traits in Guanzhong dairy goats. In this study, we sequenced the whole genomes of 20 Guanzhong dairy goats, 10 of which had high milk yield (HM) and 10 of which had low milk yield (LM). We detected candidate signatures of selection in HM goats using Fst and π-ratio statistics and identified several candidate genes including ANPEP, ADRA1A and PRKG1 associated with milk production. Our results provide the basis for molecular breeding of milk production traits in Guanzhong dairy goats.

Genomic Selection in Chinese Holsteins Using Regularized Regression Models for Feature Selection of Whole Genome Sequencing Data.

Genomic selection (GS) is an efficient method to improve genetically economic traits. Feature selection is an important method for GS based on whole-genome sequencing (WGS) data. We investigated the prediction performance of GS of milk production traits using imputed WGS data on 7957 Chinese Holsteins. We used two regularized regression models, least absolute shrinkage and selection operator (LASSO) and elastic net (EN) for feature selection. For comparison, we performed genome-wide association studies based on a linear mixed model (LMM), and the N single nucleotide polymorphisms (SNPs) with the lowest p-values were selected (LMMLASSO and LMMEN), where N was the number of non-zero effect SNPs selected by LASSO or EN. GS was conducted using a genomic best linear unbiased prediction (GBLUP) model and several sets of SNPs: (1) selected WGS SNPs; (2) 50K SNP chip data; (3) WGS data; and (4) a combined set of selected WGS SNPs and 50K SNP chip data. The results showed that the prediction accuracies of GS with features selected using LASSO or EN were comparable to those using features selected with LMMLASSO or LMMEN. For milk and protein yields, GS using a combination of SNPs selected with LASSO and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with LMMLASSO combined with 50K SNP chip data performed best for fat yield. The proposed method, feature selection using regularization regression models, provides a valuable novel strategy for WGS-based GS.

Single-step genomic evaluation of milk production traits in Canadian Alpine and Saanen dairy goats.

Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (-0.01 to -0.07 versus -0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.

The Prediction of the Expected Current Selection Coefficient of Single Nucleotide Polymorphism Associated with Holstein Milk Yield, Fat and Protein Contents.

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to 2*SNP effect.

Climatic effects on milk production traits and somatic cell score in lactating Holstein-Friesian cows in different housing systems.

The objective of this study was to compare the effect of the temperature-humidity index (THI) on milk production traits and somatic cell score (SCS) of dairy cows raised in 4 different housing systems: (1) warm loose housing with access to grazing (WG), (2) warm loose housing without access to grazing (WI), (3) cold loose housing with access to grazing (CG), and (4) cold loose housing without access to grazing (CI). For each of the 4 housing systems, 5 farms with a herd size of 70 to 200 lactating cows in Lower Saxony, Germany, were studied. Ambient temperature and relative humidity were recorded hourly in each barn to calculate THI. Milk production data included 21,546 test-day records for milk, fat, and protein yield, and SCS. These data were associated with the average THI of the 3 d preceding the respective measurement, which was divided into 6 classes (<45, ≥45 to <50, ≥50 to <55, ≥55 to <60, ≥60 to <65, and ≥65). Furthermore, bulk milk samples including the fat and protein percentage, and SCS taken 4 to 6 times per month were associated with the average and maximum THI of the 3 d before sampling. Data were recorded from April 2010 to March 2011. In each of the housing systems, monthly THI values above 60, indicating heat stress, were recorded between June and September, with higher values in WI and WG. In all systems, fat-corrected milk, fat, and protein yields of the test-day records decreased in tendency from 60 ≤ THI<65 to THI >65. In WI and CI, values for SCS were greater in the class THI > 65 than in 60 ≤ THI<65, whereas no difference between any of the THI classes was found in WG and CG. The fat and protein percentage of the bulk milk samples decreased with increasing 3-d maximum THI in all 4 systems, whereas the SCS increased with increasing 3-d average THI. In conclusion, negative effects of heat stress conditions under a temperate climate on milk production traits and SCS were found, although a housing system being superior to the other systems in altering heat stress effects was not identified.