Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle

Short communication: Genetic correlations between methane and milk production, conformation, and functional traits

Livestock produce CH₄, contributing to the global warming effect. One of the currently investigated solutions to reduce CH₄ production is selective breeding. The goal of this study was to estimate the genetic correlations between CH₄ and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH₄ production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH₄ was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH₄ were estimated with the use of single-step genomic BLUP, and breeding values for remaining traits were obtained from the Polish national genomic evaluation. Genetic correlations between CH₄ production and remaining traits were estimated using bivariate analyses. The estimated genetic correlations were in general low. The highest values were estimated for fat yield (0.21), milk yield (0.15), chest width (0.15), size (0.15), dairy strength (0.11), and somatic cell count (0.11). These estimates, as opposed to estimates for the remaining traits, were significantly different from zero.

High-frequency marker haplotypes in the genomic selection of dairy cattle

The aim of this study was to predict the genomic breeding value (DGV) of production, selected conformation and reproductive traits, and somatic cell score of dairy cattle in Poland using high-frequency marker haplotypes. The dataset consisted of phenotypic, genotypic, and pedigree data of 1216 Polish Holstein-Friesian bulls. The genotypic data consisted of 54,000 single-nucleotide polymorphisms (SNPs). The data were divided into two subsets: a test dataset (n = 1064) and a validation dataset (n = 152). Genotypic data were selected using three criteria: the percentage of missing genotypes, minor allele frequency, and linkage disequilibrium. The purpose of the data selection was to identify blocks of SNPs that were then used for the construction of haplotypes. Only haplotypes with a frequency higher than 25% were selected. DGV was predicted using four variants of a linear model with random haplotype effects and deregressed breeding values as the response variables. The accuracy of genomic prediction was checked by comparing DGVs with estimated breeding values (EBVs) using two methods: Pearson’s correlations and the regression of EBV on DGV. The use of high-frequency haplotypes showed a tendency to underestimate DGVs. None of the models tested was clearly superior with regard to the traits studied. DGVs of production and conformation traits as well as somatic cell score (medium or high heritability traits) were more accurate than those estimated for fertility traits (low heritability traits).

Comparison of significant single nucleotide polymorphisms selections in GWAS for complex traits

The goal of this study was to compare significant SNP selection approaches in the context of complex traits based on SNP estimates obtained by models: a model fitting a single SNP (M1), a model fitting a single SNP and a random polygenic effect (M2), the nonparametric CAR score (M3), a SNP-BLUP model with random effects of all SNPs fitted simultaneously (M4). There were 46,267 SNPs tested in a population of 2601 Holstein Friesian bulls, four traits (milk and fat yields, somatic cell score, non-return rate for heifers) were considered. The numbers of SNPs selected as significant differed among models. M1 selected a very large number of SNPs, except for a NRH in which no SNPs were significant. M2 and M3 both selected similar and low number of SNPs for each trait. M4 selected more SNPs than M2 and M3. Considering linkage disequilibrium between SNPs, for MY M2 and M3 selected SNPs more highly correlated with each other than in the case of M4, while for FY M3 selection contained more correlated SNPs than M2 and M4. In conclusion, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple-SNP model is recommended. Such models are capable of accounting for at least a part of linkage disequilibrium between SNPs through the design matrix of SNP effects. Functional annotation of SNPs significant in M4 reveals good correspondence between selected polymorphisms and functional information as well as with QTL mapping results.

Utilization of information from gene networks towards a better understanding of functional similarities between complex traits: a dairy cattle model

Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore — trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP - Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

Short communication: Reliability of single-step genomic BLUP breeding values by multi-trait test-day model analysis

The purpose of our study was to develop an approximation procedure to estimate reliabilities of single-step genomic BLUP breeding values in a test-day model for routine evaluation of milk yield in a dairy cattle population. Input data consisted of 20,220,047 first-, second-, and third-lactation test-day milk yield records of 1,126,102 Czech Holstein cows (each lactation being considered a separate trait), with 1,844,679 animals in the pedigree file and with genomic data from 2,236 bulls. Evaluation was according to a multi-lactation model. The procedure was based on the effective number of records per animal from milk recording as well as from genomic and pedigree relationships. Traits were analyzed individually, and genetic covariances among traits were subsequently taken into account. The use of genomic information increased average reliability in young bulls from 0.276 to 0.505, but increased reliability in proven bulls only from 0.828 to 0.855. The reliabilities of genomic breeding values in multi-trait evaluation for first, second and third lactations, respectively, averaged 0.652, 0.673, and 0.633 for young bulls and 0.907, 0.894, and 0.852 for proven bulls. For an index combining all 3 lactations, the average reliability of a single-step genomic BLUP prediction was 0.712 and 0.925 for younger and proven bulls, respectively. Increased reliability due to genotyping in the population of all genotyped and nongenotyped animals was very small (<0.01) because of the small proportion of genotyped animals in the population.

Association of polymorphism within LTF gene promoter with lactoferrin concentration in milk of Holstein cows

This study analyzed the association between single nucleotide polymorphism (A/C) in position -28 located in the TATA box of LTF gene and the lactoferrin concentration in bovine milk secreted by healthy and infected udders. Out of 241, 69 cows were selected into the experimental group and were divided into 3 groups according to mean value of somatic cell count (SCC): I < 180,000 cells/mL, II: 180,000-350,000 cells/mL and III > 350,000 cells/mL. In each SCC group, three LTF genotypes: AA, AC and CC were identified by PCR-SSCP method. A total of 604 milk samples were collected monthly and lactoferrin concentration was measured by ELISA. The 1-way ANOVA within SCC groups was performed to estimate association of -28 A/C genotypes with mean lactoferrin concentration per lactation. In the group of healthy cows (< 180,000 cells/mL) LTF concentration in milk cows with the AA genotype (107.58 ± 17.92 μg/mL) was significantly higher than in homozygotes CC (52.09 ± 19.01 μg/mL). Unexpectedly, in cows with elevated SCC (> 350,000 cells/mL) we observed a significant opposite relationship (207.21 ± 28.50 in CC vs 115.0 ± 28.6 μg/mL in AA). We hypothesized that a promoter with allele C, which cannot be recognized as a TATA sequence is becoming more accessible for other transcription factors, which may induce alternative LTF gene expression. We assume that our results demonstrate a very interesting effect of differential gene expression depending on polymorphism in a key regulatory motif (TATA box) and also on the health status of mammary tissues.

Genetic variances of SNP loci for milk yield in dairy cattle

Regression coefficients and genetic variances for 40,890 single nucleotide polymorphisms (SNPs) for milk yield were calculated using mixed model equations, with deregressed proof (DRP) as the dependent variable. Bulls were genotyped using the Illumina BovineSNP50 v2 BeadChip and SNPs were edited according the minor allele frequency (MAF) and high incidence of missing genotype. Evaluation was conducted in two rounds. In the preliminary round, the direct genetic values (DGVs) of all genotyped bulls (2,904) were computed and the absolute difference between the DGV and the input DRP of each bull was investigated. Bulls with an absolute difference greater than the mean absolute difference plus two standard deviations were eliminated from the data set prior to the final analysis (2,766 bulls remaining). SNP regression coefficients from the final analysis had a mean absolute value of 0.506 kg and a standard deviation of 0.409 kg. The SNP with the highest regression coefficient and genetic variance was ARSBFGLNGS4939 on chromosome 14. This SNP is located within the gene DGAT1 (diacylglycerol O-acyltransferase 1). Other SNPs with high regression coefficients and genetic variance are localised in proximity to DGAT1. The mean genetic variance of an individual SNP was 0.170, with a standard deviation of 0.384 and a mean heterozygosity of 0.372. The sum of genetic variances of all SNPs was only 6,968.8, probably because of the existence of genetic covariances between loci. The largest sum of genetic variances was on chromosome 14 (498.4, 7.15 % of the total). After the final analysis, the correlation between the DGV and the input DRP was 0.951 for all bulls. The variance of the predicted DGV was 98.11 % of the variance of the input estimated breeding value (EBV) and 63.65 % of the variance of the DRP.