An updated Axiom buffalo genotyping array map and mapping of cattle quantitative trait loci to the new water buffalo reference genome assembly.

The objectives of this study were to provide the buffalo research community with an updated SNP map for the Axiom Buffalo Genotyping (ABG) array with genomic positions for SNP currently unmapped and to map all cattle QTL from the CattleQTLdb onto the buffalo reference assembly. To update the ABG array map, all SNP probe sequences from the ABG array were re-aligned against the UOA_WB_1 assembly. With the new map, the number of mapped markers increased by approximately 10% and went from 106 778 to 116 708, which reduced the average marker spacing by approximately 2 kb. A comparison of results between signatures of autozygosity study using the ABG and the new map showed that, when the additional markers were used there was an increase in the autozygosity peaks and additional peaks in BBU5 and BBU11 could be identified. After sequence alignment and quality control, 64 650 (UMD3.1) and 76 530 (ARS_UCD1.2) cattle QTL were mapped onto the buffalo genome. The mapping of the bovine QTL database onto the buffalo genome should be useful for genome-wide association studies in buffalo and, given the high homology between the two species, the positions of cattle QTL on the buffalo genome can serve as a stepping stone towards a water buffalo QTL database.

Inbreeding coefficients and runs of homozygosity islands in Brazilian water buffalo.

Characterization of autozygosity is relevant to monitor genetic diversity and manage inbreeding levels in breeding programs. Identification of autozygosity hotspots can unravel genomic regions targeted by selection for economically important traits and can help identify candidate genes for selection. In this study, we estimated the inbreeding levels of a Brazilian population of Murrah buffalo undergoing selection for milk production traits, particularly milk yield. We also studied the distribution of runs of homozygosity (ROH) islands and identified putative genes and quantitative trait loci (QTL) under selection. We genotyped 422 Murrah buffalo for 51,611 SNP; 350 of these had ROH longer than 10 Mb, indicating the occurrence of inbreeding in the last 5 generations. The mean length of the ROH per animal was 4.28 ± 1.85 Mb. Inbreeding coefficients were calculated from the genomic relationship matrix, the pedigree, and the ROH, with estimates varying between 0.242 and 0.035. Inbreeding estimates from the pedigree had a low correlation with the genomic estimates, and estimates from the genomic relationship matrix were much higher than those from the pedigree or the ROH. Signatures of selection were identified in 6 genomic regions, located on chromosomes 1, 2, 3, 5, 16, and 18, encompassing a total of 190 genes and 174 QTL. Many of the genes (e.g., APRT and ACSF3) and QTL identified are related to milk production traits, such as milk yield, milk fat yield and percentage, and milk protein yield and percentage. Other genes are associated with reproduction and immune response traits as well as morphological aspects of the buffalo species. Inbreeding levels in this population are still low but are increasing due to selection and should be managed to avoid future losses due to inbreeding depression. The proximity of genes linked to milk production traits with genes associated with reproduction and immune system traits suggests the need to include these latter genes in the breeding program to avoid negatively affecting them due to selection for production traits.

Variance of gametic diversity and its application in selection programs.

The variance of gametic diversity ( σgamete2) can be used to find individuals that more likely produce progeny with extreme breeding values. The aim of this study was to obtain this variance for individuals from routine genomic evaluations, and to apply gametic variance in a selection criterion in conjunction with breeding values to improve genetic progress. An analytical approach was developed to estimate σgamete2 by the sum of binomial variances of all individual quantitative trait loci across the genome. Simulation was used to verify the predictability of this variance in a range of scenarios. The accuracy of prediction ranged from 0.49 to 0.85, depending on the scenario and model used. Compared with sequence data, SNP data are sufficient for estimating σgamete2 Results also suggested that markers with low minor allele frequency and the covariance between markers should be included in the estimation. To incorporate σgamete2 into selective breeding programs, we proposed a new index, relative predicted transmitting ability, which better utilizes the genetic potential of individuals than traditional predicted transmitting ability. Simulation with a small genome showed an additional genetic gain of up to 16% in 10 generations, depending on the number of quantitative trait loci and selection intensity. Finally, we applied σgamete2 to the US genomic evaluations for Holstein and Jersey cattle. As expected, the DGAT1 gene had a strong effect on the estimation of σgamete2 for several production traits. However, inbreeding had a small impact on gametic variability, with greater effect for more polygenic traits. In conclusion, gametic variance, a potentially important parameter for selection programs, can be easily computed and is useful for improving genetic progress and controlling genetic diversity.

Differential expression of immune response genes associated with subclinical mastitis in dairy buffaloes.

Buffalo milk production has become of significant importance on the world scale, however, there are few studies involving biotechnological tools specifically for buffalo. To verify the effects caused by subclinical mastitis on the components of milk and to study the innate immune system in the udder of dairy buffaloes with subclinical mastitis, we evaluated the levels of expression of the lactoferrin (LTF), tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1ß), interleukin-8 (IL-8), and toll-like receptors 2 (TLR-2) and 4 (TLR-4) genes in buffaloes with and without subclinical mastitis. Milk samples were collected for the determination of milk components: somatic cell score (SCS), fat, protein, lactose, total solids and solids-not-fat (SNF), as well as for RNA extraction of milk cells, complementary DNA synthesis, and expression profile quantification by quantitative real-time PCR. For gene expression, the ΔΔCt was estimated using contrasts of the target genes expression adjusted for the expression of the housekeeping genes between both groups. Linear regression analysis was performed to determine the relationship between the genes studied and the milk components. Subclinical mastitis induced changes in the fat, lactose and SNF in milk of buffaloes, and the messenger RNA abundance was upregulated for TLR-2, TLR-4, TNF-α, IL-1ß and IL-8 genes in milk cells of buffaloes with subclinical mastitis, whereas the LTF gene was not differentially expressed. Results of linear regression analysis showed that TLR-2 gene expression most explains the variation in SCS, and the change in a unit of ΔCt of the TNF-α gene would result in a higher increase in SCS. The study of these immune function genes that are active in the mammary gland is important to characterize the action mechanism of the innate immunity that occurs in subclinical mastitis in dairy buffaloes and may aid the development of strategies to preserve the health of the udder.

Genetic and nongenetic profiling of milk pregnancy-associated glycoproteins in Holstein cattle.

Pregnancy-associated glycoproteins (PAG) are secreted by the trophoblast and are detectable in maternal circulation around the time of attachment of the fetal placenta, as well as in blood and milk throughout gestation. The understanding of the genetic mechanisms controlling PAG levels can confer advantages for livestock breeding programs given the precocity and the ease of obtaining this phenotype from routine pregnancy diagnosis. The aim of this study was to characterize PAG levels by estimating genetic parameters and correlations with other dairy traits, and to identify genomic regions and candidate genes associated with PAG levels in milk. The PAG data consisted of pregnancy diagnoses using commercial assays from 2012 to 2017, and genotype data consisted of 54,123 SNP markers for 2,352 individuals (embryos and dams). The model included contemporary group (herd, year, and season) and embryo age as fixed effects, and random embryonic (direct) and maternal (indirect) genetic effects. Using genomic data, the estimated heritability for direct and maternal genetic effects (± standard deviations) were 0.23 ± 0.05 and 0.11 ± 0.05, respectively. The genetic correlation between these effects was almost zero (0.001 ± 0.06). A preliminary analysis revealed low correlations between milk PAG levels and other dairy traits. The genome-wide association analysis was performed using 2 approaches: single-marker and single-step using all markers. Four genomic regions with direct genetic effects were detected on Bos taurus autosome (BTA) 6, BTA7, BTA19, and BTA29 of the embryonic genome. The BTA29 locus was within the bovine PAG gene cluster, suggesting a cis-regulatory quantitative trait locus on the PAG expression. However, other associations, without an obvious link to PAG expression, could be related to the transportation of PAG and their concentration in milk. Only 1 region from the maternal genome, on BTA4, had a significant indirect effect, where WNT2 is a candidate gene related to placenta vascularization and gestation health. Collectively, our results suggest a moderate genetic control of milk PAG levels from both maternal and fetal genomes, but larger studies are needed to fully evaluate the usefulness of milk PAG in the genetic evaluation of fetal growth and cow fertility.

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation.

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle.

Genomic selection may accelerate genetic progress in breeding programs of indicine breeds when compared with traditional selection methods. We present results of genomic predictions in Gyr (Bos indicus) dairy cattle of Brazil for milk yield (MY), fat yield (FY), protein yield (PY), and age at first calving using information from bulls and cows. Four different single nucleotide polymorphism (SNP) chips were studied. Additionally, the effect of the use of imputed data on genomic prediction accuracy was studied. A total of 474 bulls and 1,688 cows were genotyped with the Illumina BovineHD (HD; San Diego, CA) and BovineSNP50 (50K) chip, respectively. Genotypes of cows were imputed to HD using FImpute v2.2. After quality check of data, 496,606 markers remained. The HD markers present on the GeneSeek SGGP-20Ki (15,727; Lincoln, NE), 50K (22,152), and GeneSeek GGP-75Ki (65,018) were subset and used to assess the effect of lower SNP density on accuracy of prediction. Deregressed breeding values were used as pseudophenotypes for model training. Data were split into reference and validation to mimic a forward prediction scheme. The reference population consisted of animals whose birth year was ≤2004 and consisted of either only bulls (TR1) or a combination of bulls and dams (TR2), whereas the validation set consisted of younger bulls (born after 2004). Genomic BLUP was used to estimate genomic breeding values (GEBV) and reliability of GEBV (R2PEV) was based on the prediction error variance approach. Reliability of GEBV ranged from ∼0.46 (FY and PY) to 0.56 (MY) with TR1 and from 0.51 (PY) to 0.65 (MY) with TR2. When averaged across all traits, R2PEV were substantially higher (R2PEV of TR1 = 0.50 and TR2 = 0.57) compared with reliabilities of parent averages (0.35) computed from pedigree data and based on diagonals of the coefficient matrix (prediction error variance approach). Reliability was similar for all the 4 marker panels using either TR1 or TR2, except that imputed HD cow data set led to an inflation of reliability. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. A reduced panel of ∼15K markers resulted in reliabilities similar to using HD markers. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information.

Prospecting major genes in dairy buffaloes.

BACKGROUND: Asian buffaloes (Bubalus bubalis) have an important socio-economic role. The majority of the population is situated in developing countries. Due to the scarce resources in these countries, very few species-specific biotechnology tools exist and a lot of cattle-derived technologies are applied to buffaloes. However, the application of cattle genomic tools to buffaloes is not straightforward and, as results suggested, despite genome sequences similarity the genetic polymorphisms are different. RESULTS: The first SNP chip genotyping platform designed specifically for buffaloes has recently become available. Herein, a genome-wide association study (GWAS) and gene network analysis carried out in buffaloes is presented. Target phenotypes were six milk production and four reproductive traits. GWAS identified SNP with significant associations and suggested candidate genes that were specific to each trait and also genes with pleiotropic effect, associated to multiple traits. CONCLUSIONS: Network predictions of interactions between these candidate genes may guide further molecular analyses in search of disruptive mutations, help select genes for functional experiments and evidence metabolism differences in comparison to cattle. The cattle SNP chip does not offer an optimal coverage of buffalo genome, thereafter the development of new buffalo-specific genetic technologies is warranted. An annotated reference genome would greatly facilitate genetic research, with potential impact to buffalo-based dairy production.

Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips.

Genotype imputation is widely used as a cost-effective strategy in genomic evaluation of cattle. Key determinants of imputation accuracies, such as linkage disequilibrium patterns, marker densities, and ascertainment bias, differ between Bos indicus and Bos taurus breeds. Consequently, there is a need to investigate effectiveness of genotype imputation in indicine breeds. Thus, the objective of the study was to investigate strategies and factors affecting the accuracy of genotype imputation in Gyr (Bos indicus) dairy cattle. Four imputation scenarios were studied using 471 sires and 1,644 dams genotyped on Illumina BovineHD (HD-777K; San Diego, CA) and BovineSNP50 (50K) chips, respectively. Scenarios were based on which reference high-density single nucleotide polymorphism (SNP) panel (HDP) should be adopted [HD-777K, 50K, and GeneSeek GGP-75Ki (Lincoln, NE)]. Depending on the scenario, validation animals had their genotypes masked for one of the lower-density panels: Illumina (3K, 7K, and 50K) and GeneSeek (SGGP-20Ki and GGP-75Ki). We randomly selected 171 sires as reference and 300 as validation for all the scenarios. Additionally, all sires were used as reference and the 1,644 dams were imputed for validation. Genotypes of 98 individuals with 4 and more offspring were completely masked and imputed. Imputation algorithms FImpute and Beagle v3.3 and v4 were used. Imputation accuracies were measured using the correlation and allelic correct rate. FImpute resulted in highest accuracies, whereas Beagle 3.3 gave the least-accurate imputations. Accuracies evaluated as correlation (allelic correct rate) ranged from 0.910 (0.942) to 0.961 (0.974) using 50K as HDP and with 3K (7K) as low-density panels. With GGP-75Ki as HDP, accuracies were moderate for 3K, 7K, and 50K, but high for SGGP-20Ki. The use of HD-777K as HDP resulted in accuracies of 0.888 (3K), 0.941 (7K), 0.980 (SGGP-20Ki), 0.982 (50K), and 0.993 (GGP-75Ki). Ungenotyped individuals were imputed with an average accuracy of 0.970. The average top 5 kinship coefficients between reference and imputed individuals was a strong predictor of imputation accuracy. FImpute was faster and used less memory than Beagle v4. Beagle v4 outperformed Beagle v3.3 in accuracy and speed of computation. A genotyping strategy that uses the HD-777K SNP chip as a reference panel and SGGP-20Ki as the lower-density SNP panel should be adopted as accuracy was high and similar to that of the 50K. However, the effect of using imputed HD-777K genotypes from the SGGP-20Ki on genomic evaluation is yet to be studied.

Multiple-trait genomic evaluation for milk yield and milk quality traits using genomic and phenotypic data in buffalo in Brazil.

The objective of this study was to compare the multi-trait model using pedigree information and a model using genomic information in addition to pedigree information. We used data from 5896 lactations of 2021 buffalo cows, of which 384 were genotyped using the Illumina Infinium(®) bovine HD BeadChip, considering seven traits related to milk yield (MY305), fat (FY305), protein (PY305), and lactose (LY305), percentages of fat (%F) and protein (%P), and somatic cell score (SCS). We carried out two analyses, one using phenotype and pedigree information (matrix A) and the other using the relationship matrix based on pedigree and genomics information (a single step, matrix H). The (co)variance components were estimated using multiple-trait analysis by the Bayesian inference method. The model included the fixed effects of contemporary groups (herd-year and calving season), and the age of cow at calving as (co)variables (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The estimates of heritability using matrix A were 0.25, 0.22, 0.26, 0.25, 0.37, 0.42, and 0.17, while using matrix H the heritability values were 0.25, 0.24, 0.26, 0.26, 0.38, 0.47, and 0.18 for MY305, FY305, PY305, LY305, %F, %P, and SCS, respectively. The estimates of breeding values in the two analyses were similar for the traits studied, but the accuracies were greater when using matrix H (higher than 8% in the traits studied). Therefore, the use of genomic information in the analyses improved the accuracy.

Association between single-nucleotide polymorphisms and milk production traits in buffalo.

The aim of this study was to identify single-nucleotide polymorphisms (SNPs) in buffaloes associated with milk yield and content, in addition to somatic cell scores based on the cross-species transferability of SNPs from cattle to buffalo. A total of 15,745 SNPs were analyzed, of which 1562 showed 1% significance and 4742 with 5% significance, which were associated for all traits studied. After application of Bonferroni's correction for multiple tests of the traits analyzed, we found 2 significant SNPs placed on cattle chromosomes BTA15 and BTA20, which are homologous to buffalo chromosomes BBU16 and BBU19, respectively. In this genome association study, we found several significant SNPs affecting buffalo milk production and quality. Furthermore, the use of the high-density bovine BeadChip was suitable for genomic analysis in buffaloes. Although extensive chromosome arm homology was described between cattle and buffalo, the exact chromosomal position of SNP markers associated with these economically important traits in buffalo can be determined only through buffalo genome sequencing.

Water buffalo genome characterization by the Illumina BovineHD BeadChip.

To define the best strategies for genomic association studies and genomic selection, it is necessary to determine the extent of linkage disequilibrium (LD) and the genetic structure of the study population. The current study evaluated the transference of genomic information contained in the Illumina BovineHD BeadChip from cattle to buffaloes, and assessed the extent of the LD in buffaloes. Of the 688,593 bovine single nucleotide polymorphism (SNP) that were successfully genotyped from the 384 buffalo samples, only 16,580 markers were polymorphic, and had minor allele frequencies greater than 0.05. A total of 16,580 polymorphic SNPs were identified, which were uniformly distributed throughout the autosomes, because the density and mean distance between markers were similar for all autosomes. The average minor allele frequency for the 16,580 SNPs was 0.23. The overall mean LD for pairs of adjacent markers was 0.29 and 0.71, when measured as for r2 and |D'|, respectively. The 16,580 polymorphic SNPs were matched to Bos taurus chromosome in the current bovine genome assembly (Btau 4.2), and could be utilized in association studies. In conclusion, the Illumina BovineHD BeadChip contains approximately 16,580 polymorphic markers for the water buffalo, which are broadly distributed across the genome. These data could be used in genomic association and genomic selection studies; however, it might be necessary to develop a panel with specific SNP markers for water buffaloes.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows.

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, 3, 6, or 10 classes. The models gave similar hereditability estimates, ranging from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data.