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Employing forced aeration (FA) in composting static windrows (SW) from fish waste (FW) has the potential to enhance the development of process and, organic fertiliser quality. However, due to the impact of season, the FA may lead to excessive drying of SW and, difficulty in thermophilic temperature maintenance. The aim of this study was to assess the effects of passive aeration (PA) and FA on the composting of FW in SW during the summer and winter seasons. The temperatures of the windrows remained within the thermophilic range for most of the composting period, with peak temperatures observed shortly after starting and turning the windrows (at 50 and 70 days). The aeration benefited the initial TS degradations, resulting in 86.66 and 45.99% of the TS total reduced to FA and PA piles, at 50 days during the winter. The C organic reduction was 77.77 and 76.33% in summer and winter to FA piles, respectively, but this reduction was 59.24% and 67.82% for winter and summer, respectively, in PA windrows. At 50 days, the N reduction in FA piles was already at 70.32% and 71.87% for winter and summer. The volatile solids reductions were significantly higher ( p  < 0.01) in FA piles during the summer. Although the FA has been shown to enhance the organic constituents' degradation during the composting of FW, its adoption was not enough to improve the compost composition. Thus, by conducting piles on a small scale, with the perforated wall, as described in this study, the FA could be dispensed.

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity., (© 2024 Wiley‐VCH GmbH. Published by John Wiley & Sons Ltd.)

In Brazil, water buffaloes have been used to produce milk for mozzarella cheese production. Consequently, the main selection criterion applied for the buffalo genetic improvement is the estimated mozzarella yield as a function of milk, fat and protein production. However, given the importance of reproductive traits in production systems, this study aimed to use techniques for identifying genomic regions that affect the age at first calving (AFC) and first calving interval (FCI) in buffalo cows and to select candidate genes for the identification of QTL and gene expression studies. The single-step GBLUP method was used for the identification of genomic regions. Windows of 1 Mb containing single-nucleotide polymorphisms were constructed and the 10 windows that explained the greatest proportion of genetic variance were considered candidate regions for each trait. Genes present into the selected windows were identified using the UOA&#95;WB&#95;1 assembly as the reference, and their ontology was defined with the Panther tool. Candidate regions for both traits were identified on BBU 3, 12, 21 and 22; for AFC, candidates were detected on BBU 6, 7, 8, 9 and 15 and for first calving interval on BBU 4, 14 and 19. This study identified regions with great contribution to the additive genetic variance of age at first calving and first calving interval in the population of buffalo cows studied. The ROCK2, PMVK, ADCY2, MAP2K6, BMP10 and GFPT1 genes are main candidates for reproductive traits in water dairy buffaloes, and these results may have future applications in animal breeding programs or in gene expression studies of the species., (© 2020 Wiley-VCH GmbH.)

The aim of this study was to evaluate the population structure of Brazilian buffaloes using of pedigree information. The pedigree used in the analyses included records of 16,915 animals. The population parameters were obtained through the ENDOG software. The estimates of mean of inbreeding (F) and average relatedness (AR) coefficients were 3.22% and 5.99%, respectively. The average generation interval was 6.39 years. The effective number of founders and ancestors was 28 and 22, respectively. In this study, we concluded that the selection of individuals with lower AR is necessary to avoid an increase in matings between inbreeding individuals in this population, in order to obtain greater genetic gain by selection.

The aim of this study was to estimate the (co)variance components and breeding values for birthweight (BW) in Nellore cattle by considering or not identical weights that exhibit a high frequency within the contemporary group (CG). A total of 175,258 BW records of Nellore cattle born between 2002 and 2018 were used. The CG was formed by farm, year of birth, sex and feeding regime at birth. CGs with more than 16% of identical BW values were eliminated, generating a data file called BWd. Another file was created without removing these animals (BWt). A mixed linear model was used for statistical analysis, which included fixed and random effects. In both data files analysed, single-trait analysis was performed by Bayesian inference. The mean direct and maternal heritability for BW and the correlation between direct and maternal effects were 0.27, 0.07 and -0.07 for BWt, respectively, and 0.30, 0.093 and -0.07 for BWd. This method should affect the estimation of genetic merits of animals for BW, providing greater safety in the choice of sires., (© 2020 Wiley-VCH GmbH.)

The aim of this study was to analyze the genomic homology between cattle ( Bos taurus ) and buffaloes ( Bubalus bubalis ) and to propose a rearrangement of the buffalo genome through linkage disequilibrium analyses of buffalo SNP markers referenced in the cattle genome assembly and also compare it to the buffalo genome assembly. A panel of bovine SNPs (single nucleotide polymorphisms) was used for hierarchical, non-hierarchical and admixture cluster analyses. Thus, the linkage disequilibrium information between markers of a specific panel of buffalo was used to infer chromosomal rearrangement. Haplotype diversity and imputation accuracy of the submetacentric chromosomes were also analyzed. The genomic homology between the species enabled us to use the bovine genome assembly to recreate a buffalo genomic reference by rearranging the submetacentric chromosomes. The centromere of the submetacentric chromosomes exhibited high linkage disequilibrium and low haplotype diversity. It allowed hypothesizing about chromosome evolution. It indicated that buffalo submetacentric chromosomes are a centric fusion of ancestral acrocentric chromosomes. The chronology of fusions was also suggested. Moreover, a linear regression between buffalo and cattle rearranged assembly and the imputation accuracy indicated that the rearrangement of the chromosomes was adequate. When using the bovine reference genome assembly, the rearrangement of the buffalo submetacentric chromosomes could be done by SNP BTA (chromosome of Bos taurus ) calculations: shorter BTA (shorter arm of buffalo chromosome) was given as [(shorter BTA length - SNP position in shorter BTA)] and larger BTA length as [shorter BTA length + (larger BTA length - SNP position in larger BTA)]. Finally, the proposed linkage disequilibrium-based method can be applied to elucidate other chromosomal rearrangement events in other species with the possibility of better understanding the evolutionary relationship between their genomes., (Copyright © 2020 Jordan de Abreu Santos et al.)

Growth traits are important for the profitability of buffalo breeding systems, since in general, these animals are raised both for meat and milk. In this study, the single-step genomic BLUP method was employed to prospect the genomic regions' associated with weight at standard ages of 100, 210, 365, and 550 days in a buffalo population, aiming to identify genes with stronger expression for those characteristics. We found 6, 1, 2, and 5 SNPs significantly associated (p value < 10 -5 ) with weight at 100, 210, 365, and 550 days of age, respectively, where those SNPs respectively explained 0.164, 0.040, 0.044, and 0.213% of the additive variance of each trait. SNP AX-85099682 (BBU24) was significant for weight at 100, 210, and 365 days, indicating the existence of a possible QTL affecting the initial growth rate of buffaloes. All told, eight genes (CBLB, TRNAG-UCC, GADD45B, LOC112583811, MGAT4C, KCNMA1, SLC5A2, and TGFB1I1) were identified as candidates for the growth traits of buffaloes. However, molecular and gene expression studies are necessary to validate these genes for subsequent use in programs for genetic improvement of the species.

This study was carried out to estimate genetic parameters for morphology, body weight, and tonic immobility traits in the red-winged tinamou (Rhynchotus rufescens). Information on 690 birds was used and genetic parameters were estimated using Bayesian methods under a multi-trait animal model. The following traits were considered in this study: tarsal length (TL), bill length (BL), wing length (WL), head width (HW), bill width (BW), mature weight (MW), weight at 90 days (W90), and tonic immobility (TI). The heritability showed estimates between 0.15 for wing length and 0.56 for bill length. Positive and negative genetic correlations were estimated, ranging from - 0.33 to 0.81. All the morphological, production, and behavioral traits studied will have moderate to high response to selection. The body weight at 90 days is a better alternative for use in breeding programs and its selection would not lead to an increase in the time of tonic immobility. Both the selection for weight gain and for reduction of tonic immobility time would lead to an increase in the size of the legs of the red-winged tinamou, which could be advantageous for thermal control of these birds in tropical systems.

Linkage disequilibrium (LD) plays an important role in genomic selection and mapping of quantitative trait loci (QTL). This study investigated the pattern of LD and effective population size (N e ) in Gir cattle selected for yearling weight. For this purpose, 173 animals with imputed genotypes (from 18 animals genotyped with the Illumina BovineHD BeadChip and 155 animals genotyped with the Bovine LDv4 panel) were analysed. The LD was evaluated at distances of 25-50 kb, 50-100 kb, 100-500 kb and 0.5-1 Mb. The N e was estimated based on 5 past generations. The r 2 values (a measure of LD) were, respectively, .35, .29, .18 and .032 for the distances evaluated. The LD estimates decreased with increasing distance of SNP pairs and LD persisted up to a distance of 100 kb (r 2  = .29). The N e was greater in generations 4 and 5 (24 and 30 animals, respectively) and declined drastically after the last generation (12 animals). The results showed high levels of LD and low N e , which were probably due to the loss of genetic variability as a consequence of the structure of the Gir population studied., (© 2019 Blackwell Verlag GmbH.)

The objective of this study was to evaluate the genotype-environment interaction effect on age at first calving in buffaloes. The records were analysed using two approaches: (a) standard animal model and (b) reaction norm model. For the reaction norm analysis, two environmental gradients were formed, using age of first calving or milk yield group contemporary average. The results showed differences in the heritability estimates when using the two approaches. The reaction norm model indicated high heritability in more favourable environments and low magnitude genetic correlations between extreme environments. Based on our findings, we verified the significance of the genotype-environment interaction effect on age at first calving in buffaloes., (© 2019 Blackwell Verlag GmbH.)

The aim of this research communication was to identify chromosome regions and genes that could be related to milk yield (MY), milk fat (%F) and protein percentage (%P) in Brazilian buffalo cows using information from genotyped and non-genotyped animals. We used the 90 K Axiom® Buffalo Genotyping array. A repeatability model was used. An iterative process was performed to calculate the weights of markers as a function of the squared effects of Single Nucleotide Polymorphism (SNP) and allele frequencies. The 10 SNPs with the largest effects for MY, %F and %P were studied and they explained 7·48, 9·94 and 6·56% of the genetic variance, respectively. These regions harbor genes with biological functions that could be related to the traits analyzed. The identification of such regions and genes will contribute to a better understanding of their influence on milk production and milk quality traits of buffaloes.

This study investigated the effects of genotype-environment interaction on yearling weight, age at first calving and post-weaning weight gain in Nellore cattle using multi-trait reaction norm models. The environmental gradient was defined as a function of the mean yearling weight of the contemporary groups. A first-order random regression sire model with four classes of residual variance was used in the analyses and Bayesian methods were applied to estimate the (co)variance components. The heritability estimates ranged from 0.284 to 0.547, 0.222 to 0.316 and 0.256 to 0.522 for yearling weight, age at first calving and post-weaning weight gain, respectively. The lowest genetic correlations between environment groups for each trait were 0.38, 0.02 and 0.04 for yearling weight, age at first calving and post-weaning weight gain, respectively. Differences in the correlation estimates were observed between traits in the same environments, with the magnitude of the estimates tending toward zero as the environment improved. The results highlight the importance of including genotype-environment interactions in genetic evaluation programs considering the differences observed between environmental groups not only in terms of heritability, but also of genetic correlations., (© 2018 Japanese Society of Animal Science.)

Genomic selection is arguably the most promising tool for improving genetic gain in domestic animals to emerge in the last few decades, but is an expensive process. The aim of this study was to evaluate the economic impact related to the implementation of genomic selection in a simulated dairy cattle population. The software QMSim was used to simulate genomic and phenotypic data. The simulated genome contained 30 chromosomes with 100 cm each, 1666 SNPs markers equally spread and 266 QTLs randomly designated for each chromosome. The numbers of markers and QTLs were designated according to information available from Animal QTL (http://www.animalgenome.org/QTLdb) and Bovine QTL (http://bovineqtl.tamu.edu/). The allelic frequency changes were assigned in a gamma distribution with alpha parameters equal to 0·4. Recurrent mutation rates of 1·0e-4 were assumed to apply to markers and QTLs. A historic population of 1000 individuals was generated and the total number of animals was reduced gradually along 850 generations until we obtained a number of 200 animals in the last generation, characterizing a bottleneck effect. Progenies were created along generations from random mating of the male and female gametes, assuming the same proportion of both genders. Than the population was extended for another 150 generations until we obtained 17 000 animals, with only 320 male individuals in the last generation. After this period a 25 year of selection was simulated taking into account a trait limited by sex with heritability of 0·30 (i.e. milk yield), one progeny/cow/year and variance equal to 1·0. Annually, 320 bulls were mated with 16 000 dams, assuming a replacement rate of 60 and 40% for males and females, respectively. Selection and discard criteria were based in four strategies to obtain the EBVs assuming as breeding objective to maximize milk yield. The progeny replaced the discarded animals creating an overlapping generation structure. The selection strategies were: RS is selection based on random values; PS is selection based on phenotypic values; Blup is selection based on EBVs estimated by BLUP; and GEBV is selection based on genomic estimated breeding values in one step, using high (GBlup) and low (GBlupi) density panels. Results indicated that the genetic evaluation using the aid of genomic information could provide better genetic gain rates in dairy cattle breeding programs as well as reduce the average inbreeding coefficient in the population. The economic viability indicators showed that only Blup and GBlup/GBlupi strategies, the ones that used milk control and genetic evaluation were economic viable, considering a discount rate of 6·32% per year.

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.

Reproductive traits are of the utmost importance for any livestock farming, but are difficult to measure and to interpret since they are influenced by various factors. The objective of this study was to detect associations between known polymorphisms in candidate genes related to sexual precocity in Nellore heifers, which could be used in breeding programs. Records of 1,689 precocious and non-precocious heifers from farms participating in the Conexão Delta G breeding program were analyzed. A subset of single nucleotide polymorphisms (SNP) located in the region of the candidate genes at a distance of up to 5 kb from the boundaries of each gene, were selected from the panel of 777,000 SNPs of the High-Density Bovine SNP BeadChip. Linear mixed models were used for statistical analysis of early heifer pregnancy, relating the trait with isolated SNPs or with haplotype groups. The model included the contemporary group (year and month of birth) as fixed effect and parent of the animal (sire effect) as random effect. The fastPHASE® and GenomeStudio® were used for reconstruction of the haplotypes and for analysis of linkage disequilibrium based on r2 statistics. A total of 125 candidate genes and 2,024 SNPs forming haplotypes were analyzed. Statistical analysis after Bonferroni correction showed that nine haplotypes exerted a significant effect (p<0.05) on sexual precocity. Four of these haplotypes were located in the Pregnancy-associated plasma protein-A2 gene (PAPP-A2), two in the Estrogen-related receptor gamma gene (ESRRG), and one each in the Pregnancy-associated plasma protein-A gene (PAPP-A), Kell blood group complex subunit-related family (XKR4) and mannose-binding lectin genes (MBL-1) genes. Although the present results indicate that the PAPP-A2, PAPP-A, XKR4, MBL-1 and ESRRG genes influence sexual precocity in Nellore heifers, further studies are needed to evaluate their possible use in breeding programs.

This article reports genetic analysis of the weight at different ages of Murrah water buffaloes, using random regression models (RRM). Models ranging from third to sixth order polynomial were used to describe direct genetic and animal permanent environmental effects. Contemporary group was included as a fixed effect, and a cubic polynomial was used to model the mean curve of the population. The residual was modeled considering a log-linear function. Two models were selected for study of genetic parameters. The first model included third and sixth order polynomials for direct genetic and animal permanent environmental effects (M36). The second model included sixth order polynomials for all random effects (M66). The estimates of heritability varied from 0.16 + 0.04 (44 days) to 0.38 + 0.04 (568 days) for model M36 and from 0.16 + 0.05 (33 days) to 0.42 + 0.05 (600 days) for model M66. Regarding estimates of the correlation for all effects, the magnitude tended to decline with the increase of the time span between measurements. These results indicate that the species has potential for genetic selection based on weight at different ages, since we found favorable genetic variability within the herd, with selection likely to be more efficient at ages near 600 days., (© 2016 Japanese Society of Animal Science.)

This study identified polymorphisms in the DGAT1 gene in Murrah buffaloes and investigated the associations to milk production and quality traits (milk, fat and protein yields and percentages, somatic cell count). Genomic DNA was extracted from hair follicles collected from the tail of 196 females. Three SNPs were identified in DGAT1 gene by sequencing. Statistical analyses were performed to verify the linkage and the association between polymorphisms and traits. The estimated value of r (2) between two SNPs in exon 17 (g.11,783G > A and g.11,785 T > C) was 0.029. SNP g.11,785 T > C was significantly associated (P < 0.05) to fat and protein percentage. Dominance effect was significant for milk and fat yields and protein percentage (P < 0.05). The additive effect of the SNP g.11,785 T > C was significant for protein production and somatic cell count (P < 0.05). It indicates that assisted marker selection might be done with considerations to balance production and udder health.

The study of genes associated with host defense mechanisms, such as the A2M gene, plays a critical role in preventing diseases that reduce milk yield and its constituents. The aim of this study was to identify polymorphisms in the A2M gene in Murrah buffaloes (Bubalus bubalis), and investigate their associations with milk yield, fat and protein production, fat and protein percentages, and somatic cell count. Hair follicle samples of 136 animals were collected for DNA extraction, and polymorphisms were identified by polymerase chain reactions and sequencing. Statistical analyses were performed to ascertain the allelic and genotypic frequencies, the Hardy-Weinberg equilibrium, and association analysis was conducted between the polymorphisms and the traits studied. Comparative analysis between buffalo and bovine sequences revealed seven nucleotide substitutions. Alignments among the buffalo sequences identified three single nucleotide polymorphisms (SNPs), including one in exon 29, g.241A>G, which was used in subsequent statistical analyses. A Hardy-Weinberg test indicated that this SNP was in equilibrium in this population, and was significantly associated (P < 0.05) with fat production and fat and protein percentages. Therefore, this SNP can be used as a molecular marker for these traits.

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.

Random regression models (RRM) and multitrait models (MTM) were used to estimate genetic parameters for growth traits in Brazilian Brahman cattle and to compare the estimated breeding values obtained by these 2 methodologies. For RRM, 78,641 weight records taken between 60 and 550 d of age from 16,204 cattle were analyzed, and for MTM, the analysis consisted of 17,385 weight records taken at the same ages from 12,925 cattle. All models included the fixed effects of contemporary group and the additive genetic, maternal genetic, and animal permanent environmental effects and the quadratic effect of age at calving (AAC) as covariate. For RRM, the AAC was nested in the animal's age class. The best RRM considered cubic polynomials and the residual variance heterogeneity (5 levels). For MTM, the weights were adjusted for standard ages. For RRM, additive heritability estimates ranged from 0.42 to 0.75, and for MTM, the estimates ranged from 0.44 to 0.72 for both models at 60, 120, 205, 365, and 550 d of age. The maximum maternal heritability estimate (0.08) was at 140 d for RRM, but for MTM, it was highest at weaning (0.09). The magnitude of the genetic correlations was generally from moderate to high. The RRM adequately modeled changes in variance or covariance with age, and provided there was sufficient number of samples, increased accuracy in the estimation of the genetic parameters can be expected. Correlation of bull classifications were different in both methods and at all the ages evaluated, especially at high selection intensities, which could affect the response to selection.

The diacylglycerol-O-transferase 1 gene is a positional and functional candidate for milk composition traits. The objective of this study was to evaluate the segregation of the variable number of tandem repeat polymorphisms in the regulatory region of diacylglycerol-O-transferase 1 gene in a water buffalo herd, and to assess the association of this mutation with milk production traits. For this purpose, 196 Murrah buffalo cows were genotyped by PCR. The association of the marker with total milk, fat, and protein yields at 305 d of lactation, milk fat and protein percentage, and somatic cell scores were evaluated by single-trait analyses using a generalized mixed model. Two segregating alleles were identified in the population. The allele with 2 repeats affected fat percentage favorably. The present results suggest that this polymorphism is an interesting marker to include in the genetic evaluation of buffaloes., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The use of molecular markers may auxiliary the buffalo breeding. The oxytocin (OXT) and the adrenergic receptor α1A (ADRA1A) may be involved in milk ejection in ruminants. The aim of this study was to verify the existence of polymorphisms in the OXT and ADRA1A genes and their associations with milk production traits. A total of 220 buffaloes were genotyped using PCR-RFLP for both genes. The SNP identified in the ADRA1A gene was associated with protein percentage in dairy buffaloes. This is the first report of such association in the literature, which has not been studied in other species.

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.

The main environmental factor that affects the regulation of reproductive seasonality is photoperiod through its effects on melatonin secretion. The melatonin receptor MTRN1A appears to be involved in regulating the reproductive seasonality and milk production in the period. The aim of this study was to identify polymorphisms in the MTRN1A gene and their possible associations with milk, fat and protein productions, fat and protein percentages, age at first calving, and first calving interval in buffaloes. Three genotypes (CC, CT, and TT) were identified by PCR-RFLP, and there was a significant association with protein percentage (P < 0.0001). Further studies are necessary to better understand the influence of melatonin gene and their receptors in the productive functions of buffaloes.

The gene responsible for coding the leptin hormone has been associated with productive and reproductive traits in cattle. In dairy cattle, different polymorphisms found in the leptin gene have been associated with several traits of economic interest, such as energy balance, milk yield and composition, live weight, fertility and dry matter consumption. The aim of this study was to detect genetic variability in the leptin gene of buffaloes and to test possible associations with milk yield, fat and protein percentages, age at first calving and first calving interval. Three genotypes (AA, AG and GG) were identified by polymerase chain reaction-restriction fragment length polymorphism, which presented genotypic frequencies of 0.30, 0.54 and 0.16, respectively. The allele frequencies were 0.57 for the A allele and 0.43 for the G allele. No significant effects were found in the present study, but there is an indicative that leptin gene affects lipid metabolism.

Ghrelin is a gastrointestinal hormone that acts in releasing growth hormone and influences the body general metabolism. It has been proposed as a candidate gene for traits such as growth, carcass quality, and milk production of livestock because it influences feed intake. In this context, the aim of this study was to verify the existence of polymorphisms in the ghrelin gene and their associations with milk, fat and protein yield, and percentage in water buffaloes (Bubalus bubalis). A group of 240 animals was studied. Five primer pairs were used and 11 single nucleotide polymorphisms (SNP) were found in the ghrelin gene by sequencing. The animals were genotyped for 8 SNP by PCR-RFLP. The SNP g.960G>A and g.778C>T were associated with fat yield and the SNP g.905T>C was associated with fat yield and percentage and protein percentage. These SNP are located in intronic regions of DNA and may be in noncoding RNA sites or affect transcriptional efciency. The ghrelin gene in buffaloes influences milk fat and protein synthesis. The polymorphisms observed can be used as molecular markers to assist selection., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Protein JY-1 is a bovine oocyte-specific protein that regulates granulosa cell function and is involved in early embryonic development, influencing the chance of pregnancy. This study investigated molecular markers for the JY-1 gene. Seven SNPs were identified in exon 3 of the gene. The positions of the SNPs in the exon and the respective substitutions are: 163 (T/C), 281 (T/C), 321 (T/C), 532 (T/C), 652 (A/G), 679 (T/C), and 722 (G/C) (GenBank: JN592587 and JF262042.2). SNP 163 is located in a coding region and causes a proline-to-leucine substitution. The other SNPs are located in the 3'UTR region. SNPs 163, 281, 321, and 679 were genotyped in 297 Nellore heifers and the haplotypes were constructed. The haplotypes of JY-1 were not correlated with the traits studied at 5 %.

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to 0·99±0·004, for milk, fat and protein production, respectively, indicating that whatever the selection criterion used, indirect genetic gains can be expected throughout the lactation curve.

The objective of this study was to apply factor analysis to describe lactation curves in dairy buffaloes in order to estimate the phenotypic and genetic association between common latent factors and cumulative milk yield. A total of 31 257 monthly test-day milk yield records from buffaloes belonging to herds located in the state of São Paulo were used to estimate two common latent factors, which were then analysed in a multi-trait animal model for estimating genetic parameters. Estimates of (co)variance components for the two common latent factors and cumulated 270-d milk yield were obtained by Bayesian inference using a multiple trait animal model. Contemporary group, number of milkings per day (two levels) and age of buffalo cow at calving (linear and quadratic) as covariate were included in the model as fixed effects. The additive genetic, permanent environmental and residual effects were included as random effects. The first common latent factor (F1) was associated with persistency of lactation and the second common latent factor (F2) with the level of production in early lactation. Heritability estimates for F1 and F2 were 0·12 and 0·07, respectively. Genetic correlation estimates between F1 and F2 with cumulative milk yield were positive and moderate (0·63 and 0·52). Multivariate statistics employing factor analysis allowed the extraction of two variables (latent factors) that described the shape of the lactation curve. It is expected that the response to selection to increase lactation persistency is higher than the response obtained from selecting animals to increase lactation peak. Selection for higher total milk yield would result in a favourable correlated response to increase the level of production in early lactation and the lactation persistency.

To provide data for conservation, selection, and expansion programs of buffalo herds, this study evaluated the history of a population of Murrah buffaloes based on population structure and the effect of inbreeding on accumulated 305-d milk yield (MY), fat yield (FY), protein yield (PY), mozzarella production (MProd), and somatic cell score (SCS). The usefulness of including the individual inbreeding coefficient (F) or individual increase in inbreeding coefficient (ΔF) in the model to describe inbreeding depression was evaluated. Pedigree information from 8,054 animals born between 1976 and 2008 and 4,497 lactation records obtained from 12 herds were used. The realized effective population size was 40.10 ± 1.27, and the mean F of the entire population was 2.14%. The ratio between the number of founders and ancestors demonstrated the existence of a bottleneck in the pedigree of this population, which may contribute to a reduction of genetic diversity. The effect of F on MY, FY, PY, MProd, and SCS was -1.005 kg, -0.299 kg, -0.246 kg, -1.201 kg, and -0.002 units, and the effect of ΔF transformed to equivalent F (%) for a mean of 2.57 equivalent generations was -4.287 kg, -0.581 kg, -0.383 kg, -2.001 kg, and -0.007 units, respectively. The inbreeding depression observed may have important economic repercussions for production systems. The ΔF can be considered the better of the two indicators of inbreeding depression due to its properties that prevent underestimation of this effect. A designed mating system to avoid inbreeding may be applied to this population to maintain genetic diversity., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Buffalo milk has excellent physical and chemical qualities as a consequence of the high percentage of constituents. This milk property is desirable for the dairy industry because it facilitates manufacture of mozzarella cheese. We estimated genetic parameters for milk yield, milk fat and protein and their effects on mozzarella cheese production using Bayesian inference. Using information from 4907 lactation records of buffaloes, genetic and non-genetic parameters were estimated for accumulated 305-day milk yield (MY), milk fat (%F) and protein (%P) percentages and mozzarella production per lactation (MP). The (co)variance components were obtained by Bayesian inference using a multiple trait model, which included as fixed effects contemporary group, milking number and buffalo age at calving as covariables (linear and quadratic), along with the additive genetic, permanent environmental and residual random effects. Mean a posteriori heritability distributions for MY, %F, %P, and MP were 0.25, 0.30, 0.38, and 0.23, respectively. The genetic correlation estimates between MY with %P and %F were negative and moderate. Positive genetic correlation estimates varying from 0.19 (%P/MP) to 0.95 (MY/MP) were obtained among the traits. Milk yield, milk components, and mozzarella production in Murrah buffaloes have enough genetic variation for selection purposes. We conclude that selection to increase milk yield would be effective in improving mozzarella production.

The availability of accurate genetic parameters for important economic traits in milking buffaloes is critical for implementation of a genetic evaluation program. In the present study, heritabilities and genetic correlations for fat (FY305), protein (PY305), and milk (MY305) yields, milk fat (%F) and protein (%P) percentages, and SCS were estimated using Bayesian methodology. A total of 4,907 lactations from 1,985 cows were used. The (co)variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co)variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The posterior means of heritability distributions for MY305, FY305, PY305, %F, P%, and SCS were 0.22, 0.21, 0.23, 0.33, 0.39, and 0.26, respectively. The genetic correlation estimates ranged from -0.13 (between %P and SCS) to 0.94 (between MY305 and PY305). The permanent environmental correlation estimates ranged from -0.38 (between MY305 and %P) to 0.97 (between MY305 and PY305). Residual and phenotypic correlation estimates ranged from -0.26 (between PY305 and SCS) to 0.97 (between MY305 and PY305) and from -0.26 (between MY305 and SCS) to 0.97 (between MY305 and PY305), respectively. Milk yield, milk components, and milk somatic cells counts have enough genetic variation for selection purposes. The genetic correlation estimates suggest that milk components and milk somatic cell counts would be only slightly affected if increasing milk yield were the selection goal. Selecting to increase FY305 or PY305 will also increase MY305, %P, and %F., (Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

In order to contribute to the breeding programmes of Asian water buffalo, the aim of this study was to analyse the influence of genetic effects in the stayability of Murrah dairy buffaloes. The stayability trait (ST) was defined as the female's ability to stay in the herd for one (ST1), two (ST2), three (ST3), four (ST4), five (ST5) or six years (ST6) after the first calving. The same trait was also considered as continuous and was designated stayability in days up to one (STD1), two (STD2), three (STD3), four (STD4), five (STD5) or six years (STD6) after the first calving. Data from 1016 females reared in nine herds located in the State of São Paulo, Brazil, were analysed. Statistical models included the additive genetic effect of the animal and the fixed effects of the buffalo breeding herd, birth year and birth season. Additive effects for ST were estimated by approximate restricted maximum likelihood using a threshold model, while for STD, the additive effects were estimated by restricted maximum likelihood. Heritability estimates were lower for ST, except for ST1, (0.11+/-0.07, 0.17+/-0.06, 0.23+/-0.06, 0.16+/-0.08, 0.14+/-0.09 and 0.16+/-0.10 for ST1, ST2, ST3, ST4, ST5 and ST6, respectively) when compared with STD (0.05+/-0.06, 0.18+/-0.08, 0.40+/-0.10, 0.49+/-0.11, 0.41+/-0.11 and 0.30+/-0.13, for STD1, STD2, STD3, STD4, STD5 and STD6, respectively). Considering the values of heritability and owing to the serial nature of STD to a specific age, selection for STD3 should have a favourable influence on STD to other ages.

The objective of the present study was to estimate genetic parameters for test-day milk, fat and protein yields and 305-day-yields in Murrah buffaloes. 4,757 complete lactations of Murrah buffaloes were analyzed. Co-variance components were estimated by the restricted maximum likelihood method. The models included additive direct genetic and permanent environmental effects as random effects, and the fixed effects of contemporary group, milking number and age of the cow at calving as linear and quadratic covariables. Contemporary groups were defined by herd-year-month of test for test-day yields and by herd-year-season of calving for 305-day yields. The heritability estimates obtained by two-trait analysis ranged from 0.15 to 0.24 for milk, 0.16 to 0.23 for protein and 0.13 to 0.22 for fat, yields. Genetic and phenotypic correlations were all positive. The observed population additive genetic variation indicated that selection might be an effective tool in changing population means in milk, fat and protein yields.