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Made available in DSpace on 2021-06-25T11:12:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-05-01 Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from −0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies. Department of Animal Sciences Purdue University Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Centre for Genetic Improvement of Livestock Department of Animal Biosciences University of Guelph Department of Animal and Avian Science University of Maryland Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP)

This study focused on the identification of QTL regions, candidate genes, and network related genes based on the first 3 lactations (LAC3) of milk, fat, and protein yields, and somatic cell score (SCS) in Portuguese Holstein cattle. Additionally, the results were compared with those from only first lactation (LAC1) data. The analyses were performed using the weighted single-step GWAS under an autoregressive test-day (TD) multiple lactations model. A total of 11,434,294 and 4,725,673 TD records from LAC3 and LAC1, respectively, including 38,323 autosomal SNPs and 1338 genotyped animals were used in GWAS analyses. A total of 51 (milk), 5 (fat), 24 (protein), and 4 (SCS) genes were associated to previously annotated relevant QTL regions for LAC3. The CACNA2D1 at BTA4 explained the highest proportion of genetic variance respectively for milk, fat, and protein yields. For SCS, the TRNAG-CCC at BTA14, MAPK10, and PTPN3 genes, both at BTA6 were considered important candidate genes. The accessed network refined the importance of the reported genes. CACNA2D1 regulates calcium density and activation/inactivation kinetics of calcium transport in the mammary gland; whereas TRNAG-CCC, MAPK10, and PTPN3 are directly involved with inflammatory processes widely derived from mastitis. In conclusion, potential candidate genes (TRNAG-CCC, MAPK10, and PTPN3) associated with somatic cell were highlighted, which further validation studies are needed to clarify its mechanism action in response to mastitis. Moreover, most of the candidate genes identified were present in both (LAC3 and LAC1) for milk, fat and protein yields, except for SCS, in which no candidate genes were shared between LAC3 and LAC1. The larger phenotypic information provided by LAC3 dataset was more effective to identify relevant genes, providing a better understanding of the genetic architecture of these traits over all lactations simultaneously.

We investigated the applicability of ssGBLUP methodology under the autoregressive model (H-AR) for genomic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle. The genotype data of 1,230 bulls and 1,645 cows were considered in our study. The reproductive traits evaluated were interval from calving to first service (ICF), calving interval (CI) and daughter pregnancy rate (DPR) measured during the first four parities. Reliability and rank correlation were used to compare the H-AR with the traditional pedigree-based autoregressive models (A-AR). In addition, a validation study was performed considering different scenarios. Higher genomic estimated breeding values (GEBV) reliabilities were obtained for genotyped bulls when evaluated under the H-AR model, with emphasis on bulls with less than 9 daughters. For this group, the averages of GEBV reliabilities corresponded to 0.62, 0.69 and 0.62 for ICF, CI and DPR, respectively, while the averages obtained by the A-AR model were 0.27, 0.15 and 0.16. The validation study was favourable to H-AR. The best results were observed in the scenario where genotyped cows were combined with contributing bulls (genotyped bulls with daughter or relationship information in the population). Overall, the results suggest that ssGBLUP methodology under the autoregressive model is a feasible and applicable approach to be used in genomic analyses of longitudinal reproductive traits in Portuguese Holstein cattle.

We investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

The profitability of dairy activity is influenced by the genetic selection of economically important traits. In this context, the correct estimation of genetic parameters and the knowledge regarding candidate genes involved in the expression of genetically associated traits may help develop appropriate breeding strategies for the Guzera breed. In this study, genetic parameters and candidate genes were estimated for accumulated 305-day milk yield (305MY), age at n calving (ANC), and milk yield efficiency (MYE) in Guzera cattle. The (co)variance components were estimated by the restricted maximum likelihood method, and single nucleotide polymorphisms (SNPs) effects were estimated using the weighted single-step genome-wide association approach. The numbers of SNPs and samples used after quality control were 29,869 and 870, respectively. Candidate genes identified in a GWAS were selected to build gene networks by using the ClueGO Cytoscape plug-in (kappa score level ≥ 0.4). Heritabilities estimated for 305MY, ANC, and MYE were moderate to high (0.23, 0.41, and 0.42, respectively). Genetic correlations were positive between 305MY × ANC and 305MY × MYE (0.10 and 0.81, respectively) and negative between MYE × ANC (-0.53). Based on the GWAS analysis, a total of 89 significant SNPs (threshold value of 5.77 on the –log10 scale) were found for 305MY, and no significant SNPs were found for the other traits. Based on the top 5 significant SNPs (i.e., significant SNPs that explained the largest proportion of additive genetic variance), the following positional candidate genes were found to be related to 305MY in Guzera cattle: CIB4, OTOF, LOXL2, PPFIA2, NXN, MRM3, GEMIN4, DBIL5, and FAM57A. In addition, the gene network analysis revealed the candidate genes BCR, P2RY12, P2RY14, GPR87, IFT81, SYT1, IL16, and HOOK3, which shared important biological processes and functions (e.g., Rho guanine nucleotide exchange factor activity, G-protein coupled purinergic nucleotide receptor signaling, intraciliary transport involved in cilium assembly, calcium ion-dependent exocytosis, chemotaxis regulation, and microtubule anchoring at centrosome), for 305MY.

p. 254-258 Submitted by Edileide Reis (leyde-landy@hotmail.com) on 2014-12-16T12:11:00Z No. of bitstreams: 1 Thiago Bruno Ribeiro da Silva.pdf: 288792 bytes, checksum: b7eb8840458d8a5946c856136302defb (MD5) Approved for entry into archive by Alda Lima da Silva (sivalda@ufba.br) on 2014-12-17T21:45:17Z (GMT) No. of bitstreams: 1 Thiago Bruno Ribeiro da Silva.pdf: 288792 bytes, checksum: b7eb8840458d8a5946c856136302defb (MD5) Made available in DSpace on 2014-12-17T21:45:17Z (GMT). No. of bitstreams: 1 Thiago Bruno Ribeiro da Silva.pdf: 288792 bytes, checksum: b7eb8840458d8a5946c856136302defb (MD5) Previous issue date: 2013 A total of 204,912 records of birth weights up to 550 days of age, of 24,890 Nellore cattle, offspring of 375 sires and 16,917 dams from five herds in the state of Mato Grosso, Brazil, were used in order to describe the variability of the weight development by random regression models. The model evaluated as the most suitable used the covariance function of fourth order to describe the variability of the effects of additive genetic, animal permanent environmental and maternal effects of third order to describe the maternal genetic effect, with four classes of residual variance. Heritability estimates ranged from 0.18 to 0.46 from the beginning of trajectory to 210 days of age, from 0.45 to 0.48 post-weaning to 365 days of age and from 0.47 to 0.57 at later ages. The values of additive genetic correlations for different ages showed higher estimates between the closest ages, while birth weight was not very related to the weights at older ages. The body weight performance of the animals has additive genetic variation to respond to selection.

Resumo Foi simulado um teste de progênie com o objetivo de avaliar o efeito da heterogeneidade de variância ambiental sobre a avaliação genética de reprodutores, em situações de número desigual de progênie por reprodutor entre rebanhos. A situação do número desigual de progênie por reprodutor foi representada eliminando-se, de forma aleatória, o número de progênies por reprodutor em ambientes de baixa e alta variabilidade ambiental. Nas situações que envolveram perda de informação no ambiente de menor variabilidade ambiental, houve maiores superestimações na estimação da variância residual, ocasionando menores estimativas de herdabilidade. Por outro lado, quando se diminuiu a quantidade de informação proveniente de rebanhos de alta variabilidade ambiental, houve maior superestimação da variância genética aditiva e menor superestimação da variância ambiental. Embora tenham ocorrido alterações na magnitude dos valores genéticos para os reprodutores, as correlações de Spearman e Pearson entre os valores genéticos dos mesmos, em todas as situaçãoes, foram maiores do que 0,90. O número desigual de progênie por reprodutor na presença de heterogeneidade de variância ambiental provoca superestimação das estimativas de componentes de variância e valores absolutos de predição de valores genéticos dos reprodutores, sem, contudo, alterar a classificação dos mesmos. Abstract A progeny test representing the milk yield was simulated to evaluate the effect of heterogeneity of environmental variance on genetic evaluation of sires in situations of unequal number of progeny per sire between herds, eliminating, at random, daughters per sires in environments of low and high environmental variability. In all situations involving loss of information in the environment of lower environmental variability, there was larger overestimation of the residual variance component, resulting in lower estimates of heritability. On the other hand, the decrease in the amount of information of herds from high environmental variability led to greater overestimation of additive genetic variance and lower overestimation of environmental variance. Although there were alterations in breeding values regarding sires, Spearman and Pearson correlations between breeding values, in all situations, were higher than 0.90. The uneven number of progeny per sire in the presence of heterogeneity of environmental variance led to the overestimation of variance components and changes in absolute breeding values of the sires, without, however, changing the classification values.

O descarte de vacas inaptas à reprodução é prática rotineira no sistema de produção de bovinos e bubalinos de corte, sendo realizado na ocasião do desmame do bezerro. As causas principais do descarte dessas vacas do rebanho são: problemas reprodutivos, baixa habilidade materna e elevada idade. A conseqüência do abate desses animais é o menor rendimento de carcaça. Registros de peso vivo, peso de carcaça, peso de cortes primários e rendimento de carcaça foram utilizados para discriminar possíveis diferenças entre fêmeas bubalinas e bovinas, abatidas durante o ano de 2007. Registros de peso vivo foram utilizados para separar a amostra em fêmeas leves e pesadas, separadamente, em cada espécie, utilizando como limiar o valor correspondente à mediana do peso vivo em cada espécie. Para comparação entre fêmeas, vetores de médias do peso vivo, peso carcaça quente, carcaça quente esquerda e direita, peso da carcaça fria, peso dianteiro, peso traseiro, peso ponta-de-agulha e peso couro foram utilizados procedimentos de inferência multivariada. Os resultados revelaram a heterogeneidade para o peso de abate dos animais, dentro e entre espécies, não havendo um padrão definido de peso ideal ao abate. Búfalas obtiveram maiores pesos para componente de carcaça, quando comparadas às do grupo bovino. Dentro do grupo de fêmeas bovinas não houve diferença estatística para o peso da carcaça fria (p>0,05). Verifica-se que, em média, fêmeas bovinas apresentaram maior rendimento, exceto para o dianteiro. Os grupos intrarraciais apresentaram-se significativamente diferentes entre si (p

Although several studies have investigated the factors affecting imputation accuracy, most of these studies involved a large number of genotyped animals. Thus, results from these studies cannot be directly applied to small populations, since the population structure affects imputation accuracy. In addition, factors affecting imputation accuracy may also be intensified in small populations. Therefore, we aimed to compare different imputation strate-gies for the Portuguese Holstein cattle population considering several commercially available single nucleotide poly-morphism (SNP) panels in a relatively small number of genotyped animals. Data from 1359 genotyped animals were used to evaluate imputation in 7 different scenarios. In the S1 to S6 scenarios, imputations were performed from LDv1, 50Kv1, 57K, 77K, HDv3 and Ax58K panels to 50Kv2 panel. In these scenarios, the bulls in 50Kv2 were divided into reference (352) and validation (101) populations based on the year of birth. In the S7 scenario, the validation population consisted of 566 cows genotyped with the Ax58K panel with theirgenotypes masked to LDv1. In general, all sample imputation accuracies were high with correlations ranging from 0.94 to 0.99 and concordance rate rang-ing from 92.59 to 98.18%. SNP-specific accuracy was consistent with that of sample imputation. S4 (40.32% of SNPs imputed) had higher accuracy than S2 and S3, both with less than 7.59% of SNPs imputed. Most probably, this was due to the high number of imputed SNPs with minor allele frequency (MAF) < 0.05 in S2 and S3 (by 18.43% and 16.06% higher than in S4, respectively). Therefore, for these two scenarios, MAF was more relevant than the panel density. These results suggest that genotype imputation using several commercially available SNP panels is feasible for the Portuguese national genomic evaluation. Made available in DSpace on 2019-12-30T00:36:34Z (GMT). No. of bitstreams: 1 1202019CJAS.pdf: 1178178 bytes, checksum: 114bbd751830ee7632d89d5f814b3518 (MD5) Previous issue date: 2019