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Additional file 1: Figure S1. Boxplot of calving rest interval (CRI) of REB: All records of rebreeding of females; REBB: Rebreeding of females that entered reproduction at two years old; REBA: Rebreeding of precocity heifers; IR: Interval of rest (in days); N: Number of animals in each class.

The objective of this study was to estimate (co)variances and genetic parameters and to predict genetic trends for weight at 120 (W120), 210 (W210), 365 (W365), and 450 (W450) days of age in Nelore cattle raised in the northern region of Brazil. The database comprised records of 30,387 animals born between 2000 and 2013 in the Brazilian North. Estimates were calculated by the Restricted Maximum Likelihood (REML) method, in single- and multi-trait analyses in an animal model. Heritability as obtained using single- and multi-trait models for W120 (0.22 and 0.31), W210 (0.20 and 0.34), W365 (0.51 and 0.51), and W450 (0.49 and 0.51) indicated moderate to high magnitudes, with the possibility of genetic selection and incorporation into the herd. Genetic correlations between growth traits were favorable, ranging from 0.78 to 0.96. Genetic trends for W120 and W210 varied largely, from -0.31 to 4.68 and -0.53 to 7.62 kg/year, respectively. Smaller fluctuations were observed in genetic trends for W365 and W450, which ranged from -1.08 to 10.90 and -1.29 to 12.51 kg/year, respectively. Selection for W365 and W450 proved to be the criterion of choice for Nelore herds raised in the region; however, it may compromise adult performance because of higher costs and time for production. A thorough analysis of mattings is recommended to allow the selection of earlier-developing animals. O presente trabalho foi delineado para estimar as (co) variâncias, parâmetros genéticos e de predizer as tendências genéticas para o peso aos 120 (W120), 210 (W210), 365 (W365) e 450 (W450) dias de idade de gado Nelore criado na região norte do Brasil. A base de dados foi constituituída por registro de 30387 animais, nascidos entre 2000 e 2013 no norte do Brasil. As estimativas foram calculadas pelo método de máxima restrição de probabilidade (REML) em um modelo animal com análises isoladas e multi variadas. A herdabilidade obtida para os modelos utilizados foi: W120 (0,22 e 0,31); W210 (0,20 e 0,34); W365 (0,51 e 0,51) e W450 (0,49 e 0,51), indicando moderada e alta magnitude com a possibilidade de seleção genética e incorporação no rebanho. As correlações genéticas entre grupos de tendências foram favoráveis variando de 0,78 a 0,96. As tendências genéticas para W120 e W210 apresentaram ampla variação de -0,31 a 4,68 e -0,53 a 7,62 kg/ano, respectivamente. Menores flutuações foram observadas nas tendências genéticas para W365 e W450, as quais variaram de -1,08 a 10,90 e -1,29 a12,51 kg/ano, respectivamente. Foi constatado que a seleção para W365 e W450 deve ser um critério de escolha para os rebanhos Nelore criados na região; contudo ela pode comprometer a performance dos adultos devido aos elevados custos e da duração da produção. Uma completa análise de cruzamentos é recomendada para possibilitar a seleção de animais jovens em desenvolvimento.

QQ plot for single-SNP genome-wide association analysis. (PDF 14 kb)

QQ plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 2144 kb)

Manhattan plot for single-SNP genome-wide association analysis. (PDF 55 kb)

Manhattan plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 8648 kb)

Made available in DSpace on 2018-11-26T17:55:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-09-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The objective of this study was to estimate genetic parameters for carcass and meat quality traits, as well as their genetic correlations using pedigree and genomic information. A total of 3,716; 3,702; 3,439; 3,705; and 3,714 records of 12th-13th rib LM area (LMA), backfat thickness (BF), HCW, marbling score (MARB), and Warner-Bratzler peak shear force (WBSF), respectively, were used. Animals were geno-typed with BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki panel. The (co) variance components were estimated by Bayesian inference using a multitrait ssGBLUP analysis. The animal model included fixed effects of contemporary group (defined by the combination of farm and year of birth, and management group at yearling) and age of animal at slaughtering as a covariate (linear). Direct additive genetic and residual effects were fitted as random. The posterior means and SD of heritabilities for LMA, BF, HCW, MARB, and WBSF were 0.28 (0.03), 0.21 (0.04), 0.21 (0.04), 0.12 (0.04), and 0.11 (0.03), respectively. The posterior means for genetic correlations between LMA and meat quality were positive and moderate with MARB (0.38 +/- 0.12) and negative with WBSF (-0.47 +/- 0.12). Low genetic correlations were estimated between BF and WBSF (-0.03 +/- 0.16) and between HCW and MARB (-0.04 +/- 0.14), indicating that these traits are not controlled by the same set or linked genes. Carcass traits (LMA, BF, and HCW) presented moderate heritability providing quick response to the selection purpose. The estimates of heritability for meat quality traits (MARB and WBSF) were low and indicate that the rate of genetic improvement for these traits would be slow. Genetic correlations indicated that selection for carcass traits would not be strongly antagonistic for improving meat quality. Sao Paulo State Univ, UNESP, Sch Agr & Vet Sci, Anim Sci Dept, Via Acesso Prof Paulo Donato Castellane S-N, BR-14488490 Jaboticabal, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Via Acesso Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil Sao Paulo State Univ, UNESP, Sch Agr & Vet Sci, Anim Sci Dept, Via Acesso Prof Paulo Donato Castellane S-N, BR-14488490 Jaboticabal, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Via Acesso Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil FAPESP: 2009/16118-5

Submitted by RAFAEL ESPIGOLAN (espigolan@yahoo.com.br) on 2017-03-17T22:04:14Z No. of bitstreams: 1 Tese_Rafael_Espigolan.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2017-03-22T12:50:50Z (GMT) No. of bitstreams: 1 espigolan_r_dr_jabo.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) Made available in DSpace on 2017-03-22T12:50:50Z (GMT). No. of bitstreams: 1 espigolan_r_dr_jabo.pdf: 1532864 bytes, checksum: c79ad7471b25137c47529f25762a83a2 (MD5) Previous issue date: 2017-02-23 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) O melhoramento genético animal visa melhorar a produtividade econômica das futuras gerações de espécies domésticas por meio da seleção. A maioria das características de interesse econômico na pecuária é de expressão quantitativa e complexa, isto é, são influenciadas por vários genes e afetadas por fatores ambientais. As análises estatísticas de informações de fenótipo e pedigree permite estimar os valores genéticos dos candidatos à seleção com base no modelo infinitesimal. Uma grande quantidade de dados genômicos está atualmente disponível para a identificação e seleção de indivíduos geneticamente superiores com o potencial de aumentar a acurácia de predição dos valores genéticos e, portanto, a eficiência dos programas de melhoramento genético animal. Vários estudos têm sido conduzidos com o objetivo de identificar metodologias apropriadas para raças e características específicas, o que resultará em estimativas de valores genéticos genômicos (GEBVs) mais acurados. Portanto, o objetivo deste estudo foi verificar a possibilidade de aplicação de modelos semiparamétricos para a seleção genômica e comparar a habilidade de predição com os modelos paramétricos para dados reais (características de carcaça, qualidade da carne, crescimento e reprodutiva) e simulados. As informações fenotípicas e de pedigree utilizadas foram fornecidas por onze fazendas pertencentes a quatro programas de melhoramento genético animal. Para as características de carcaça e qualidade da carne, o banco de dados continha 3.643 registros para área de olho de lombo (REA), 3.619 registros para espessura de gordura (BFT), 3.670 registros para maciez da carne (TEN) e 3.378 observações para peso de carcaça quente (HCW). Um total de 825.364 registros para peso ao sobreano (YW) e 166.398 para idade ao primeiro parto (AFC) foi utilizado para as características de crescimento e reprodutiva. Genótipos de 2.710, 2.656, 2.749, 2.495, 4.455 e 1.760 animais para REA, BFT, TEN, HCW, YW e AFC foram disponibilizados, respectivamente. Após o controle de qualidade, restaram dados de, aproximadamente, 450.000 polimorfismos de base única (SNP). Os modelos de análise utilizados foram BLUP genômico (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) e as abordagens semiparamétricas Reproducing Kernel Hilbert Spaces (RKHS) e Kernel Averaging (KA). Para cada característica foi realizada uma validação cruzada composta por cinco “folds” e replicada aleatoriamente trinta vezes. Os modelos estatísticos foram comparados em termos do erro do quadrado médio (MSE) e acurácia de predição (ACC). Os valores de ACC variaram de 0,39 a 0,40 (REA), 0,38 a 0,41 (BFT), 0,23 a 0,28 (TEN), 0,33 a 0,35 (HCW), 0,36 a 0,51 (YW) e 0,49 a 0,56 (AFC). Para todas as características, os modelos GBLUP e BL apresentaram acurácias de predição similares. Para REA, BFT e HCW, todos os modelos apresentaram ACC similares, entretanto a regressão RKHS obteve o melhor ajuste comparado ao KA. Para características com maior quantidade de registros fenotípicos comparada ao número de animais genotipados (YW e AFC) o modelo ssGBLUP é indicado. Considerando o desempenho geral, para todas as características estudadas, a regressão RKHS é, particularmente, uma alternativa interessante para a aplicação na seleção genômica, especialmente para características de baixa herdabilidade. No estudo de simulação, genótipos, pedigree e fenótipos para quatro características (A, B, C e D) foram simulados utilizando valores de herdabilidade baseados nos obtidos com os dados reais (0,09, 0,12, 0,36 e 0,39 para cada característica, respectivamente). O genoma simulado consistiu de 735.293 marcadores e 1.000 QTLs distribuídos aleatoriamente por 29 pares de autossomos, com comprimento variando de 40 a 146 centimorgans (cM), totalizando 2.333 cM. Assumiu-se que os QTLs explicavam 100% da variação genética. Considerando as frequências do alelo menor maiores ou iguais a 0,01, um total de 430.000 marcadores foram selecionados aleatoriamente. Os fenótipos foram obtidos pela soma dos resíduos (aleatoriamente amostrados de uma distribuição normal com média igual a zero) aos valores genéticos verdadeiros, e todo o processo de simulação foi replicado 10 vezes. A ACC foi calculada por meio da correlação entre o valor genético genômico estimado e o valor genético verdadeiro, simulados da 12a a 15a geração. A média do desequilíbrio de ligação, medido entre os pares de marcadores adjacentes para todas as características simuladas foi de 0,21 para as gerações recentes (12a, 13a e 14a), e 0,22 para a 15a geração. A ACC para as características simuladas A, B, C e D variou de 0,43 a 0,44, 0,47 a 0,48, 0,80 a 0,82 e 0,72 a 0,73, respectivamente. Diferentes metodologias de seleção genômica implementadas neste estudo mostraram valores similares de acurácia de predição, e o método mais adequado é dependente da característica explorada. Em geral, as regressões RKHS obtiveram melhor desempenho em termos de ACC com menor valor de MSE em comparação com os outros modelos. Animal breeding aims to improve economic productivity of future generations of domestic species through selection. Most of the traits of economic interest in livestock have a complex and quantitative expression i.e. are influenced by a large number of genes and affected by environmental factors. Statistical analysis of phenotypes and pedigree information allows estimating the breeding values of the selection candidates based on infinitesimal model. A large amount of genomic data is now available for the identification and selection of genetically superior individuals with the potential to increase the accuracy of prediction of genetic values and thus, the efficiency of animal breeding programs. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate genomic estimated breeding values (GEBVs). Therefore, the objective of this study was to verify the possibility of applying semi-parametric models for genomic selection and to compare their ability of prediction with those of parametric models for real (carcass, meat quality, growth and reproductive traits) and simulated data. The phenotypic and pedigree information used were provided by farms belonging to four animal breeding programs which represent eleven farms. For carcass and meat quality traits, the data set contained 3,643 records for rib eye area (REA), 3,619 records for backfat thickness (BFT), 3,670 records for meat tenderness (TEN) and 3,378 observations for hot carcass weight (HCW). A total of 825,364 records for yearling weight (YW) and 166,398 for age at first calving (AFC) were used as growth and reproductive traits of Nelore cattle. Genotypes of 2,710, 2,656, 2,749, 2,495, 4,455 and 1,760 animals were available for REA, BFT, TEN, HCW, YW and AFC, respectively. After quality control, approximately 450,000 single nucleotide polymorphisms (SNP) remained. Methods of analysis were genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) and the semi-parametric approaches Reproducing Kernel Hilbert Spaces (RKHS) regression and Kernel Averaging (KA). A five-fold cross-validation with thirty random replicates was carried out and models were compared in terms of their prediction mean squared error (MSE) and accuracy of prediction (ACC). The ACC ranged from 0.39 to 0.40 (REA), 0.38 to 0.41 (BFT), 0.23 to 0.28 (TEN), 0.33 to 0.35 (HCW), 0.36 to 0.51 (YW) and 0.49 to 0.56 (AFC). For all traits, the GBLUP and BL models showed very similar prediction accuracies. For REA, BFT and HCW, models provided similar prediction accuracies, however RKHS regression had the best fit across traits considering multiple-step models and compared to KA. For traits which have a higher number of animals with phenotypes compared to the number of those with genotypes (YW and AFC), the ssGBLUP is indicated. Judged by overall performance, across all traits, the RKHS regression is particularly appealing for application in genomic selection, especially for low heritability traits. Simulated genotypes, pedigree, and phenotypes for four traits A, B, C and D were obtained using heritabilities based on real data (0.09, 0.12, 0.36 and 0.39 for each trait, respectively). The simulated genome consisted of 735,293 markers and 1,000 QTLs randomly distributed over 29 pairs of autosomes, with length varying from 40 to 146 centimorgans (cM), totaling 2,333 cM. It was assumed that QTLs explained 100% of genetic variance. Considering Minor Allele Frequencies greater or equal to 0.01, a total of 430,000 markers were randomly selected. The phenotypes were generated by adding residuals, randomly drawn from a normal distribution with mean equal to zero, to the true breeding values and all simulation process was replicated 10 times. ACC was quantified using correlations between the predicted genomic breeding value and true breeding values simulated for the generations of 12 to 15. The average linkage disequilibrium, measured between pairs of adjacent markers for all simulated traits was 0.21 for recent generations (12, 13 and 14), and 0.22 for generation 15. The ACC for simulated traits A, B, C and D ranged from 0.43 to 0.44, 0.47 to 0.48, 0.80 to 0.82 and 0.72 to 0.73, respectively. Different genomic selection methodologies implemented in this study showed similar accuracies of prediction, and the optimal method was sometimes trait dependent. In general, RKHS regressions were preferable in terms of ACC and provided smallest MSE estimates compared to other models. FAPESP: 2014/00779-0 FAPESP: 2015/13084-3

Principal Component Analysis (PCA) of gene FPKM values between the high and low groups with fatty acids profile: (a) PCA for sum of SFA; (b) PCA for sum of MUFA; (c) PCA for sum of PUFA; (d) PCA for sum of PUFA/SFA ratio; (e) PCA for sum of n3;.(f) PCA for sum of n6; (g) PCA for n6/n3 ratio;(h) PCA for myristic acid (C14:0); (i) PCA for palmitic acid (C16:0); (j) PCA for stearic acid (C18:0);(k) PCA for oleic acid (C18:1 cis-9); (l) PCA for conjugated linoleic acid (C18:2 cis-9 trans-11); (m) PCA for linoleic acid (C18:2 cis6 cis12 n6); (n) PCA for Îą-linolenic (C18:3 n3). (PDF 459 kb)

Boxplot of the transcript abundance distribution for each beef fatty acid and gene expressed. (PDF 42 kb)