Diversidade genética de bovinos Brahman no Brasil por meio da análise de pedigree

The objective of this work was to evaluate the genetic diversity of Brahman cattle in Brazil with pedigree analysis. Genealogical records of a subpopulation were used considering all pedigree information (Pt) and the pedigree information divided into two periods (P1, from 1994 to 2004; and P2, from 2005 to 2012) or according to the raising system (Ppt, animals on pasture; or Pst, on stable). Estimates were obtained for average inbreeding coefficients, generation intervals (GI), number of equivalent known generation (CGE), number of founders (Nf), effective number of founders (fe), effective number of ancestors (fa), and founder genome equivalents (fg). The average inbreeding coefficients were 11.97, 7.79, 11.95, 11.74, and 11.31% for Pt, P1, P2, Ppt, and Pst, respectively. Average GI was 4.4 years, whereas CGE was 3.18. The fe values were similar to those of fa, which were greater than those of fg. The fe/fa and fg/fe ratios were close to 1, which indicates no genetic bottleneck and small losses by genetic drift. The genetic diversity in the Brazilian population of Brahman breed is not significantly reduced.O objetivo deste trabalho foi avaliar a diversidade genética de bovinos da raça Brahman no Brasil com análise de pedigree. Registros genealógicos de uma subpopulação foram usados, tendo-se considerado toda a informação de pedigree (Pt) e a informação de pedigree dividida em dois períodos (P1, de 1994 a 2004; e P2, de 2005 a 2012) ou de acordo com sistema de criação (Ppt, animais a pasto; ou Pst, estabulados). Foram obtidas as estimativas de coeficientes médios de endogamia, intervalos de geração (IG), número equivalente de gerações conhecidas (EGC), número de fundadores (Nf), número efetivo de fundadores (fe), número efetivo de ancestrais (fa) e número efetivo de genomas remanescentes (fg). Os coeficientes médios de endogamia foram de 11,97, 7,79, 11,95, 11,74 e 11,31% para Pt, P1, P2, Ppt e Pst, respectivamente. O IG médio foi de 4,4 anos, enquanto o EGC médio foi de 3,18. O fe foi próximo de fa, cujos valores foram maiores que os de fg. As razões fe/fa e fg/fe foram próximas de 1, o que indica ausência de gargalo genético e pequenas perdas por deriva genética. A diversidade genética na raça Brahman no Brasil não está significativamente reduzida

A genome-wide association study for morphometric traits in quarter horse

A genome-wide association study for morphometric traits was conducted in 184 Quarter Horses, 120 from a racing population, and 64 from a cutting population, which were genotyped using the Illumina EquineSNP50 chip. Association analysis was performed with 42,058 single-nucleotide polymorphisms (SNPs) (after quality control) using Qxpak5 software. The following traits were measured: weight (W), rump length (RL), and body length (BL). These morphometric traits are important for the best performance in race and cutting events. For weight, three SNPs associated (P < .0001) were found on chromosomes (Equus caballus autosomes [ECA]) 2 and 3. For rump length, eight SNPs associated (P < .0001) were found on ECA 2, 3, 6, 7, 9, 21, and 26. On ECA 3 and ECA 8, two SNPs were associated (P < .0001) with body length. So, a total of 13 important chromosomal regions were identified with Q values of 0.53 (SNPs for W), 0.40 (SNPs for RL), and 0.99 (SNPs for BL). Positional and functional candidate genes emerging from this study were WWOX and AAVPR1A. Further studies are required to confirm these associations in other populations. (c) 2014 Elsevier Inc. All rights reserved

Acurácia da predição genômica para altura do quadril em bovinos Brahman com uso de diferentes matrizes de parentesco

The objective of this work was to evaluate the effects of genomic information on the genetic evaluation of hip height in Brahman cattle using different matrices built from genomic and pedigree data. Hip height measurements from 1,695 animals, genotyped with high-density SNP chip or imputed from 50 K high-density SNP chip, were used. The numerator relationship matrix (NRM) was compared with the H matrix, which incorporated the NRM and genomic relationship (G) matrix simultaneously. The genotypes were used to estimate three versions of G: observed allele frequency (HGOF), average minor allele frequency (HGMF), and frequency of 0.5 for all markers (HG50). For matrix comparisons, animal data were either used in full or divided into calibration (80% older animals) and validation (20% younger animals) datasets. The accuracy values for the NRM, HGOF, and HG50 were 0.776, 0.813, and 0.594, respectively. The NRM and HGOF showed similar minor variances for diagonal and off-diagonal elements, as well as for estimated breeding values. The use of genomic information resulted in relationship estimates similar to those obtained based on pedigree; however, HGOF is the best option for estimating the genomic relationship matrix and results in a higher prediction accuracy. The ranking of the top 20% animals was very similar for all matrices, but the ranking within them varies depending on the method used.O objetivo deste trabalho foi avaliar os efeitos da informação genômica na avaliação genética para altura do quadril em bovinos da raça Brahman, por meio de diferentes matrizes construídas com dados genômicos e de pedigree. Utilizaram-se medidas de altura do quadril de 1.695 animais, genotipados com SNP chip de alta densidade ou imputados do 50 K SNP chip de alta densidade. A matriz de pedigree “numerator relationship matrix” (NRM) foi comparada à matriz H, a qual incorporou as matrizes NRM e de parentesco genômico (G) simultaneamente. Os genótipos foram utilizados para estimar três versões de G: frequência observada dos alelos (HGOF), média da menor frequência alélica (HGMF) e frequência de 0,5 para todos os marcadores (HG50). Para a comparação das matrizes, foram utilizadas informações completas ou divididas em conjuntos de dados de calibração (80% dos animais mais velhos) e de validação (20% dos mais jovens). Os valores de acurácia para NRM, HGOF e HG50 foram 0,776, 0,813 e 0,594, respectivamente. NRM e HGOF foram semelhantes, com menores variâncias para os elementos da diagonal e fora da diagonal, bem como para os valores genéticos estimados. O uso de informações genômicas resultou em estimativas de parentesco semelhantes às obtidas com base em pedigree; entretanto, H­GOF é a melhor opção para estimar a matriz de parentesco genômico e resulta em maiores acurácias de predição. O ranking dos animais top 20% foi muito semelhante para as matrizes, mas a classificação dentro destas varia dependendo do método

Simulation of individual and gene level applied to animal breeding

Submitted by Rodrigo Martins Cruz ([email protected]) on 2015-12-17T16:32:54Z No. of bitstreams: 2 michel_marques_farah.pdf: 437133 bytes, checksum: efc5c1b8937d6edbcc7aaf0f1481a293 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Rodrigo Martins Cruz ([email protected]) on 2015-12-17T16:33:17Z (GMT) No. of bitstreams: 2 michel_marques_farah.pdf: 437133 bytes, checksum: efc5c1b8937d6edbcc7aaf0f1481a293 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2015-12-17T16:33:19Z (GMT). No. of bitstreams: 2 michel_marques_farah.pdf: 437133 bytes, checksum: efc5c1b8937d6edbcc7aaf0f1481a293 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2010Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)A simula??o de dados apresenta diversas vantagens, como proporcionar a obten??o de respostas ? sele??o e diminuir o tempo necess?rio para a avalia??o das metodologias estudadas no melhoramento gen?tico animal. Por?m, os trabalhos que utilizam simula??o empregam v?rios termos como simula??o estoc?stica, simula??o determin?stica, simula??o de Monte Carlo, simula??o em n?vel de gene e simula??o em n?vel de indiv?duo e, muitas vezes, estes termos s?o utilizados de maneiras diferentes ou em outras condi??es, causando uma diverg?ncia nos termos utilizados. Assim, os objetivos deste trabalho foram agrupar, definir e diferenciar os termos t?cnicos utilizados nos trabalhos de simula??o em melhoramento gen?tico animal e comparar e definir as propriedades dos procedimentos de simula??o em n?vel de indiv?duo e em n?vel de gene. Foram desenvolvidos tr?s cen?rios de simula??o, em n?vel de indiv?duo, em n?vel de gene com e sem marcador utilizando o software LZ5. Foram simuladas tr?s popula??es de su?nos para cada cen?rio e com diferentes herdabilidades (0,12, 0,27 e 0,47). A popula??o-base foi constitu?da de 1500 animais, sendo 750 machos e 750 f?meas e para as duas simula??es em n?vel de gene foi considerado um genoma de 2800 cM e 18 cromossomos de tamanhos aleat?rios, as caracter?sticas foram governadas por 500 locos polig?nicos dial?licos, com freq??ncias al?licas iguais e taxa de recombina??o de 0,01. Para a simula??o em n?vel de gene com marcadores, ainda foram distribu?dos marcadores distanciados igualmente a 50 cM e distribu?dos aleatoriamente 5 QTLs por todo o genoma. Os valores amostrados apresentaram bem semelhantes para os tr?s tipos de simula??o, apresentando um aumento das vari?ncias aditiva e fenot?pica e da herdabilidade nas primeiras gera??es e depois decrescendo ao longo das gera??es. J? para a m?dia fenot?pica, houve um ganho gen?tico por gera??o, indicando que todos os m?todos utilizados s?o eficientes para a obten??o de dados simulados. Assim, a vantagem da simula??o em n?vel de gene ? que ? poss?vel simular marcadores moleculares e QTLs, enquanto a simula??o em n?vel de indiv?duo ? muito eficiente para obten??o de dados como o valor gen?tico do indiv?duo e da m?dia fenot?pica da popula??o em um per?odo de tempo muito menor, pois demanda menos recursos computacionais e de algoritmos estruturados para desenvolver quando comparado com a simula??o em n?vel de gene. Portanto, define-se simula??o em n?vel de indiv?duo como uma metodologia de simula??o que consiste em gerar valores gen?ticos (G) a partir de uma distribui??o normal com m?dia e vari?ncia previamente definidas; enquanto para a simula??o em n?vel de gene a metodologia consiste em gerar os valores dos efeitos de cada loco polig?nico e seus QTLs, a partir de uma distribui??o normal com m?dia e vari?ncia previamente definidas para cada componente, e pela soma destes, obt?m-se o G de cada indiv?duo da popula??o. Para a gera??o do efeito residual (E) as duas metodologias de simula??o s?o feitas da mesma forma, gerando-se um efeito aleat?rio amostrado, tamb?m, de uma distribui??o normal e assim obt?m-se os valores fenot?picos (P) de cada indiv?duo pela soma destes dois componentes (G+E).Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Zootecnia, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2010.ABSTRACT The simulation data has several advantages, such as providing the obtaining responses to selection and reduce the time required for evaluation methodologies studied in animal breeding. However, simulation studies employ various terms such as simulation stochastic, deterministic simulation, Monte Carlo simulation, simulation level of gene and simulation at the individual level and often these terms are used in different ways or in other conditions, causing a divergence in the terms used. Thus, the objectives were cluster, define and differentiate the technical terms used in the work of simulation in animal breeding and compare and define the properties procedures for simulation-level and individual-level gene. There had been developed three scenarios for simulation at the individual-level and level gene, with and without marker, using the software LZ5. There had been simulated three pig populations for each scenario, with different heritabilities (0.12, 0.27 and 0.47). The base population consisted of 1500 animals, 750 males and 750 females and for both simulations at the level of the gene was considered a genome of 2800 cM, and 18 chromosomes in random sizes, the characteristics were governed by 500 loci diallelic polygenic, with equal allele frequencies and recombination rate of 0.01. For the simulation Level with gene markers, were also distributed bookmarks equally spaced at 50 cM and five QTL distributed randomly across the genome. The sampled values were very similar for the three types of simulation, an increase of additive variance and phenotype and heritability in the first generations and then decreasing to over the generations. As for the average phenotype was a genetic per generation, indicating that all methods used are efficient for obtain simulated data. Thus, the advantage of gene-level simulation is that it can simulate molecular markers and QTLs, while the simulation at individual level is very efficient for obtaining data as the individual's genetic value and phenotypic average of the population over a period of much less time, since it requires less computational resources and algorithms structured to develop, when compared with the simulation-level gene. Therefore, it is defined as the individual level simulation a methodology simulation that generates breeding values (G) from a normal distribution with mean and variance as previously defined; and the gene level simulation is defined as a methodology that generates the values of effects of each locus and their polygenic QTLs from a normal distribution with mean and variance previously defined for each component, and the sum of these gives the G of each individual in the population. For the generation of residual effect (E) the two simulation methodologies are made in the same way, generating a random effects sampled also a normal distribution and so it was obtained the phenotypic values (P) of each individual by summing these two components (G+E)

Efeito da utilização de diferentes matrizes genômicas e parentesco na avaliação genética de bovinos de corte

No melhoramento genético animal a forma tradicional de realizar seleção é com base no fenótipo dos indivíduos e na informação do parentesco entre estes, porém é um processo lento, sendo assim, programas de melhoramento estão procurando identificar os genes responsáveis pela característica de interesse e assim realizar a seleção dos animais que carregam a informação desejada. Com as informações dos indivíduos genotipados, tornou-se possível a utilização da informação de genes idênticos em estado tornando viável a utilização de uma matriz de parentesco (G) permitindo aumentar a precisão das avaliações genéticas, porém, devido à dificuldade de se obter o genótipo de todos os animais de uma população, foi proposto um método que realiza a integração da matriz G com a matriz de parentesco (A) em uma matriz de parentesco-genômica (H). Embora tenham trabalhos que indiquem uma similaridade no progresso genético utilizando estas diferentes matrizes é importante a avaliação da contribuição da avaliação genômica nos processos de avaliação genética em populações com estruturas de parentesco diferentes, bem como avaliar a metodologia de seleção genômica em populações multirraciais, a fim de atender o sistema de criação de animais cruzados. Assim, o objetivo geral deste trabalho foi estudar os efeitos da informação genômica na avaliação genética animal por meio de diferentes matrizes genômicas, utilizando dados de bovinos de corte com diferentes estruturas populacionais e composições raciais. Primeiramente avaliou-se 3 diferentes metodologias de se obter a matriz H, com a frequência alélica observada (HGOF), menor frequência alélica (HGMF) e uma frequência de 0,5 para todos os SNPs (HG50). Foram feitas comparações entre estas matrizes genômicas e a matriz de parentesco tradicional (A) utilizando uma população de 1695 animais da raça Brahman (BB). De acordo com os ...In animal breeding methodologies, the traditional method of performing selection is based on the phenotype of individuals and information of relationship between them, but it is a slow process, so breeding programs are trying to identify the genes responsible for the trait of interest and thus achieve selection of animals that carry the interesting genes. With the information of genotyped individuals, it became possible to use the information of genes identical in state making it feasible to use a relationship matrix (G) which increase the accuracy of genetic evaluations, however, due to difficulty of obtaining the genotype of all animals in a population, we propose a method that performs the integration of the G matrix with the relationship matrix (A) in a pedigree-genomic relationship matrix (H). Although studies indicating a similarity in genetic progress using these matrices is important to evaluate the contribution of genomic evaluation in the process of genetic evaluation in populations with different structures of kinship, as well as evaluating the methodology of genomic selection in multiracial populations in order to cater to the creation of crossbred system. Thus the objective of this work was to study the effects of genomic information in genetic evaluation through different genomic arrays using data from beef cattle with different population structures and racial compositions. First we evaluated three different methods of obtaining the H matrix with the observed allele frequency (HGOF), lower allele frequency (HGMF) and a frequency of 0.5 for all SNPs (HG50). Comparisons between these genomic arrays and traditional kinship (A) using a population of 1695 animals breed Brahman (BB) matrix were made. According to the results , the HGOF was a matrix that showed the greatest similarity to the matrix A but the greatest differences were found in the classification of animals, when we evaluated the classification of animals ..

Impact of reproductive and productive rates on levels of inbreeding and genetic gain of pigs through data simulation

The objective of this study was to evaluate the impact of farrowing and mortality rates on inbreeding levels and genetic gain through data simulation. Data came from two real populations A and B, composed of Pietrain and Landrace breed pigs, respectively. To generate the simulated populations, a Fortran-language simulator was developed using the (co)variances of the breeding values and productive and reproductive information obtained from populations A and B, as well as restrictions on mating and animals selected per generation. Two data files were created. The first contained the pedigree of the previous 10 years, with 21,906 and 251,343 animals in populations A and B, respectively. The second included the breeding values for age, backfat thickness, and feed conversion, all of which were adjusted for 110 kg live weight, for both populations; longissimus dorsi muscle depth adjusted for 110 kg live weight, for population A only; and number of live piglets at the fifth day of life per farrowing, for population B only. Three scenarios were simulated by varying the farrowing and mortality rates during the lactation period. Ten generations were simulated, with 30 replicates for each generation and scenario. Inbreeding levels in closed production units increase with productive and reproductive losses, and these reduce the variances of breeding values, selection intensity, and genetic gains by reducing the number of animals available for selection. Actions that maximize farrowing rates are more important than those that minimize mortality rates during the lactation period, since a reduction in simulated farrowing resulted in greater losses of genetic gain

Accuracy of genomic selection predictions for hip height in Brahman cattle using different relationship matrices

The objective of this work was to evaluate the effects of genomic information on the genetic evaluation of hip height in Brahman cattle using different matrices built from genomic and pedigree data. Hip height measurements from 1,695 animals, genotyped with high-density SNP chip or imputed from 50 K high-density SNP chip, were used. The numerator relationship matrix (NRM) was compared with the H matrix, which incorporated the NRM and genomic relationship (G) matrix simultaneously. The genotypes were used to estimate three versions of G: observed allele frequency (H-GOF), average minor allele frequency (H-GMF), and frequency of 0.5 for all markers (H-G50). For matrix comparisons, animal data were either used in full or divided into calibration (80% older animals) and validation (20% younger animals) datasets. The accuracy values for the NRM, H-GOF, and H-G50 were 0.776, 0.813, and 0.594, respectively. The NRM and H-GOF showed similar minor variances for diagonal and off-diagonal elements, as well as for estimated breeding values. The use of genomic information resulted in relationship estimates similar to those obtained based on pedigree; however, H-GOF is the best option for estimating the genomic relationship matrix and results in a higher prediction accuracy. The ranking of the top 20% animals was very similar for all matrices, but the ranking within them varies depending on the method used

Speed Index in the Racing Quarter Horse: A Genome-wide Association Study

The racing line of Quarter Horses is characterized by great sprinting speed over short distances on straight tracks. To perform selection on racing horses, the speed index (SI) and conformation traits are often used. A genome-wide association study (GWAS) on 112 racing Quarter Horses was performed for the SI trait, and markers and genes associated were reported. The GWAS was carried out using the Qxpak.5 software and the genotyping data obtained from the Equine SNP50 BeadChip. A total of eight significant single-nucleotide polymorphisms (SNPs) (P < .0001; Q = 0.25) distributed on Equus caballus autosomal chromosomes 2, 4, 10, 18, and 27 were found and served as markers for genomic regions mined for candidate genes associated with SI. For candidate gene, annotation was considered 100 kb windows upstream and downstream to each important SNP. The highlighted genes were GRM8, GRIK2, NEB, ANK1, and KAT6A because their function could be related to racing performance. Future studies should consider a validation study with an independent population, and sequencing of these candidate genes should be done to identify causal alleles. (C) 2014 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES