Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Background: Many studies have provided evidence of the existence of genetic heterogeneity of environmental variance, suggesting that it could be exploited to improve robustness and uniformity of livestock by selection. However, little is known about the perspectives of such a selection strategy in beef cattle.Methods: A two-step approach was applied to study the genetic heterogeneity of residual variance of weight gain from birth to weaning and long-yearling weight in a Nellore beef cattle population. First, an animal model was fitted to the data and second, the influence of additive and environmental effects on the residual variance of these traits was investigated with different models, in which the log squared estimated residuals for each phenotypic record were analyzed using the restricted maximum likelihood method. Monte Carlo simulation was performed to assess the reliability of variance component estimates from the second step and the accuracy of estimated breeding values for residual variation.Results: The results suggest that both genetic and environmental factors have an effect on the residual variance of weight gain from birth to weaning and long-yearling in Nellore beef cattle and that uniformity of these traits could be improved by selecting for lower residual variance, when considering a large amount of information to predict genetic merit for this criterion. Simulations suggested that using the two-step approach would lead to biased estimates of variance components, such that more adequate methods are needed to study the genetic heterogeneity of residual variance in beef cattle

A comparison of statistical methods for genomic selection in a mice population

Background: The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. Because the number of markers is often much larger than the number of phenotypes, marker effect estimation is not a trivial task. The objective of this research was to compare the predictive performance of ten different statistical methods employed in genomic selection, by analyzing data from a heterogeneous stock mice population.Results: For the five traits analyzed (W6W: weight at six weeks, WGS: growth slope, BL: body length, %CD8+: percentage of CD8+ cells, CD4+/ CD8+: ratio between CD4+ and CD8+ cells), within-family predictions were more accurate than across-family predictions, although this superiority in accuracy varied markedly across traits. For within-family prediction, two kernel methods, Reproducing Kernel Hilbert Spaces Regression (RKHS) and Support Vector Regression (SVR), were the most accurate for W6W, while a polygenic model also had comparable performance. A form of ridge regression assuming that all markers contribute to the additive variance (RR_GBLUP) figured among the most accurate for WGS and BL, while two variable selection methods (LASSO and Random Forest, RF) had the greatest predictive abilities for % CD8+ and CD4+/ CD8+. RF, RKHS, SVR and RR_GBLUP outperformed the remainder methods in terms of bias and inflation of predictions.Conclusions: Methods with large conceptual differences reached very similar predictive abilities and a clear re-ranking of methods was observed in function of the trait analyzed. Variable selection methods were more accurate than the remainder in the case of % CD8+ and CD4+/ CD8+ and these traits are likely to be influenced by a smaller number of QTL than the remainder. Judged by their overall performance across traits and computational requirements, RR_GBLUP, RKHS and SVR are particularly appealing for application in genomic selection

Two pre-weaning selection criteria in Gyr cattle. 2. Effect in animal classification

A utilização de dois critérios de seleção na pré-desmama, ganho médio diário do nascimento à desmama (GMD) e dias para ganhar 160 kg do nascimento à desmama (D160), foi estudada, analisando-se informações de 16.592 animais, provenientes do controle de desenvolvimento ponderal da Associação Brasileira dos Criadores de Zebu, nascidos no período de 1978 a 1994. Foram incluídos no modelo o efeito fixo de grupo de contemporâneos e os efeitos aleatórios genético aditivo de animal e materno, de ambiente permanente materno e o erro. A covariância entre os efeitos direto e materno foi considerada igual a zero. As estimativas dos componentes de variância e herdabilidade foram obtidas pelo método da máxima verossimilhança restrita e os valores genéticos preditos (VGs), por modelos animais uni-característica. As estimativas de herdabilidade foram: 0,12; 0,05; 0,10 e 0,05 para GMD (efeito direto), GMD (efeito materno), D160 (efeito direto) e D160 (efeito materno), respectivamente. Foram estimadas a correlação genética entre GMD e D160 (efeito direto e materno) e a correlação de classificação (Spearman) entre os valores genéticos para as categorias de touros, vacas e bezerros. As estimativas de correlação genética entre GMD e D160 foram 0,86 e 0,88, para o efeito direto e materno, respectivamente. As estimativas de correlação de ;rank;, também foram altas, entretanto, nenhuma foi igual a um, resultando em alterações na classificação dos animais. A relação entre as médias aritmética (A) e harmônica (H) e o desvio-padrão (S) do GMD ajustado para efeitos ambientais e maternos (GMDc) foi verificada utilizando-se um modelo restrito, sem intercepto, mediante as regressões linear e quadrática do S do GMDc sobre a diferença entre a média aritmética e média harmônica (A-H). Os resultados evidenciaram que, semelhantemente a H, o critério D160 apresentou a propriedade de discriminar touros com progênie mais uniforme.Two different preweaning selection criteria in Gyr cattle: average daily gain from birth to weaning (ADG) and number of days to gain 160 kg from birth to weaning (D160) were evaluated in this study. Records from the Brazilian Zebu Breeders Association obtained from 1978 to 1994, were studied. (Co)variance components and heritabilities were estimated by restricted maximum likelihood derivative free method, and expected breeding values (EBV) were predicted using an single trait animal model. In the model, contemporary group was considered as fixed effect and direct and maternal genetic, permanent environment and error as random effects. The covariance between the genetic direct and maternal genetic effects was assumed to be zero. Pearson's and Spearman's correlations of direct and maternal breeding values for ADG and D160 were estimated. Estimated heritability values were 0.12, 0.05, 0.10 and 0.05 for ADG (direct effect), ADG (maternal effect), D160 (direct effect), and D160 (maternal effect), respectivelly. The genetic correlation estimates between ADG and D160 for direct (0.86) and maternal effects (0.88) showed a strong and positive association. Rank correlation estimates were also high but not the unity, so changes occured in the animals rankings according to the trait adopted as the criterion. The relationship between the harmonic mean (H), arithmetic mean (A) and standard deviation (S) of ADG preajusted for environmental and maternal effects was also studied according to linear and quadratic regression of S on the diffrence between A and H (A-H). The results indicated that similarly to H, D160 favoured sires with more homogeneous progeny

Study of whole genome linkage disequilibrium in Nellore cattle

Background: Knowledge of the linkage disequilibrium (LD) between markers is important to establish the number of markers necessary for association studies and genomic selection. The objective of this study was to evaluate the extent of LD in Nellore cattle using a high density SNP panel and 795 genotyped steers.Results: After data editing, 446,986 SNPs were used for the estimation of LD, comprising 2508.4 Mb of the genome. The mean distance between adjacent markers was 4.90 ± 2.89 kb. The minor allele frequency (MAF) was less than 0.20 in a considerable proportion of SNPs. The overall mean LD between marker pairs measured by r2 and |D'| was 0.17 and 0.52, respectively. The LD (r2) decreased with increasing physical distance between markers from 0.34 (1 kb) to 0.11 (100 kb). In contrast to this clear decrease of LD measured by r2, the changes in |D'| indicated a less pronounced decline of LD. Chromosomes BTA1, BTA27, BTA28 and BTA29 showed lower levels of LD at any distance between markers. Except for these four chromosomes, the level of LD (r2) was higher than 0.20 for markers separated by less than 20 kb. At distances < 3 kb, the level of LD was higher than 0.30. The LD (r2) between markers was higher when the MAF threshold was high (0.15), especially when the distance between markers was short.Conclusions: The level of LD estimated for markers separated by less than 30 kb indicates that the High Density Bovine SNP BeadChip will likely be a suitable tool for prediction of genomic breeding values in Nellore cattle. © 2013 Espigolan et al.; licensee BioMed Central Ltd

Accuracy of genotype imputation in Nelore cattle

Background: Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.Methods: Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs Flmpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina (R) BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.Results: Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. Flmpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.Conclusions: If the Illumina (R) BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with Flmpute

Strategies for genotype imputation in composite beef cattle

Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R(2)). The highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE. The genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals