A note on transgenerational epigenetics affecting egg quality traits in meat-type quail.

1. The aim of the following experiment was to estimate transgenerational epigenetic variance for egg quality traits using genealogical and phenotypic information in meat-type quail. Measured traits included egg length (EL) and width (EWD), albumen weight (AW), shell weight (SW), yolk weight (YW) and egg weight (EW). 2. A total of 391 birds were evaluated for egg quality by collecting a sample of one egg per bird, during three consecutive days, starting on the 14th d of production. Analyses were performed using mixed models including the random epigenetic effect. Variance components were estimated by the restricted maximum likelihood method. A grid-search for values for the auto-recursive parameter (λ) was used in the variance components estimation. This parameter is directly related to the reset (v) and epigenetic transmissibility (1 - v) coefficients. 3. The epigenetic effect was not significant for any of the egg quality traits evaluated. Direct heritability estimates for egg quality traits ranged in magnitude from 0.06 to 0.33, whereby the higher estimates were found for AW and SW. Epigenetic heritability estimates were low and close to zero (ranging from 0.00 to 0.07) for all evaluated traits. 4. The current breeding strategies accounting for additive genetic effect seem to be suitable for egg quality traits in meat-type quail.

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.

New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program.

Genomic selection is the main force driving applied breeding programs and accuracy is the main measure for evaluating its efficiency. The traditional estimator (TE) of experimental accuracy is not fully adequate. This study proposes and evaluates the performance and efficiency of two new accuracy estimators, called regularized estimator (RE) and hybrid estimator (HE), which were applied to a practical cassava breeding program and also to simulated data. The simulation study considered two individual narrow sense heritability levels and two genetic architectures for traits. TE, RE, and HE were compared under four validation procedures: without validation (WV), independent validation, ten-fold validation through jacknife allowing different markers, and with the same markers selected in each cycle. RE presented accuracies closer to the parametric ones and less biased and more precise ones than TE. HE proved to be very effective in the WV procedure. The estimators were applied to five traits evaluated in a cassava experiment, including 358 clones genotyped for 390 SNPs. Accuracies ranged from 0.67 to 1.12 with TE and from 0.22 to 0.51 with RE. These results indicated that TE overestimated the accuracy and led to one accuracy estimate (1.12) higher than one, which is outside of the parameter space. Use of RE turned the accuracy into the parameter space. Cassava breeding programs can be more realistically implemented using the new estimators proposed in this study, providing less risky practical inferences.

Population structure and genetic diversity of maize inbreds derived from tropical hybrids.

The objectives of this study were to identify the population structure and to assess the genetic diversity of maize inbreds. We genotyped 81 microsatellite loci of 90 maize inbreds that were derived from tropical hybrids and populations. The population structure analysis was based on a Bayesian approach. Each subpopulation was characterized for the effective number of alleles, gene diversity, and number of private alleles. We also performed an analysis of molecular variance and computed a measure of population differentiation (FST). The genetic distances were computed from the similarity index of Lynch and the dissimilarity measures proposed by Smouse and Peakall. The cluster analyses were based on the unweighted pair-group method using arithmetic averages and Tocher method. The clustering efficiency was assessed by the error rate of the discriminant analysis. We also performed a principal coordinates analysis. The population structure analysis revealed three tropical heterotic pools, which have been used by worldwide and Brazilian maize seed companies. The degree of genetic differentiation and of intra- and inter-population genetic diversity for these tropical heterotic pools are comparable to that observed for temperate and subtropical heterotic pools. The higher allelic frequency variation within each tropical heterotic pool and the high genetic diversity between the inbreds were evidence of heterotic groups within the main tropical heterotic pools.

Supervised independent component analysis as an alternative method for genomic selection in pigs.

The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR-BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.

Sire evaluation for total number born in pigs using a genomic reaction norms approach.

In the era of genome-wide selection (GWS), genotype-by-environment (G×E) interactions can be studied using genomic information, thus enabling the estimation of SNP marker effects and the prediction of genomic estimated breeding values (GEBV) for young candidates for selection in different environments. Although G×E studies in pigs are scarce, the use of artificial insemination has enabled the distribution of genetic material from sires across multiple environments. Given the relevance of reproductive traits, such as the total number born (TNB) and the variation in environmental conditions encountered by commercial dams, understanding G×E interactions can be essential for choosing the best sires for different environments. The present work proposes a two-step reaction norm approach for G×E analysis using genomic information. The first step provided estimates of environmental effects (herd-year-season, HYS), and the second step provided estimates of the intercept and slope for the TNB across different HYS levels, obtained from the first step, using a random regression model. In both steps, pedigree ( A: ) and genomic ( G: ) relationship matrices were considered. The genetic parameters (variance components, h(2) and genetic correlations) were very similar when estimated using the A: and G: relationship matrices. The reaction norm graphs showed considerable differences in environmental sensitivity between sires, indicating a reranking of sires in terms of genetic merit across the HYS levels. Based on the G: matrix analysis, SNP by environment interactions were observed. For some SNP, the effects increased at increasing HYS levels, while for others, the effects decreased at increasing HYS levels or showed no changes between HYS levels. Cross-validation analysis demonstrated better performance of the genomic approach with respect to traditional pedigrees for both the G×E and standard models. The genomic reaction norm model resulted in an accuracy of GEBV for "juvenile" boars varying from 0.14 to 0.44 across different HYS levels, while the accuracy of the standard genomic prediction model, without reaction norms, varied from 0.09 to 0.28. These results show that it is important and feasible to consider G×E interactions in evaluations of sires using genomic prediction models and that genomic information can increase the accuracy of selection across environments.