Polynomial order selection in random regression models via penalizing adaptively the likelihood

Heat stress on breeding value prediction for milk yield and composition of a Brazilian Holstein cattle population

Due to the high milk production of Holstein cows, many countries have chosen to import semen to improve local dairy herds. This strategy would be more effective if this semen was used in the same environment conditions in which the bulls were selected. If the effect of genotype by environment (G × E) interaction is not considered, the estimated breeding values (EBVs) may vary, potentially reducing the selection response. We evaluate the impact of heat stress on selection for milk yield and composition of Holstein cows using random regression models. To verify the interference of heat stress in milk yield (MY) and composition traits (fat, protein, total saturated, and total unsaturated fatty acids content in milk), temperature-humidity index (THI) on test-day milk records was used. The threshold value to divide the environments using test-day information from Brazilian Holstein cows was 72 units of THI, i.e., < 72 represented no heat stress and > 72 represented heat stress. Legendre polynomials of second-order (Leg 2) model and two lactation points (33 and 122 DIM) were used to estimate heritabilities and EBVs for five important dairy traits. The heritabilities of milk components and fatty acids were low (0.09-0.29), regardless of lactation period and degree of heat stress, with the exception of protein content (0.30-0.35). Fat content was the only milk component that was reduced according to the degree of heat stress and lactation period. The EBVs tended to decrease in heat stress conditions, thus animals with high genetic potential demonstrated evidence of G × E interaction. However, acclimatization of dairy cows to heat stress in the farm production systems may have been responsible for the low differences among genetic parameters and EBVs with and without heat stress found in this study.

Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle

Simple Summary

Cattle longevity is usually defined as the duration of life of a cow from first calving to death. In addition to a longer lifespan, it is crucial that cows are productive throughout their lives. Incorporating optimal indicators of productive longevity in breeding schemes will directly improve the economic profitability of the beef cattle herd and long-term sustainability of the industry. Thus, the impact of different longevity indicators in the selection of North American Angus cattle was evaluated and optimal parameters were defined to perform the evaluations.

Abstract

This study aimed to propose novel longevity indicators by comparing genetic parameters for traditional (TL; i.e., the cow’s lifespan after the first calving) and functional (FL; i.e., how long the cow stayed in the herd while also calving; assuming no missing (FLa) or missing (FLb) records for unknown calving) longevity, considering different culling reasons (natural death, structural problems, disease, fertility, performance, and miscellaneous). Longevity definitions were evaluated from 2 to 15 years of age, using single- and multiple-trait Bayesian random regression models (RRM). The RRM fitting heterogenous residual variance and fourth order Legendre polynomials were considered as the optimal models for the majority of longevity indicators. The average heritability estimates over ages for FLb (from 0.08 to 0.25) were always higher than those for FLa (from 0.07 to 0.19), and higher or equal to the ones estimated for TL (from 0.07 to 0.23), considering the different culling reasons. The average genetic correlations estimated between ages were low to moderate (~0.40), for all longevity definitions and culling reasons. However, removing the extreme ages (i.e., 2 and >12 years) increased the average correlation between ages (from ~0.40 to >0.70). The genetic correlations estimated between culling reasons were low (0.12 and 0.20 on average, considering all ages and ages between 3 and 12 years old, respectively), indicating that longevity based on different culling reasons should be considered as different traits in the genetic evaluations. Higher average genetic correlations (estimated from 3 to 12 years old) were observed between TL and FLb (0.73) in comparison to TL and FLa (0.64), or FLa and FLb (0.65). Consequently, a higher average proportion of commonly-selected sires, for the top 1% sires, was also observed between TL and FLb (91.74%), compared to TL and FLa (59.68%), or FLa and FLb (61.01%). Higher prediction accuracies for the expected daughter performances (calculated based on the pedigree information) were obtained for FLb in comparison to TL and FLa. Our findings indicate that FLb is preferred for the genetic evaluation of longevity. In addition, it is recommended including multiple longevity traits based on different groups of culling reasons in a selection sub-index, as they are genetically-different traits. Genetic selection based on breeding values at the age of four years is expected to result in greater selection responses for increased longevity in North American Angus cattle.

Adaptability and stability analyses of plants using random regression models

The evaluation of cultivars using multi-environment trials (MET) is an important step in plant breeding programs. One of the objectives of these evaluations is to understand the genotype by environment interaction (GEI). A method of determining the effect of GEI on the performance of cultivars is based on studies of adaptability and stability. Initial studies were based on linear regression; however, these methodologies have limitations, mainly in trials with genetic or statistical unbalanced, heterogeneity of residual variances, and genetic covariance. An alternative would be the use of random regression models (RRM), in which the behavior of the genotypes is characterized as a reaction norm using longitudinal data or repeated measurements and information regarding a covariance function. The objective of this work was the application of RRM in the study of the behavior of common bean cultivars using a MET, based on Legendre polynomials and genotype-ideotype distances. We used a set of 13 trials, which were classified as unfavorable or favorable environments. The results revealed that RRM enables the prediction of the genotypic values of cultivars in environments where they were not evaluated with high accuracy values, thereby circumventing the unbalanced of the experiments. From these values, it was possible to measure the genotypic adaptability according to ideotypes, according to their reaction norms. In addition, the stability of the cultivars can be interpreted as variation in the behavior of the ideotype. The use of ideotypes based on real data allowed a better comparison of the performance of cultivars across environments. The use of RRM in plant breeding is a good alternative to understand the behavior of cultivars in a MET, especially when we want to quantify the adaptability and stability of genotypes.

Genetic insights into elephantgrass persistence for bioenergy purpose

Persistence may be defined as high sustained yield over multi-harvest. Genetic insights about persistence are essential to ensure the success of breeding programs and any biomass-based project. This paper focuses on assessing the biomass yield persistence for bioenergy purpose of 100 elephantgrass clones measured in six growth seasons in Brazil. To assess the clones' persistence, an index based on random regression models and genotype-ideotype distance was proposed. Results suggested the existence of wide genetic variability between elephantgrass clones, and that the yield trajectories along the harvests generate genetic insights into elephantgrass clones' persistence and G x E interaction. A gene pool that acts over the biomass yield (regardless of the harvest) was detected, as well as other gene pools, which show differences on genes expression (these genes are the major responsible for clones' persistence). The lower and higher clones' persistence was discussed based on genome dosage effect and natural biological nitrogen fixation ability applied to bioenergy industry. The huge potential of energy crops necessarily is associated with genetic insights into persistence, so just this way, breeding programs could breed a new cultivar that fulfills the bioenergy industries.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus)

Background

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding.

Methods

We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion.

Conclusions

In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Electronic supplementary material

The online version of this article (10.1186/s12711-017-0357-7) contains supplementary material, which is available to authorized users.

Genetic analysis of partial egg production records in Japanese quail using random regression models

The main objectives of this study were to detect the most appropriate random regression model (RRM) to fit the data of monthly egg production in 2 lines (selected and control) of Japanese quail and to test the consistency of different criteria of model choice. Data from 1,200 female Japanese quails for the first 5 months of egg production from 4 consecutive generations of an egg line selected for egg production in the first month (EP1) was analyzed. Eight RRMs with different orders of Legendre polynomials were compared to determine the proper model for analysis. All criteria of model choice suggested that the adequate model included the second-order Legendre polynomials for fixed effects, and the third-order for additive genetic effects and permanent environmental effects. Predictive ability of the best model was the highest among all models (ρ = 0.987). According to the best model fitted to the data, estimates of heritability were relatively low to moderate (0.10 to 0.17) showed a descending pattern from the first to the fifth month of production. A similar pattern was observed for permanent environmental effects with greater estimates in the first (0.36) and second (0.23) months of production than heritability estimates. Genetic correlations between separate production periods were higher (0.18 to 0.93) than their phenotypic counterparts (0.15 to 0.87). The superiority of the selected line over the control was observed through significant (P < 0.05) linear contrast estimates. Significant (P < 0.05) estimates of covariate effect (age at sexual maturity) showed a decreased pattern with greater impact on egg production in earlier ages (first and second months) than later ones. A methodology based on random regression animal models can be recommended for genetic evaluation of egg production in Japanese quail.