The objective of this study was to estimate genetic parameters for female mature weight (FMW), age at first calving (AFC), weight gain from birth to 120 days (WG_B_120), from 210 to 365 days (WG_210_365), rib eye area (REA), back fat thickness (BF), rump fat (RF) and body weight at scanning date (BWS) using single and multiple-trait animal models by the REML method from Nellore cattle data. The estimates of heritability ranged from 0.1637 0.011 for WG_210_365 to 0.309 70.028 for RF using the single-trait model and from 0.163 70.010 for WG_210_365 to 0.382 70.025 for BWS using the multiple-trait model. The estimates of genetic correlations ranged from −0.357 0.08 between AFC with BF to 0.69 7 0.04 between WG_B_120 with BWS. Selection for weights gains, REA, and BWS can improve FMW.

Brazilian Registry of Clinical Trials, RBR-95sxqv. [de Andrade RL, Bø K, Antonio FI, Driusso P, Mateus-Vasconcelos ECL, Ramos S, Julio MP, Ferreira CHJ (2018) An education program about pelvic floor muscles improved women's knowledge but not pelvic floor muscle function, urinary incontinence or sexual function: a randomised trial. Journal of Physiotherapy 64: 91-96].

The objectives of this paper were to identify the phenotypic egg-laying patterns in a White Leghorn line mainly selected for egg production, to estimate genetic parameters of traits related to egg production and to evaluate the genetic association between these by principal components analysis to identify trait(s) that could be used as selection criteria to improve egg production. Records of 54 wk of egg production from a White Leghorn population were used. The data set contained records of the length:width ratio of eggs at 32, 37, and 40 wk of age; egg weight at 32, 37, and 40 wk of age; BW at 54 and 62 wk of age; age at first egg; early partial egg production rate from 17 to 30 wk and from 17 to 40 wk of age; late partial egg production rate from 30 to 70 wk and from 40 to 70 wk of age; and total egg production rate (TEP). The estimates of genetic parameters between these traits were estimated by the restricted maximum likelihood method. Multivariate analyses were performed: a hierarchical cluster analysis, a nonhierarchical clustering analysis by the k-means method of weekly egg production rate to describe the egg-laying patterns of hens, and a principal components analysis using the breeding values of all traits. The highest heritability estimates were obtained for BW at 54 wk of age (0.68 ± 0.07) and age at first egg (0.53 ± 0.07). It is recommended that a preliminary clustering analysis be performed to obtain the population structure that takes into account the pattern of egg production, rather than the TEP, because hens may have the same final egg production with different patterns of egg laying. Early partial production periods were not good indicators for use in improving total egg production because these traits presented an overestimated genetic correlation with TEP because of the part-whole genetic correlation component. Egg production might be improved by selecting individuals based on TEP.

Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.

Identification of genotype–environment interaction in beef cattle may help the artificial selection process and increase the efficiency of genetic evaluation on sires submitted to different environments. Post-weaning traits are economically important and are more influenced by the effects of genotype–environment interactions than pre-weaning traits. Thus, the aim of this study was to investigate whether this interaction has any effect on bodyweight at 365, 450, and 550 days of age in Nellore cattle reared in Brazil. Analyses considered the states of Goiás, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Pará, and São Paulo. Genetic parameters were estimated for each trait, per state, using the restricted maximum likelihood method, in two-trait analysis under an animal model. Genetic correlations regarding the same trait in two different states were used to evaluate the effect of the genotype–environment interaction on the traits studied. Genetic correlation estimates smaller than 0.80 between observations for the same trait in different states were taken to be indicative of genotype–environment interaction. It was observed that there is evidence of genotype–environment interaction in some of the states studied, and they tend to increase when the weight measurements are made at later ages. From this, it was concluded that selection conducted using data from one state might be different from selection based on data from another state. Summaries of bulls that consider different environments could contribute greatly to the genetic improvement of livestock.