A cross-sectional sample of 3136 scalp hair drawn from 392 individuals aged 10 to 60 years and belonging to the Bania (n = 201) and Brahmin (n = 191) caste groups of Punjab State of India were examined for diameters of hair shaft and medulla, scale count, medulla type, hair index, medullary index and scale-count index, employing standard procedures. The mean hair shaft diameter, medullary diameter, incidence of medullation and scale-count index was higher in males, while the mean scale count was higher in females. However, with a few exceptions, the gender differences were not statistically significant (p
This study aimed at estimating genetic parameters of sex-influenced production traits, evaluating the impact of genotype-by-sex interaction, and identifying the selection criteria that could be included in multiple-trait genetic evaluation to increase the rate of genetic improvement in both sexes. To achieve this goal, we used 10 male and 10 female phenotypes, which were measured in a population of 2111 Australian Brahman cattle genotyped at high-density.
Sex male brahmin!
Hip height in cows and PNS24, as well as blood insulin-like growth factor 1 (IGF1) concentration in bulls at 6 months of age are efficient selection criteria to improve male growth and female reproductive traits, simultaneously. In the presence of genotype-by-sex interactions, selection for traits in each sex results in high rates of genetic improvement, however, for the identification of animals with the highest breeding value, data for males and females may be considered a single trait.
Genetic improvement using breeding techniques such as best linear unbiased prediction of breeding values relies on recording phenotypes. Growth and reproductive traits are two of the most important production traits for cattle breeding systems. On the one hand, growth traits are directly associated to meat, the main sales product of beef cattle [1] and, on the other hand, reproductive traits are a relevant component of economic performance in beef cattle industry [2, 3]. In some cases, these traits are difficult to record or cannot be recorded on the selection candidate, for example, when they are expressed late in life or only in one sex (sex-limited). Furthermore, traits with a low heritability (h2) are expected to have small rates of genetic improvement. In this situation, indirect selection offers an efficient means of increasing response to selection. For instance, scrotal circumference at a young age, semen quality, and some male hormone levels have been suggested as selection criteria to improve female reproduction traits [4, 5].
Phenotypic means of many traits differ between sexes, and this pattern, which is termed sexual dimorphism, is generally believed to be adaptive [6]. Within-sex variability may have implications for population dynamics, for example, if selection promotes the fixation (or loss) of mutations having sex-limited beneficial (or detrimental) effects [7]. Therefore, if the expression of homologous traits in both sexes is determined to a large extent by different genes, female and male expressions should be treated as separate traits [8]. In scenarios with extreme sexual dimorphism, this could require the implementation of specific selection breeding programs for females and males. Thus, having knowledge about the associations between male and female traits allows us to choose the most efficient selection methods and criteria for better selection decisions. Thus, the aim of this study was to estimate genetic parameters of male and female growth and reproductive traits to guide the identification of selection criteria in multiple-trait genetic evaluation to increase the rate of genetic improvement in both sexes and, for sex-influenced traits, to evaluate the impact of sex-by-genotype interactions on growth and reproductive traits.
All analyses were carried out using the AIRemlf90 software program [15]. Estimates of variance components for each pair of male and female traits were obtained using the genomic best linear unbiased prediction method in a series of 100 bivariate analyses (i.e. from 10 traits in males and 10 traits in females). In all cases, the same genomic relationship matrix (G) was used and computed following Method 1 of VanRaden [16]. The distribution of the genomic relationship coefficients both within and across sexes is shown in Fig. 1.
where \(\mathbfy_\textM\) and \(\mathbfy_\textF\) represent the phenotypic observations for males (\(\textM\)) and females (\(\textF\)), respectively, \(\mathbfX\) is the incidence matrix relating fixed effects in \(\varvec\upbeta\) with observations in \(\mathbfy_\textM\) and \(\mathbfy_\textF\), \(\mathbfZ\) is the incidence matrix that allocates records to breeding values in \(\mathbfu\) for every individual in the relationship matrix (both males and females), and \(\mathbfe_\textM\) and \(\mathbfe_\textF\) are the random residual effects for males and females, respectively. The fixed effects included in the model were specific for each evaluated trait and are described in Table 1. The fixed effects of contemporary group (72 levels for females and 60 levels for male), laboratory assay batch (52 levels), age of dam and age of the animal at the time of recording (linear covariable) were considered and included in the model when significant.
where \(\Delta \textG_\textFM\) is the expected correlated response per generation relative to a given female trait by selecting for the male trait, \(\Delta \textG_\textF\) is the expected direct response per generation relative to a given female trait, \(\textr_\texta_\textFM \) represents the genetic correlation of a trait measured in females and males obtained in bivariate analysis, \(\texth_\textF\) and \(\texth_\textM\) represent the square root of the heritability h2 for females and males, respectively.
In addition to the analyses previously described, six single-trait analyses were performed, in which female and male homologous traits were combined into a single trait. These analyses, which included sex and all the other fixed effects cited above, were performed to estimate the genetic parameters and GEBV, and to compare the results from the analyses that treat traits either separately for each sex or combined for both sexes.
Furthermore, Pearson correlations of 0.94, 0.87, 0.74, 0.95, 0.92, and 0.77 were obtained between GEBV of the six male and female homologous IGF1, YWT, AWT, HH, BCS, and EMA phenotypes in the bivariate analyses, respectively [see Additional file 2: Figure S2], and similarly Pearson correlations between GEBV from the analyses for sex-combined traits and for traits separately for each sex ranged from 0.88 to 0.99 [see Additional file 1: Figure S1]. Therefore, in spite of differences between direct and indirect selection responses, small variations in ranking would be expected regardless of whether the GEBV was obtained from the same sex phenotype or the opposite-sex phenotype when both male and female phenotypes are available.
In addition, the six homologous traits allowed us to explore the relative weight of each (male or female) source of information in the resulting GEBV when this GEBV is used to predict the opposite sex. We based these calculations on the correlation of the GEBV of a given trait and sex with the adjusted phenotype for the same or opposite sex. Thus, when exploring the efficiency with which GEBV predict the six homologous traits across sexes, GEBV for one sex was explored against (correlated with) the adjusted phenotype separately for each sex. The ratio between these two correlations reflects the efficiency of predictive power of a GEBV when used to predict the opposite sex. We found that the GEBV benefits for the opposite sex phenotype were around 10% smaller than those obtained from the same sex phenotype (Table 3). For instance, the correlation between the GEBV for IGF1 blood concentration measured in females and the adjusted phenotype for females is 0.48, whereas the correlation between the GEBV for IGF1 blood concentration measured in females and the adjusted phenotype for males is 0.42, i.e. equivalent to 88% of 0.48. Similarly, the correlation between the GEBV for IGF1 blood concentration measured in males and the adjusted phenotype for females is 0.35, which is 95% of the 0.37 correlation observed between the GEBV for IGF1 blood concentration measured in males and the adjusted phenotype for males. The values for efficiency of predictive power reflect the within-sex heritability estimates and the across-sex genetic correlations.
Although males and females share close genetic architectures, sexual differences are widespread [18]. In our study, we found differences in the additive and residual variances of homologous traits in males and females, which could be partially explained by a distinct expression of alleles or genes through sexual antagonism [19], distinct mutational effects between males and females [20, 21], presence of sex-specific dominance effects [22], and/or differences in environmental treatments (such as differences in age at measurement of EMA in this study). Rowe and Houle [23] suggested that females are expected to experience stabilizing natural selection on most traits, leading to a reduction in additive variance, while males are expected to experience directional selection on mating-related traits, which could reduce or increase additive variance. In breeding systems, differences in the intensity of selection between sexes could change the additive genetic variance in each sex. 2ff7e9595c
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