Genetic associations of temperament and facial hair whorls with productive, reproductive, and carcass traits in Canchim beef cattle

Genetic associations of temperament and facial hair whorls with productive, reproductive, and carcass traits in Canchim beef cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic associations of temperament and facial hair whorls with productive, reproductive, and carcass traits in Canchim beef cattle Ayrton Fernandes de Oliveira Bessa, Giovanna Maria dos Santos Câmara, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8833805/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to estimate genetic parameters and evaluate genetic correlations between temperament and facial hair whorl traits with productive, reproductive, and carcass traits in Canchim beef cattle. Methods Phenotypic records from animals born between 2013 and 2022 were analyzed. Temperament was assessed using behavioral scores (movement, tension, breathing, vocalization), reactivity measured by accelerometers, and flight time, while facial hair whorls were characterized by presence, number, position, and height. Productive, reproductive, and carcass traits included body weights, scrotal circumference, and ribeye area at different ages. Genetic parameters were estimated using single- and two-trait animal models. Results Heritability estimates ranged from low to high (0.09 to 0.67), indicating sufficient additive genetic variability for all trait groups. Favorable genetic correlations were observed between body weight traits and temperament measures, particularly vocalization and flight time, as well as between ribeye area and reactivity flight time. Scrotal circumference at 12 and 18 months of age showed moderate to high genetic correlations with facial hair whorl traits, especially whorl number and position. Conclusion These results indicate that selection for productive and reproductive traits can lead to indirect improvements in temperament. Moreover, pre-selection for the absence of facial hair whorls, combined with selection for body weight and scrotal circumference, may promote favorable genetic gains in temperament, supporting their inclusion in Canchim breeding programs. Animal Science Animal Breeding Behavior Composite Breed Pleiotropy INTRODUCTION Brazil plays a key role in the production of animal protein, being one of the largest beef producers in the world [ 1 ]. The country relies on important scientific and technological advances that have contributed to improving quality and sustainability. Beef cattle breeding programs are aimed at evaluating a set of traits according to the necessities of breeders and the consumer market and at identifying those traits that will compose the selection indexes according to their economic importance. Such indexes mainly consider traits related to body weight and weight gain, reproductive precocity and longevity, and carcass quality [ 2 ]. In view of the concerns of society and producers in terms of animal welfare, studies on new traits have been conducted in order to evaluate the possibility of reducing injuries related to animal handling [ 3 ]. Within this context, the temperament of animals as a measure for assessing welfare comprises a broad set of traits that are related to and determine the behavioral responses of an individual. Therefore, temperament is evaluated based on the animal’s reaction to human interaction [ 4 ]. Studies indicate that animals that are less reactive to handling tend to be more productive [ 5 ]. In situations in which animals are more reactive, lower weight gain, poorer meat quality, susceptibility to disease, and low reproductive performance have been reported [ 6 ]. Furthermore, the presence of these animals in herds may increase production and labor costs, as well as the risk of accidents involving employees [ 7 ]. There are several approaches to assessing temperament in cattle, which consider the animal’s reactions to different external stimuli [ 8 ]. The most common temperament measures are obtained by the chute exit velocity, flight speed, flight time [ 9 ], and chute score [ 10 ]. Furthermore, studies reported the existence of an association between temperament and facial hair whorls. In cattle, temperament and facial hair whorls presented important genetic background, where facial hair whorl traits can contribute to the pre-selection of animals with desirable temperaments [ 8 ]. Grandin et al. [ 11 ] showed that animals with hair whorls positioned above the eye line were more reactive to human presence. Taken together, the evidence indicates that both temperament and facial hair whorl traits possess a genetic component and may respond to selection [ 8 , 12 , 13 ]. Therefore, to contribute to the Canchim breeding program, this study estimated genetic parameters for temperament, facial hair whorl, productive, reproductive, and carcass traits, and evaluated the genetic association of temperament and facial hair whorl traits with the other traits. MATERIAL AND METHODS The phenotypic data used belong to an experimental herd maintained by Embrapa Southeast Livestock, São Carlos/SP, Brazil. The Canchim cattle were developed in the 1940s, aiming to use crossbreeding between Charolais and Zebu cattle (mainly Nelore) to obtain animals adapted to the production system and climate of Brazil, resulting in a breed composition of 5/8 Charolais and 3/8 Zebu [ 14 ]. Further details on the history of the formation of the Canchim breed can be found in previous studies [ 14 , 15 ]. The studied animals were born between 2013 and 2022. The temperament traits were assessed based on scores [ 15 ], which were: movement (MOV: 1 = no movement to 5 = animal jumping, raising its forelimbs at least 2.5 cm from the ground); tension (TEN: 1 = animal with regular muscle tonus, without sudden movements of the tail, head or neck, and a relaxed gaze to 4 = animal appears to be paralyzed, with muscle tremors); breathing (BRE: 1 = normal, rhythmic, poorly or non-audible breathing or 2 = blowing or puffing, with non-rhythmic breathing); and vocalization (VOC: 1 = absent or 2 = occurrence of mooing regardless of frequency or intensity). Reactivity (REACT) was measured with the REATEST®. This device is coupled to the weighing scale and uses accelerometers to rate the intensity and frequency of animal movements on a numerical scale ranging from 1 to 99,999 [ 16 ]. Flight time (FT) was obtained with the Duboi® sensor, which measures the time, in hundredths of a second, that it takes an animal to traverse 1.83 meters after leaving the weighing scale. The animals were evaluated at 4 months of age, at weaning, at yearling, and at post-yearling. Temperament traits were measured on a weighing scale by trained evaluators. Regarding the facial hair whorls, the lateral position was defined as absent (score 1) or present (score 2) [whorl on the left side (LW), whorl in the middle (MW), and whorl on the right side (RW)]. The vertical position of facial hair whorls in relation to the eyes was defined as absent (score 1) or present (score 2) [low whorl (LoW), mid whorl (MidW), and high whorl (HiW)]. The number of hair whorls (NW) was defined as absent (score 1) or present (scores 2 to 5 corresponding to one to four whorls, respectively). Whorl height (WH) was defined as absent (score 1) or present (scores 2 to 5 corresponding to low, medium, high, or very high whorls, respectively). Very high whorl indicates the presence of the hair whorl in the upper part of the animal’s face, i.e., an area between the horns. The whorl trait (WHORL) was defined as absent (score 1) or present (score 2), regardless of the number or position of the whorls. The description and visualization of facial hair whorls were previously described by Bessa et al. [ 8 ]. The following productive, reproductive, and carcass traits were studied: weaning weight (WW); body weight at 12 (W12) and 18 months of age (W18); scrotal circumference at weaning (SCW), scrotal circumference at 12 (SC12) and 18 months of age (SC18); and ribeye area at 12 (REA12) and 18 months of age (REA18). REA12 and REA18 were measured using a Piemedical Scanner 200 Vet equipped with an 18 cm linear transducer operating at 3.5 MHz, and an ALOKA 500V with a 17.2 cm linear probe at 3.5 MHz, positioned over the longissimus dorsi muscle between the 12th and 13th ribs. Quality control of the phenotypic data was performed using the R program ( https://www.r-project.org/foundation ). A generalized linear model was used for the study of fixed effects. For REACT, the data were square-root transformed. The contemporary groups were formed considering combinations of the tested fixed effects (Supplementary Table S1). Contemporary groups with fewer than three individuals and groups composed of offspring derived from only one sire and that did not show variability in the phenotypes, i.e., with only one of the scores, were excluded. The genetic parameters were estimated in single- and two-trait analyses under an animal model using the GIBBSF90 + program [ 17 ]. The matrix notation of the model for the temperament traits is represented by: $$\:y=X\beta\:+Za+Wpe+e$$ in which y is the vector of observations; β is the vector of fixed effects; a is the vector of random additive genetic effects; pe is the vector of random permanent environmental effects; X , Z and W are incidence matrices that relate β , a , and pe to y , and e is the vector of random environmental effects. The variance of the permanent environment obtained with this model was used to estimate the repeatability. The following model was used for WW and SCW: $$\:y=X\beta\:+Za+Mm+e$$ where y is the vector of observations; β is the vector of fixed effects; a is the vector of random additive genetic effects; m is the vector of random maternal additive genetic effects; X , Z and M are incidence matrices that relate β , a and m to y , and e is the vector of random environmental effects. The maternal additive genetic variance obtained with this model was used to estimate maternal heritability. For the other analyses (facial hair whorls, W12, W18, SC12, SC18, REA12, and REA18), random permanent environmental and maternal additive genetic effects were not considered. The relationship matrix included 10,308 animals. For genetic parameter estimation, 1,100,000 iterations were considered, with a burn-in period of 100,000 iterations and a thinning interval of 500 iterations, totaling 2,000 samples for convergence analysis. The Geweke and Heidelberger and Welch convergence tests were performed using the boa package of the R software [ 18 ]. Convergence was established when the heritability, repeatability, and genetic correlation estimates met the criteria of one or both tests. RESULTS The data structure of the facial hair whorls HiW and RW did not allow the genetic parameters to meet the convergence criteria and the results of these analyses will therefore not be reported. The other traits showed convergence in at least one of the tests used. The descriptive statistics of the traits studied are displayed in Supplementary Tables S2 and S3. Estimates of direct heritability, maternal heritability, and repeatability estimates are presented in Table 1 . The heritability estimates varied from 0.09 (BRE) to 0.67 (SC12), while the repeatability varied from 0.16 (BRE) to 0.55 (FT). Maternal heritability estimates were equal to 0.18 (WW) and 0.13 (SCW). Table 1 Genetic parameter estimates and highest density intervals (in brackets) for the traits studied in Canchim cattle Temperament traits Facial hair whorl traits Productive, reproductive, and carcass traits \(\:{\varvec{h}}_{\varvec{d}}^{2}\) \(\:\varvec{t}\) \(\:{\varvec{h}}_{\varvec{d}}^{2}\) \(\:{\varvec{h}}_{\varvec{d}}^{2}\) \(\:{\varvec{h}}_{\varvec{m}}^{2}\) MOV 0.20 ± 0.04 (0.11;0.28) 0.35 ± 0.02 (0.31;0.39) LW 0.57 ± 0.12 (0.33;0.81) WW 0.10 ± 0.03 (0.03;0.15) 0.18 ± 0.03 (0.12;0.22) TEN 0.30 ± 0.04 (0.21;0.38) 0.45 ± 0.03 (0.40;0.05) MW 0.30 ± 0.08 (0.15;0.45) W12 0.53 ± 0.07 (0.39;0.65) - BRE 0.09 ± 0.03 (0.02;0.14) 0.16 ± 0.04 (0.08;0.16) LoW 0.57 ± 0.12 (0.33;0.81) W18 0.32 ± 0.07 (0.18;0.44) - VOC 0.35 ± 0.08 (0.20;0.49) 0.43 ± 0.06 (0.30;0.55) MidW 0.30 ± 0.08 (0.15;0.45) SCW 0.24 ± 0.10 (0.07;0.43) 0.13 ± 0.04 (0.04;0.21) REA 0.21 ± 0.04 (0.13;0.27) 0.41 ± 0.02 (0.37;0.44) NW 0.18 ± 0.04 (0.10;0.25) SC12 0.67 ± 0.15 (0.36;0.93) - FT 0.35 ± 0.06 (0.23;0.48) 0.55 ± 0.02 (0.50;0.59) WH 0.27 ± 0.07 (0.14;0.39) SC18 0.53 ± 0.12 (0.29;0.76) - - - - WHORL 0.20 ± 0.06 (0.08;0.32) REA12 0.48 ± 0.12 (0.24;0.70) - - - - - - REA18 0.36 ± 0.09 (0.18;0.52) - ± = standard deviations, MOV = movement, TEN = tension, BRE = breathing, VOC = vocalization, REA = reactivity, FT = flight time, LW = facial whorl on the left side, MW = facial whorl in the middle, LoW = low whorl, MidW = mid whorl, NW = number of facial whorls, WH = whorl height, WHORL = absence or presence of facial whorls, WW = weaning weight, W12 and W18 = body weight at 12 and 18 months of age, SCW = scrotal circumference at weaning, SC12 and SC18 = scrotal circumference at 12 and 18 months of age, REA12 and REA18 = ribeye area at 12 and 18 months of age, \(\:{h}_{d}^{2}\) = direct heritability estimate, \(\:{h}_{m}^{2}\) = maternal heritability estimate, \(\:t\) = repeatability. In Table 2 , the genetic correlations that presented the highest posterior density range, which encompassed the estimate and presented the same sign, were between WW and VOC (-0.71), W12 and VOC (-0.71), WW and FT (-0.24), W12 and FT (-0.26), REA12 and REACT (-0.51), and REA12 and FT (-0.44). The highlighted phenotypic correlations (Table 3 ) among productive, reproductive, and carcass traits with temperament were between WW and VOC (-0.88), WW and FT (-0.15), W12 and VOC (-0.88), W12 and FT (-0.13), W18 and VOC (-0.91), and REA12 and FT (-0.21). SC12 and SC18 presented genetic correlations with MW, MidW, and NW (Table 4 ) ranging from − 0.74 to -0.41. In Table 5 , phenotypic correlations between productive, reproductive, and carcass traits with facial hair whorl traits varied from − 0.44 to 0.14 but did not present estimates within the highest posterior density range. Genetic correlations between productive, reproductive, and carcass traits are presented in Supplementary Table S4. Table 2 Estimates of genetic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with temperament traits in Canchim cattle MOV TEN BRE VOC REACT FT WW 0.02 ± 0.11 (-0.23;0.20) 0.08 ± 0.12 (-0.14;0.32) 0.11 ± 0.25 (-0.15;0.98) -0.71 ± 0.32* (-1.00;-0.11) -0.09 ± 0.15 (-0.37;0.20) -0.24 ± 0.13 * (-0.48;-0.03) W12 0.02 ± 0.11 (-0.20;0.22) 0.06 ± 0.10 (-013;0.24) 0.32 ± 0.25 (-0.18;0.83) -0.71 ± 0.32 * (-1.00;-0.08) -0.04 ± 0.14 (-0.29;0.24) -0.26 ± 0.14 * (-0.53;-0.01) W18 0.03 ± 0.13 (-0.20;0.30) 0.03 ± 0.11 (-0.18;0.26) 0.32 ± 0.30 (-015;0.98) 0.07 ± 0.57 (-0.99;0.99) 0.00 ± 0.16 (-0.31;0.30) -0.09 ± 0.19 (-0.45;0.29) SCW -0.27 ± 0.55 (-1.00;0.47) -0.27 ± 0.44 (-1.00;0.20) -0.24 ± 0.33 (-0.87;0.35) -0.09 ± 0.24 (-0.64;0.34) 0.08 ± 0.18 (-0.27;0.41) 0.19 ± 0.25 (-0.64.0.30) SC12 -0.17 ± 0.68 (-1.00;0.97) 0.00 ± 0.37 (-1.00;0.30) 0.09 ± 0.25 (-0.40;0.56) -0.03 ± 0.34 (-0.54;0.51) 0.22 ± 0.17 (-0.14;0.52) 0.26 ± 0.25 (-0.19;0.76) SC18 -0.12 ± 0.38 (-1.00;0.27) -0.03 ± 0.34 (-1.00;0.32) -0.01 ± 0.26 (-0.55;0.47) -0.10 ± 0.29 (-0.65;0.52) -0.04 ± 0.19 (-0.41;0.34) 0.36 ± 0.27 (-0.18;0.84) REA12 0.06 ± 0.14 (-0.22;0.30) 0.04 ± 0.11 (-0.19;0.23) 0.16 ± 0.23 (-0.31;0.63) -0.02 ± 0.11 (-0.48;0.47) -0.51 ± 0.30 * (-0.99;-0.10) -0.44 ± 0.15* (-0.73;-0.14) REA18 0.12 ± 0.13 (-0.13;0.36) 0.21 ± 0.13 (-0.01;0.45) 0.29 ± 0.22 (-0.16;0.70) 0.11 ± 0.24 (-0.34;0.60) 0.19 ± 0.15 (-0.07;0.50) -0.08 ± 0.18 (-0.41;0.27) ± = standard deviations, WW = weaning weight, W12 and W18 = body weight at 12 and 18 months of age, SCW = scrotal circumference at weaning, SC12 and SC18 = scrotal circumference at 12 and 18 months of age, REA12 and REA18 = ribeye area at 12 and 18 months of age, MOV = movement, TEN = tension, BRE = breathing, VOC = vocalization, REACT = reactivity, FT = flight time, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values. Table 3 Estimates of phenotypic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with temperament traits in Canchim cattle MOV TEN BRE VOC REACT FT WW 0.05 ± 0.08 (-0.09;0.27) 0.04 ± 0.09 (-0.14;0.21) -0.21 ± 0.20 (-0.59;0.18) -0.88 ± 0.03* (-0.93;-0.81) -0.08 ± 0.08 (-0.23;0.04) -0.15 ± 0.09* (-0.30;-0.05) W12 0.06 ± 0.07 (-0.08;0.19) -0.01 ± 0.09 (-0.18;0.17) -0.17 ± 0.17 (-0.54;0.16) -0.88 ± 0.03 * (-0.93;-0.81) 0.03 ± 0.07 (-0.10;0.16) -0.13 ± 0.08* (-0.29;-0.03) W18 -0.07 ± 0.08 (-0.22;0.08) -0.08 ± 0.11 (-0.29;0.14) -0.21 ± 0.20 (-0.59;0.18) -0.91 ± 0.03* (-0.96;-0.84) 0.05 ± 0.08 (-0.19;0.08) -0.04 ± 0.09 (-0.21;0.14) SCW -0.07 ± 0.17 (-0.40;0.28) 0.00 ± 0.12 (-0.25;0.21) -0.20 ± 0.21 (-0.58;0.21) -0.05 ± 0.21 (-0.44;0.39) -0.02 ± 0.10 (-0.22;0.16) -0.05 ± 0.08 (-0.19;0.11) SC12 0.15 ± 0.13 (-0.11;0.38) 0.04 ± 0.17 (-0.25;0.35) 0.02 ± 0.23 (-0.41;0.44) 0.11 ± 0.31 (-0.54;0.64) 0.06 ± 0.12 (-0.18;0.28) 0.19 ± 0.15 (-0.10;0.46) SC18 -0.14 ± 0.14 (-0.40;0.15) -0.08 ± 0.18 (-0.44;0.24) -0.20 ± 0.24 (-0.66;0.29) 0.22 ± 0.32 (-0.51;0.72) -0.14 ± 0.14 (-0.38;0.09) 0.03 ± 0.14 (-0.24;0.26) REA12 0.02 ± 0.11 (-0.21;0.20) -0.02 ± 0.19 (-0.39;0.32) -0.08 ± 0.24 (-0.52;0.38) -0.08 ± 0.48 (-0.65;0.71) -0.29 ± 0.20 (-0.74;0.03) -0.21 ± 0.10* (-0.42;-0.01) REA18 0.06 ± 0.10 (-0.12;0.25) -0.26 ± 0.14 (-0.53;0.38) -0.06 ± 0.42 (-0.60;0.78) 0.65 ± 0.13 (-0.40;0.89) 0.07 ± 0.10 (-0.11;0.26) 0.00 ± 0.09 (-0.19;0.16) ± = standard deviations, WW = weaning weight, W12 and W18 = body weight at 12 and 18 months of age, SCW = scrotal circumference at weaning, SC12 and SC18 = scrotal circumference at 12 and 18 months of age, REA12 and REA18 = ribeye area at 12 and 18 months of age, MOV = movement, TEN = tension, BRE = breathing, VOC = vocalization, REACT = reactivity, FT = flight time, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values. Table 4 Estimates of genetic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with facial hair whorl traits in Canchim cattle LW MW LOW MidW NW WH WHORL WW 0.20 ± 0.19 (-0.14;0.60) 0.03 ± 0.23 (-0.39;0.48) 0.20 ± 0.19 (-0.14;0.60) 0.03 ± 0.23 (-0.39;0.48) 0.07 ± 0.21 (-0.32;0.48) 0.27 ± 0.17 (-0.06;0.59) 0.20 ± 0.20 (-0.19;0.59) W12 -0.06 ± 0.15 (-0.36;0.21) -0.01 ± 0.24 (-0.51;0.42) -0.06 ± 0.15 (-0.36;0.21) -0.09 ± 0.21 (-0.48;0.32) -0.06 ± 0.21 (-0.47;0.33) 0.10 ± 0.18 (-0.25;0.43) -0.08 ± 0.19 (-0.44;0.29) W18 -0.20 ± 0.20 (-0.55;0.20) -0.13 ± 0.24 (-0.59;0.34) -0.20 ± 0.20 (-0.55;0.20) -0.13 ± 0.24 (-0.59;0.34) -0.23 ± 0.22 (-0.64;0.21) 0.25 ± 0.20 (-0.11;0.66) 0.21 ± 0.23 (-0.22;0.65) SCW 0.38 ± 0.26 (-0.15;0.83) 0.38 ± 0.26 (-0.15;0.83) 0.38 ± 0.26 (-0.15;0.83) -0.24 ± 0.29 (-0.73;0.37) -0.06 ± 0.31 (-0.62;0.55) -0.63 ± 0.42 (-0.98;0.44) -0.06 ± 0.42 (-1.00;0.62) SC12 0.19 ± 0.22 (-0.19;0.63) -0.57 ± 0.23* (-1.00;-0.15) 0.19 ± 0.22 (-0.19;0.63) -0.57 ± 0.23 * (-1.00;-0.15) -0.41 ± 0.23 * (-0.83;-0.02) 0.01 ± 0.32 (-0.64;0.55) 0.13 ± 0.44 (-0.61;1.00) SC18 0.02 ± 0.26 (-0.50;0.50) -0.74 ± 0.18* (-0.99;-0.41) 0.02 ± 0.26 (-0.50;0.50) -0.74 ± 0.19* (-1.00;-0.40) -0.74 ± 0.18* (-0.99;-0.40) -0.14 ± 0.27 (-0.65;0.39) -0.29 ± 0.30 (-0.82;0.30) REA12 0.03 ± 0.21 (-0.38;0.42) -0.21 ± 0.22 (-0.63;0.18) 0.03 ± 0.21 (-0.38;0.42) -0.21 ± 0.22 (-0.63;0.18) -0.15 ± 0.21 (-0.52;0.27) 0.12 ± 0.28 (-0.39;0.66) -0.13 ± 0.23 (-0.56;0.31) REA18 0.00 ± 0.20 (-0.40;0.38) 0.02 ± 0.23 (-0.42;0.46) 0.00 ± 0.20 (-0.40;0.38) 0.02 ± 0.23 (-0.42;0.46) 0.05 ± 0.21 (-0.38;0.44) 0.17 ± 0.22 (-0.22;0.62) -0.03 ± 0.26 (-0.53;0.45) ± = standard deviations, WW = weaning weight, W12 and W18 = body weight at 12 and 18 months of age, SCW = scrotal circumference at weaning, SC12 and SC18 = scrotal circumference at 12 and 18 months of age, REA12 and REA18 = ribeye area at 12 and 18 months of age, LW = facial whorl on the left side, MW = facial whorl in the middle, LoW = low whorl, MidW = mid whorl, NW = number of facial whorls, WH = whorl height, WHORL = absence or presence of facial whorls, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values. Table 5 Estimates of phenotypic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with facial hair whorl traits in Canchim cattle LW MW LOW MidW NW WH WHORL WW 0.04 ± 0.03 (-0.01;0.10) 0.01 ± 0.03 (-0.05;0.05) 0.04 ± 0.03 (-0.01;0.10) 0.01 ± 0.03 (-0.05;0.05) 0.01 ± 0.03 (-0.04;0.06) -0.03 ± 0.47 (-0.72;0.78) 0.04 ± 0.52 (-0.77;0.85) W12 0.02 ± 0.03 (-0.03;0.08) -0.02 ± 0.03 (-0.03;0.07) 0.02 ± 0.03 (-0.03;0.08) -0.01 ± 0.03 (-0.04;0.07) 0.01 ± 0.13 (-0.04;0.07) 0.02 ± 0.04 (-0.70;0.69) 0.00 ± 0.45 (-0.69;0.82) W18 -0.02 ± 0.03 (-0.08;0.04) -0.03 ± 0.03 (-0.09;0.02) -0.02 ± 0.03 (-0.08;0.04) -0.03 ± 0.03 (-0.09;0.02) -0.04 ± 0.03 (-0.10;0.02) 0.14 ± 0.46 (-0.66;0.84) 0.10 ± 0.53 (-0.74;0.86) SCW 0.07 ± 0.04 (-0.02;0.15) 0.07 ± 0.04 (-0.02;0.15) 0.07 ± 0.04 (-0.02;0.15) 0.05 ± 0.04 (-0.03;0.12) 0.04 ± 0.04 (-0.03;0.12) -0.44 ± 0.39 (-0.96;0.33) 0.00 ± 0.44 (-0.72;0.73) SC12 0.06 ± 0.05 (-0.04;0.15) -0.03 ± 0.04 (-0.11;0.05) 0.0.6 ± 0.05 (-0.04;0.15) -0.03 ± 0.04 (-0.11;0.05) -0.03 ± 0.04 (-0.11;0.05) 0.01 ± 0.35 (-0.65;0.63) 0.09 ± 0.41 (0.63;0.81) SC18 0.04 ± 0.05 (-0.05;0.13) 0.01 ± 0.04 (-0.07;0.09) 0.04 ± 0.05 (-0.05;0.13) -0.12 ± 0.35 (-0.83;0.43) 0.01 ± 0.04 (-0.07;0.10) 0.03 ± 0.36 (-0.69;0.62) 0.00 ± 0.37 (-0.68;0.61) REA12 0.03 ± 0.04 (-0.04;0.10) 0.06 ± 0.04 (-0.01;0.13) 0.03 ± 0.04 (-0.04;0.10) 0.06 ± 0.04 (-0.01;0.13) 0.05 ± 0.04 (-0.02;0.11) 0.08 ± 0.38 (-0.57;0.77) -0.05 ± 0.33 (-0.66;0.54) REA18 0.06 ± 0.04 (-0.01;0.13) 0.03 ± 0.04 (-0.03;0.10) 0.06 ± 0.04 (-0.01;0.13) 0.03 ± 0.04 (-0.03;0.10) 0.04 ± 0.04 (-0.02;0.11) 0.13 ± 0.37 (-0.53;0.75) 0.08 ± 0.46 (-0.73;0.74) ± = standard deviations, WW = weaning weight, W12 and W18 = body weight at 12 and 18 months of age, SCW = scrotal circumference at weaning, SC12 and SC18 = scrotal circumference at 12 and 18 months of age, REA12 and REA18 = ribeye area at 12 and 18 months of age, LW = facial whorl on the left side, MW = facial whorl in the middle, LoW = low whorl, MidW = mid whorl, NW = number of facial whorls, WH = whorl height, WHORL = absence or presence of facial whorls. DISCUSSION Heritability estimates Except for BRE, the heritability estimates for the temperament traits (Table 1 ) were classified as moderate to high, suggesting that these traits will respond satisfactorily to selection and less reactive offspring are expected. On the other hand, the repeatability of these traits indicated some consistency in the expression of the phenotypes measured at different ages, especially for FT. Thus, the temperament of Canchim cattle is influenced by genetic and permanent environmental components. The behavior of young animals tends to change gradually, possibly because of cumulative exposure to the stimuli presented; hence, for selection purposes, the collection of information at different ages is necessary [ 19 ]. Other studies reported heritability estimates for MOV, TEN, BRE, VOC, REACT, and flight speed of 0.17 [ 20 ], 0.07 ± 0.04 [ 21 ], 0.04 ± 0.01 [ 22 ], 0.71 ± 0.03 [ 23 ], 0.39 [ 24 ], and 0.33 ± 0.03 [ 25 ], respectively. These divergences in the results compared to the present study are related to differences in the breeds used and in the assessment methods of the animals. The heritability estimates for the facial hair whorl traits ranged from low (0.18 ± 0.04 for NW) to high (0.57 ± 0.12 for LW and LoW). Overall, hair whorl traits in the Canchim breed showed sufficient genetic variability to respond to selection. Although these traits appear to be similar, differences were observed in genetic variability for height, position, and number of whorls. One of the advantages of evaluating facial hair whorls in cattle is that these traits are easy to obtain; they can be evaluated shortly after the animal is born and only require one observation, facilitating the implementation of these assessments on farms. In Holstein cattle, heritability estimates of 0.54 ± 0.08 and 0.11 ± 0.06 were obtained for WH and whorl position in relation to the midline of the face, respectively [ 26 ]. Hair whorls are more frequently studied in horses and heritability estimates of 0.88 ± 0.03 and 0.99 ± 0.02 have been reported for position and number of facial hair whorls, respectively [ 12 ]. These authors found that animals with a larger number of hair whorls tend to be shyer during taming. For whorl position and NW, heritability estimates equal to 0.75 ± 0.05 [ 27 ] and 0.66 ± 0.097 [ 28 ] were observed in horses. The high direct heritability estimates for body weights indicated that animals are likely to respond to direct selection, especially for W12. For SCW, SC12, and SC18, direct heritability estimates of 0.24 ± 0.10, 0.67 ± 0.15 and 0.53 ± 0.12, respectively, were obtained, indicating that greater responses to selection can be expected for SC12. In Canchim animals, heritability estimates of 0.31 ± 0.03, 0.29 ± 0.02, and 0.28 ± 0.01 were obtained for WW, W12, and W18, respectively [ 29 ]. In Nellore cattle, estimates of 0.31 ± 0.03 and 0.38 ± 0.02 were obtained for SC measured at 365 and 450 days of age, respectively [ 30 ]. The maternal heritabilities for WW and SCW suggest that the maternal genetic component plays an important role in the expression of these traits. The results observed agree with those reported by Vargas et al. [ 31 ]. These authors highlighted the need for incorporating maternal effects in genetic parameter estimation models for pre-weaning traits to reduce bias. The heritability estimates for REA12 and REA18 were 0.48 ± 0.12 and 0.36 ± 0.09, respectively. Thus, selection for these traits is expected to promote satisfactory genetic gains. Genetic and phenotypic correlations As can be seen in Tables 2 and 4 , a large part of the genetic correlation estimates was of low magnitude and had high standard deviations, indicating the absence of associations between most traits. One explanation for this observation could be the number of phenotypes; it is therefore recommended to continue these measurements to reduce potential bias in future studies of these traits in the Canchim breed. Negative genetic correlations of WW and W12 with VOC and FT were observed, indicating that lower body weight performance is associated with higher VOC scores and a shorter time at which the animal leaves the scale and traverses 1.83 meters. This result suggests the existence of an additive genetic component that acts favorably on these traits, i.e., animals with higher WW and W12 are expected to have a desirable temperament in terms of VOC and FT, being calmer during handling in the pen. Animals with slower flight speed exhibited greater body weight gains (mean of 1.54 kg/day) than animals with faster flight speed (mean of 1.37 kg/day) [ 32 ]. In a study with Charolais and Nellore, animals with slower FT, i.e., that traverse a certain distance more slowly, had greater weight gain [ 33 ]. The genetic correlations of REA12 with REACT and FT were negative and of moderate magnitude. This result suggests that more reactive animals and animals with faster FT have a smaller ribeye area. Furthermore, the genetic correlations of WW and W12 with REA12 (Supplementary Table S4) suggest the existence of a genetic component shared between these traits, indicating that they affect each other and that selection for one will indirectly and favorably affect the others. According to Nkrumah et al. [ 34 ], temperament influences carcass traits in beef cattle, highlighting the importance of including temperament in the definitions of selection objectives, as well as the culling of animals that are difficult to handle and the use of appropriate facilities to improve the handling experience of animals. Furthermore, Coutinho et al. [ 35 ] observed that higher chute score and exit velocity were correlated with shear force and tenderness of the Longissimus lumborum muscle, indicating that calmer animals produce better quality meat. Other studies on cattle reported genetic correlations of scrotal circumference with flight speed and temperament scores of -0.28 ± 0.02 [ 36 ] and − 0.07 ± 0.03 [ 37 ], respectively. According to Brandão & Cooke [ 4 ], animals with a more reactive temperament exhibit poorer reproductive performance than calmer animals; this fact can affect traits such as age at first calving of females, which also shows high genetic correlations with scrotal circumference of males. The phenotypic correlations between productive, reproductive and carcass traits with temperament traits showed estimates that were mostly negative (Table 3 ). Favorable and high phenotypic correlations were observed between VOC with WW, W12, and W18 indicating that there is an association and that the variation in the expression of these traits in cattle is probably influenced by other environmental factors. A negative phenotypic correlation was also observed between FT with WW, W12, and REA12. The phenotypic correlations for MOV and flight speed with body weight in Nellore animals were previously reported by Sant’Anna et al. [ 21 ]. The authors observed that these correlations showed negative and low magnitude estimates of -0.05 ± 0.01 and − 0.08 ± 0.01, respectively, which reflects a weak relationship between the traits. In crossbred animals, Burrow [ 38 ] observed phenotypic correlations between body weight and flight speed that ranged from − 0.02 to -0.05, corroborating the results observed in our study. These same authors also observed phenotypic correlations for flight speed and scrotal circumference at different ages that ranged from 0.07 to 0.11. In Table 4 , only SC12 and SC18 showed important genetic correlations with MW, MidW, and NW. These results demonstrate that the position and number of facial hair whorls of the animal can influence male reproductive traits. Therefore, the genetic correlations of SC12 and SC18 with the highlighted whorl traits indicate that the genes responsible for the presence of whorls in these regions are also responsible for undesirable reproductive performance in males. In a study with Angus cattle, Meola et al. [ 39 ] found that animals with a hair whorl pattern classified as round epicenter had a higher percentage of normal spermatozoa than animals with a non-round (or misshapen) epicenter. Investigating WH in beef cattle, Silveira et al. [ 33 ] observed negative correlations with weight gain and FT. In contrast, Olmos & Turner [ 40 ] found no correlations of whorl position with weight gain or flight speed in beef cattle. The phenotypic correlations between the productive, reproductive and carcass traits with the whirlpool traits are presented in Table 5 , where they were observed as being of low magnitude, accompanied by high standard errors. This indicates that the phenotypic interactions between these estimates are not very relevant, suggesting a weak or inconsistent relationship between them. In general, the genetic correlations between productive, reproductive, and carcass traits were of moderate to high magnitude (Supplementary Table S4), suggesting that a set of genes with additive effect act simultaneously and favorably on these traits. The genetic correlations of WW with SC12, SC18, and REA18; of SCW with REA12 and REA18; of SC12 with REA18; and of SC18 with REA18 were of low magnitude and had high standard deviations. CONCLUSION This study showed that most of the traits exhibit additive genetic variability that can be explored by the Canchim breeding program, aiming at selecting animals with adequate performance and temperament. Genetic associations were observed between body weight traits with temperament and between scrotal circumference and carcass traits with facial hair whorls. Thus, pre-selecting animals for the absence of facial hair whorls, in conjunction with selection for W12 and SC12, may result in indirect and favorable genetic gain in offspring temperament. Declarations CONFLICT OF INTEREST The authors declare that there is no conflict of interest. AUTHOR CONTRIBUTIONS Conceptualization: Bessa AFO, Souza VAF, Ribeiro ARB, Marcondes CR, Buzanskas ME. Data curation: Bugner ALP, Souza VAF, Maffei WE, Ribeiro ARB, Marcondes CR. Formal analysis: Bessa AFO, Buzanskas ME. Methodology: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. Software: Bessa AFO, Buzanskas ME. Validation: Bessa AFO, Câmara GMS, Zucatelle C, Futema FK, Teixeira R, Cenedeze G, Buzanskas ME. Investigation: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. Writing - original draft: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. Writing - review & editing: Bessa AFO, Câmara GMS, Zucatelle C, Futema FK, Teixeira R, Cenedeze G, Bugner ALP, Souza VAF, Maffei WE, Ribeiro ARB, Marcondes CR, Buzanskas ME. FUNDING Ayrton Bessa received a scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001. ACKNOWLEGEMENTS Not applicable. SUPPLEMENTARY MATERIAL Supplementary Table S1. Fixed effects included in the genetic evaluation model Supplementary Table S2. Descriptive statistics of temperament and facial hair whorl traits Supplementary Table S3. Descriptive statistics of productive, reproductive and carcass traits Supplementary Table S4 . 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BMC Res Notes 12:346. https://doi.org/10.1186/s13104-019-4386-x Baldi F, de Alencar MM, de Albuquerque LG (2010) Estimativas de parâmetros genéticos para características de crescimento em bovinos da raça Canchim utilizando modelos de dimensão finita. Rev Bras Zootec 39:2409–2417. https://doi.org/10.1590/S1516-35982010001100013 Buzanskas ME, Pires PS, Chud TCS, Bernardes PA, Rola LD, Savegnago RP et al (2017) Parameter estimates for reproductive and carcass traits in Nelore beef cattle. Theriogenology 92:204–209. https://doi.org/10.1016/j.theriogenology.2016.09.057 Vargas G, Buzanskas ME, Guidolin DGF, Grossi DA, Bonifácio AS, Lôbo RB et al (2014) Genetic parameter estimation for pre- and post-weaning traits in Brahman cattle in Brazil. Trop Anim Health Prod 46:1271–1278. https://doi.org/10.1007/s11250-014-0640-3 Petherick JC, Holroyd RG, Doogan VJ, Venus BK (2002) Productivity, carcass and meat quality of lot-fed Bos indicus cross steers grouped according to temperament. Aust J Exp Agric 42:389. https://doi.org/10.1071/EA01084 Silveira IDB, Fischer V, Farinatti LHE, Restle J, Alves Filho DC (2008) Relação entre genótipos e temperamento de novilhos Charolês x Nelore em confinamento. Rev Bras Zootec 37:1808–1814. https://doi.org/10.1590/S1516-35982008001000014 Nkrumah JD, Crews DH, Basarab JA, Price MA, Okine EK, Wang Z et al (2007) Genetic and phenotypic relationships of feeding behavior and temperament with performance, feed efficiency, ultrasound, and carcass merit of beef cattle1. J Anim Sci 85:2382–2390. https://doi.org/10.2527/jas.2006-657 Coutinho MAdaS, Ramos PM, da Luz e Silva S, Martello LS, Pereira ASC, Delgado EF (2017) Divergent temperaments are associated with beef tenderness and the inhibitory activity of calpastatin. Meat Sci 134:61–67. https://doi.org/10.1016/j.meatsci.2017.06.017 Valente TS, Baldi F, Sant’Anna AC, Albuquerque LG (2016) Paranhos da Costa MJR. Genome-Wide Association Study between Single Nucleotide Polymorphisms and Flight Speed in Nellore Cattle. Barendse W, editor. PLoS One ;11: e0156956. https://doi.org/10.1371/journal.pone.0156956 Barrozo D, Buzanskas ME, Oliveira JA, Munari DP, Neves HHR, Queiroz SA (2012) Genetic parameters and environmental effects on temperament score and reproductive traits of Nellore cattle. Animal 6:36–40. https://doi.org/10.1017/S1751731111001169 Burrow H (2001) Variances and covariances between productive and adaptive traits and temperament in a composite breed of tropical beef cattle. Livest Prod Sci 70:213–233. https://doi.org/10.1016/S0301-6226(01)00178-6 Meola MG, Grandin T, Burns P, Deesing M (2004) Hair whorl patterns on the bovine forehead may be related to breeding soundness measures. Theriogenology 62:450–457. https://doi.org/10.1016/j.theriogenology.2003.10.021 Olmos G, Turner SP (2008) The relationships between temperament during routine handling tasks, weight gain and facial hair whorl position in frequently handled beef cattle. Appl Anim Behav Sci 115:25–36. https://doi.org/10.1016/j.applanim.2008.05.001 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8833805","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588523318,"identity":"3944a352-835d-43bb-8638-4009701c90a1","order_by":0,"name":"Ayrton Fernandes de Oliveira Bessa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ayrton","middleName":"Fernandes de Oliveira","lastName":"Bessa","suffix":""},{"id":588523319,"identity":"eb8762ef-f597-4ad5-a25d-8014dc8c6968","order_by":1,"name":"Giovanna Maria dos Santos 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19:20:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8833805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8833805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102298088,"identity":"ef1cb829-bfb8-4237-b06b-a783b461f3d6","added_by":"auto","created_at":"2026-02-10 10:30:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1008643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8833805/v1/9b42792d-11a4-4baf-88e5-98ae3ab09db2.pdf"},{"id":102281971,"identity":"c12e2e6d-148f-4b83-9397-708d4f4f5b54","added_by":"auto","created_at":"2026-02-10 07:22:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51504,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8833805/v1/855db38e6eee40a47decad25.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenetic associations of temperament and facial hair whorls with productive, reproductive, and carcass traits in Canchim beef cattle\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBrazil plays a key role in the production of animal protein, being one of the largest beef producers in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The country relies on important scientific and technological advances that have contributed to improving quality and sustainability. Beef cattle breeding programs are aimed at evaluating a set of traits according to the necessities of breeders and the consumer market and at identifying those traits that will compose the selection indexes according to their economic importance. Such indexes mainly consider traits related to body weight and weight gain, reproductive precocity and longevity, and carcass quality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn view of the concerns of society and producers in terms of animal welfare, studies on new traits have been conducted in order to evaluate the possibility of reducing injuries related to animal handling [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Within this context, the temperament of animals as a measure for assessing welfare comprises a broad set of traits that are related to and determine the behavioral responses of an individual. Therefore, temperament is evaluated based on the animal\u0026rsquo;s reaction to human interaction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies indicate that animals that are less reactive to handling tend to be more productive [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In situations in which animals are more reactive, lower weight gain, poorer meat quality, susceptibility to disease, and low reproductive performance have been reported [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the presence of these animals in herds may increase production and labor costs, as well as the risk of accidents involving employees [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several approaches to assessing temperament in cattle, which consider the animal\u0026rsquo;s reactions to different external stimuli [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The most common temperament measures are obtained by the chute exit velocity, flight speed, flight time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and chute score [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, studies reported the existence of an association between temperament and facial hair whorls. In cattle, temperament and facial hair whorls presented important genetic background, where facial hair whorl traits can contribute to the pre-selection of animals with desirable temperaments [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrandin et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that animals with hair whorls positioned above the eye line were more reactive to human presence. Taken together, the evidence indicates that both temperament and facial hair whorl traits possess a genetic component and may respond to selection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, to contribute to the Canchim breeding program, this study estimated genetic parameters for temperament, facial hair whorl, productive, reproductive, and carcass traits, and evaluated the genetic association of temperament and facial hair whorl traits with the other traits.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003eThe phenotypic data used belong to an experimental herd maintained by Embrapa Southeast Livestock, S\u0026atilde;o Carlos/SP, Brazil. The Canchim cattle were developed in the 1940s, aiming to use crossbreeding between Charolais and Zebu cattle (mainly Nelore) to obtain animals adapted to the production system and climate of Brazil, resulting in a breed composition of 5/8 Charolais and 3/8 Zebu [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further details on the history of the formation of the Canchim breed can be found in previous studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe studied animals were born between 2013 and 2022. The temperament traits were assessed based on scores [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which were: movement (MOV: 1\u0026thinsp;=\u0026thinsp;no movement to 5\u0026thinsp;=\u0026thinsp;animal jumping, raising its forelimbs at least 2.5 cm from the ground); tension (TEN: 1\u0026thinsp;=\u0026thinsp;animal with regular muscle tonus, without sudden movements of the tail, head or neck, and a relaxed gaze to 4\u0026thinsp;=\u0026thinsp;animal appears to be paralyzed, with muscle tremors); breathing (BRE: 1\u0026thinsp;=\u0026thinsp;normal, rhythmic, poorly or non-audible breathing or 2\u0026thinsp;=\u0026thinsp;blowing or puffing, with non-rhythmic breathing); and vocalization (VOC: 1\u0026thinsp;=\u0026thinsp;absent or 2\u0026thinsp;=\u0026thinsp;occurrence of mooing regardless of frequency or intensity).\u003c/p\u003e \u003cp\u003eReactivity (REACT) was measured with the REATEST\u0026reg;. This device is coupled to the weighing scale and uses accelerometers to rate the intensity and frequency of animal movements on a numerical scale ranging from 1 to 99,999 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Flight time (FT) was obtained with the Duboi\u0026reg; sensor, which measures the time, in hundredths of a second, that it takes an animal to traverse 1.83 meters after leaving the weighing scale. The animals were evaluated at 4 months of age, at weaning, at yearling, and at post-yearling. Temperament traits were measured on a weighing scale by trained evaluators.\u003c/p\u003e \u003cp\u003eRegarding the facial hair whorls, the lateral position was defined as absent (score 1) or present (score 2) [whorl on the left side (LW), whorl in the middle (MW), and whorl on the right side (RW)]. The vertical position of facial hair whorls in relation to the eyes was defined as absent (score 1) or present (score 2) [low whorl (LoW), mid whorl (MidW), and high whorl (HiW)]. The number of hair whorls (NW) was defined as absent (score 1) or present (scores 2 to 5 corresponding to one to four whorls, respectively). Whorl height (WH) was defined as absent (score 1) or present (scores 2 to 5 corresponding to low, medium, high, or very high whorls, respectively). Very high whorl indicates the presence of the hair whorl in the upper part of the animal\u0026rsquo;s face, i.e., an area between the horns. The whorl trait (WHORL) was defined as absent (score 1) or present (score 2), regardless of the number or position of the whorls. The description and visualization of facial hair whorls were previously described by Bessa et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe following productive, reproductive, and carcass traits were studied: weaning weight (WW); body weight at 12 (W12) and 18 months of age (W18); scrotal circumference at weaning (SCW), scrotal circumference at 12 (SC12) and 18 months of age (SC18); and ribeye area at 12 (REA12) and 18 months of age (REA18). REA12 and REA18 were measured using a Piemedical Scanner 200 Vet equipped with an 18 cm linear transducer operating at 3.5 MHz, and an ALOKA 500V with a 17.2 cm linear probe at 3.5 MHz, positioned over the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle between the 12th and 13th ribs.\u003c/p\u003e \u003cp\u003eQuality control of the phenotypic data was performed using the R program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/foundation\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/foundation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A generalized linear model was used for the study of fixed effects. For REACT, the data were square-root transformed. The contemporary groups were formed considering combinations of the tested fixed effects (Supplementary Table S1). Contemporary groups with fewer than three individuals and groups composed of offspring derived from only one sire and that did not show variability in the phenotypes, i.e., with only one of the scores, were excluded.\u003c/p\u003e \u003cp\u003eThe genetic parameters were estimated in single- and two-trait analyses under an animal model using the GIBBSF90\u0026thinsp;+\u0026thinsp;program [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The matrix notation of the model for the temperament traits is represented by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:y=X\\beta\\:+Za+Wpe+e$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ein which \u003cem\u003ey\u003c/em\u003e is the vector of observations; \u003cem\u003eβ\u003c/em\u003e is the vector of fixed effects; \u003cem\u003ea\u003c/em\u003e is the vector of random additive genetic effects; \u003cem\u003epe\u003c/em\u003e is the vector of random permanent environmental effects; \u003cem\u003eX\u003c/em\u003e, \u003cem\u003eZ\u003c/em\u003e and \u003cem\u003eW\u003c/em\u003e are incidence matrices that relate \u003cem\u003eβ\u003c/em\u003e, \u003cem\u003ea\u003c/em\u003e, and \u003cem\u003epe\u003c/em\u003e to \u003cem\u003ey\u003c/em\u003e, and \u003cem\u003ee\u003c/em\u003e is the vector of random environmental effects. The variance of the permanent environment obtained with this model was used to estimate the repeatability.\u003c/p\u003e \u003cp\u003eThe following model was used for WW and SCW:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:y=X\\beta\\:+Za+Mm+e$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ey\u003c/em\u003e is the vector of observations; \u003cem\u003eβ\u003c/em\u003e is the vector of fixed effects; \u003cem\u003ea\u003c/em\u003e is the vector of random additive genetic effects; \u003cem\u003em\u003c/em\u003e is the vector of random maternal additive genetic effects; \u003cem\u003eX\u003c/em\u003e, \u003cem\u003eZ\u003c/em\u003e and \u003cem\u003eM\u003c/em\u003e are incidence matrices that relate \u003cem\u003eβ\u003c/em\u003e, \u003cem\u003ea\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e to \u003cem\u003ey\u003c/em\u003e, and \u003cem\u003ee\u003c/em\u003e is the vector of random environmental effects. The maternal additive genetic variance obtained with this model was used to estimate maternal heritability. For the other analyses (facial hair whorls, W12, W18, SC12, SC18, REA12, and REA18), random permanent environmental and maternal additive genetic effects were not considered. The relationship matrix included 10,308 animals.\u003c/p\u003e \u003cp\u003eFor genetic parameter estimation, 1,100,000 iterations were considered, with a burn-in period of 100,000 iterations and a thinning interval of 500 iterations, totaling 2,000 samples for convergence analysis. The Geweke and Heidelberger and Welch convergence tests were performed using the boa package of the R software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Convergence was established when the heritability, repeatability, and genetic correlation estimates met the criteria of one or both tests.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe data structure of the facial hair whorls HiW and RW did not allow the genetic parameters to meet the convergence criteria and the results of these analyses will therefore not be reported. The other traits showed convergence in at least one of the tests used. The descriptive statistics of the traits studied are displayed in Supplementary Tables S2 and S3. Estimates of direct heritability, maternal heritability, and repeatability estimates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The heritability estimates varied from 0.09 (BRE) to 0.67 (SC12), while the repeatability varied from 0.16 (BRE) to 0.55 (FT). Maternal heritability estimates were equal to 0.18 (WW) and 0.13 (SCW).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic parameter estimates and highest density intervals (in brackets) for the traits studied in Canchim cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTemperament traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eFacial hair whorl traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eProductive, reproductive,\u003c/p\u003e \u003cp\u003eand carcass traits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{h}}_{\\varvec{d}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{h}}_{\\varvec{d}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{h}}_{\\varvec{d}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{h}}_{\\varvec{m}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMOV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.11;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e(0.31;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(0.33;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eWW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(0.03;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(0.12;0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTEN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.21;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(0.40;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(0.15;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eW12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e(0.39;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBRE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(0.02;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.08;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLoW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(0.33;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eW18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e(0.18;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(0.20;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003cp\u003e(0.30;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMidW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(0.15;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSCW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e(0.07;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.04;0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.13;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e(0.37;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(0.10;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSC12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(0.36;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003cp\u003e(0.23;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e(0.50;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eWH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e(0.14;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSC18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(0.29;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eWHORL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003cp\u003e(0.08;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eREA12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(0.24;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eREA18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e(0.18;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u0026plusmn; = standard deviations, MOV\u0026thinsp;=\u0026thinsp;movement, TEN\u0026thinsp;=\u0026thinsp;tension, BRE\u0026thinsp;=\u0026thinsp;breathing, VOC\u0026thinsp;=\u0026thinsp;vocalization, REA\u0026thinsp;=\u0026thinsp;reactivity, FT\u0026thinsp;=\u0026thinsp;flight time, LW\u0026thinsp;=\u0026thinsp;facial whorl on the left side, MW\u0026thinsp;=\u0026thinsp;facial whorl in the middle, LoW\u0026thinsp;=\u0026thinsp;low whorl, MidW\u0026thinsp;=\u0026thinsp;mid whorl, NW\u0026thinsp;=\u0026thinsp;number of facial whorls, WH\u0026thinsp;=\u0026thinsp;whorl height, WHORL\u0026thinsp;=\u0026thinsp;absence or presence of facial whorls, WW\u0026thinsp;=\u0026thinsp;weaning weight, W12 and W18\u0026thinsp;=\u0026thinsp;body weight at 12 and 18 months of age, SCW\u0026thinsp;=\u0026thinsp;scrotal circumference at weaning, SC12 and SC18\u0026thinsp;=\u0026thinsp;scrotal circumference at 12 and 18 months of age, REA12 and REA18\u0026thinsp;=\u0026thinsp;ribeye area at 12 and 18 months of age, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{d}^{2}\\)\u003c/span\u003e\u003c/span\u003e = direct heritability estimate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{m}^{2}\\)\u003c/span\u003e\u003c/span\u003e = maternal heritability estimate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e = repeatability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the genetic correlations that presented the highest posterior density range, which encompassed the estimate and presented the same sign, were between WW and VOC (-0.71), W12 and VOC (-0.71), WW and FT (-0.24), W12 and FT (-0.26), REA12 and REACT (-0.51), and REA12 and FT (-0.44). The highlighted phenotypic correlations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) among productive, reproductive, and carcass traits with temperament were between WW and VOC (-0.88), WW and FT (-0.15), W12 and VOC (-0.88), W12 and FT (-0.13), W18 and VOC (-0.91), and REA12 and FT (-0.21). SC12 and SC18 presented genetic correlations with MW, MidW, and NW (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) ranging from \u0026minus;\u0026thinsp;0.74 to -0.41. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, phenotypic correlations between productive, reproductive, and carcass traits with facial hair whorl traits varied from \u0026minus;\u0026thinsp;0.44 to 0.14 but did not present estimates within the highest posterior density range. Genetic correlations between productive, reproductive, and carcass traits are presented in Supplementary Table S4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of genetic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with temperament traits in Canchim cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTEN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.23;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(-0.14;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(-0.15;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32*\u003c/p\u003e \u003cp\u003e(-1.00;-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(-0.37;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-0.48;-0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.20;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e(-013;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(-0.18;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-1.00;-0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.29;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-0.53;-0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.20;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.18;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003cp\u003e(-015;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003cp\u003e(-0.99;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003cp\u003e(-0.31;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.45;0.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003cp\u003e(-1.00;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003cp\u003e(-1.00;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003cp\u003e(-0.87;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.64;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003cp\u003e(-0.27;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(-0.64.0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003cp\u003e(-1.00;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003cp\u003e(-1.00;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(-0.40;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003cp\u003e(-0.54;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e(-0.14;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(-0.19;0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003cp\u003e(-1.00;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003cp\u003e(-1.00;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.55;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e(-0.65;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.41;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003cp\u003e(-0.18;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.22;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.19;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.31;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.48;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-0.99;-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15*\u003c/p\u003e \u003cp\u003e(-0.73;-0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.13;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.01;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.16;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.34;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(-0.07;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003cp\u003e(-0.41;0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026plusmn; = standard deviations, WW\u0026thinsp;=\u0026thinsp;weaning weight, W12 and W18\u0026thinsp;=\u0026thinsp;body weight at 12 and 18 months of age, SCW\u0026thinsp;=\u0026thinsp;scrotal circumference at weaning, SC12 and SC18\u0026thinsp;=\u0026thinsp;scrotal circumference at 12 and 18 months of age, REA12 and REA18\u0026thinsp;=\u0026thinsp;ribeye area at 12 and 18 months of age, MOV\u0026thinsp;=\u0026thinsp;movement, TEN\u0026thinsp;=\u0026thinsp;tension, BRE\u0026thinsp;=\u0026thinsp;breathing, VOC\u0026thinsp;=\u0026thinsp;vocalization, REACT\u0026thinsp;=\u0026thinsp;reactivity, FT\u0026thinsp;=\u0026thinsp;flight time, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of phenotypic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with temperament traits in Canchim cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTEN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(-0.09;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e(-0.14;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.59;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03*\u003c/p\u003e \u003cp\u003e(-0.93;-0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(-0.23;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09*\u003c/p\u003e \u003cp\u003e(-0.30;-0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e(-0.08;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e(-0.18;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e(-0.54;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-0.93;-0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e(-0.10;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08*\u003c/p\u003e \u003cp\u003e(-0.29;-0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(-0.22;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.29;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.59;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03*\u003c/p\u003e \u003cp\u003e(-0.96;-0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(-0.19;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e(-0.21;0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e(-0.40;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(-0.25;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.58;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.44;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e(-0.22;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003cp\u003e(-0.19;0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.11;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e(-0.25;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.41;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003cp\u003e(-0.54;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(-0.18;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(-0.10;0.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.40;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003cp\u003e(-0.44;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.66;0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003cp\u003e(-0.51;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.38;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.24;0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e(-0.21;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.39;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.52;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003cp\u003e(-0.65;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.74;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10*\u003c/p\u003e \u003cp\u003e(-0.42;-0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e(-0.12;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.53;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e(-0.60;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.40;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e(-0.11;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e(-0.19;0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026plusmn; = standard deviations, WW\u0026thinsp;=\u0026thinsp;weaning weight, W12 and W18\u0026thinsp;=\u0026thinsp;body weight at 12 and 18 months of age, SCW\u0026thinsp;=\u0026thinsp;scrotal circumference at weaning, SC12 and SC18\u0026thinsp;=\u0026thinsp;scrotal circumference at 12 and 18 months of age, REA12 and REA18\u0026thinsp;=\u0026thinsp;ribeye area at 12 and 18 months of age, MOV\u0026thinsp;=\u0026thinsp;movement, TEN\u0026thinsp;=\u0026thinsp;tension, BRE\u0026thinsp;=\u0026thinsp;breathing, VOC\u0026thinsp;=\u0026thinsp;vocalization, REACT\u0026thinsp;=\u0026thinsp;reactivity, FT\u0026thinsp;=\u0026thinsp;flight time, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of genetic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with facial hair whorl traits in Canchim cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMidW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWHORL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.14;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.39;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.14;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.39;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.32;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e(-0.06;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.19;0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(-0.36;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.51;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(-0.36;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.48;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.47;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003cp\u003e(-0.25;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(-0.44;0.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.55;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.59;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.55;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.59;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.64;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.11;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.22;0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.15;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.15;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.15;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e(-0.73;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003cp\u003e(-0.62;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e(-0.98;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e(-1.00;0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.19;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23*\u003c/p\u003e \u003cp\u003e(-1.00;-0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.19;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-1.00;-0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(-0.83;-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003cp\u003e(-0.64;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003cp\u003e(-0.61;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.50;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18*\u003c/p\u003e \u003cp\u003e(-0.99;-0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.50;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19*\u003c/p\u003e \u003cp\u003e(-1.00;-0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18*\u003c/p\u003e \u003cp\u003e(-0.99;-0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003cp\u003e(-0.65;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003cp\u003e(-0.82;0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.38;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.63;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.38;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.63;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.52;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003cp\u003e(-0.39;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.56;0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.40;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.42;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003cp\u003e(-0.40;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(-0.42;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003cp\u003e(-0.38;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e(-0.22;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e(-0.53;0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026plusmn; = standard deviations, WW\u0026thinsp;=\u0026thinsp;weaning weight, W12 and W18\u0026thinsp;=\u0026thinsp;body weight at 12 and 18 months of age, SCW\u0026thinsp;=\u0026thinsp;scrotal circumference at weaning, SC12 and SC18\u0026thinsp;=\u0026thinsp;scrotal circumference at 12 and 18 months of age, REA12 and REA18\u0026thinsp;=\u0026thinsp;ribeye area at 12 and 18 months of age, LW\u0026thinsp;=\u0026thinsp;facial whorl on the left side, MW\u0026thinsp;=\u0026thinsp;facial whorl in the middle, LoW\u0026thinsp;=\u0026thinsp;low whorl, MidW\u0026thinsp;=\u0026thinsp;mid whorl, NW\u0026thinsp;=\u0026thinsp;number of facial whorls, WH\u0026thinsp;=\u0026thinsp;whorl height, WHORL\u0026thinsp;=\u0026thinsp;absence or presence of facial whorls, * genetic correlations that presented estimates within the highest density intervals and that did not include zero values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of phenotypic correlations and highest posterior density (in brackets) of productive, reproductive, and carcass traits with facial hair whorl traits in Canchim cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMidW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWHORL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.01;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.05;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.01;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.05;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.04;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003cp\u003e(-0.72;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003cp\u003e(-0.77;0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.03;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.03;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.03;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.04;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e(-0.04;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.70;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003cp\u003e(-0.69;0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eW18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.08;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.09;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.08;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.09;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e(-0.10;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003cp\u003e(-0.66;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003cp\u003e(-0.74;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.02;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.02;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.02;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.03;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.03;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003cp\u003e(-0.96;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003cp\u003e(-0.72;0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e(-0.04;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.11;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e(-0.04;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.11;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.11;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003cp\u003e(-0.65;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003cp\u003e(0.63;0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSC18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e(-0.05;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.07;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e(-0.05;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003cp\u003e(-0.83;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.07;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003cp\u003e(-0.69;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003cp\u003e(-0.68;0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.04;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.01;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.04;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.01;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.02;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003cp\u003e(-0.57;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003cp\u003e(-0.66;0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREA18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.01;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.03;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.01;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.03;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e(-0.02;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003cp\u003e(-0.53;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003cp\u003e(-0.73;0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026plusmn; = standard deviations, WW\u0026thinsp;=\u0026thinsp;weaning weight, W12 and W18\u0026thinsp;=\u0026thinsp;body weight at 12 and 18 months of age, SCW\u0026thinsp;=\u0026thinsp;scrotal circumference at weaning, SC12 and SC18\u0026thinsp;=\u0026thinsp;scrotal circumference at 12 and 18 months of age, REA12 and REA18\u0026thinsp;=\u0026thinsp;ribeye area at 12 and 18 months of age, LW\u0026thinsp;=\u0026thinsp;facial whorl on the left side, MW\u0026thinsp;=\u0026thinsp;facial whorl in the middle, LoW\u0026thinsp;=\u0026thinsp;low whorl, MidW\u0026thinsp;=\u0026thinsp;mid whorl, NW\u0026thinsp;=\u0026thinsp;number of facial whorls, WH\u0026thinsp;=\u0026thinsp;whorl height, WHORL\u0026thinsp;=\u0026thinsp;absence or presence of facial whorls.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHeritability estimates\u003c/h2\u003e \u003cp\u003eExcept for BRE, the heritability estimates for the temperament traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were classified as moderate to high, suggesting that these traits will respond satisfactorily to selection and less reactive offspring are expected. On the other hand, the repeatability of these traits indicated some consistency in the expression of the phenotypes measured at different ages, especially for FT. Thus, the temperament of Canchim cattle is influenced by genetic and permanent environmental components. The behavior of young animals tends to change gradually, possibly because of cumulative exposure to the stimuli presented; hence, for selection purposes, the collection of information at different ages is necessary [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOther studies reported heritability estimates for MOV, TEN, BRE, VOC, REACT, and flight speed of 0.17 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], 0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], 0.39 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and 0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], respectively. These divergences in the results compared to the present study are related to differences in the breeds used and in the assessment methods of the animals.\u003c/p\u003e \u003cp\u003eThe heritability estimates for the facial hair whorl traits ranged from low (0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 for NW) to high (0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 for LW and LoW). Overall, hair whorl traits in the Canchim breed showed sufficient genetic variability to respond to selection. Although these traits appear to be similar, differences were observed in genetic variability for height, position, and number of whorls. One of the advantages of evaluating facial hair whorls in cattle is that these traits are easy to obtain; they can be evaluated shortly after the animal is born and only require one observation, facilitating the implementation of these assessments on farms.\u003c/p\u003e \u003cp\u003eIn Holstein cattle, heritability estimates of 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 and 0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 were obtained for WH and whorl position in relation to the midline of the face, respectively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hair whorls are more frequently studied in horses and heritability estimates of 0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 and 0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 have been reported for position and number of facial hair whorls, respectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These authors found that animals with a larger number of hair whorls tend to be shyer during taming. For whorl position and NW, heritability estimates equal to 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and 0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.097 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were observed in horses.\u003c/p\u003e \u003cp\u003eThe high direct heritability estimates for body weights indicated that animals are likely to respond to direct selection, especially for W12. For SCW, SC12, and SC18, direct heritability estimates of 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, 0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 and 0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, respectively, were obtained, indicating that greater responses to selection can be expected for SC12. In Canchim animals, heritability estimates of 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, 0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, and 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 were obtained for WW, W12, and W18, respectively [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Nellore cattle, estimates of 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 and 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 were obtained for SC measured at 365 and 450 days of age, respectively [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The maternal heritabilities for WW and SCW suggest that the maternal genetic component plays an important role in the expression of these traits. The results observed agree with those reported by Vargas et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These authors highlighted the need for incorporating maternal effects in genetic parameter estimation models for pre-weaning traits to reduce bias. The heritability estimates for REA12 and REA18 were 0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 and 0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, respectively. Thus, selection for these traits is expected to promote satisfactory genetic gains.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic and phenotypic correlations\u003c/h3\u003e\n\u003cp\u003eAs can be seen in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a large part of the genetic correlation estimates was of low magnitude and had high standard deviations, indicating the absence of associations between most traits. One explanation for this observation could be the number of phenotypes; it is therefore recommended to continue these measurements to reduce potential bias in future studies of these traits in the Canchim breed.\u003c/p\u003e \u003cp\u003eNegative genetic correlations of WW and W12 with VOC and FT were observed, indicating that lower body weight performance is associated with higher VOC scores and a shorter time at which the animal leaves the scale and traverses 1.83 meters. This result suggests the existence of an additive genetic component that acts favorably on these traits, i.e., animals with higher WW and W12 are expected to have a desirable temperament in terms of VOC and FT, being calmer during handling in the pen. Animals with slower flight speed exhibited greater body weight gains (mean of 1.54 kg/day) than animals with faster flight speed (mean of 1.37 kg/day) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In a study with Charolais and Nellore, animals with slower FT, i.e., that traverse a certain distance more slowly, had greater weight gain [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genetic correlations of REA12 with REACT and FT were negative and of moderate magnitude. This result suggests that more reactive animals and animals with faster FT have a smaller ribeye area. Furthermore, the genetic correlations of WW and W12 with REA12 (Supplementary Table S4) suggest the existence of a genetic component shared between these traits, indicating that they affect each other and that selection for one will indirectly and favorably affect the others.\u003c/p\u003e \u003cp\u003eAccording to Nkrumah et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], temperament influences carcass traits in beef cattle, highlighting the importance of including temperament in the definitions of selection objectives, as well as the culling of animals that are difficult to handle and the use of appropriate facilities to improve the handling experience of animals. Furthermore, Coutinho et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] observed that higher chute score and exit velocity were correlated with shear force and tenderness of the \u003cem\u003eLongissimus lumborum\u003c/em\u003e muscle, indicating that calmer animals produce better quality meat.\u003c/p\u003e \u003cp\u003eOther studies on cattle reported genetic correlations of scrotal circumference with flight speed and temperament scores of -0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and \u0026minus;\u0026thinsp;0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], respectively. According to Brand\u0026atilde;o \u0026amp; Cooke [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], animals with a more reactive temperament exhibit poorer reproductive performance than calmer animals; this fact can affect traits such as age at first calving of females, which also shows high genetic correlations with scrotal circumference of males.\u003c/p\u003e \u003cp\u003eThe phenotypic correlations between productive, reproductive and carcass traits with temperament traits showed estimates that were mostly negative (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Favorable and high phenotypic correlations were observed between VOC with WW, W12, and W18 indicating that there is an association and that the variation in the expression of these traits in cattle is probably influenced by other environmental factors. A negative phenotypic correlation was also observed between FT with WW, W12, and REA12.\u003c/p\u003e \u003cp\u003eThe phenotypic correlations for MOV and flight speed with body weight in Nellore animals were previously reported by Sant\u0026rsquo;Anna et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The authors observed that these correlations showed negative and low magnitude estimates of -0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 and \u0026minus;\u0026thinsp;0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, respectively, which reflects a weak relationship between the traits. In crossbred animals, Burrow [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] observed phenotypic correlations between body weight and flight speed that ranged from \u0026minus;\u0026thinsp;0.02 to -0.05, corroborating the results observed in our study. These same authors also observed phenotypic correlations for flight speed and scrotal circumference at different ages that ranged from 0.07 to 0.11.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, only SC12 and SC18 showed important genetic correlations with MW, MidW, and NW. These results demonstrate that the position and number of facial hair whorls of the animal can influence male reproductive traits. Therefore, the genetic correlations of SC12 and SC18 with the highlighted whorl traits indicate that the genes responsible for the presence of whorls in these regions are also responsible for undesirable reproductive performance in males. In a study with Angus cattle, Meola et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that animals with a hair whorl pattern classified as round epicenter had a higher percentage of normal spermatozoa than animals with a non-round (or misshapen) epicenter. Investigating WH in beef cattle, Silveira et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] observed negative correlations with weight gain and FT. In contrast, Olmos \u0026amp; Turner [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] found no correlations of whorl position with weight gain or flight speed in beef cattle.\u003c/p\u003e \u003cp\u003eThe phenotypic correlations between the productive, reproductive and carcass traits with the whirlpool traits are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, where they were observed as being of low magnitude, accompanied by high standard errors. This indicates that the phenotypic interactions between these estimates are not very relevant, suggesting a weak or inconsistent relationship between them. In general, the genetic correlations between productive, reproductive, and carcass traits were of moderate to high magnitude (Supplementary Table S4), suggesting that a set of genes with additive effect act simultaneously and favorably on these traits. The genetic correlations of WW with SC12, SC18, and REA18; of SCW with REA12 and REA18; of SC12 with REA18; and of SC18 with REA18 were of low magnitude and had high standard deviations.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study showed that most of the traits exhibit additive genetic variability that can be explored by the Canchim breeding program, aiming at selecting animals with adequate performance and temperament. Genetic associations were observed between body weight traits with temperament and between scrotal circumference and carcass traits with facial hair whorls. Thus, pre-selecting animals for the absence of facial hair whorls, in conjunction with selection for W12 and SC12, may result in indirect and favorable genetic gain in offspring temperament.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Bessa AFO, Souza VAF, Ribeiro ARB, Marcondes CR, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eData curation: Bugner ALP, Souza VAF, Maffei WE, Ribeiro ARB, Marcondes CR. \u003c/p\u003e\n\u003cp\u003eFormal analysis: Bessa AFO, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eMethodology: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eSoftware: Bessa AFO, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eValidation: Bessa AFO, C\u0026acirc;mara GMS, Zucatelle C, Futema FK, Teixeira R, Cenedeze G,\u003csup\u003e \u003c/sup\u003eBuzanskas ME. \u003c/p\u003e\n\u003cp\u003eInvestigation: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eWriting - original draft: Bessa AFO, Ribeiro ARB, Marcondes CR, Buzanskas ME. \u003c/p\u003e\n\u003cp\u003eWriting - review \u0026amp; editing: Bessa AFO, C\u0026acirc;mara GMS, Zucatelle C, Futema FK, Teixeira R, Cenedeze G,\u003csup\u003e \u003c/sup\u003eBugner ALP, Souza VAF, Maffei WE, Ribeiro ARB, Marcondes CR, Buzanskas ME.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAyrton Bessa received a scholarship from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior \u0026ndash; Brazil (CAPES) \u0026ndash; Finance Code 001.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eACKNOWLEGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY MATERIAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e Fixed effects included in the genetic evaluation model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S2.\u003c/strong\u003e Descriptive statistics of temperament and facial hair whorl traits\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S3.\u003c/strong\u003e Descriptive statistics of productive, reproductive and carcass traits\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Estimates of genetic correlations (\u0026plusmn; standard deviation) between productive, reproductive, and carcass traits\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon reasonable request, the datasets of this study can be available from the corresponding author.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Animal Use Ethics Committee of Embrapa approved the study (Protocol 02/2024).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF GENERATIVE AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo AI tools were used in this article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eABIEC. 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Appl Anim Behav Sci 115:25\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.applanim.2008.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.applanim.2008.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sao Paulo State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Animal Breeding, Behavior, Composite Breed, Pleiotropy","lastPublishedDoi":"10.21203/rs.3.rs-8833805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8833805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to estimate genetic parameters and evaluate genetic correlations between temperament and facial hair whorl traits with productive, reproductive, and carcass traits in Canchim beef cattle.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePhenotypic records from animals born between 2013 and 2022 were analyzed. Temperament was assessed using behavioral scores (movement, tension, breathing, vocalization), reactivity measured by accelerometers, and flight time, while facial hair whorls were characterized by presence, number, position, and height. Productive, reproductive, and carcass traits included body weights, scrotal circumference, and ribeye area at different ages. Genetic parameters were estimated using single- and two-trait animal models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHeritability estimates ranged from low to high (0.09 to 0.67), indicating sufficient additive genetic variability for all trait groups. Favorable genetic correlations were observed between body weight traits and temperament measures, particularly vocalization and flight time, as well as between ribeye area and reactivity flight time. Scrotal circumference at 12 and 18 months of age showed moderate to high genetic correlations with facial hair whorl traits, especially whorl number and position.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese results indicate that selection for productive and reproductive traits can lead to indirect improvements in temperament. Moreover, pre-selection for the absence of facial hair whorls, combined with selection for body weight and scrotal circumference, may promote favorable genetic gains in temperament, supporting their inclusion in Canchim breeding programs.\u003c/p\u003e","manuscriptTitle":"Genetic associations of temperament and facial hair whorls with productive, reproductive, and carcass traits in Canchim beef cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 07:22:38","doi":"10.21203/rs.3.rs-8833805/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21bb9025-5c99-4f32-b39f-fa28f37b176d","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62614123,"name":"Animal Science"}],"tags":[],"updatedAt":"2026-02-10T07:22:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 07:22:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8833805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8833805","identity":"rs-8833805","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}