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Journal of Animal Science - Animal Genetics

Heritabilities and genetic correlations of pulmonary arterial pressure and performance traits in Angus cattle at high altitude1

 

This article in JAS

  1. Vol. 94 No. 11, p. 4483-4490
     
    Received: June 06, 2016
    Accepted: Aug 16, 2016
    Published: October 27, 2016


    2 Corresponding author(s): mark.enns@colostate.edu
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doi:10.2527/jas.2016-0703
  1. N. F. Crawford*,
  2. M. G. Thomas*,
  3. T. N. Holt,
  4. S. E. Speidel* and
  5. R. M. Enns 2*
  1. * Department of Animal Sciences, Colorado State University, Fort Collins 80523
     Department of Clinical Sciences, Colorado State University, Fort Collins 80523

Abstract

Pulmonary arterial pressure (PAP) is an indicator trait for pulmonary hypertension and for the risk of developing high-altitude disease (HAD) in cattle. Pulmonary arterial pressures provide a tool for selection of breeding cattle for tolerance to high altitude in mountainous regions of the United States. The objective of this study was to evaluate relationships between growth performance traits and yearling PAP (42.4 ± 9.9 mmHg; n = 5,776; elevation 2,150 m) using data from 1993 to 2014 in the John E. Rouse Colorado State University Beef Improvement Center (CSU-BIC) Angus herd. The breeding program used sires (n = 299) from both low- and high-elevation environments. We hypothesized that little to no genetic relationship exists between PAP and birth weight (BWT; direct and maternal), weaning weight (WW; direct and maternal), yearling weight (YW; direct and maternal), and postweaning gain (PWG). Historic selection of natural service sires from within the herd required a PAP of ≤ 42 mmHg. Outside AI sires (n = 156) used in this breeding program were not PAP tested and therefore were used with little knowledge of these sires’ high-altitude adaptability. Performance traits (± SD) routinely recorded included BWT (36.2 ± 5.1 kg; n = 8,695), WW (213.5 ± 31.8 kg; n = 8,010), YW (345.6 ± 83.8 kg; n = 5,580), and PWG (122.0 ± 63.7 kg; n = 5,449), where PWG represented the total weight gained from weaning to yearling age. Four-trait analyses using REML were conducted with an animal model. The heritability estimates (± SE) for PAP (0.26 ± 0.03), BWT direct (0.42 ± 0.04) and maternal (0.14 ± 0.02), WW direct (0.29 ± 0.04) and maternal (0.19 ± 0.03), YW direct (0.45 ± 0.04) and maternal (0.23 ± 0.03), and PWG (0.14 ± 0.02) were in the range of those reported in previous literature. Estimates of genetic correlations (± SE) revealed weak relationships between PAP and direct and maternal BWT, direct and maternal WW, direct and maternal YW, and PWG of 0.15 ± 0.09, 0.14 ± 0.10, 0.23 ± 0.09, −0.01 ± 0.10, 0.12 ± 0.08, 0.00 ± 0.09, and −0.10 ± 0.10, respectively. The results of this study suggest that selection for lower PAP measures should have minimal influence on the growth performance of yearling Angus bulls and heifers at the CSU-BIC, supporting our hypothesis.



INTRODUCTION

A risk for cattle raised in high elevations (>1,800 m) is development of high-altitude disease (HAD), commonly referred to as brisket disease, a disease first reported in 1889 (Glover and Newsom, 1917). High-altitude disease is a consequence of hypoxia-induced pulmonary hypertension, which has a multifactorial pathophysiology, where not all contributing factors for the disease are fully understood (Pugliese et al., 2015b). Preventative measures for HAD typically involve identification and culling of high-risk individuals from a breeding program. Pulmonary arterial pressures (PAP) are used as an indicator of altitude-associated pulmonary hypertension in cattle (Holt and Callan, 2007). With a moderate heritability (0.20 to 0.46), PAP measures have been used to make selection decisions for genetic improvement and reduced incidence of HAD for several decades (Enns et al., 1992; Shirley et al., 2008; Cockrum et al., 2014). The John E. Rouse Colorado State University Beef Improvement Center (CSU-BIC) has routinely made selection decisions for genetic improvement simultaneously on both growth traits and yearling PAP measures, yet the disease continues to be observed at low incidence rates. A better understanding of the relationships between PAP and performance traits would indicate what effect, if any, selection for favorable PAP has on growth performance of cattle at high elevations. Therefore, the objective of this research was to determine genetic and environmental relationships between yearling PAP and performance traits, including birth weight (BWT), weaning weight (WW), yearling weight (YW), and postweaning gain (PWG). Our hypothesis was that little to no genetic relationship exists between PAP measures and these performance traits.


MATERIALS AND METHODS

This study received approval from the Colorado State University Animal Care and Use Committee under protocol number 13-4136.

Environment and Cattle Management

The CSU-BIC is located approximately 14 km east of Encampment, WY, with an elevation ranging from 2,150 to 2,411 m. The research facility consists of approximately 2,596 ha of land that supports grazing of approximately 420 Angus cows. Heifers (around May 15) and mature cows (around June 15) were artificially inseminated each spring after a progestogen-based estrous synchronization protocol. Two weeks after AI, natural service sires were put with the groups of cows and heifers for approximately 60 d. Parentage of calves was traced on the basis of birth dates and a gestation length of 278 d. Since 2011, all calves at the CSU-BIC have been genotyped, allowing for parentage verification if necessary. Weaning occurred in the fall (around October 1) of each year.

Approximately 150 yearling heifers were developed each year with grazing of Timothy and Brome grass pastures and alfalfa/grass hay supplementation in winter months, typically from December to April. Target gain for developing heifers was 0.5 kg/d. Replacement heifers with yearling PAP ≤ 45 mmHg were selected as herd replacements, with numbers depending on cow culling rates for that year, and the remainder was marketed each year.

At weaning, male calves were divided into 2 groups, with 1 group being a performance-gain bull test and the other group being steers destined for the feedlot. Between 37 and 99 bulls were performance tested each year. The selection criterion of bulls for this gain test was typically BWT < 41 kg and a WW ratio > 100%. This WW ratio was calculated by taking the actual WW of an animal, dividing it by the average WW of its contemporaries, and multiplying by 100 to get a percentage. To achieve a greater than 100% ratio, a bull calf needed to weigh more than the average of its contemporaries. If bull calves did not achieve this criterion, they were castrated. After weaning, bull calves entering the gain test were fed a 50% concentrate and 50% hay diet for 120 d, targeting an expected ADG of 1.5 kg/d. Mean PAP measures were collected on bulls and heifers at or near the end of the gain tests in March or April, when bulls and heifers were approximately 1 yr of age. The number of steers with PAP measures was relatively few in comparison to bulls and heifers, as they were often transported to a low-elevation experiment station for finishing. As such, steers were omitted from the analyses for postweaning traits YW, PWG, and PAP, resulting in postweaning data comprising only that from bulls and heifers.

PAP Measurements and Use

A veterinarian licensed in the states of Colorado and Wyoming collected all PAP measures while cattle were restrained in a squeeze chute. This is a measure of the mean pressure based on pulmonary artery wave forms and was described in more depth by Holt and Callan (2007). Since the formation of the CSU-BIC research facility in 1986, culling decisions for replacement heifers and within-herd bulls have considered PAP measures. Generally, PAP ≤ 42 mmHg were considered suitable for herd bulls and replacement heifers, with the exception of a few females retained with higher PAP for research purposes. High PAP measures aid in the identification of animals susceptible to the development of pulmonary hypertension and HAD. Although the range in PAP measures was 29 to 139 mmHg, eliminating records of high PAP would result in loss of valuable information on potential animals or genetic lines susceptible to the disease, and these records were therefore kept in the analyses (Holt and Callan, 2007). Semen from commercially available external AI sires (n = 156) was used as part of a PAP progeny-testing program. These bulls were not PAP tested, as most were raised at low elevations where PAP testing does not occur. Progeny from these outside AI sires were subsequently evaluated for PAP, and those observations were included in this study. Pulmonary arterial pressures in this study were subjected to the same age requirements as those for YW as outlined in Beef Improvement Federation (BIF) guidelines (BIF, 2010). The average PAP testing age in this study was 356 d, with a range of 320 to 410 d and a SD of 19 d.

Data Summary

Growth records, PAP records, and the number of cattle evaluated from 1993 to 2014 are summarized in Table 1. A 3-generation pedigree was constructed beginning with animals having at least 1 observation. The resulting pedigree contained 9,459 animals, with 299 sires and 1,721 dams.


View Full Table | Close Full ViewTable 1.

Descriptive statistics for pulmonary arterial pressure and weight traits in the CSU-BIC Angus herd1

 
Item2 n Minimum Mean Maximum SD
PAP, mmHg 5,776 21.0 42.4 139.0 9.9
BWT, kg 8,695 12.2 36.2 56.7 5.1
WW, kg 8,010 58.5 213.5 368.3 31.8
YW, kg 5,580 166.9 345.6 584.2 83.8
PWG, kg 5,449 11.7 122.0 382.9 63.7
1CSU-BIC = Colorado State University Beef Improvement Center, Saratoga, WY, elevation 2,150 to 2,411 m.
2PAP = mean pulmonary arterial pressure; BWT = birth weight; WW = weaning weight; YW = yearling weight. PWG = postweaning gain = [(YW − WW)/days between weights] × 160.

Age criteria for acceptable weaning weights of cattle were based on BIF guidelines (BIF, 2010) and resulted in an average age of 190 d, with a range of 160 to 250 d and a SD of 17 d. A similar approach using BIF guidelines was applied to YW and resulted in an average age of 358 d and a SD of 24 d. Postweaning gain, a measure of the weight change from weaning to yearling age, was calculated as PWG = [(YW − WW)/days between weights] × 160.

Contemporary groups for each trait were designed to account for differences in management and environment. Weaning contemporary group (CG) was defined as weaning date. This resulted in 24 weaning CG, with an average CG size of 354 animals and SD of 93 animals. Yearling CG was defined as yearling date and weaning date (i.e., single concatenated effect), resulting in 54 yearling CG, with an average CG size of 111 animals and SD of 58 animals. All single-animal CG observations were removed from the analysis. Age of dam (AOD) of the calves was classified according to BIF (2010) recommended age categories on the basis of days of age, with the final groupings being ages of 2, 3, 4, 5 to 9, 10, 11, 12, and ≥13 yr.

Statistical Analysis

Heritabilities and genetic and environmental correlations were obtained using the software package ASReml 3.0 (Gilmour et al., 2009). Single-trait and 2-trait analyses were conducted to obtain genetic and residual variances to serve as starting values for two 4-trait analyses reported in this study. The first 4-trait analysis included PAP, BWT, WW, and YW for estimation of heritabilities and genetic and environmental correlations. Direct and maternal additive genetic effects were fit for BWT, WW, and YW in the analysis. A likelihood ratio test (Ott and Longnecker, 2008) was initially conducted to determine if having a maternal additive genetic effect in the model for YW was statistically significant. The second 4-trait analysis was conducted with PAP, BWT, WW, and PWG for estimation of heritabilities and genetic and environmental correlations. Yearling weight and PWG were modeled separately because of the part-whole relationship between the 2 and a failure of the equation system to converge to a unique solution as a 5-trait model. The 4-trait animal models were expressed aswhere yi was a vector of observations for trait i, Xi was an incidence matrix relating fixed effects to observations in yi, bi was a vector of fixed effects, Zdi was an incidence matrix relating direct (d) random effects to observations in yi, udi was a vector of direct additive genetic random effects, Zmi was an incidence matrix relating maternal (m) random effects (BWT, WW, and YW only) to observations in yi, umi was a vector of maternal additive genetic random effects, Zpi was an incidence matrix relating maternal permanent environmental (p) random effects (BWT, WW, and YW only) to observations in yi, upi was a vector of permanent environmental random effects, and ei is a vector of random residual errors for each record for trait .

The (co)variance structure for the random effects in the 4-trait analyses were expressed as follows:where d, m, p and e were vectors of direct, maternal, maternal permanent environmental, and residual variances, respectively, for each trait i. A was Wright’s numerator relationship matrix, ⊗ was the Kronecker product operator, Gd was the random direct additive genetic (co)variance matrix, Gm was the random maternal additive genetic (co)variance matrix, Gdm was the direct and maternal additive genetic covariance matrix, P was the random maternal permanent environmental (co)variance matrix, R was a (co)variance matrix of random residual effects, Ic was an identity matrix whose order is the number of dams, and In was an identity matrix whose order was the number of animals. Given that direct maternal and maternal permanent environmental effects were included only for BWT, WW, and YW, the orders of Gm, Gdm, and P were used for BWT, WW, and YW or PWG, depending on the model.

Wald F statistics were utilized to test the significance of the fixed effects used in the analyses. Single-trait analyses were used initially to determine the significance of each fixed effect and whether to include those in the 4-trait analyses. In the single-trait models, all tested fixed effects (i.e., year of birth, sex, animal age, date, weaning CG, yearling CG), except AOD, were significant (P < 0.001). Age of dam was significant (P < 0.001) for only BWT, WW, and YW and was not significant (P > 0.05) for both PWG and PAP. Weaning age, yearling age, and PAP age showed significant (P < 0.001) effects on their respective traits. From these preliminary analyses, significant fixed effects were included in the analysis for all traits, as shown in Table 2. The fixed effects used in the models included year of birth, sex, and AOD for BWT; sex, animal age, AOD, and weaning CG for WW; yearling sex (i.e., bulls and heifers), animal age, AOD, and yearling CG for YW; and yearling sex and yearling CG for PWG. The fixed effects used for analysis of PAP included yearling sex, animal age, date of PAP test, and yearling CG (Table 2).


View Full Table | Close Full ViewTable 2.

Fixed and random effects included in the 4-trait animal models analyzed for each trait in the CSU-BIC Angus herd data1

 
Model
Effect Birth Weight Weaning Weight Yearling Weight Postweaning Gain PAP2
Fixed
    Year of birth X
    Sex3 X X X X X
    Age of Dam X X X
    Age4 X X X
    Date5 X
    Weaning CG6 X
    Yearling CG7 X X X
Random
    Direct additive X X X X X
    Maternal additive X X X
    Permanent environment X X X
1CSU-BIC = Colorado State University Beef Improvement Center, Saratoga, WY, elevation of 2,150 to 2,411 m; average observations of n = 6,702.
2Mean pulmonary arterial pressure, mm Hg.
3Preweaning traits females (1) and males (2); postweaning traits heifers (1) and bulls (2), excluding steers because of limited PAP measures.
4Age of animals when phenotypic observation was measured.
5Date when phenotypic observation was measured on the animals.
6Weaning contemporary group (CG) = weaning date.
7Yearling contemporary group (CG) = yearling date and weaning date.


RESULTS AND DISCUSSION

High-altitude disease involves vascular narrowing and remodeling of the pulmonary arteriolar vessels as a consequence of hypoxia-induced pulmonary hypertension (Brown et al., 2015; Neary et al., 2015a,b; Pugliese et al., 2015a,b). Pulmonary arterial pressure measures indicate a bovine’s ability to adapt to high-elevation regions and therefore predict risk of HAD (Holt and Callan, 2007).

Hohenboken et al. (2005) stated that the adaptation of animals to a specific environment declines when outside or nonnative animals are used for breeding purposes. After a survey of cattle producers across the mountainous west of the United States, Will and Alexander (1970) reported HAD incidence rates of 0.5% to 2% in cattle native to production systems 2,133 m or higher. Will and Alexander (1970) reported incidence rates increased to 10% to 40% for low-altitude-raised calves transported to high altitudes. More recently, Holt and Callan (2007) reported 3% to 5% calf crop losses associated with HAD in mountainous beef production systems. In the last 5 yr, the overall incidence rate (percent ± SE) of HAD in postweaning Angus cattle at CSU-BIC was 0.5% ± 0.002%, with the highest incidence in bulls at 1.5% ± 0.007%. With the use of outside AI sires, there is potential for introduction of nonadapted sires to the breeding program. Therefore, determining factors affecting PAP measures and how selection may influence other important production traits is critical for genetic improvement.

Heritabilities

The heritability estimate for PAP fell within the range of previous reports (0.20 to 0.46; Enns et al., 1992; Shirley et al., 2008; Cockrum et al., 2014). This range varies depending on factors such as sex, age, and breed of cattle evaluated. For instance, Cockrum et al. (2014) estimated heritabilities separately for heifers (0.20) and bulls (0.31). Likewise, another study by Schimmel (1981) reported a range of heritability estimates for cows (0.20), male (0.77) and female (0.60) calves, and differing breeds (0.06 to 0.21).

In a review of heritability estimates of weight traits BWT, WW, and YW, various results were observed (Mackinnon et al., 1991; Robinson, 1996; Costa et al., 2011; Williams et al., 2012; Boddhireddy et al., 2014; Campos et al., 2014; Chen et al., 2014). Postweaning gain heritability in our study was slightly less than previously reported estimates ranging from 0.19 to 0.36 (Garrick et al., 1989; Elzo et al., 2012; Williams et al., 2012). Differences observed between heritability estimates for weight traits in this paper and those of previous research can be explained through differences in analytical techniques or source of variation such as models, age, sex, and breed, among others. In addition, a paper regarding environmental changes on genetic parameters, DeNise et al. (1988) reported that heritability estimates have the potential to vary because of the differing influence of genes on the phenotype measured, in contrasting environments, or a change in the relative contribution of single genes to the phenotype. The current study evaluated a single herd across multiple years, and therefore, the environmental component may be lower, which could result in greater heritability estimates when compared to other studies. Consequently, although the majority of our heritability estimates fell within the range of previously reported estimates on weight traits BWT, WW, YW, and PWG, population parameters have the potential to differ between reports.

Correlations

Yearling Weight Analysis.

The results of the PAP, BWT, WW, and YW analysis are presented in Table 3. Genetic correlations (displayed above the diagonal in Table 3) of PAP and maternal WW and PAP and maternal YW were not different than zero. Weak relationships were observed between PAP and BWT direct, WW direct, and YW direct. Although these correlations are of moderate magnitude, particularly for WW, the results provide evidence to suggest the potential for higher weaning weights to counteract selection for lower PAP measures. On the contrary, given the positive correlation of PAP and BWT direct, selection for lower PAP measures could be favorable for reducing dystocia through lower BWT. However, overall, the genetic correlations are low enough that selection based on PAP would likely not affect genetic change in these growth traits. In contrast to the results of this analysis, Shirley et al. (2008) reported moderate correlations (± SE) between PAP and BWT (0.49 ± 0.12) and PAP and WW direct (0.51 ± 0.02) in another Colorado Angus herd managed at a slightly lower elevation (1,981 m). The results may differ because of the use of weaning PAP measures in the study of Shirley et al. (2008), whereas the current study used yearling PAP measures. Shirley et al. (2008) reported results similar to those of the current research with regard to the genetic correlation between PAP and WW maternal additive genetic effects (−0.05 ± 0.14). Other than the current study, the only other reported correlation between PAP and YW came from Schimmel (1981), who found a strong negative genetic correlation between PAP and YW (−0.75 ± 0.65) with bulls from multiple sire lines. Given Schimmel’s SE of 0.65, the true genetic correlation between these traits has a highly variable range. Computing capabilities and analytical techniques have advanced considerably since that report. Also, the estimate by Schimmel (1981) was derived from sire model analysis of data from Hereford, Angus, and Red Angus bull calves, whereas the analyses in this study included relationships among Angus bulls and heifers from a larger, single-breed population.


View Full Table | Close Full ViewTable 3.

Heritabilities (diagonal), genetic correlations (above diagonal), and environmental correlations (below diagonal) ± SE from the 4-trait model for mean pulmonary arterial pressure, birth weight (direct and maternal), weaning weight (direct and maternal), and yearling weight (direct and maternal) in the CSU-BIC herd1

 
Trait2 PAP BWTd BWTm WWd WWm YWd YWm
PAP 0.26 ± 0.03 0.15 ± 0.09 0.14 ± 0.10 0.22 ± 0.08 −0.03 ± 0.08 0.12 ± 0.08 0.00 ± 0.09
BWTd −0.02 ± 0.03 0.42 ± 0.04 −0.06 ± 0.10 0.23 ± 0.07 −0.15 ± 0.08 0.16 ± 0.08 −0.07 ± 0.08
BWTm 0.13 ± 0.02 0.23 ± 0.10 0.15 ± 0.09 0.30 ± 0.10 0.10 ± 0.09
WWd -0.03 ± 0.03 0.26 ± 0.04 0.38 ± 0.04 −0.51 ± 0.06 0.92 ± 0.08 −0.65 ± 0.05
WWm 0.28 ± 0.03 −0.43 ± 0.06 0.96 ± 0.02
YWd −0.03 ± 0.03 0.23 ± 0.04 0.68 ± 0.02 0.45 ± 0.04 −0.61 ± 0.05
YWm 0.23 ± 0.03
1CSU-BIC = Colorado State University Beef Improvement Center, Saratoga, WY, elevation of 2,150 to 2,411 m; 1993 to 2014; average observations of n = 6,702.
2PAP = mean pulmonary arterial pressure; BWT = birth weight; WW = weaning weight; YW = yearling weight. Subscript d indicates additive direct. Subscript m indicates additive maternal.

Genetic correlations between direct and maternal components of both WW and YW were moderately to strongly negative (−0.51 to −0.61; Table 3). Potential explanations for the strong, negative relationship include the consequences of selection for an intermediate optimum, experimental design, size of the data set, and bias due to unknown missing components in the model (Garrick et al., 1989; Meyer, 1992; Robinson, 1996).

Postweaning Gain Analysis

Table 4 presents the results of the 4-trait analysis including PWG as a dependent variable, rather than YW. The utilization of PWG in the current and many other studies over the years comes from the desire to improve rate of gain in beef cattle (Swiger, 1961; Arthur et al., 2001; Thompson et al., 2015), as well as to more closely follow current genetic evaluations of YW as a function of WW and PWG. Both YW and PWG yield meaningful information on gain from weaning to yearling, independent of weaning weight. The decision to analyze both YW and PWG came from the desire to better understand the relationship between PAP and these traits individually. High genetic correlations (± SE) exist between direct components of WW and YW (0.92 ± 0.08) and between the maternal components of WW and YW (0.96 ± 0.02; Table 3). Solvability and collinearity issues arise because of these high correlations, and as a result, PWG is typically used in genetic evaluations (Speidel, 2011). Therefore, the different heritability estimates observed for direct and maternal WW between Tables 3 and 4 provide evidence for the use of PAP, BWT, and WW estimates from the model containing PWG.


View Full Table | Close Full ViewTable 4.

Heritability estimates (diagonal), genetic correlations (above diagonal), and environmental correlations (below diagonal) ± SE from the 4-trait model for mean pulmonary arterial pressure, birth weight (direct and maternal), weaning weight (direct and maternal), and postweaning gain in the CSU-BIC herd1

 
Trait2 PAP BWTd BWTm WWd WWm PWG
PAP 0.26 ± 0.03 0.15 ± 0.09 0.14 ± 0.10 0.23 ± 0.09 −0.01 ± 0.10 −0.10 ± 0.10
BWTd −0.02 ± 0.03 0.42 ± 0.04 −0.06 ± 0.10 0.35 ± 0.08 −0.20 ± 0.09 0.18 ± 0.10
BWTm 0.14 ± 0.02 0.24 ± 0.11 0.20 ± 0.10 0.24 ± 0.11
WWd -0.03 ± 0.03 0.24 ± 0.03 0.29 ± 0.04 −0.49 ± 0.08 0.26 ± 0.11
WWm 0.19 ± 0.03 −0.13 ± 0.11
PWG −0.02 ± 0.02 0.06 ± 0.03 -0.02 ± 0.03 0.14 ± 0.02
1CSU-BIC = Colorado State University-Beef Improvement Center, Saratoga, WY, elevation of 2,150 to 2,411 m; 1993 to 2014; average observations of n = 6,702.
2PAP = mean pulmonary arterial pressure; BWT = birth weight; WW = weaning weight; PWG = postweaning gain. Subscript d indicates additive direct. Subscript m indicates additive maternal.

The most noteworthy observations from this analysis were the weak to modest genetic correlations between PAP measures and weight traits. Neary et al. (2015b) suggested that factors such as greater hypobaric hypoxia, dietary differences (finishing ration vs. pasture grazing), a heavier body mass, or a combination of these factors contribute to differences in PAP measures. Likewise, total weight at a year of age relative to PAP may further clarify the relationship at a genetic level, as suggested by Neary et al. (2015b), but that was not supported by the results of this study.

Environmental correlations (displayed below the diagonal in Tables 3 and 4) represent the extent of the relationship between PAP and each of the weight traits not due to genetics. From these results, there appears to be little relationship between the environment for performance traits and the environment for PAP. These findings are in contrast to the suggestion of Neary et al. (2015a) in which a good environment for 1 trait could adversely affect how the environment influences PAP measures. Specifically, Neary et al. (2015a) discussed the influence of rainfall in mountain pastures on PAP. Logically, additional rainfall would bolster forage production and calf weight gain in a semiarid environment such as Colorado, but the inverse was observed for PAP in the current study, suggesting the wetness may have challenged calf health. We expected to see high environmental correlations between weight traits WW and YW (Table 3), considering the environment necessary to gain weight from birth to weaning age would also be needed to gain weight from birth to yearling age.

Results of this study revealed only weak or modest genetic relationships between PAP and weight performance traits in the CSU-BIC Angus herd. Neary et al. (2015b) suggested that the relationship of PAP measures with heavier body mass was likely not due to the genetic relationship among them, supporting this study’s results. Accordingly, genetic selection to reduce the incidence of pulmonary hypertension through lower PAP could occur without adversely affecting growth performance in yearling Angus bulls and heifers at the CSU-BIC, supporting our hypothesis.

 

References

Footnotes


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