Genetic parameter estimates for feet and leg traits in Red Angus cattle

Abstract

The objective of this study was to estimate genetic parameters for feet and leg traits, relationships within feet and leg traits, and relationships between feet and leg traits and production traits in Red Angus cattle. Subjective scores for 14 traits including body condition score (BCS), front hoof angle (FHA), front heel depth (FHD), front claw shape (FCS), rear hoof angle (RHA), rear heel depth (RHD), rear claw shape (RCS), size of hoof (SIZE), front side view (FSV), knee orientation (KNEE), front hoof orientation (FHO), rear side view (RSV), rear view (RV), and a composite score (COMP) were collected by trained evaluators on 1,720 Red Angus cattle. All traits except COMP were scored as intermediate optimum traits. Performance data, and EPD were obtained on all animals measured and a three-generation pedigree was obtained from the Red Angus Association of America (RAAA) that contained 13,306 animals. Data were modeled using a linear bivariate animal model with random additive genetic and residual effects, and fixed effects of age and contemporary group (herd-year) implemented in ASREML 4.0. Heritability estimates of BCS, FHA, FHD, FCS, RHA, RHD, RCS, SIZE, FSV, KNEE, FHO, RSV, RV, and COMP were 0.11, 0.20, 0.17, 0.09, 0.19, 0.25, 0.17, 0.36, 0.16, 0.17, 0.17, 0.30, 0.14, and 0.12, respectively. These results demonstrate feet and leg traits are lowly to moderately heritable. Strong, positive genetic correlations were observed between FHA and FHD (0.89), FHA and RHA (0.88), FHD and RHA (0.85), FHA and RHD (0.85), FHD and RHD (0.94), and FHO and KNEE (0.95), indicating these traits were highly related to each other. Strong and negative genetic correlations were observed between KNEE and FSV (−0.59) and FHO and FSV (−0.75). The strongest Pearson correlation between front limb traits (FHA, FHD, FSV, FHO, KNEE, and COMP) and Stayability EPD (STAY) was FSV (r = 0.16) and for rear limb traits (RHA, RHD, RCS, RSV, RV, and COMP) and STAY was RCS (r = −0.12). This suggests cattle with more slope to the angle of the shoulder and cattle with less evidence of hoof curl may stay in the herd longer. Further investigation into the economic viability of feet and leg trait genetic prediction with a larger population of animals is required to help validate these findings.

Introduction

Hoof, foot, and leg (HFL) conformational issues represent a major concern for beef and dairy cattle production globally. Dairy production systems are particularly invested as animals spend a large proportion of their life in confined or semi-confined environments. The earliest predictions for genetic merit in Holstein cattle date to the 1980s (Thompson et al., 1983). Nearly 25% of dairy cows are treated for foot disorders every year, and economic losses accrue through veterinary expenses, reduced milk yield, suppressed feed intake, and increased labor (Politiek et al., 1986; Kossaivati et al., 1999). Dairy producers involuntarily cull cattle with lameness and foot issues, where culling due to lameness ranks fourth in terms of prevalence rate behind infertility, mastitis, and poor performance (USDA, 2007). Holstein Association USA, Inc. (2017) revealed for every 1.0 unit increase in Feet and Leg Composite standard transmitting ability there was an increase of 0.3 for Productive Life. In Holstein, a moderate genetic correlation exists among composite Feet and Legs score and increased productive herd life (r = 0.05–0.32; Dekkers et al., 1994; Vollema and Groen, 1997; Perez-Cabal et al., 2006).

Hoof and foot health traits are well documented in dairy cattle, yet literature relating to the soundness of beef cattle is sparse (Daniel and Kreise-Anderson, 2013; Gadberry et al., 2016). The primary concern for beef producers is the impact of HFL issues on the longevity or sustained productivity of cows in the herd. Costs of developing replacement heifers can be offset by increasing the number of productive years they stay in the herd (Cundiff et al., 1992). A useful metric to select for increased probability of a female surviving to pay for her development and maintenance costs is stayability EPD (STAY), which is the probability a female will stay in the herd to 6 yr of age. A possible influence for reduced longevity is involuntary culling due to poor soundness and/or feet and leg structure. Animals with poor HFL traits were culled at a higher rate than animals with ideal structure (Forabosco et al., 2004).

The beef industry’s understanding of HFL traits in beef cattle has advanced in recent years. Low to moderate heritability estimates for varying feet and leg traits have been reported in Angus cattle ($h^2$= 0.10–0.40; Jeyaruban et al., 2012). Currently, the Australian Angus Association (2019) publishes estimated breeding values (EBV) for feet and leg structure traits. The Australian Angus Association EBV for front hoof angle, rear hoof angle, front claw shape, rear view, and rear leg side view. The American Angus Association reports similar heritability estimates and predicts EPD on two similar traits; claw set and hoof angle (Retallick, 2019) with no distinction between front and rear anatomical position. Phenotypes for both traits in the American Angus evaluation were collected on the worst hoof.

Favorable HFL characteristics have been reported to impact sustained longevity (Norman et al., 1996; Perez-Cabal and Alenda, 2002) and thus impact the profit of cattle enterprises. The objective of this research was to estimate the genetic parameters of feet and leg traits, as well as identify relationships within feet and leg traits, and evaluate the relationships between feet and leg traits and RAAA production traits.

Materials and Methods

Phenotypes were collected on 1,885 registered Red Angus cattle at operations located in the Midwestern United States by trained observers from Kansas State University between August 2015 and April 2017. Data were collected using a protocol (#3635) approved by the Institutional Animal Care and Use Committee at Kansas State University. The traits recorded were body condition score (BCS), front hoof angle (FHA), front heel depth (FHD), front claw shape (FCS), rear hoof angle (RHA), rear heel depth (RHD), rear claw shape (RCS), size of hoof (SIZE), front side view (FSV), front view knee orientation (KNEE), front view hoof orientation (FHO), rear leg side view (RSV), rear leg rear view (RV), and composite feet and leg rank (COMP). All scores were assigned subjectively, and each animal was scored simultaneously by at least two observers. Data were recorded using an offline, electronic survey application on a mobile device where visual rubrics (when applicable) were included above the data collection function of the survey for immediate visual reference, and a function was implemented that forced an observation for all traits before a response could be submitted. Supplementary Figures S1 through S11 on the Supplementary Material depict the phenotype rubrics and data collection functions implemented in the study. Table 1 contains phenotypic descriptions for all 14 traits subjectively assessed in the study by low extreme (undesirable), intermediate optimum (desirable), and high extreme (undesirable). Scores were averaged to produce a single observation for each animal and to reduce scorer bias. Body condition score was ranked from 1 to 9 (BIF, 2016). Front hoof angle, FHD, FCS, RHA, RHD, RCS, SIZE, FSV, KNEE, FHO, RSV, and RV were scored on a scale of 0 to 100 with 50 as “ideal”. Composite score was scored subjectively as a linear score from 0 to 50, where 0 was unsound and 50 was the most ideal level of soundness. Rubrics on the 1-9 scale depicted by Hermel (2015) were adapted and used to assign phenotypes for FHA, FHD, FCS, RHA, RHD, RCS. Rubrics on the 1-9 scale depicted by Jeyaruban et al. (2012), were adapted and used to assign phenotypes for RSV. For the remaining traits observed in the study, visual rubrics obtained from industry sources were used to assign a phenotype for each trait except SIZE and COMP. The remaining visual rubrics were obtained from breed association websites, livestock judging manuals, and online resource guides (Ashwood, 2011, Bruns, 2003, Jeyaruban et al., 2012, Hermel, 2015). Though many existing visual rubrics use a scale of discrete variables from 1 to 9, this project expanded these to a more robust scale (0-100) to ultimately test differences in granularity of scale.

Table 1.

Body condition score, hoof, foot and leg phenotype trait descriptions

	Phenotype descriptions by low extreme, mid, and high extreme
Intermediate-Optimum (1-9)	Low Score (1)	Mid Score (5)	High Score (9)
Body Condition Score	Emaciated (extremely thin)	Optimum (moderate fat cover)	Wasty (extremely fat)
Intermediate-Optimum (0-100)	Low Score (0)	Mid Score (50)	High Score (100)
Front Hoof Angle	Extremely steep and rigid	Moderate angle to the hoof	Extremely low angle
Front Heel Depth	Too deep and straight	Moderate substance with mobility	Too shallow with little substance
Front Claw Shape	Extremely weak and open divergent	Symmetrical and appropriately spaced	Extreme curling of claws and/or crossing
Rear Hoof Angle	Extremely steep and rigid	Moderate angle to the hoof	Extremely low angle
Rear Heel Depth	Too deep and straight	Moderate substance with mobility	Too shallow with little substance
Rear Claw Shape	Extremely weak and open divergent	Symmetrical and appropriately spaced	Extreme curling of claws and/or crossing
Foot Size	Extremely small foot relative to bone size	Moderate hoof size, similar to bone	Extremely large hoof, obstructive
Front Side View	Extremely straight shoulder angle, low head carriage	Moderate angle to shoulder, appropriate head carriage	Extremely set back in the angle of the shoulder, difficulty taking steps
Front Hoof Orientation	Extreme inward toe orientation (pigeon-toe)	Symmetrical and forward-facing	Extreme outward facing toes (splay-foot)
Knee Orientation	Extremely bowlegged	Symmetrical and sturdy	Extreme inward knee orientation (knock-kneed)
Rear Leg Side View	Extremely straight, and rigid hind leg set	Desirable, with flexibility	Extremely sickle-hocked
Rear Leg Hind View	Extremely bowlegged	Symmetrical and sturdy	Extremely cow-hocked
Linear (0-40)	Low Score, Undesirable (0)		High Score, Desirable (50)
Composite Score	Extremely unsound, should be considered for culling		Extremely sound, desirable mobility and foot trait conformation

Animals were assessed at registered Red Angus seedstock enterprises where cattle handling facilities varied in design. All animals were observed freestanding and mobile on a dry, flat surface, typically in a small and enclosed pen with one point of entry and one point of exit. Animals were scored in small groups (2-4) separate from the much larger group sequentially to ensure proper identification, appropriate speed of appraisal and whole contemporary group reporting. No animals were evaluated while in a processing chute, so as not to assign an inappropriate phenotype when animals pull back on the head-catch and disrupt natural weight distribution across all four limbs. Since not all animals were assessed during semi-annual processing or no digital scale was available for use at every facility, no animal weights were recorded and instead BCS was used as a subjective measurement for animal conditioning and weight.

The Red Angus Association of America (RAAA) provided a three-generation pedigree for each animal scored. In the pedigree file, there were 13,306 animals including 3,157 sires, and 8,724 dams. Contemporary group (n = 48) was defined as herd in which the animal was scored and birth year. Records were removed if they were only scored by one observer or did not have a corresponding registration number. The final dataset used for analysis consisted of 1,720 Red Angus animals.

A bivariate animal model was utilized with additive genetic and residual effects fit as random. Fixed effects included contemporary group (herd/year) and a covariate for age in months. The bivariate animal model was:

$${\displaystyle \begin{array}{l}{\displaystyle \left[\begin{array}{l}{Y}_1\\ Y_2\end{array}\right]=\left[\begin{array}{l}{X}_1{\mathrm{\beta }}_1\\ X_2{\mathrm{\beta }}_2\end{array}\right]+\left[\begin{array}{l}{Z}_1{u}_1\\ Z_2{u}_2\end{array}\right]+\left[\begin{array}{l}{e}_1\\ e_2\end{array}\right]}\end{array}}$$

where $Y_i$ was a vector of observations for trait 1 and trait 2, $X_i$ as an incidence matrix relating observations to the fixed effects, ${\mathrm{\beta }}_i$ was a vector of fixed effects for contemporary group and age, $Z_i$ was an incidence matrix relating observations to additive genetic effects, $u_i$ was a vector of additive genetic effects, and $e_i$ was a vector of random residuals. Ideally all 14 traits would be fit in the linear model simultaneously; however, the model was too complex to allow estimation of genetic parameters from the data available. Therefore, all pairwise combinations of the feet and leg traits were evaluated in 169 bivariate analyses. The reported heritability was calculated from the average of the 13 additive variances and 13 phenotypic variances that resulted from each individual bivariate analysis.

The structure for residual (co)variances was:

$${\displaystyle \begin{array}{l}{\displaystyle \left[\begin{array}{l}{e}_1\\ e_2\end{array}\right]=\left[\begin{array}{cc}I{\sigma }_{e1}^2& {\displaystyle I{\sigma }_{e1,e2}}\\ I{\sigma }_{e2,e1}& {\displaystyle I{\sigma }_{e2}^2}\end{array}\right]}\end{array}}$$

where the matrix I represents an identity matrix with dimensions equal to the number of records for each trait. Residual covariances between trait one and trait two can be calculated because every trait was measured on every animal. The structure for genetic (co)variances was:

$${\displaystyle \begin{array}{l}{\displaystyle \left[\begin{array}{l}{u}_1\\ u_2\end{array}\right]=\left[\begin{array}{cc}A{\sigma }_{u1}^2& {\displaystyle A{\sigma }_{u1,u2}}\\ A{\sigma }_{u2,u1}& {\displaystyle A{\sigma }_{u2}^2}\end{array}\right]}\end{array}}$$

where matrix A was the relationship matrix populated using pedigree data. Variances and covariances were estimated using ASREML (Ver 4.0, VSN International, Ltd., Hemel Hempstead, UK).

A dataset of adjusted weights and the full suite of EPD on every animal classified in this study was obtained from RAAA. Pearson and Spearman correlation coefficients between BV estimated on feet and leg traits in this study and adjusted weights and EPD from RAAA were calculated utilizing SAS 9.2 (SAS Institute Inc., Cary, NC). Feet and leg BV for HFL traits collected in this study were generated using ASREML (Ver 4.0, VSN International, Ltd., Hemel Hempstead, UK) in 4-6 trait animal models grouped by anatomical category: front limb traits (FHA, FHD, FSV, KNEE, FHO, and COMP), rear limb traits (RHA, RHD, RCS, RSV, RV, and COMP), and associated traits including BCS, FCS, SIZE, and COMP. When FCS was included in the front limb traits model it failed to converge, so FCS was instead included in the associated traits analysis. Composite score was included in all three categories to anchor each model, so three separate COMP BV were estimated for every animal.

Results and Discussion

The mean, standard deviation, minimum, and maximum for each trait is provided in Table 2. A total of 1,217 females and 503 males were scored, and the distribution of age on all animals is described in Figure 1. Only males 2 years old and younger were scored, because production systems generally do not possess sizable contemporary groups of bulls over 24 months of age. The age distribution of females ranged from less than 1 year to 18 years. Most female phenotypes were from young cows or replacement heifers and fewer scores were recorded as age increased. Future incorporation of feet and leg guidelines for national beef cattle evaluation should investigate the appropriate age of classification in both males and females.

Table 2.

Summary statistics for all 14 feet and leg traits evaluated

Trait	Mean	SD	Min	Max
Body Condition Score	5.65	0.52	3.7	8.55
Front Hoof Angle	56.59	4.57	38	82.5
Front Heel Depth	57.21	4.56	37	75
Front Claw Shape	57.47	6.43	38	93.5
Rear Hoof Angle	58.4	5.57	37.5	80
Rear Hoof Depth	59.69	5.73	34.5	83
Rear Claw Shape	52.76	5.76	29.5	95.33
Size of Hoof	49.63	5.35	25.5	74
Front Side View	46.04	3.66	29	61
Knee Orientation	53.71	2.97	32	70
Front Hoof Orientation	55.78	4.96	23.5	74.5
Rear Side View	55.17	5.43	30	82
Rear View	56.62	3.81	37	77.5
Composite Score	31.37	4.04	15	44

Figure 1.

Distribution of animals scored by age category in years.

Table 3 contains the covariate for age in months for each of the feet and leg traits when predicted in the anatomical grouping models. Across all three groupings, COMP was consistently affected by age. In the rear limb anatomical grouping, age appears to have a significant effect on RHA, RCS, RSV, RV, and COMP (0.26, 0.16, 0.25, −0.18, and 0.20); thus, as animals age, RHA presents with increased angularity and slope to the hoof, RCS is prone to increased curvature of the hoof wall and lateral or medial claw, RSV shows increased angularity and set to the hock and hind leg, RV shows orientation closer toward the middle of the phenotypic scale or increasingly less outward bowing of the rear leg set, while COMP improves with age. This could be due to the fact fewer older cows were scored versus younger cows and poorer structured cows have already been removed from the herd at higher ages. In the associated trait grouping, Body Condition score appears to decrease with age (−0.03) and the size of the hoof appears to increase with age (0.22).

Table 3.

Age covariates (months) for all traits estimated from three models separated by anatomical category (front limb traits, rear limb traits, and associated traits)

Trait	Age covariate	Standard error
Front Hoof Angle1	−0.05	0.06
Front Hoof Depth1	−0.07	0.06
Front Side View1	0.02	0.05
Knee Orientation1	0.09	0.05
Front Hoof Orientation1	0.06	0.07
Composite Score1	0.14	0.05
Rear Hoof Angle2	0.26	0.06
Rear Heel Depth2	−0.12	0.07
Rear Claw Shape2	0.16	0.08
Rear Side View2	0.25	0.08
Rear View2	−0.18	0.06
Composite Score2	0.2	0.05
Body Condition Score3	−0.03	0.01
Front Claw Shape3	−0.06	0.08
Hoof Size3	0.22	0.07
Composite Score3	0.13	0.05

¹Trait predictions included in the Front Limb anatomical category.

²Trait predictions included in the Rear Limb anatomical category.

³Trait predictions included in the Associated anatomical category.

Summaries of additive genetic variances, residual variances, and heritabilities averaged from multiple bivariate analyses for the 14 traits measured are provided in Table 4. Heritability estimates for FHA and RHA were 0.20 and 0.19, respectively. Jeyaruban et al. (2012) reported higher heritability estimates in a larger sample size (n = 7,387 to 7,572) using a similar scoring system and model (FHA = 0.32 and RHA = 0.29). A threshold animal model with the same data reported higher heritabilities, ranging from 0.35 to 0.50, for FHA and RHA (Jeyaruban et al., 2012). The estimates of FHA and RHA heritability in this study were higher than estimates in dairy cattle populations, which range from 0.09 to 0.18 (Fatehi et al., 2003; Van Dorp et al., 2004; Van Der Waaij et al. 2005, Wiggans et al., 2006; Perez-Cabal et al., 2006; Onyiro and Brotherstone, 2008; Laursen et al., 2009; Wright et al., 2013).

Table 4.

Average additive genetic variance (${\sigma }_a^2)$ range of additive genetic variances (${\sigma }_a^2$Range), average residual variance (${\sigma }_e^2)$, range of residual variances (${\sigma }_e^2$Range), average heritability, and range of heritability estimates for all 14 feet and leg traits

Trait	${\sigma }_a^2$ ± SE	${\sigma }_a^2$ Range	${\sigma }_e^2$ ± SE	${\sigma }_e^2$ Range	h2	h2 Range
Body Condition Score	0.02 ± 0.01	0.015–0.017	0.13 ± 0.01	0.128–0.130	0.11 ± 0.04	0.10–0.12
Front Hoof Angle	2.77 ± 0.86	2.62–2.95	11.36 ± 0.77	11.22–11.48	0.2 ± 0.06	0.19–0.21
Front Heel Depth	2.65 ± 0.85	2.41–3.26	12.84 ± 0.8	12.37–13.02	0.17 ± 0.05	0.16–0.21
Front Claw Shape	2.26 ± 1.06	2.14–2.45	23.84 ± 1.2	23.68–23.94	0.09 ± 0.04	0.08–0.09
Rear Hoof Angle	3.02 ± 0.95	2.83–3.31	13.29 ± 0.87	13.01–13.44	0.19 ± 0.06	0.17–0.20
Rear Heel Depth	5.2 ± 1.39	4.77–5.52	15.49 ± 1.18	15.24–15.78	0.25 ± 0.06	0.23–0.26
Rear Claw Shape	4.21 ± 1.35	4.07–4.45	21.25 ± 1.29	21.06–21.36	0.17 ± 0.05	0.16–0.17
Size of Hoof	7.24 ± 1.46	7.11–7.50	12.94 ± 1.14	12.75–13.04	0.36 ± 0.06	0.35–0.37
Front Side View	1.88 ± 0.6	1.79–2.06	9.91 ± 0.59	9.77–9.98	0.16 ± 0.05	0.15–0.17
Knee Orientation	1.43 ± 0.48	1.25–1.52	6.91 ± 0.44	6.86–7.07	0.17 ± 0.05	0.15–0.18
Front Hoof Orientation	3.68 ± 1.24	3.13–4.08	18.25 ± 1.16	17.94–18.68	0.17 ± 0.05	0.14–0.19
Rear Side View	7.71 ± 2.01	7.40–7.99	17.8 ± 1.47	17.58–18.03	0.3 ± 0.06	0.29–0.31
Rear View	1.74 ± 0.61	1.68–1.83	10.35 ± 0.6	10.27–10.40	0.14 ± 0.05	0.14–0.15
Composite Score	1.27 ± 0.52	0.97–1.42	9.26 ± 0.53	9.09–9.98	0.12 ± 0.05	0.09–0.14

Front heel depth and RHD had heritabilities in the present study of 0.17 and 0.25, respectively. Fatehi et al. (2003) reported heritability estimates for hoof depth, with no differentiation between front and rear, but separated by floor type (solid = 0.06, slatted = 0.09). Hahn et al. (1984) reported heritability of FHD and RHD to be 0.58 and 0.19, respectively. Hahn et al. (1984) physically measured heel depth with a ruler and did not subjectively appraise the trait. Though physical measurements of heel depth and angle on hoof traits may be ideal, it is unreasonable to assume large amounts of field data would be collected using this method. Heritability estimates observed in this study using subjective measurements on front hoof traits suggest there could be reasonable opportunity to select for improved hoof conformation without the need for a more intensive data collection methodology.

Heritability estimates in the present study were 0.09 and 0.17 for FCS and RCS, respectively. Jeyaruban et al. (2012) reported higher heritability estimates in Australian Angus using a linear model (FCS = 0.33 and RCS = 0.29) and a threshold model (FCS = 0.46 and 0.44). In the present study, FCS and RCS had the highest average residual variance of all traits measured at ${\mathrm{\sigma }}_{\mathrm{e}}^2$ = 23.84 and 21.25, respectively compared to all traits measured on the same numerical scale. In general, increased anatomical mass is associated with the anterior portion of the animal, due to the weight of the head, neck, anterior skeleton, internal organs, and anterior muscle groups (Swett and Graves, 1939; Charles and Johnson, 1976). A possible explanation for the increased residual variance of FCS may be the increased weight associated with the anterior mass of the animal causing increased downward pressure on the front hooves, resulting in uneven weight distribution and hoof claw curvature.

Hoof size was the most heritable trait (0.36) in this Red Angus population. Literature describing a subjective hoof size phenotype was not found, though some studies did physically measure the circumference of the hoof in Holstein cattle with heritability estimates ranging from 0.16 to 0.33 (Hahn et al., 1984). It appears incorporating a subjective visual measurement may be just as effective as attempting to measure large numbers of hoof circumference phenotypes, which would be much more feasible for producers and increase the likelihood of obtaining phenotypes.

Front side view, KNEE, and FHO had moderate heritability estimates at 0.16, 0.17, and 0.17, respectively. No estimates for heritability of cattle shoulder angularity and front limb orientation were found in literature. This was not unexpected as this study developed a unique subjective scoring system for these traits.

Body condition score was included in this study to have an estimate of body mass at the time of phenotypic appraisal. Body condition score was lowly heritable at 0.11 which was consistent with the reported value from Nephawe et al. (2004) at 0.16. Varying age ranges, seasonality of the data collection, and a limited dataset with marginal sire connectivity were likely the factors reducing heritability for BCS reported in this study.

The heritability for RSV in this population of Red Angus cattle was 0.30. In Australian Angus, estimates for heritability of RSV have been reported, ranging from 0.10 to 0.22 (Jeyaruban et al., 2012). Heritability of RSV in dairy cattle also appears to be lower than the estimates in the present study (0.14–0.21; Vollema and Groen, 1997; Fatehi et al., 2003; Van Dorp et al., 2004; Van der Waaij et al, 2005; Perez-Cabal et al., 2006; Wiggans et al., 2006; Onyiro and Brotherstone, 2008; Laursen et al., 2009; Wright et al., 2013). There was no clear explanation as to why the heritability measurement from the current study was higher than previous research in beef and dairy cattle, though it may be due the difference in the granularity of the scoring system or population sampling.

The heritability estimate for RV was 0.14 in this population. Estimates for RV in dairy and beef cattle were similar, and range from 0.06 to 0.17 (Fatehi et al., 2003; Van der Waaij et al., 2005; Laursen et al., 2009; Jeyaruban et al., 2012; Wright et al., 2013). The estimate observed in this study was similar to the 0.17 estimate reported in Australian Angus cattle (Jeyaruban et al., 2012). However, like many of the traits in the present study, there was indication using a threshold animal model could improve the ability to explain more of the genetic variability among these animals.

The estimated heritability in this study for COMP was 0.12. Though few examples of a phenotype for overall soundness exist, the Holstein association requests observations for Locomotion (LOC)—a trait subjectively scored by trained classifiers that reflects overall soundness as a type trait. Estimates for LOC heritability range from 0.05 to 0.11 (Van Dorp et al., 2004; Onyiro and Brotherstone, 2008). These heritability findings suggest selection for improved soundness could be accomplished by using one singular subjective measure. The comparableness between COMP and estimates for LOC present an opportunity for beef breed associations to consider this in future genetic evaluations. It can be assumed these subjective overall soundness measurements must be gathered by trained evaluators to ensure consistency, which may prevent significant industry adoption or utilization in the beef industry. Additionally, if overall soundness can more easily be determined using feet and leg indicator traits, which have shown to be more highly heritable, perhaps a more appropriate model would abandon composite traits like COMP due to lower heritability and data collection constraints.

Heritabilities of foot and leg traits were moderate in this study suggesting Red Angus producers would be able to make genetic progress using selection. However, investigation into the economic relevance of these foot and leg traits and the appropriateness of EPD in national genetic evaluations should be considered before cattle producers start selecting to improve HFL conformational characteristics. Thus, correlations of foot and leg traits from the sample population among HFL traits, as well as HFL correlations with RAAA produced EPD and adjusted weights were generated.

Table 5 contains the genetic and phenotypic correlations between all 14 traits measured. Interpretation of correlations between two traits that are both intermediate-optimum or those between intermediate-optimum traits and linear trait predictions can be difficult. Jeyaruban et al. (2012) described the difficulty in modeling intermediate-optimum and ordered category traits using conventional approaches. Supplementary Figures S12 through S25 in the Supplementary Material depict the distribution of observations for each HFL trait. In general, scores for each HFL trait in the present study tended to be normally distributed and above optimum on the 0 to 100 scale, with fewer scores being observed <50 compared with those observed >50. As such, many of the subsequent correlation interpretations focus solely on the directionality of relationships between intermediate-optimum traits on the above optimum side of the scale and do not attempt to classify desirable or undesirable relationships. Future studies should investigate identifying alternative methods for modeling these effects in large populations and may shed additional light on the relationships between these traits and best practices for making comparisons between them.

Table 5.

Genetic correlations (± SE) above the diagonal and phenotypic correlations (± SE) below diagonal for all 14 feet and leg traits

Trait1	BCS	FHA	FHD	FCS	RHA	RHD	RCS	SIZE	FSV	Knee	FHO	RSV	RV	COMP
BCS		0.27 ± 0.25	0.2 ± 0.26	0.51 ± 0.28	0.08 ± 0.26	−0.04 ± 0.24	0.19 ± 0.25	0.4 ± 0.19	0.38 ± 0.25	−0.68 ± 0.26	−0.7 ± 0.24	−0.27 ± 0.22	−0.26 ± 0.26	0.07 ± 0.29
FHA	−0.03 ± 0.03		0.89 ± 0.06	−0.21 ± 0.27	0.88 ± 0.08	0.85 ± 0.09	−0.17 ± 0.22	0.11 ± 0.18	0.46 ± 0.19	−0.05 ± 0.23	−0.25 ± 0.23	0.63 ± 0.15	0.36 ± 0.23	−0.33 ± 0.24
FHD	−0.02 ± 0.03	0.82 ± 0.01		−0.31 ± 0.27	0.85 ± 0.1	0.94 ± 0.06	−0.12 ± 0.24	−0.06 ± 0.19	0.45 ± 0.19	0.05 ± 0.24	−0.2 ± 0.23	0.51 ± 0.17	0.51 ± 0.22	−0.36 ± 0.24
FCS	0.05 ± 0.03	0.1 ± 0.03	0.1 ± 0.03		0.13 ± 0.28	−0.05 ± 0.26	0.75 ± 0.17	0.2 ± 0.24	0.08 ± 0.28	0.15 ± 0.28	0.12 ± 0.28	−0.01 ± 0.25	0.17 ± 0.29	−0.13 ± 0.31
RHA	−0.01 ± 0.03	0.51 ± 0.02	0.47 ± 0.02	0.14 ± 0.03		0.86 ± 0.06	−0.09 ± 0.23	0.00 ± 0.18	0.29 ± 0.21	−0.04 ± 0.23	−0.24 ± 0.22	0.72 ± 0.15	0.51 ± 0.21	−0.44 ± 0.22
RHD	−0.01 ± 0.03	0.46 ± 0.02	0.52 ± 0.02	0.12 ± 0.03	0.83 ± 0.01		0.11 ± 0.21	−0.23 ± 0.16	0.19 ± 0.21	0.01 ± 0.21	−0.24 ± 0.21	0.56 ± 0.15	0.63 ± 0.19	−0.57 ± 0.18
RCS	0.03 ± 0.03	0.03 ± 0.03	0.05 ± 0.03	0.38 ± 0.02	0.18 ± 0.03	0.18 ± 0.03		−0.11 ± 0.19	0.03 ± 0.23	0.41 ± 0.21	0.38 ± 0.21	−0.36 ± 0.18	0.14 ± 0.24	−0.06 ± 0.26
SIZE	0.23 ± 0.03	0.00 ± 0.03	−0.05 ± 0.03	0.04 ± 0.03	0.01 ± 0.03	−0.03 ± 0.03	0.02 ± 0.03		0.11 ± 0.18	0.06 ± 0.19	0.17 ± 0.18	0.03 ± 0.16	−0.17 ± 0.19	0.32 ± 0.19
FSV	0.13 ± 0.03	0.07 ± 0.03	0.05 ± 0.03	−0.05 ± 0.03	0.08 ± 0.03	0.08 ± 0.03	−0.02 ± 0.03	0.02 ± 0.03		−0.59 ± 0.21	−0.75 ± 0.18	−0.07 ± 0.2	−0.1 ± 0.24	0.87 ± 0.19
Knee	−0.11 ± 0.03	0.03 ± 0.03	0.03 ± 0.03	0.11 ± 0.03	0.04 ± 0.03	0.05 ± 0.03	0.05 ± 0.03	−0.11 ± 0.03	−0.13 ± 0.03		0.95 ± 0.07	−0.38 ± 0.19	0.19 ± 0.23	0.07 ± 0.26
FHO	−0.14 ± 0.03	0.02 ± 0.03	0.03 ± 0.03	0.12 ± 0.03	0.02 ± 0.03	0.01 ± 0.03	0.03 ± 0.03	−0.13 ± 0.03	−0.24 ± 0.03	0.73 ± 0.01		−0.46 ± 0.18	0.16 ± 0.24	−0.25 ± 0.27
RSV	−0.12 ± 0.03	0.16 ± 0.06	0.15 ± 0.03	0.00 ± 0.03	0.24 ± 0.03	0.23 ± 0.03	−0.03 ± 0.03	−0.01 ± 0.03	0.09 ± 0.03	0.1 ± 0.03	0.08 ± 0.03		0.31 ± 0.2	−0.4 ± 0.21
RV	−0.12 ± 0.03	0.1 ± 0.03	0.13 ± 0.03	0.07 ± 0.03	0.19 ± 0.03	0.21 ± 0.03	0.05 ± 0.03	−0.1 ± 0.03	−0.1 ± 0.03	0.21 ± 0.03	0.23 ± 0.03	0.32 ± 0.02		−0.64 ± 0.18
COMP	0.15 ± 0.03	−0.15 ± 0.03	−0.2 ± 0.03	−0.3 ± 0.02	−0.21 ± 0.03	−0.23 ± 0.03	−0.28 ± 0.03	0.25 ± 0.03	0.38 ± 0.02	−0.07 ± 0.03	−0.09 ± 0.03	−0.06 ± 0.03	−0.32 ± 0.02

¹Traits: Body Condition Score (BCS), Front Hoof Angle (FHA), Front Heel Depth (FHD), Front Claw Shape (FCS), Rear Hoof Angle (RHA), Rear Claw Shape (RCS), Size of Hoof (SIZE), Front Side View (FSV), Knee Orientation (KNEE), Front Hoof Orientation (FHO), Rear Side View (RSV), and Rear View (RV), and Composite Score (COMP).

Genetic correlations identified in this study illustrate the opportunity to reduce the number of traits necessary to record for a national beef cattle genetic evaluation. Front hoof angle, FHD, RHA, and RHD were all highly genetically correlated (r = 0.85 to 0.94). Front hoof angle and FHD had a strong phenotypic relationship (r = 0.82), as did RHA and RHD (r = 0.83). A strong genetic relationship between FHA and RHA has been reported in Australian Angus cattle using a linear animal model (r = 0.87; Jeyaruban et al., 2012). The strong genetic relationships between FHA, FHD, RHA, and RHD would indicate pleiotropy or mutations in similar areas of the genome subject to linkage drag could control these traits. It is important scoring systems for cattle producers be as simple as possible to encourage ample data collection, and because these traits are strongly genetically correlated and similar in evaluation, they may not need to be scored separately. From these findings, it is possible only a single observation for a hoof angle/depth trait is required to explain an appropriate level of differences between hoof angularity or depth.

Front claw shape and RCS had a genetic correlation of 0.75 and a phenotypic correlation of 0.38. Genetic correlations between claw shape traits (FCS and RCS) and other hoof traits (FHA, FHD, RHA, and RHD) were not high, indicating in the current population, FCS and RCS were genetically independent from FHA, FHD, RHA, and RHD. Jeyaruban et al. (2012) reported a genetic correlation of 0.69 between FCS and RCS utilizing a linear animal model analysis. Jeyaruban et al. (2012) noted stronger genetic relationships between FHA and RHA with FCS and RCS, ranging from 0.40 to 0.79. It is likely the number of animals in each sample population, breed differences and phenotypic scale of measurement were the factors influencing the differences in claw shape and hoof trait genetic correlations between the present study and the Jeyaruben et al. (2012) study. The genetic correlation identified in this study between FCS and RCS suggest only one of these traits needs to be recorded. Current recommendations by the American Angus Association suggest producers score the worst hoof when variation in hoof conformation is present (Retallick, 2019). The genetic correlations between FCS and RCS suggest scoring a single curvature trait may be enough to capture the genetic variability of the trait; however, a separate score for hoof angle or depth should be collected due to the low genetic correlations between the hoof angle/depth traits and curvature traits. The strong genetic correlations between FHA, RHA, FHD, and RHD suggest a single measure for hoof angle or depth is likely a reasonable approach for mass field data collection and beef cattle genetic evaluation. Future investigation should focus on evaluating the merit of worst hoof data collection for these traits.

Front side view was genetically correlated with FHA, FHD, KNEE, and FHO (r = 0.46, 0.45, −0.59, and −0.75, respectively) and KNEE and FHO have a high phenotypic (r = 0.73) and genetic correlation (r = 0.95), which signifies the traits were controlled by many of the same alleles, and cattle that display outward rotation and placement of front hooves also display inward orientation of the knee. The negative phenotypic relationship between FSV and KNEE and FHO (r = −0.13 and −0.24, respectively) suggested cattle with less angle in their shoulder tend to display an inward orientation of the knee with more outward placement of the front hooves. Front limb traits including FHA, FHD, FSV, KNEE, and FHO appear to be genetically related, and selection of front hoof traits will lead to an indirect response for front limb traits. These front limb trait correlations suggest the skeletal requirements for these traits interact to a degree where a biological change in one trait will result in compensation of the others.

Body condition score exhibited strong, significant genetic correlations with FCS, SIZE, KNEE, and FHO at 0.51, 0.40, −0.68, and −0.70, respectively and moderate genetic correlations with FHA, FHD, RCS, FSV, RSV, and RV at 0.27, 0.20, 0.19, 0.38, −0.27, and −0.26, respectively. It is commonly understood BCS is an indicator of an animal’s nutritional status compared to a live weight (Eversole et al., 2009). Obtaining live weights on animals evaluated in the present study was not possible since not every animal evaluated could be weighed at the time of appraisal. As such, BCS was captured as an alternative estimate of an animal’s body mass. Though BCS is affected by seasonality, location, and age, all of these were accounted for in the contemporary grouping structure and model development of this study. A strong genetic relationship between BCS and FCS suggested a heavier body mass, or the increased energy consumption associated with higher BCS, leads to more curvature of the front hoof claws. The genetic correlations between BCS and FCS versus BCS and RCS may be explained by increased anterior body mass compared to posterior. It is possible the heavier weight associated with increased BCS adds increased downward pressure on the front hooves, which may cause this curvature; however, this relationship needs to be further investigated. A strong genetic relationship between BCS and SIZE suggested animals with increased body mass are genetically associated with having increased hoof size. Further investigation into these relationships with more objective measurements such as mature weight at time of appraisal would be beneficial.

Rear leg side view had positive genetic correlations with FHA, FHD, RHA, and RHD (r = 0.51 to 0.72) and negative genetic correlations with RCS, KNEE, and FHO (r = −0.36 to −0.46). Therefore, cattle with a straighter angle to the hock tended to have steeper hoof angles, deeper heel depths, more curvature of the hoof wall, more outward weakness at the knee, and increased outward placement of the front feet commonly referred to as “splay-footed.” Jeyaruban et al. (2012) reported positive genetic correlations with FHA and RHA with RSV at 0.32 and 0.68, respectively. In dairy, Perez-Cabal et al. (2006) reported a strong, negative genetic correlation between FA and RSV (−0.44); however, the interpretation would be similar because the scoring system in this study was the reverse of Perez-Cabal et al. (2006). These literature estimates were consistent with the results of this study.

Rear leg rear view had positive genetic (r = 0.51, 0.51, and 0.63) and phenotypic relationships (r = 0.21, 0.23, and 0.32) with FHD, RHA, and RHD, respectively. Jeyaruban et al. (2012) reported positive genetic correlations existed in Australian Angus cattle using a linear animal model between RV and hoof angle traits in Australian Angus cattle. Wiggans et al. (2006) reported a positive genetic relationship in Brown Swiss and Guernsey cattle between foot angle and RV (r = 0.19 to 0.31, respectively). Therefore, cattle with more depth to their front and rear heel and more angle to their hoof tend to be exhibit more “cow-hocked” angularity from the rear view.

Studies in beef and dairy cattle suggest a genetic relationship exists between RSV and RV (Wiggans et al., 2006; Jeyaruban et al., 2012). The present study observed a genetic correlation of 0.31 between RSV and RV, indicating cattle with more angularity to the set of the hind leg were more cow-hocked from the rear.

Composite score in this study was an attempt to classify overall feet and leg soundness in beef cattle. The dairy industry uses this type of metric with Feet and Legs score (FL), a composite value incorporating RV, RSV, foot angle (no distinction between front or rear), and locomotion along with consideration of thurl position, hocks, bone substance and pastern flexibility. The Holstein Association reports a heritability estimate of 0.17 for FL (Holstein Association USA, Inc., 2017). Several reports indicate strong genetic correlations between foot angle and FL in dairy cattle range from 0.51 to 0.73 (Van Der Waaij et al., 2005; Perez-Cabal et al., 2006). Van der Waaij et al. (2005) reported a positive phenotypic correlation between FL and foot angle at 0.42. The present study observed significant genetic correlations between COMP and RHA, RHD, FSV, RV at −0.44, −0.57, 0.87, and −0.64, respectively. These correlations suggest Red Angus cattle with steeper rear hoof angles, more angularity of the shoulder and straightness of the hock from the rear view tend to have higher COMP scores. Van Der Waaij et al. (2005) reported rear leg rear view was positively genetically correlated (r = 0.79) to FL. Differences in the standard Holstein scoring system result in opposite scales of measurement compared to the present study; however, both can be interpreted similarly: cattle with increased outward angularity of the hock from the rear view have poorer scores for subjective measures of overall feet leg structure. Negative correlations exist between feet and leg scores and RLSV (r = −0.36 to −0.52; Vollema and Groen, 1997; Van Der Waaij et al., 2005). This study observed a negative genetic correlation (−0.40) between RSV and COMP score.

Correlations of feet and leg traits with EPD and adjusted weights from RAAA including birth weight (BWA), adjusted weaning weight (WWA), adjusted yearling weight (YWA), post weaning gain (PWG), birth weight EPD (BW), weaning weight EPD (WW), yearling weight EPD (YW), daughters’ milk EPD (Milk), maintenance energy EPD (ME), heifer pregnancy EPD (HPG), maternal calving ease EPD (CEM), stayability EPD (STAY), HerdBuilder index (HERD), and GridMaster index (GRID) were calculated.

Table 6 contains the Pearson correlation coefficients (r) and Spearman ($r_s)$ correlation coefficients, respectively, between front limb traits (FHA, FHD, FSV, KNEE, FHO, and COMP) and adjusted weights, PWG and EPD. Front hoof traits had little to no association with adjusted weights or EPD. The ME EPD showed small negative relationships with FSV, KNEE, and FHV (r = −0.15, −0.19, and −0.21;$r_s$ = −0.12, −0.19, and −0.21) and small positive relationships with FHA, FHD, and COMP (r = 0.10, 0.14, and 0.26; $r_s$ = 0.11, 0.15, and 0.23). Small positive relationships were observed between STAY and FSV, KNEE, and FHV (r = 0.16, 0.11, and 0.12; $r_s$= 0.20, 0.09, and 0.11) and a small negative relationship between COMP and STAY at r = −0.11 was calculated. FHD appeared to have small negative relationships with BW, WW, and YW (r = −0.18, −0.11, and −0.15; $r_s$ = −0.17, −0.12, and −0.17). Front Side View exhibited a positive relationship with WW and YW (r = 0.10 and 0.13; $r_s$= 0.12 and 0.17). The greatest relationship between front limb traits was FSV between STAY, HERD, and GRID ($r_s$= 0.20, 0.23, and 0.16). The FCS and STAY/HERD correlations suggest a possible impactful connection between shoulder angularity and an animal’s longevity.

Table 6.

Pearson and Spearman (within parentheses) correlation coefficients between front limb² breeding values and Red Angus Association of America adjusted weights and expected progeny differences (EPD)¹

	BWA	WWA	YWA	PWG	BW	WW	YW	MILK	ME	HPG	CEM	STAY	HERD	GRID
n	1,710	1,723	1,723	1,592	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727
FHA	−0.04	−0.02	−0.07**	−0.07**	−0.11***	−0.05*	−0.07**	0.03	0.10***	−0.04	0.06*	−0.04	−0.02	−0.03
	(−0.03)	(−0.02)	(−0.06*)	(−0.06*)	(−0.11***)	(−0.03)	(−0.06**)	(0.05*)	(0.11***)	(−0.05*)	(0.07**)	(−0.04)	(−0.02)	(−0.03)
FHD	−0.08**	−0.07**	−0.05	−0.02	−0.18***	−0.11***	−0.15***	0.07**	0.14***	−0.09**	0.14***	−0.04	−0.02	−0.11**
	(−0.08**)	(−0.09**)	(−0.06*)	(−0.03)	(−0.17***)	(−0.12***)	(−0.17***)	(0.07**)	(0.15***)	(−0.10***)	(0.11***)	(−0.03)	(−0.01)	(−0.12***)
FSV	−0.02	0.03	−0.01	−0.04	−0.003	0.10***	0.13***	−0.03	−0.15***	0.05	−0.13***	0.16***	0.16***	0.12***
	(−0.05*)	(0.04)	(−0.02)	(−0.05)	(−0.06*)	(0.12***)	(0.17***)	(−0.03)	(−0.12***)	(0.04)	(−0.11***)	(0.20***)	(0.23***)	(0.16***)
KNEE	0.02	−0.06*	−0.09**	−0.09**	0.05*	−0.13***	−0.06**	−0.05*	−0.19***	0.05*	−0.02*	0.11***	0.07**	−0.04
	(0.03)	(−0.04)	(−0.07**)	(−0.08**)	(0.05*)	(−0.10***)	(−0.04)	(−0.05*)	(−0.19***)	(0.06*)	(−0.01)	(0.09**)	(0.07**)	(−0.02)
FHO	0.01	−0.04	−0.09**	−0.08**	0.06*	−0.13***	−0.06*	−0.06**	−0.21***	0.08**	−0.03	0.12***	0.09***	−0.02
	(0.03)	(−0.02)	(−0.05)	(−0.06*)	(0.05*)	(−0.11***)	(−0.04)	(−0.07**)	(−0.21***)	(0.1***)	(−0.01)	(0.11***)	(0.09**)	(0.003)
COMP	0.01	0.09**	0.12***	0.11***	−0.05	0.14***	0.07**	−0.01	0.26***	−0.07**	0.11***	−0.11***	−0.07**	0.01
	(−0.001)	(0.07)	(0.09**)	(0.08**)	(−0.01)	(0.13***)	(0.05*)	(0.01)	(0.23***)	(−0.07*)	(0.05*)	(−0.09***)	(−0.07**)	(−0.01)

¹Production data and EPD: Adjusted birth weight (BWA), adjusted weaning weight (WWA, adjusted yearling weight (YWA), post weaning gain (PWG), birth weight EPD (BW), weaning weight EPD (WW), yearling weight EPD (YW), daughter’s milk EPD (MILK), maintenance energy EPD (ME), heifer pregnancy EPD (HPG), calving ease maternal EPD (CEM), stayability EPD (STAY), herdbuilder index (HERD), and gridmaster index (GRID).

²Front Limb traits: Front Hoof Angle (FHA), Front Heel Depth (FHD), Front Side View (FSV), Knee Orientation (KNEE), Front Hoof Orientation (FHO), and Composite Score (COMP).

*P < 0.05.

**P < 0.01.

***P < 0.0001.

Table 7 contains the relationships between rear limb traits (RHA, RHD, RCS, RSV, RV, and COMP), adjusted weights, PWG and EPD. The strongest positive relationships among rear limb traits were observed between RSV and STAY/HERD (r = 0.11 and 0.12; $r_{}$= 0.12 and 0.15, respectively). The strongest negative relationship was between RCS with STAY and HERD (r = −0.12 and −0.11;$r_s$= −0.14 and −0.11). The relationship between RCS and STAY suggested animals that have increased curvature of their claw conformation have reduced genetic merit for longevity. The relationship between RSV and STAY suggest animals that have increased hock angularity, or less straightness through their hock, are more likely to have increased genetic merit for longevity.

Table 7.

Pearson and Spearman (within parentheses) correlation coefficients between rear limb breeding values² and Red Angus Association of America adjusted weights and expected progeny differences (EPD)¹

	BWA	WWA	YWA	PWG	BW	WW	YW	MILK	ME	HPG	CEM	STAY	HERD	GRID
n	1,710	1,723	1,592	1,592	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727
RHA	−0.01	−0.02	−0.04	−0.04	−0.07**	−0.09**	−0.07**	0.06*	−0.002	0.02	−0.01	0.004	0.004	−0.03
	(−0.01)	(−0.02)	(−0.03)	(−0.04)	(−0.06*)	(−0.08**)	(−0.06*)	(0.07**)	(−0.002)	(0.01)	(−0.004)	(0.01)	(0.01)	(−0.01)
RHD	0.01	0.04	0.04	0.03	0.03	−0.03	−0.04	−0.01	−0.01	0.01	−0.06*	−0.06*	−0.07**	−0.05
	(0.02)	(0.05*)	(0.07**)	(0.04)	(0.06**)	(−0.01)	(−0.01)	(−0.01)	(−0.03)	(0.001)	(−0.08**)	(−0.06*)	(−0.09**)	(−0.03)
RCS	0.03	−0.04	0.001	0.03	0.04	−0.01	−0.02	0.07**	0.06*	−0.06*	0.04	−0.12***	−0.11***	−0.03
	(0.04)	(−0.04)	(−0.004)	(0.03)	(0.03)	(−0.04)	(−0.04)	(0.06**)	(0.07**)	(−0.06**)	(0.06*)	(−0.14***)	(−0.11***)	(−0.02)
RSV	−0.03	−0.07**	−0.08**	−0.06**	−0.07**	0.02	0.03	0.01	0.01	0.01	0.07**	0.11***	0.12***	0.05*
	(−0.04)	(−0.08**)	(−0.11***)	(−0.09**)	(−0.1***)	(−0.01)	(0.002)	(0.02)	(0.02)	(0.02)	(0.10***)	(0.12***)	(0.15***)	(0.03)
RV	0.01	0.04	0.04	0.03	0.03	−0.03	−0.04	−0.01	−0.01	0.01	−0.06*	−0.06*	−0.07**	−0.04
	(0.02)	(0.05*)	(0.07**)	(0.05)	(0.06*)	(−0.005)	(−0.01)	(−0.01)	(−0.03)	(0.001)	(−0.08**)	(−0.06*)	(−0.09**)	(−0.03)
COMP	−0.01	−0.04	−0.04	−0.03	−0.03	0.03	0.04	0.01	0.01	−0.01	0.06*	0.06**	0.08**	0.05
	(−0.02)	(−0.05*)	(−0.07**)	(−0.05)	(−0.07**)	(0.01)	(0.01)	(0.01)	(0.03)	(0.0001)	(0.08**)	(0.06**)	(0.10***)	(0.03)

¹Production data and EPD: Adjusted birth weight (BWA), adjusted weaning weight (WWA, adjusted yearling weight (YWA), post weaning gain (PWG), birth weight EPD (BW), weaning weight EPD (WW), yearling weight EPD (YW), daughter’s milk EPD (MILK), maintenance energy EPD (ME), heifer pregnancy EPD (HPG), calving ease maternal EPD (CEM), stayability EPD (STAY), herdbuilder index (HERD), and gridmaster index (GRID).

²Rear Limb traits: Rear Hoof Angle (RHA), Rear Heel Depth (RHD), Rear Claw Shape (RCS), Rear Side View (RSV), Rear View (RV), and Composite Score (COMP).

*P < 0.05

**P < 0.01

***P < 0.0001

Table 8 includes the Pearson and Spearman correlations between associated traits (BCS, FCS, SIZE, COMP) with adjusted weights and RAAA EPD. Body condition score appeared to be lowly correlated with WWA, YWA, PWG, BW, WW, YW, HPG, CEM, STAY, and HERD (r = 0.09, 0.19, 0.20, 0.10, 0.18, 0.14, −0.14, −0.20, −0.13, and −0.13;$r_s$= 0.10, 0.19, 0.22, 0.11, 0.20, 0.16, −0.13, −0.18, −0.14, and −0.12, respectively). Therefore, cattle that have more genetic potential for increased weaning weight and yearling weight showed higher BCS. Increased genetic potential for BCS was associated with poorer CEM, reduced heifer fertility, less STAY, and lower HERD. Front claw shape had small relationships with BW, WW, YW, ME, HPG, and GRID (r = −0.08, 0.14, 0.16, 0.21, 0.10, and 0.20; $r_s$= −0.10, 0.12, 0.14, 0.22, 0.10, and 0.19). The strongest relationship observed between all feet and leg traits, adjusted weights, and EPD were between SIZE and BWA, WWA, YWA, PWG, BW, WW, YW, ME, and GRID (r = 0.24, 0.30, 0.32, 0.23, 0.19, 0.36, 0.37, 0.16, and 0.24;$r_s$= 0.24, 0.30, 0.34, 0.27, 0.17, 0.36, 0.38, 0.14, and 0.25). Cattle with larger hooves appeared to have the genetic potential for heavier weights and increased energy demand for maintenance.

Table 8.

Pearson and Spearman (within parentheses) correlation coefficients between associated trait breeding values² and Red Angus Association of America adjusted weights and expected progeny differences (EPD)¹

	BWA	WWA	YWA	PWG	BW	WW	YW	MILK	ME	HPG	CEM	STAY	HERD	GRID
n	1,710	1,723	1,592	1,592	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727	1,727
BCS	0.01	0.09**	0.19***	0.20***	0.10***	0.18***	0.14***	−0.01	0.02	−0.14***	−0.20***	−0.13***	−0.13***	0.04
	(0.01)	(0.10***)	(0.19***)	(0.22***)	(0.11***)	(0.20***)	(0.16***)	(−0.03)	(0.03)	(−0.13***)	(−0.18***)	(−0.14***)	(−0.12***)	(0.10***)
FCS	0.002	0.06**	0.01	−0.01	−0.08**	0.14***	0.16***	0.05*	0.21***	0.10***	0.07**	−0.02	0.05*	0.20***
	(−0.02)	(0.05*)	(0.003)	(−0.01)	(−0.10***)	(0.12***)	(0.14***)	(0.02)	(0.22***)	(0.10***)	(0.06**)	(0.02*)	(0.09**)	(0.19***)
SIZE	0.24***	0.30***	0.32***	0.23***	0.19***	0.36***	0.37***	−0.09**	0.16***	0.02	−0.07**	−0.07**	−0.03	0.24***
	(0.24***)	(0.30***)	(0.34***)	(0.27***)	(0.17***)	(0.36***)	(0.38***)	(−0.07**)	(0.14***)	(0.03)	(−0.05*)	(−0.06*)	(−0.01)	(0.25***)
COMP	0.02	0.11***	0.09**	0.04	0.03	0.19***	0.17***	−0.11***	0.04	0.03	−0.08**	0.08**	0.09**	0.10***
	(0.02)	(0.11***)	(0.07**)	(0.02)	(0.02)	(0.18***)	(0.17***)	(−0.10***)	(0.03)	(0.04)	(−0.09**)	(0.10***)	(0.11***)	(0.11***)

¹Production data and EPD: Adjusted birth weight (BWA), adjusted weaning weight (WWA, adjusted yearling weight (YWA), post weaning gain (PWG), birth weight EPD (BW), weaning weight EPD (WW), yearling weight EPD (YW), daughter’s milk EPD (MILK), maintenance energy EPD (ME), heifer pregnancy EPD (HPG), calving ease maternal EPD (CEM), stayability EPD (STAY), herdbuilder index (HERD), and gridmaster index (GRID).

²Feet and Leg traits: Body Condition Score (BCS), Front Claw Shape (FCS), Size of Hoof (SIZE), and Composite Score (COMP).

*P < 0.05

**P < 0.01

***P < 0.0001

Each of the three models predicting BV for feet and leg traits included COMP as a linear trait. This resulted in three separate BV predictions for COMP. In each anatomical category for COMP, Pearson and Spearman correlations were consistent in magnitude of differences across the three separate models. When BV for COMP were evaluated in the front limb traits model, moderate relationships with adjusted weights and RAAA EPD existed, with the most significant and highest Pearson correlation observed with ME (r = 0.26). When COMP was evaluated with associated traits, the most significant Pearson correlation was between WW and YW (r = 0.19, and 0.17). When COMP was evaluated with the rear limb traits, no moderate or high Pearson and Spearman correlations were reported. This is likely explained by how COMP was evaluated in the present study. As a subjective measurement, it is possible that COMP included significant scorer bias, resulting in less influence of rear limb traits on the overall phenotype. Measurements for COMP were taken at the end of each scoring rotation, after scorers had already assigned phenotypes for the other HFL traits included for data collection, possibly contributing bias to the phenotypes reported. The lack of variation in COMP among a small population of animals evaluated could have contributed to the differences of COMP BV correlations with Red Angus adjusted weights and EPD when evaluated in three separate models (front limb traits, rear limb traits, and associated traits). An effort should be made to investigate the merit of an overall soundness measurement in beef cattle to determine if it can be an appropriate measure for improving soundness, without the need for collecting indicator trait phenotypes.

A primary goal of the study was to look at the relationship between HFL traits and STAY. The strongest relationship between STAY and HFL traits was with FSV (r = 0.16; $r_s$= 0.20). This suggested populations selected for higher STAY may possess more angularity of the shoulder. A moderate negative correlation exists (r = −0.13) between BCS and STAY, indicating cattle that have been selected for higher STAY will have increased potential for appropriate BCS with fewer obese animals. A moderate negative correlation between BCS and STAY also suggested that animals with poorer STAY were more likely to be obese. Berry et al. (2003) identified a positive favorable genetic correlation between BCS and improved fertility traits, with the implication that females with higher BCS had a lower first service interval, showed an increase in the percentage of pregnancy to first service, reported a reduction in the number of services required to become pregnant, and reported an increase in the percentage of females pregnant by 63 days. A moderate negative relationship between BCS and STAY, and a moderate positive relationship between BCS and fertility measures in literature provide evidence that suggests STAY is influenced by multiple performance traits, and early culling in females is not solely dependent on fertility and reproductive performance. This study observed a positive correlation between RSV BV and STAY (r = 0.11 and $r_s$= 0.12), indicating cattle with genetic merit for increased angle to the hock and hind leg may remain in the herd longer as compared to cattle genetically predisposed to sire calves with more straightness to the hock. Forabosco et al. (2004) reported Italian Chianina cattle with a straighter hind leg were 59% more likely to be culled versus an ideal set to the hind leg whereas cattle with a slightly more sickled hock were only 3% more likely to be culled versus those with an ideal set to the hind leg. However, studies in dairy cattle have reported small negative correlations between RSV with productive life and functional herd life (r = −0.01 to −0.21; Vollema and Groen, 1997; Perez-Cabal et al., 2006). The discrepancy between dairy and beef correlations of RSV with early culling predictions may be due to the differences in production systems of beef versus dairy cattle. While dairy cattle live in more intensive management systems, beef cattle experience more diverse environments and management systems where mobility is of a higher concern. Beef cattle tend to have increased daily movement across larger spaces compared to dairy cattle, and hoof health management practices such as trimming are less opportunistic in beef production systems. Furthermore, a concentrated look at reviewing the interpretations of genetic correlations between an intermediate optimum trait and linear EPD should be a high priority in future studies.

The current study implemented a scoring methodology where the assumed optimum level for many of the traits included for phenotypic appraisal resided in the middle of the scale. Though most scoring methods for type traits included this methodology, it is worth noting determining the true optimum level of a type trait can be difficult. Given this reality, the issue of interpreting linear correlations of type traits with an intermediate optimum level against adjusted weights and EPD should be done cautiously. Further understanding of the true optimum level of type traits and their correlations with beef breed association produced EPD should be explored in future studies.

Due to the lack of understanding of optimal phenotype or directional change in HFL traits, it may be reasoned directly selecting for feet and leg improvement is inappropriate. Production of foot and leg EPD within a national genetic evaluation may introduce the opportunity for cattle producers to select for improvement of feet and legs. However, direct improvement of HFL traits may not lead to direct economic improvements as HFL are not considered an economically relevant trait (ERT) in beef cattle production; rather the true ERT would be soundness as a contributor to sustained longevity. Inappropriate selection for direct improvement of feet and leg indicator traits may cause unexpected or negative genetic progress for other ERT. Perhaps, investigation should instead focus on utilizing HFL traits as indicators to help improve the prediction of STAY and ME at younger ages.

Conclusion

Feet and leg traits were lowly to moderately heritable in this population of Red Angus cattle. Thus, producers can select for feet and leg traits and realize genetic change. Genetic correlations were extremely strong among some feet and leg traits, suggesting fewer traits would need to be classified when this methodology is incorporated into national beef cattle genetic evaluations. It is logical many more cattle producers would be willing to classify feet and leg structure if there are fewer traits necessary for robust genetic evaluation. Small correlations existed between feet and leg traits and RAAA produced EPD, and only minimal associations with adjusted weights existed. A small to nonexistent relationship between feet and leg traits and growth is a benefit because cattle producers will not have indirect, adverse effects on growth when selecting for improved structure or, conversely, have seen severe negative impacts on feet and leg structure due to the recent and sustained improvement in genetic merit for growth seen in the industry. Cattle producers may notice small genetic change in the EPD for other traits when selecting on feet and leg structure. This does merit increased scrutiny as production of genetic prediction(s) for HFL traits may not be appropriate for national beef cattle genetic evaluation as no direct economic association between HFL traits and beef production profitability has been identified. Though this study was not able to test the merit of feet and leg traits as an indicator for STAY, further investigation into the viability of HFL traits as indicators to improve prediction for STAY or ME is a high priority. The relationships between RCS, FSV, FHO, RSV, STAY, and ME provide a starting point for future investigations into using these HFL traits as indicator observations. Moderate relationships with SIZE and RAAA adjusted weights and EPD exist, suggesting SIZE warrants further investigation as a primary type trait for genetic evaluation. In order to further explore the viability of these traits within national beef cattle genetic evaluation, a significantly larger data set, representative of the national genetic diversity of the Red Angus breed, is required. Recommendations for the standard practices of HFL data collection are required for large amounts of field data, and our data suggests that hoof angle/depth, claw shape, foot size, front side view, and rear side view would be likely candidates due to their low genetic correlation among each other and moderate heritability estimates.

Glossary

Abbreviations

BCS

body condition score

BIF

Beef Improvement Federation

BW

birth weight EPD

BWA

adjusted birth weight

CEM

calving ease maternal EPD

COMP

composite score

EBV

estimated breeding values

EPD

expected progeny differences

ERT

economically relevant trait

FCS

front claw shape

FHA

front hoof angle

FHD

front heel depth

FHO

front hoof orientation

FL

feet and legs score

FSV

front side view

GRID

Gridmaster $index

HERD

Herdbuilder $index

HFL

hoof, foot and leg traits

HPG

heifer pregnancy EPD

KNEE

knee orientation

LOC

Locomotion

ME

maintenance energy EPD

MILK

daughter's milk EPD

PWG

post weaning gain

RAAA

Red Angus Association of America

RCS

rear claw shape

RHA

rear hoof angle

RHD

rear heel depth

RSV

rear side view

RV

rear view

SAS

SAS Institute

SE

standard error

SIZE

hoof size

STAY

stayability EPD

USDA

United States Department of Agriculture

WW

weaning weight EPD

WWA

adjusted weaning weight

YW

yearling weight EPD

YWA

adjusted yearling weight

r

correlation

h²

heritability

σ² _a

additive genetic variance

σ² _e

residual variance

Conflict of interest statement

The authors declare they have no conflict of interest.