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D W Newcom

Publications and source records attributed to D W Newcom.

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Genetic and phenotypic relationships between individual subcutaneous backfat layers and percentage of longissimus intramuscular fat in Duroc swine.

Progeny (n = 589) of randomly mated Duroc pigs were used to determine the genetic and phenotypic relationships between individual s.c. backfat layers and i.m. fat percent (IMF) of the longissimus. Five days before slaughter, cross-sectional ultrasound images were collected at the 10th rib by a National Swine Improvement Federation-certified ultrasound technician using an ultrasound machine (Aloka 500 SSD) fitted with a 12-cm linear array transducer. Off-midline backfat (SBF) and loin muscle area (SLMA) were measured. Individual s.c. backfat layers were measured at the same location: outer (OBF), middle (MBF), and inner (IBF). Off-midline backfat (CBF) and loin muscle area (CLMA) were measured on the carcass 24 h postmortem. A slice from the 10th rib of the loin muscle was obtained for determination of IMF. Heritability estimates and genetic correlations were calculated fitting all possible two-trait animal models in MATVEC (Wang et al., 2003). The heritabilities for OBF, MBF, IBF, CBF, SBF, and IMF were 0.63, 0.45, 0.53, 0.48, 0.44, and 0.69, respectively. The genetic correlations of OBF, MBF, and IBF with IMF were 0.36, 0.16, and 0.28, respectively, and the genetic correlations of CBF and SBF with IMF were 0.25 and 0.27, respectively. Genetic correlations between OBF and MBF, OBF and IBF, and MBF and IBF were 0.43, 0.45, and 0.67, respectively. Results demonstrate that individual backfat layers are highly heritable, of similar magnitude to total backfat, and have similar genetic correlations with IMF. Individual backfat layers could become candidate traits for implementation into a multiple-trait genetic evaluation to improve IMF, while minimizing the detrimental effect on total backfat depth.

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Comparison of three models to estimate breeding values for percentage of loin intramuscular fat in Duroc swine.

Three selection models were evaluated to compare selection candidate rankings based on EBV and to evaluate subsequent effects of model-derived EBV on the selection differential and expected genetic response in the population. Data were collected from carcass- and ultrasound-derived estimates of loin i.m. fat percent (IMF) in a population of Duroc swine under selection to increase IMF. The models compared were Model 1, a two-trait animal model used in the selection experiment that included ultrasound IMF from all pigs scanned and carcass IMF from pigs slaughtered to estimate breeding values for both carcass (C1) and ultrasound IMF (U1); Model 2, a single-trait animal model that included ultrasound IMF values on all pigs scanned to estimate breeding values for ultrasound IMF (U2); and Model 3, a multiple-trait animal model including carcass IMF from slaughtered pigs and the first three principal components from a total of 10 image parameters averaged across four longitudinal ultrasound images to estimate breeding values for carcass IMF (C3). Rank correlations between breeding value estimates for U1 and C1, U1 and U2, and C1 and C3 were 0.95, 0.97, and 0.92, respectively. Other rank correlations were 0.86 or less. In the selection experiment, approximately the top 10% of boars and 50% of gilts were selected. Selection differentials for pigs in Generation 3 were greatest when ranking pigs based on C1, followed by U1, U2, and C3. In addition, selection differential and estimated response were evaluated when simulating selection of the top 1, 5, and 10% of sires and 50% of dams. Results of this analysis indicated the greatest selection differential was for selection based on C1. The greatest loss in selection differential was found for selection based on C3 when selecting the top 10 and 1% of boars and 50% of gilts. The loss in estimated response when selecting varying percentages of boars and the top 50% of gilts was greatest when selection was based on C3 (16.0 to 25.8%) and least for selection based on U1 (1.3 to 10.9%). Estimated genetic change from selection based on carcass IMF was greater than selection based on ultrasound IMF. Results show that selection based on a combination of ultrasonically predicted IMF and sib carcass IMF produced the greatest selection differentials and should lead to the greatest genetic change.

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Breed differences and genetic parameters of myoglobin concentration in porcine longissimus muscle.

An evaluation of porcine longissimus myoglobin concentration was conducted to determine breed and gender differences for myoglobin content, estimate genetic parameters for myoglobin concentration, and determine the relationship between myoglobin content and objective measures of muscle color. Data from centrally tested (n = 255), purebred Yorkshire (42), Duroc (61), Hampshire (17), Chester White (28), Berkshire (67), Poland China (28), and Landrace (12) barrows and gilts from the 1999 National Barrow Show Sire Progeny Test were used. Ultimate pH and Hunter L were measured on the 10th-rib face 24 h postmortem. A section of bone-in loin containing the 10th rib was taken to the Iowa State University Meats Laboratory. At 48 h postmortem, Hunter L, CIE L*, a*, and b*, Japanese color score, and water-holding capacity were measured on the face of the 10th-rib loin chop. A slice from the 10th-rib loin section was evaluated for percentage of i.m. fat. The resulting loin chop was used for the determination of soluble myoglobin concentration (mg/g, wet basis). Chester White, Hampshire, and Duroc pigs had the highest (P < 0.05) myoglobin concentration (0.92, 0.95, and 0.85 mg/g, respectively), whereas Landrace had the lowest (0.62 mg/g; P < 0.05). No gender differences were detected for myoglobin concentration. The heritability estimate for soluble myoglobin concentration was 0.27. Residual correlations between soluble myoglobin and CIE L*, a*, b*, Hunter L (24 h), Hunter L (48 h), and Japanese color score were -0.17, 0.23, -0.15, -0.16, -0.13, and 0.13, respectively. These correlations are low but in the desired direction. The residual correlation between soluble myoglobin and intramuscular fat percent was 0.18. Results show that myoglobin concentration has a moderate heritability and could be used in a selection program to make pork loins darker in color.

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Prediction of intramuscular fat percentage in live swine using real-time ultrasound.

Purebred Durocs (n = 207) were used to develop a model to predict loin intramuscular fat percentage (PIMF) of the longissimus muscle in live pigs. A minimum of four longitudinal, real-time ultrasound images were collected 7 cm off-midline across the 10th to the 13th ribs on the live animal. A trained technician used texture analysis software to interpret the images and produce 10 image parameters. Backfat and loin muscle area were measured from a cross-sectional image at the 10th rib. After harvest, a slice from the 10th to the 11h rib loin interface was used to determine carcass loin intramuscular fat percentage (CIMF). The model to predict loin intramuscular fat percentage was developed using linear regression analysis with CIMF as the dependent variable. Initial independent variables were off-test weight, live animal ultrasonic 10th rib backfat and loin muscle area, and the 10 image parameters. Independent variables were removed individually until all variables remaining were significant (P < 0.05). The final prediction model included live animal ultrasound backfat and five image parameters. The multiple coefficient of determination and root mean square error for the prediction model were 0.32 and 1.02%, respectively. An independent data set of Duroc (n = 331) and Yorkshire (n = 288) pigs from two replications of the National Pork Board's Genetics of Lean Efficiency Project were used for model validation. Results showed the Duroc pigs provided the beat validation of the model. The product moment correlation and rank correlation coefficients between PIMF and CIMF were 0.60 and 0.56, respectively, in the Duroc population. Results show real-time ultrasound image analysis can be used to predict intramuscular fat percentage in live swine.

Adipose Tissue↗

Genetic parameters for pork carcass components.

Data from 456 homozygous halothane normal purebred Yorkshire, Duroc, and Other-breed pigs from two national progeny testing and genetic evaluation programs were utilized to estimate genetic parameters for carcass components in pigs. Carcass components were cut and weighed according to Institutional Meat Purchase Specifications. Primal cut weights evaluated included 401 Ham (HAM), 410 Loin (LOIN), 405 Picnic shoulder (PIC), 406 Boston Butt (BB), and 409 Belly (BELLY). Individual muscle weights included the inside (INS), outside (OUT), and knuckle (KNU) muscles of the ham, the longissimus dorsi (LD) and psoas major (TEND) of the loin, and the boneless components of both the Boston Butt (BBUTT) and picnic (BPIC). Muscle weights from each primal were summed to yield a boneless subprimal weight (BHAM, BLOIN, BSHLDR), and all boneless subprimals were summed to yield total primal boneless lean (LEAN). Heritability estimates for HAM, LOIN, and BELLY were 0.57, 0.51, and 0.51, respectively. Heritability estimates for BB and PIC were 0.09 and 0.21, respectively. Heritability estimates for the boneless components of each primal were higher than those for the intact primals. Genetic correlations for HAM, LOIN, and PIC with loin muscle area (LMA) were 0.53, 0.78, and 0.70, respectively, and-0.62, -0.51, and -0.60, respectively, with 10th rib off-midline backfat (BF10). Boneless subprimal components were highly correlated with LEAN. Gilts had heavier weights (P < 0.01) than barrows for all boneless subprimals, individual muscles, LEAN, and for all primal cuts except BELLY. Gilts also had less BF10 and more LMA (P < 0.01) than barrows. Duroc pigs had a heavier (P < 0.01) weight for HAM and PIC when compared to Yorkshires. Yorkshire pigs had more (P < 0.01) LOIN weight than did the Durocs. Results suggest primal, boneless subprimal, and individual muscle weights in pigs should respond favorably to selection.

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