Selection for lean weight based on ultrasound and CT in a meat line of sheep

Kvame, T.; Vangen, O.

doi:10.1016/j.livsci.2006.08.007

Cited by 41 publications

(27 citation statements)

References 25 publications

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“…This is the first study to present heritabilities for LMP estimated from CT scanning of live pigs. A similar size in heritability has been reported by Kvame and Vangen (2007), who studied genetic variation in a Norwegian sheep population with CT. Duroc exhibited a larger genetic variation for LMP than Landrace. The heritabilities for LMP in this study are slightly higher than those estimated for these two breeds with the previous method used in Norsvin: an experimental dissection for carcass composition of half-sibs tested females and castrates (Gjerlaug-Enger et al, 2010a).…”

Section: Discussionsupporting

confidence: 80%

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Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc

Gjerlaug-Enger

Kongsro

Ødegård

et al. 2012

Animal

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In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n 5 3835) and Duroc (D; n 5 3139) boars, selection candidates to be elite boars in a breeding programme, were CT-scanned between August 2008 and August 2010 as part of an ongoing testing programme at Norsvin's boar test station. Genetic parameters in the growth rate of muscle (MG), carcass fat (FG), bone (BG) and non-carcass tissue (NCG), from birth to ,100 kg live weight, were calculated from CT data. Genetic correlations between growth of different body tissues scanned using CT, lean meat percentage (LMP) calculated from CT and more traditional production traits such as the average daily gain (ADG) from birth to 25 kg (ADG1), the ADG from 25 kg to 100 kg (ADG2) and the feed conversion ratio (FCR) from 25 kg to 100 kg were also estimated from data on the same boars. Genetic parameters were estimated based on multi-trait animal models using the average information-restricted maximum likelihood (AI-REML) methodology. The heritability estimates (s.e. These results showed the difficulty in jointly improving LMP and ADG2. ADG2 was unfavourably correlated with FG (L: 0.84 and D: 0.72), thus indicating to a large extent that selection for increased growth implies selection for fatness under an ad libitum feeding regime. Selection for MG is not expected to increase ADG2, but will yield faster growth of the desired tissues and a better carcass quality. Hence, we consider MG to be a better biological trait in selection for improved productivity and carcass quality. CT is a powerful instrument in conjunction with breeding, as it combines the high accuracy of CT data with measurements taken from the selection candidates. CT also allows the selection of new traits such as real body composition, and in particular, the actual MG on living animals.

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Section: Discussionsupporting

confidence: 80%

“…Phenotypes on many animals are required for genetic analyses. A few studies on sheep have used CT data for the estimation of genetic parameters (Jones et al, 2004;Kvame and Vangen, 2007), but to the best of the authors' knowledge, similar work has not been carried out previously on pigs.…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc

Gjerlaug-Enger

Kongsro

Ødegård

et al. 2012

Animal

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“…However, in terminal sire studies (Jones et al, 2004;Kvame and Vangen, 2007), as well as another study involving Scottish Blackface lambs (Karamichou et al, 2006), heritabilities of MUSC derived by CT were slightly higher (0.45 to 0.48) than the 0.35 found here. The heritability for UFD was at the lower end of the range found in other studies in different breeds and crosses (Wolf and Smith, 1982).…”

Section: Discussioncontrasting

confidence: 78%

“…Therefore, although the precise values of these genetic correlation estimates may be unfeasibly high, live weights from weaning to adulthood appear to be under very similar genetic control. Jones et al (2004) and Kvame and Vangen (2007) found genetic correlations between UMD and MUSC ranging from 0.42 to 0.7 in terminal sire lambs. The estimate here is substantially lower (0.13) and UMD seems to be more highly correlated to muscularity indices than MUSC in this breed.…”

Section: Discussionmentioning

confidence: 98%

“…The heritability for UFD was at the lower end of the range found in other studies in different breeds and crosses (Wolf and Smith, 1982). Estimates of heritabilities for CFAT in terminal sire breeds in the UK and Norway (Jones et al, 2004;Kvame and Vangen, 2007) ranged from 0.18 to 0.41. A much higher heritability of 0.6 was estimated for CFAT in Scottish Blackface lambs by Karamichou et al (2006).…”

Section: Discussionmentioning

confidence: 99%

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Relationships between lamb carcass quality traits measured by X-ray computed tomography and current UK hill sheep breeding goals

Lambe¹,

Conington²,

Bishop

et al. 2008

Animal

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Genetic parameters were estimated between current UK hill sheep breeding goals and lamb carcass composition and muscularity traits derived using X-ray computed tomography (CT). To produce these estimates, a total of 648 lambs from two hill farms were CT scanned at weaning (ca 120 days of age), over 3 years, and total weights of carcass muscle (MUSC), fat (CFAT) and bone (BONE) and internal fat (IFAT) were predicted. Previously derived muscularity indices were also calculated for the hind leg (HLMI) and lumbar (LRMI) regions, to assess muscle shape. Data for current breeding goals (lamb performance and maternal traits) were also included from a total of 10 297 lamb records and 12 704 ewe records. Heritabilities were estimated for each trait and genetic and phenotypic correlations were calculated between each CT trait and other lamb or ewe traits. Moderate to high positive genetic correlations were found between CT-predicted tissue weights and breeding goals, which were also weights (lamb weaning weight, carcass weight, mature ewe weight, average weight of lambs reared by the ewe). CFAT was positively genetically correlated with ultrasound backfat depth at weaning (UFD) and subjective fatness grade at slaughter (MLCF), suggesting that carcass fat could be decreased using selection on any of these predictors. Ultrasound muscle depth at weaning (UMD) and subjective conformation score at slaughter (MLCC) had high genetic correlations with the muscularity indices (HLMI and LRMI), but correlations with MUSC were not significantly different from zero. This implies that selection to improve MLCC is likely to be increasing the 'roundness' of muscle shape in the high-priced carcass region, but having little impact on total lean meat yield. Correlations of CT traits with the other ewe traits (number of lambs weaned, number of lambs lost, longevity, fleece weight) were generally small or not significantly different from zero. The genetic parameters generated in this study can now be used in selection index calculations to assess the benefits of including lamb CT traits in future selection programmes for hill sheep.

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Bayesian Mixture Models: When the Thing You Need to Know is the Thing You Cannot Measure

Alston

Mengersen

Gardner

2012

Case Studies in Bayesian Statistical Modelling and Analysis

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Selection for lean weight based on ultrasound and CT in a meat line of sheep

Cited by 41 publications

References 25 publications

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc

Relationships between lamb carcass quality traits measured by X-ray computed tomography and current UK hill sheep breeding goals

Bayesian Mixture Models: When the Thing You Need to Know is the Thing You Cannot Measure

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