12 Acta argiculturae Slovenica, Supplement 5, 12–17, Ljubljana 2016 24th Int. Symp. “Animal Science Days”, Ptuj, Slovenia, Sept. 21st−23rd, 2016. Invited lecture COBISS: 1.06 Agris category code: L01, Q04 NON-INVASIVE TECHNIQUES FOR EXACT PHENOTYPIC ASSESSMENT OF CARCASS COMPOSITION AND TISSUE GROWTH IN DOMESTIC ANIMALS 1 Invited lecture Goran KUŠEC 2, Armin M. SCHOLZ 3, Ulrich BAULAIN 4, Ivona DJURKIN KUŠEC 2, Maren BERNAU 3 Non-invasive techniques for exact phenotypic assessment of carcass composition and tissue growth in domestic animals 1 To prof. Erhardt Kallweit for his 80th birthday 2 Faculty of Agriculture in Osijek, Josip JurajStrossmayer University of Osijek, Kralja Petra Svačića 1d, 31000 Osijek, Croatia, e-mail: gkusec@pfos.hr 3 Livestock Center Oberschleißheim, Ludwig-Maximilians-University Munich, Sankt-Hubertusstrasse 12, 85764 Oberschleißheim, Germany 4 Institute ofFarmAnimalGenetics, Friedrich-Loeffler-Institut, Hoeltystr.10, 31535 Neustadt, Germany ABSTRACT The present review paper deals with the advances in exact phenotyping of domestic animals regarding carcass composition and the changes in tissue proportions during growth. The development of non-invasive methods such as computer tomography (CT), dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) is pre- sented, especially in relation to recent investigations of genetic aspects of growth. Supported by various bio-statistical approaches that can manage huge amount of data, such research could enable better understanding of complex pheno- typic traits like changes in body composition during growth. Key words: domestic animals, phenotyping, non-invasive methods, carcass composition, growth 1 INTRODUCTION In early stages of animal body composition studies, only destructive methods were at disposal for scientists. The methods commonly used at those times were total dissection, followed by separation into major tissues (Davies and Kallweit, 1979; Vincek et al., 2010). Oth- er methods, as well, can be found in literature such as grinding followed by chemical analysis (Tess et al., 1986; White et al., 1995). During the last 30 years, however, a number of non- destructive or non-invasive methods evolved which can accurately depict body composition of domestic animals, most widely used being ultrasound, computer aided tomography (CT), dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) (Scholz et al., 2015). CT has been used to measure body composition in farm animals for quite some time, for example in sheep breeding (Young et al., 2002; Bunger et al., 2011), rabbit (Szendro et al., 2012), beef (Navajas et al., 2010) or pig body composition analyses (Font i Furnols et al., 2009; Kongsro, 2014). Recently the introduction of CT as a reference method for pig carcass classification was pro- posed (Daumas and Monziols, 2011). Similarly, DXA can be used in the evaluation of the body composition of pigs both in vivo and on carcasses of with a high accu- racy (Scholz and Förster, 2006; Scholz et al., 2006, 2015; Bernau et al., 2015). To some extent better results when compared to DXA in the investigation of body composition demon- strated the usage of MRI in pigs by Bernau et al. (2015) and Lee and Kuk (2013) in humans. In the latter case, multiple image protocols that cover the entire body are used as the reference method for the whole-body com- position assessment. There are a number of studies which showed that body composition of the pig can be accurately predicted by an acquisition of a series of cross-sectional images covering the whole carcass or primal cuts (Mitchell et al., 2001; Tholen et al., 2003; Baulain et al., 2004; Collewet et al., 2005; Monziols et al., 2006; Olsen et al., 2013; Acta agriculturae Slovenica, Supplement 5 – 2016 13 NON-INVASIVE TECHNIQUES FOR EXACT PHENOTYPIC ASSESSMENT OF CARCASS ... GROWTH IN DOMESTIC ANIMALS Kremer et al., 2013). The major advantage of using the non-invasive techniques is the ability to perform inves- tigations on living animals without detrimental conse- quences to the animal. For that reason, these methods are suitable when sequential studies of the same object are required. This is particularly desired when conduct- ing longitudinal studies of phenomena such as growth, development, ageing, and seasonal influences or environ- mental factors. Based on repeated measures of cross-sectional MR images on the same animals, tissue composition of grow- ing pigs and their body parts were examined in studies of Kastelic et al. (1995), Baulain et al. (1996) and Kusec et al. (2007). DXA allows an even easier approach, be- cause whole body composition results are available im- mediately after scanning as was demonstrated in DXA growth studies on calves e.g. by Scholz et al. (2003) or in pigs by Scholz and Mitchell (2003) and Mitchell and Scholz (2007, 2008). 2 PHENOTYPIC EVALUATIONS OF ANI- MALS’ GROWTH IN LIVE WEIGHT AS WELL AS IN SPECIFIC TISSUES Growth can be considered as complex phenotypic trait that results from a number of biological processes in the animal. According to Schinkel and Einstein (1995), the genotype determines the maximum level at which these processes can occur, whereas environmental fac- tors determine the degree to which genetic potential is expressed. Thus, an understanding of the relationships between genotype and environmental factors is essen- tial in order to implement strategies that could improve utilization of growth potential. With this in mind, Kusec et al. (2007) investigated growth characteristics of hybrid pigs distributed in four groups according to genotype at malignant hyperthermia syndrome (MHS) gene (NN and Nn) and feeding system (ad libitum and restrictive). The analysis of growth by asymmetric S-function showed different patterns of live weight growth of pigs from two feeding regimes as shown on the Figure 1a. Within the feeding regimes, no significant differ- ences in live weight growth patterns between the pigs of different MHS genotype (NN and Nn) were found. Muscle growth pattern determined by 4 consecutive MRI measurements at 4 week interval significantly dif- fered between the groups of investigated pigs (Fig. 1b); Nn pigs from the intensive feeding group were signifi- cantly superior to NN pigs in the same group and to Nn pigs fed restrictively (p < 0.05), but only in the phase of progressive growth (b-coefficient of the S-function). In this respect Nn pigs performed better under intensive feeding than under restrictive feeding regime, while no difference was found between NN pigs from two feed- ing regimes. This led to the conclusion that cost effective restrictive feeding regime can be recommended as more appropriate in fattening of pigs, since intensive feeding generally failed to improve their muscle growth. Similar- ly, authors found that inclusion of MHS-gene did not en- hance muscle growth characteristics of investigated pigs, so MHS-negative pigs (NN) can be considered as more desirable fatteners, especially when fed restrictively. Furthermore, on the basis of MRI measurements of muscle and live weight analyses by generalized logistic S-function, Kušec et al. (2008) found that the optimal slaughter time/weight for pigs in terms of maximum muscle growth potential. In their study, this point was described as the time when the intensive phase of mus- cle growth ceased and turned to the phase of retardation Model: live weight = A/(1+b*exp(-c*0.01*age)) (̂1/0.01) tB tI tC tB tI tC 30 60 90 120 150 180 210 240 age (days) 0 20 40 60 80 100 120 140 160 180 liv e w ei gh t ( kg ) intensive; NN intensive; Nn restrictive; NN restrictive; Nn Model: muscle volume = A/(1+b*exp(-c*0.01*age))^(1/0.01) tB tI tC 30 60 90 120 150 180 210 240 age (days) 0 10 20 30 40 50 60 70 m us cl e vo lu m e (d m 3 ) intensive; NN intensive; Nn restrictive; NN restrictive; Nn (a) (b) Figure 1: (a) Live weight growth curves of two MHS-genotypes (NN and Nn) of pigs kept on intensive and restrictive feeding regime; (b) Muscle growth curves of two MHS-genotypes (NN and Nn) of pigs kept on intensive and restrictive feeding regime Acta agriculturae Slovenica, Supplement 5 – 201614 G. KUŠEC et al. which occurred when muscle volume reached of 51.31 dm3.For pigs from the intensive and restrictive group, this moment occurred at the age of 164 and 167 days when their weight was estimated to 126 and 112 kg, re- spectively. After these moments pigs tend to gain weight only by fat deposition. Slightly different results were found by Scholz and Mitchell (2003) using DXA. Protein (muscle) deposi- tion rates found their maximum already between 30 and 60 kg live weight showing lower protein deposition rates between 60 and 90 kg live weight, while fat deposition rates were already higher than lean deposition rates be- tween 30 and 60 kg live weight (Table 1). NN and Nn did not differ significantly during growth in both cases (Kusec et al., 2008; Scholz and Mitchell, 2003), while nn (not studied by Kusec et al., 2008) showed a significantly higher protein deposition rate between 30 and 60 kg body weight. 3 GENETIC/GENOMIC STUDIES OF BODY COMPOSITION DURING GROWTH In the meantime genetic analyses evolved to a great extent. Genome research in domestic animals is general- ly conducted in the aim to be applied in livestock produc- tion practices. The achievements made in that directions were most certainly a great stimulus for genome sequenc- ing programs in domestic animals while molecular infor- mation became very important in practical breeding pro- grams. Silva et al. (2013) studied genomic growth curves of an outbred pig population. The authors postulated that the genetic basis of pig growth can be understood by as- sessing SNP marker effects and genomic breeding values based on estimates of these growth curve parameters as phenotypes. They compared two statistical approaches using empirical weight-age data from an outbred F2 (Brazilian Piau X commercial) population. On the basis of the obtained results they constructed genomic growth curves, which showed substantial genetic discrimination among animals in the final growth phase. The SNP ef- fect estimates allowed identification of the most relevant markers for each phenotype, the positions of which were coincident with reported QTL regions for growth traits. Andersson (2015) presented a genetic dissection of major locus responsible for the control of muscle growth and fat deposition. In this review he described the identification of single-nucleotide substitution in intron 3 of the gene for insulin-like growth factor 2 (IGF2) that is underlying a quantitative trait locus affecting muscle growth, size of the heart, and fat deposition. As it is obvious, advances in genetics and genomics with their cutting-edge bio- chemical methods and subtle statistical approaches make powerful tools in the explanation of complex biological phenomena such as animal growth. Inclusion of non-invasive techniques with their ability to give insight in the changing body composition of animals during growth can help to broaden the knowl- edge on these phenotypes. Since proportions of tissues in the carcass of domestic animals, like muscle and fat, influence the value of the final product, that is primar- ily processed carcass, the traits considered here are also of economic importance. That is why we conclude that non-invasive methods should be used in the phenotyp- ing of meat animals for the growth traits in more detail than live weight – also in order to decrease the mismatch between wide available molecular data and phenotypic data that are often based on less reliable slaughterhouse standards only (see also Bruford et al., 2015). This would in turn improve the animal breeding programs aimed at more effective meat production by the use of genetic analysis of animal’s growth in live weight, muscle, fat, and not to forget bone tissues. First reports, where non-invasive techniques in farm animals were combined with genome wide asso- ciation studies (GWAS), originate from the pig as model animal (Kogelmann et al., 2013) or as farm animal (Rot- hammer et al., 2014). DXA as a simple and affordable Growth Period (live weight, kg) RyR1-Genotype NN Nn nn Deposition rate (g/d) Protein Fat Bone Mineral Protein Fat Bone Mineral Protein Fat Bone Mineral 1.2–10 42 ± 3 13 ± 2 6 ± 1 39 ± 4 10 ± 2 5 ± 1 30 ± 4 8 ± 3 4 ± 1 10–30 104 ± 4 57 ± 4 13 ± 1 107 ± 3 59 ± 3 13 ± 0 115 ± 5 58 ± 5 14 ± 1 30–60 120 ± 6 199 ± 13 19 ± 1 123 ± 5 172 ± 11 19 ± 1 132 ± 7 168 ± 16 19 ± 1 60–90 104 ± 14 225 ± 45 9 ± 1 116 ± 14 183 ± 45 9 ± 1 112 ± 19 125 ± 58 12 ± 1 Table 1: Deposition rates for protein, fat and bone mineral (g/d) in pigs with different Ryanodine Receptor 1 (RyR1)-Genotypes [LSM ± SEE; modified from Scholz and Mitchell, 2003] Acta agriculturae Slovenica, Supplement 5 – 2016 15 NON-INVASIVE TECHNIQUES FOR EXACT PHENOTYPIC ASSESSMENT OF CARCASS ... GROWTH IN DOMESTIC ANIMALS non-invasive technique has been used in both cases to providing the body composition and bone mineraliza- tion phenotypes for the genome wide association study. Rothammer et al. (2014) identified a large number of with body composition associated SNP’s, which were not known before to be associated with body composition or bone mineralization (Fig. 2). Furthermore, Matika et al. (2016) recently per- formed a genome wide association study about the growth and carcass traits of lambs based on computed tomography (CT) measurements on live animals. They identified quantitative trait loci (QTL) on OAR1, 3, 24 and OAR6 that are associated with the traits of major tissues (muscle, fat and bone). Authors stressed that re- vealed single nucleotide polymorphisms (SNPs) have a potential for selection on improved carcass composition and meat quality in sheep breeding. Further investiga- tions are recommended in order to clarify if the effects on QTL OAR6 are caused by a single gene or more closely- linked genes. This work represents the fine example of exploiting the improved genotyping tools (the Illumina OvineSNP50 SNP chip) by which up to 54,241 independ- ent SNPs can be simultaneously characterised, in combi- nation with non-invasive measurements of carcass com- position by CT in order to identify QTL and candidate genes with significant effects. Nowadays, the genetic gain on body composition of breeding pigs is extensively studied using CT measure- ments. For example, as a part of Norsvin (now Topigs- Norsvin) breeding system, 3500 boars are scanned an- nually in Norway (Kongsro, 2014). An estimated 30 % increase of genetic gain on lean meat percentage by the use of this non-invasive technology for collecting the data for phenotyping in vivo has been reported. Due to such promising results as those presented in this review, it is to be expected that similar research will be performed more frequently in near future giving in- sights in genetics behind such complex traits as growth of animals and humans as well. 4 CONCLUSION Growth and development of whole animals as well as the growth of tissues, single muscles and organs is get- ting a new interest in animal science. It is becoming to be viewed as a complex phenotypic trait with great impor- tance as in fact the basis of animal production. Due to its ability to perform investigations on liv- ing animals without detrimental consequences to the animal, non-invasive techniques such as ultrasound, computer aided tomography (CT), dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) are becoming increasingly appealing to the ani- mal scientists, especially those interested in the genetic aspects of growth. It seems that a growing interest emerges in combin- ing the non-invasive techniques for body composition studies with up-to-date genetic and genomic analyses. Supported by various bio-statistical approaches that can manage huge amount of data, such combination rep- resents a promising way to the better understanding of such complex phenotypic traits as body composition during growth, an ever changing morphological set up of domestic or other animals; and humans. 5 REFERENCES Andersson, L. (2015). Studying Phenotypic Evolution in Do- mestic Animals: A Walk in the Footsteps of Charles Dar- win. Cold Spring Harbor Symposia on Quantitative Biology, 74, 319–325. Baulain, U., Henning, M., Kallweit, E. (1996). Bestimmung der Körperzusammensetzung von Landrasse-Schweinen un- terschiedlichen Alters mittels MRI. Archiv für Tierzucht, Dummerstorf, 39, 431–440. Baulain, U., Wiese, M., Tholen, E., Hoereth, R., Hoppenbrock, K.H. (2004). Magnet-ResonanzTomographie: Referenz- technik zur Bestimmung der Körperzusammensetzung in Figure 2: Manhattan plot of GWAS results for DXA phenotypes (BMC = bone mineral content, Fat, and Lean) in pigs (modified after Rothammer et al., 2014) Acta agriculturae Slovenica, Supplement 5 – 201616 G. KUŠEC et al. der Leistungsprüfung beim Schwein. Fleischwirtschaft, 84, 101–104. Bernau, M., Kremer, P. V., Lauterbach, E., Tholen, E., Petersen, B., Pappenberger, E., Scholz, A. M. (2015). Evaluation of carcass composition of intact boars using linear measure- ments from performance testing, dissection, dual energy X-ray absorptiometry (DXA) and magnetic resonance im- aging (MRI). Meat Science, 104, 58–66. Bruford, M. W., Ginja, C., Hoffmann, I., Joost, S., Orozcoter Wengel, P., Alberto F. J., Amaral, A.J., et al. (2015). Pros- pects and challenges for the conservation of farm ani- mal genomic resources, 2015–2025. Frontiers in Genetics, 6(314), 1–11. Bunger, L., Macfarlane, J. M., Lambe, N. R., Conington, J., McLean, K.A., Moore, K., Glasbey, C. A., Simm, G. (2011). Use of X-ray computed tomography (CT) in UK sheep pro- duction and breeding. In S. Karuppasamy (Ed.), CT Scan- ning – Techniques and Applications (pp. 329–348). Rijeka, Croatia: INTECH Open access Publisher. Collewet, G., Bogner, P., Allen, P., Busk, H., Dobrowolski, A., Olsen, E., Davenel, A. (2005): Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Science, 70, 563–572. Davies, Kallweit (1979). The effect of body weight and maturity on the carcass composition of the pig. Zeitschrift für Tier- züchtung und Züchtungsbiologie, 96, 6–17. Daumas, G., Monziols, M. (2011). An accurate and simple Computed Tomography approach for measuring the lean meat percentage of pig cuts. In ICoMST Proceedings, 57th International Congress of Meat Science and Technology, Gent, Belgium, 2011 (paper 061). Curran Associates, Inc. Font i Furnols, M., Teran, M.F., Gispert, M. (2009). Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression. Chemometrics and Intel- ligent Laboratory Systems, 98, 31–37. Kastelic, M., Baulain, U., Kallweit, E. (1995). Early prediction of body composition in living pigs. In 46th Annual Meeting of European Association for Animal Production Prague, Czech Republic, 4–7 September, 1995. (pp. 285). Wageningen Aca- demic Publishers. Kogelman, L. J. A., Haja, N., Kadarmideen, T. M., Karlskov- Mortensen, P., Bruun, C. S., Cirera, S., Jacobsen, M. J., et al. (2013). An F2 pig resource population as a model for ge- netic studies of obesity and obesity-related diseases in hu- mans: design and genetic parameters. Frontiers in Genetics, Livestock Genomics, 4(29), 1–14. Kongsro, J. (2014). Genetic gain on body composition in pigs by Computed Tomography (CT). Return on investment. In C. Maltin, C. Craigie & L. Bunger (Eds.), Farm Animal Imag- ing Copenhagen 2014 (pp. 28–30). Scotland’s Rural College. Kremer, P. V., Förster, M., Scholz, A. M. (2013). Use ofmagnetic resonance imaging (MRI) to predict the body composition of pigs in vivo. Animal, 7, 879–884. Kusec, G., Baulain, U., Kallweit, E., Glodek, P. (2007). Influence of MHS genotype and feeding regime on allometric and temporal growth of pigs assessed by magnetic resonance imaging. Livestock Science, 110, 89–100. Kušec, G., Kralik, G., Đurkin, I., Baulain, U., Kallweit, E. (2008). Optimal slaughter weight of pigs assessed by means of the asymmetric S-curve. Czech Journal of Animal Science, 53(3), 98–105. Lee, S. J., Kuk, J. L. (2013). Changes in Fat and Skeletal Muscle with Exercise Training in Obese Adolescents: Comparison of Whole–Body MRI and Dual Energy X–Ray Absorptiom- etry. Obesity, 21, 2063–2071. Matika, O., Riggio, V., Moizan, M. A., Law, A. S., Pong-Wong, R., Archibald, A. L., Bishop, S. C. (2016). Genome-wide association reveals QTL for growth, bone and in vivo car- cass traits as assessed by computed tomography in Scottish Blackface lambs. Genetics Selection Evolution, 48, 11. Mitchell, A. D., Scholz, A. M. (2007). Efficiency of energy and protein deposition in swine during compensatory growth measured by dual energy X-ray absorptiometry (DXA). In I. Ortigues-Marty, N. Miraux & W. Brand-Williams (Eds.), Energy and protein metabolism and nutrition (pp. 487–488). EAAP publication No. 124. Mitchell, A.D., Scholz, A. M. (2008). Efficiency of energy and protein deposition in swine measured by dual energy X- ray absorptiometry (DXA). Archiv für Tierzucht (Dummer- storf), 51(2), 159–171. Mitchell, A.D., Scholz, A.M., Wang, P.C., Song, H. (2001). Body composition analysis of the pig by magnetic resonance im- aging. Journal of Animal Science, 79, 1800–1813. Monziols, M., Collewet, G, Bonneau, M., Mariette, F., Davenel, A., Kouba, M. (2006): Quantification of muscle, subcutane- ous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Science, 72, 146–154. Navajas, E.A., Richardson, R.I., Fisher, A.V., Hyslop, J.J., Ross, D.W., Prieto, N., Simm, G., Roehe, R. (2010). Predicting beef carcass composition using tissue weightsof a primal cut assessed by computed tomography. Animal, 4(11), 1810–1817. Olsen, E. V., Christensen, L. B. (2013). Comparison of accuracy of reference methods based on CT and manual dissection. In C. Maltin, C. Craigie & L. Bunger (Eds.), Farm Animal Imaging Kaposvar 2013 (pp. 18–21). Quality Meat Scotland. Rothammer, S., Kremer, P.V., Bernau, M., Fernandez-Figares, I., Pfister-Schär, J., Medugorac, I., Scholz, A.M. (2014). Genome-wide QTL-mapping of nine body composition and bone mineral density traits in pigs. Genetics Selection Evolution, 46(1), 68. Silva, F. F. E., Resende, M. D. V. De Rocha, G. S., Duarte, D. A. S., Lopes, P. S., Brustolin, O. J. B., Thus, S., Viana, J. M. S., Guimarães, S. E. F. (2013). Genomic growth curves of an outbred pig population. Genetics and Molecular Biology, 36(4), 520–527. Scholz, A. M., Förster, M. (2006). Genauigkeit der Dualener- gie-Röntgenabsorptiometrie (DXA) zur Ermittlung der Körperzusammensetzung von Schweinen in vivo. Archiv für Tierzucht (Dummerstorf), 49(5), 462–476. Scholz, A. M., Mitchell, A. D. (2003). Protein, fat, and bone mineral deposition rates in growing pigs (birth to 90 kg) of different RyR1 genotypes studied by dual energy x-ray absorptiometry. In W.B. Souffrant & C.C. Metges (Eds.), Progress in Research on Energy and Protein Metabolism (pp. 543–547). EAAP Scientific Series no. 109. Scholz, A.M., Nüske, S., Förster, M. (2003). Body composition and bone mineralization in calves of different genetic origin Acta agriculturae Slovenica, Supplement 5 – 2016 17 NON-INVASIVE TECHNIQUES FOR EXACT PHENOTYPIC ASSESSMENT OF CARCASS ... GROWTH IN DOMESTIC ANIMALS by using dual energy x-ray absorptiometry. Acta Diabeto- logica, 40 (Suppl. 1): S91–S94. Scholz, A. M., Bünger, L., Kongsro, J., Baulain, U., Mitchell, A. D. (2015). Non-invasive methods for the determination of body and carcass composition in livestock – Dual Energy X-Ray Absorptiometry, Computed Tomography, Magnetic Resonance Imaging, and Ultrasound: Invited Review. Ani- mal, 9(7), 1250–1264. Schinkel, A.P., Einstein, M.E. (1995). Concepts of pig growth and composition. In Purdue swine day, 1995, Purdue. Anais… 118. Purdue University. Szendrő, Zs., Metzger, Sz., Nagy, I., Szabo, A., Petrasi, Zs., Donko, T., Horn, P. (2012). Effect of divergent selection for the computer tomography measured thigh muscle volume on productive and carcass traits of growing rabbits. Live- stock Science, 149, 167–172. Tess M. W., Dickerson, G. E., Nienaber, J. A., Ferrell, C. L. (1986). Growth, Development and Body Composition in Three Genetic Stocks of Swine. Journal of Animal Science, 62, 986–979. Tholen, E., Baulain, U., Henning, M. D., Schellander, K. (2003). Comparison of different methods to asses the composition of pig bellies in progeny testing. Journal of Animal Science, 81, 1177–1184. Vincek, D., Djurkin, I., Lukić, B., Kralik, G., Kušec, G. (2010). Variability in carcass composition of pigs during growth. Acta Agraria Kaposváriensis, 14(2), 237–243. White B. R., Lan, Y. H., McKeith, F. K., Novakofski, J., Wheeler, M. B., McLaren, D. G. (1995). Growh and body composi- tion of Meishan and Yorkshire barrows and gilts. Journal of Animal Science, 73, 738–749. Young, M.J., Simm, G., Glasbey, C.A., Merrell. B. (2002). Using CT scanning to improve carcass form in sheep. In Procee- dings of the 7th World Congress of Genetics Applied to Lives- tock Production. Montpellier, France Session 2, Communica- tion No 02-06.