Genetic Analysis of a Maternal Assistance Score in Sheep

Maternal behaviour is important for lamb survival, as ewes perform many behaviours that affect the chances of a lamb surviving. Collecting maternal behaviour data directly at lambing is time consuming and not considered suitable for acquiring the large volumes of data that would be required for using as selection criteria within commercial breeding flocks. The aim of this study was to investigate if a simple scoring system is heritable and assesses expression of behaviours that reduce the probability of lamb mortality. Ewe behaviour was scored on a 3-point Maternal Assistance Score (MAS): (1) the ewe shows a high level of maternal interest (assumed if no intervention required); (2) the ewe shows limited interest in her lamb; and (3) the ewe shows no interest in her lamb. A total of 19 453 MAS were collected over 12 years, across 24 farms (including both indoor and outdoor lambing systems) and 12 different breed lines that make up the Innovis breeding programme. Ewe parity, breed, number of lambs carried, flock, lambing batch, lambing day within flock and pre-mating weight all had a significant effect on MAS (P<0.05). The maternal assistance score was shown to be heritable (h2=0.05) and repeatable (0.10), positively genetically correlated to lambing difficulty (rg=0.29) and amount of assistance the lamb required to suckle from the ewe (rg=0.88), and negatively genetically correlated with the number of lambs successfully reared (rg=0.49). This study shows that an easy to measure score can be used by shepherds with large breeding flocks, based on whether the ewe requires further assistance to support her lamb rearing. The score could be used in breeding programmes to select for lamb rearing ability in the future and potentially lead to an improvement in lamb welfare through a reduction in mortality

Genetic Analysis of a Maternal Assistance Score in Sheep

Maternal behaviour is important for lamb survival, as ewes perform many behaviours that affect the chances of a lamb surviving. Collecting maternal behaviour data directly at lambing is time consuming and not considered suitable for acquiring the large volumes of data that would be required for using as selection criteria within commercial breeding flocks. The aim of this study was to investigate if a simple scoring system is heritable and assesses expression of behaviours that reduce the probability of lamb mortality. Ewe behaviour was scored on a 3-point Maternal Assistance Score (MAS): (1) the ewe shows a high level of maternal interest (assumed if no intervention required); (2) the ewe shows limited interest in her lamb; and (3) the ewe shows no interest in her lamb. A total of 19 453 MAS were collected over 12 years, across 24 farms (including both indoor and outdoor lambing systems) and 12 different breed lines that make up the Innovis breeding programme. Ewe parity, breed, number of lambs carried, flock, lambing batch, lambing day within flock and pre-mating weight all had a significant effect on MAS (P<0.05). The maternal assistance score was shown to be heritable (h2=0.05) and repeatable (0.10), positively genetically correlated to lambing difficulty (rg=0.29) and amount of assistance the lamb required to suckle from the ewe (rg=0.88), and negatively genetically correlated with the number of lambs successfully reared (rg=0.49). This study shows that an easy to measure score can be used by shepherds with large breeding flocks, based on whether the ewe requires further assistance to support her lamb rearing. The score could be used in breeding programmes to select for lamb rearing ability in the future and potentially lead to an improvement in lamb welfare through a reduction in mortality

Genome wide association studies for carcass traits measured by video image analysis in crossbred lambs

This is the first UK genome wide association study investigating potential links between Video Image Analysis (VIA) carcass traits and molecular polymorphisms in crossbred sheep. Phenotypic and genotypic data were collected from two crossbred lamb populations: Texel x Scotch Mule (TxSM, n = 2330) and Texel x Lleyn (TxL, n = 3816). Traits measured included live weights at birth, eight weeks and weaning (∼15 weeks). VIA-predicted traits included total weights and weights of fat, muscle and bone in the whole carcass and primal (hind leg, saddle, shoulder) regions. Within-breed heritabilities estimated for the VIA traits ranged from 0.01 to 0.70, indicating potential for inclusion of some traits in breeding programmes. The two crossbred populations differed in SNPs associated with different traits. Two SNPs on chromosomes two (s74618.1) and eight (s68536.1), respectively, reached genome-wise significance for TxSM, explaining &lt;1% of trait variance, for whole carcass fat and muscle weights, hind leg and saddle fat weights and shoulder bone weights. For TxL, four SNPs reached genome-wise significance, on chromosome two for hind leg muscle weight (OAR2_117,959,202 and OAR2_11804335), on chromosome 10 for whole carcass bone weight (OAR19_8,995,957.1), and on chromosome 19 for weaning weight (s40847.1), each explaining &lt;1% of trait genetic variation. Differences in apparent genetic control of carcass traits may be influenced by the lambs' cross-breed, but also by management decisions affecting environmental variance and trait definitions, which should be understood in order to define protocols for incorporation of carcass traits into (cross)breeding programmes. Implications: Combining VIA-measured carcass traits with conventional production traits in a breeding programme could potentially improve the production and product quality of meat sheep. Phenotypes for VIA traits could be collected relatively easily if VIA machines were present at all abattoir sites. The current study and future Genome Wide Association Studies may help to identify potentially informative molecular markers, that explain large proportions of the genetic variance observed in VIA-measured carcass traits. Including this information in the estimation of breeding values could increase the accuracy of prediction, increasing the potential rate of genetic improvement for product quality. This study confirms the polygenic architecture of the investigated carcass traits, with a small number of molecular markers that each explain a small amount of genetic variation. Further studies across breed types are recommended to further test and validate molecular markers for traits related to lamb carcass quality, as measured by video image analysis.</p

Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses

Introduction: Mechanical grading can be used to objectively classify beef carcasses. Despite its many benefits, it is scarcely used within the beef industry, often due to infrastructure and equipment costs. As technology progresses, systems become more physically compact, and data storage and processing methods are becoming more advanced. Purpose-built imaging systems can calculate 3-dimensional measurements of beef carcasses, which can be used for objective grading.Methods: This study explored the use of machine learning techniques (random forests and artificial neural networks) and their ability to predict carcass conformation class, fat class and cold carcass weight, using both 3-dimensional measurements (widths, lengths, and volumes) of beef carcasses, extracted using imaging technology, and fixed effects (kill date, breed type and sex). Cold carcass weight was also included as a fixed effect for prediction of conformation and fat classes.Results: Including the dimensional measurements improved prediction accuracies across traits and techniques compared to that of results from models built excluding the 3D measurements. Model validation of random forests resulted in moderate-high accuracies for cold carcass weight (R2 = 0.72), conformation class (71% correctly classified), and fat class (55% correctly classified). Similar accuracies were seen for the validation of the artificial neural networks, which resulted in high accuracies for cold carcass weight (R2 = 0.68) and conformation class (71%), and moderate for fat class (57%).Discussion: This study demonstrates the potential for 3D imaging technology requiring limited infrastructure, along with machine learning techniques, to predict key carcass traits in the beef industry

Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses

Introduction: Mechanical grading can be used to objectively classify beef carcasses. Despite its many benefits, it is scarcely used within the beef industry, often due to infrastructure and equipment costs. As technology progresses, systems become more physically compact, and data storage and processing methods are becoming more advanced. Purpose-built imaging systems can calculate 3-dimensional measurements of beef carcasses, which can be used for objective grading.Methods: This study explored the use of machine learning techniques (random forests and artificial neural networks) and their ability to predict carcass conformation class, fat class and cold carcass weight, using both 3-dimensional measurements (widths, lengths, and volumes) of beef carcasses, extracted using imaging technology, and fixed effects (kill date, breed type and sex). Cold carcass weight was also included as a fixed effect for prediction of conformation and fat classes.Results: Including the dimensional measurements improved prediction accuracies across traits and techniques compared to that of results from models built excluding the 3D measurements. Model validation of random forests resulted in moderate-high accuracies for cold carcass weight (R2 = 0.72), conformation class (71% correctly classified), and fat class (55% correctly classified). Similar accuracies were seen for the validation of the artificial neural networks, which resulted in high accuracies for cold carcass weight (R2 = 0.68) and conformation class (71%), and moderate for fat class (57%).Discussion: This study demonstrates the potential for 3D imaging technology requiring limited infrastructure, along with machine learning techniques, to predict key carcass traits in the beef industry

Effect of the Texel muscling QTL (TM-QTL) on spine characteristics in purebred Texel lambs

Previous work showed that the Texel muscling QTL (TM-QTL) results in pronounced hypertrophy in the loin muscle, with the largest phenotypic effects observed in lambs inheriting a single copy of the allele from the sire. As the loin runs parallel to the spinal vertebrae, and the development of muscle and bone are closely linked, the primary aim of this study was to investigate if there were any subsequent associations between TM-QTL inheritance and underlying spine characteristics (vertebrae number, VN; spine region length, SPL; average length of individual vertebrae, VL) of the thoracic, lumbar, and thoracolumbar spine regions. Spine characteristics were measured from X-ray computed tomography (CT) scans for 142 purebred Texel lambs which had been previously genotyped. Least-squares means were significantly different between genotype groups for lumbar and thoracic VN and lumbar SPL. Similarly for these traits, contrasts were shown to be significant for particular modes of gene action but overall were inconclusive. In general, the results showed little evidence that spine trait phenotypes were associated with differences in loin muscling associated with the different TM-QTL genotypes. © 2013 Published by Elsevier B.V