The objective of this paper was to estimate the heritability for shoulder ulcers and the genetic correlations between shoulder ulcers, mean piglet weight and sow body condition. The analyses were based on information on 5549 Norwegian Landrace sows and their 7614 purebred litters. The genetic analysis was performed using the Gibbs sampling method. Shoulder ulcers were analyzed as a threshold trait. Sow body condition and mean piglet weight were analyzed as linear traits. The heritability of shoulder ulcers was estimated at 0.25 (s.d. = 0.03). The heritability for sow body condition was estimated at 0.14 (s.d. = 0.02) and that for mean piglet weight at 0.23 (s.d. = 0.02). The genetic correlation between shoulder ulcers and sow body condition was negative (−0.59, s.d. = 0.09). The genetic correlation between shoulder ulcers and mean piglet weight was positive (0.23, s.d. = 0.10) and the genetic correlation between sow body condition and mean piglet weight was negative (−0.24, s.d. = 0.10).

Survival or longevity is an economically relevant trait in cattle. However, it is not currently included in cattle selection criteria because of the delayed recording of phenotypic data and the high computational demand of survival techniques under proportional hazard models. The identification of longevity-correlated traits that can be early registered in lifetime would therefore be very useful for beef cattle selection processes. The aim of this study was to estimate the genetic correlation of survival (SURV) with: growth – birth weight (BW), weight at 120 days (W120), weight at 210 days (W210); carcass – cold carcass weight (CCW), conformation (CON), fatness (FAT) and meat colour (COL); teat morphology – teat thickness (TT), teat length (TL) and udder depth (UD); leg morphology – forward (FL) and backward legs (BL); milk production (MILK) and docility (DOC). In the statistical analysis, SURV was measured in discrete-time intervals and modelled via a sequential threshold model. A series of independent bivariate Bayesian analyses between cow survival and each recorded trait were carried out. The posterior mean estimates (and posterior standard deviation) for the heritability of SURV was 0.05 (0.01); and for the relevant genetic correlations with SURV were 0.07 (0.04), 0.12 (0.05), 0.10 (0.05), 0.15 (0.05), −0.18 (0.06), 0.33 (0.06) and 0.27 (0.15) for BW, W120, W210, CCW, CON, FAT and COL, respectively.

Lpin1 was a gene with important effects on controlling lipid/energy metabolism in humans and mice. However, little was known about chicken Lpin1 gene. In the present study, two transcript isoforms of chicken Lpin1 were identified. Lpin1-α was predicted encoding one 902 amino acid protein, whereas Lpin1-δ was predicted encoding one 918 amino acid protein with an insertion of 48-bp fragment from intron 12 of chicken Lpin1-α, and a conservative element was found to be located in intron 12 of chicken Lpin1-α genomic sequence. Ten variants were identified from chicken Lpin1-α coding sequence, and two missense mutations were predicted to affect the protein function of Lpin1. Reverse transcription PCR (RT-PCR) analysis revealed that chicken total Lpin1, Lpin1-α and Lpin1-δ were expressed in all analyzed tissues, and presented clear tissue expression differences. Real-time quantitative RT-PCR revealed that 30% energy restriction significantly elevated the total Lpin1 mRNA expression level in hepatic (P < 0.01) and adipose (P < 0.01) tissues of birds. Chicken total Lpin1 gene mRNA expression level presented a significantly inverse correlation with some traits including abdominal fat rate (P < 0.01), serum high-density lipoprotein (P < 0.05) and total cholesterol (P < 0.05), which would make a foundation for the further study on chicken Lpin1 gene function.

Ducks (common, Muscovy and mule ducks) are the third most important bird species in animal production for human consumption worldwide. Our study aimed to improve the efficiency of mule duck breeding, thus contributing to the efficiency of food production in general. In the common duck, females can be bred either with males of the same species (i.e. in pure breeding (PB) subscript p) or in inter-generic crossbreeding (CB; subscript c) with Muscovy drakes to produce the hybrid mule duck. The aim of the present study was to estimate the genetic parameters of several indicators of duration of fertility, considered to be a trait of the female duck, within and between breeding schemes and, in particular, to estimate the purebred–crossbred genetic correlation (rgpc). These indicators were maximum duration of fertility (MD), that is, the time interval between insemination and the last fertilised egg, the number of fertile eggs (F) and of hatched ducklings (H) after a single artificial insemination (AI), and the fertility rate over days 2 to 12 after AI (F2,12), taking three sub-periods (F2,4, F5,8, F9,12) into account. A total of 494 females and 2655 inseminations were involved. PB resulted in longer duration of fertility (MDp = 8.1 v.MDc = 6.4 days). Heritability (h2) was higher for MDp (estimate ± s.e.: 0.27 ± 0.04) than for MDc (0.15 ± 0.04), but both traits were highly correlated with each other (rgpc = 0.85 ± 0.07). Fp and Fc had similar heritability (h2 around 0.24) and displayed a high genetic correlation (0.78 ± 0.07). The same was true for Hp and Hc (h2 around 0.17 and rgpc = 0.88 ± 0.05). The heritability estimates were 0.24 ± 0.03 for F2,12p and 0.20 ± 0.04 for F2,12c, with a 0.80 ± 0.07 genetic correlation between each other. Permanent environmental effects influenced MDp far less than MDc, Fp less than Fc, but Hp and Hc to the same extent. The high values for rgpc (>0.78) indicated that the same genes are involved in the duration of fertility for both PB and CB. Unlike CB, initial fertility for PB (F2,4p) was not correlated to overall fertility rate and to duration of fertility and probably involves different genes, if any. In both breeding schemes, indirect selection on F would be better than direct selection on H to improve H, and easier to implement than selection on MD. Moreover, any gain in one breeding scheme will have its correlated counterpart in the other one, because of the high values of rgpc.

Pig breeders in the past have adopted their breeding goals according to the needs of the producers, processors and consumers and have made remarkable genetic improvements in the traits of interest. However, it is becoming more and more challenging to meet the market needs and expectations of consumers and in general of the citizens. In view of the current and future trends, the breeding goals have to include several additional traits and new phenotypes. These phenotypes include (a) vitality from birth to slaughter, (b) uniformity at different levels of production, (c) robustness, (d) welfare and health and (e) phenotypes to reduce carbon footprint. Advancements in management, genomics, statistical models and other technologies provide opportunities for recording these phenotypes. These new developments also provide opportunities for making effective use of the new phenotypes for faster genetic improvement to meet the newly adapted breeding goals.

Sinclair swine develop an aggressive form of melanoma, which, in many cases, spontaneously regresses after a complete metastatic phase. We used Affymetrix GeneChip® Porcine Genome Arrays consisting of 24 123 probe sets to compare gene expression in white blood cells (WBCs) and various tissues including the liver, lungs, inguinal lymph nodes and spleen harvested from a Sinclair piglet afflicted by melanoma at birth and exhibiting metastatic lesions at weaning (6 weeks) with those from a full-sibling piglet that showed no incidence of melanoma at birth and weaning. The highest number (3489; ∼14%) of significantly upregulated transcripts (fold change in gene expression ⩾2.0 and t-test P-value ≤0.05) was observed in the liver, while the spleen exhibited the lowest number of upregulated transcripts (528; ∼2%). Among significantly downregulated genes, the highest numbers were observed in the inguinal lymph nodes (3651; ∼15%) and the least in WBCs (730; ∼3%). Differentially expressed transcripts included genes involved in melanoma pathogenesis including SILV, TYR and RAB28. SILV was over-expressed 784-, 430- and 164-fold, while TYR was over-expressed 138-, 81- and 28-fold in the liver, lungs and inguinal lymph nodes, respectively. Quantitative real-time RT-PCR (qRT-PCR) confirmed the microarray data of 12 selected differentially expressed sequences. These results suggest that significant changes in gene expression occur during metastasis of malignant melanoma in the Sinclair swine model. In addition, qRT-PCR analysis of the above 12 differentially expressed sequences was carried out on liver samples collected from 22 pigs (12 of which had melanoma during the first 6 weeks of life), and an ANOVA test contrasting absolute RNA expression between pigs with regressing, progressing and without tumors was significant for TYR, TACSTD1, MATP, GPNMB and CYP4A22, with P-values of 0.034, 0.015, 0.007, 0.050 and 0.022, respectively.

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (rTI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of rmg = 0.5. For a low heritability trait (h2 = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles rTI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases rTI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Infertility in cattle is a major concern of farmers worldwide and despite the enormous improvements in assisted reproduction technologies, the success rates of pregnancies are still low. Embryonic loss is considered one of the main factors of infertility in cattle. As such, the identification of genetic markers for embryo quality and development can help elucidate the molecular mechanisms involved in the formation of embryos with the highest developmental potential. In a previous study, using next-generation RNA sequencing, we identified novel transcripts and alternatively spliced genes that were associated with embryo quality. The objectives of this study were to characterize these transcripts and validate their expression in new biological replications of embryos using quantitative real-time PCR. Two types of embryos differing in morphological and developmental statuses (blastocysts and degenerate embryos) were produced using in vitro fertilization. Quantitative expression of eight novel transcripts revealed a range of 2.5- to 90-fold difference in expression between degenerate embryos and blastocysts. Some of these novel transcripts showed sequence similarity to human and cattle genes known to affect differentiation, growth and development. In addition, expression analysis of alternative splicing isoforms of five genes (MYL6, NOP10, RNF187, RPS24 and RPS28) revealed significant differential expression of these isoforms in the different embryo types. Thus, results of this study suggest that novel transcripts and alternatively spliced genes, found to be differentially expressed between blastocysts and degenerate embryos, can be used as markers for blastocyst formation and development.

Because not all animal factors influencing profitability can be included in total merit breeding indices for profitability, the association between animal total merit index and true profitability, taking cognisance of all factors associated with costs and revenues, is generally not known. One method to estimate such associations is at the herd level, associating herd average genetic merit with herd profitability. The objective of this study was to primarily relate herd average genetic merit for a range of traits, including the Irish total merit index, with indicators of performance, including profitability, using correlation and multiple regression analyses. Physical, genetic and financial performance data from 1131 Irish seasonal calving pasture-based dairy farms were available following edits; data on some herds were available for more than 1 year of the 3-year study period (2007 to 2009). Herd average economic breeding index (EBI) was associated with reduced herd average phenotypic milk yield but with greater milk composition, resulting in higher milk prices. Moderate positive correlations (0.26 to 0.61) existed between genetic merit for an individual trait and average herd performance for that trait (e.g. genetic merit for milk yield and average per cow milk yield). Following adjustment for year, stocking rate, herd size and quantity of purchased feed in the multiple regression analysis, average herd EBI was positively and linearly associated with net margin per cow and per litre as well as gross revenue output per cow and per litre. The change in net margin per cow per unit change in the total merit index was €1.94 (s.e. = 0.42), which was not different from the expectation of €2. This study, based on a large data set of commercial herds with accurate information on profitability and genetic merit, confirms that, after accounting for confounding factors, the change in herd profitability per unit change in herd genetic merit for the total merit index is within expectations.

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a ‘cow model,’ estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.

Despite many challenges faced by animal producers, including environmental problems, diseases, economic pressure, and feed availability, it is still predicted that animal production in developing countries will continue to sustain the future growth of the world's meat production. In these areas, livestock performance is generally lower than those obtained in Western Europe and North America. Although many factors can be involved, climatic factors are among the first and crucial limiting factors of the development of animal production in warm regions. In addition, global warming will further accentuate heat stress-related problems. The objective of this paper was to review the effective strategies to alleviate heat stress in the context of tropical livestock production systems. These strategies can be classified into three groups: those increasing feed intake or decreasing metabolic heat production, those enhancing heat-loss capacities, and those involving genetic selection for heat tolerance. Under heat stress, improved production should be possible through modifications of diet composition that either promotes a higher intake or compensates the low feed consumption. In addition, altering feeding management such as a change in feeding time and/or frequency, are efficient tools to avoid excessive heat load and improve survival rate, especially in poultry. Methods to enhance heat exchange between the environment and the animal and those changing the environment to prevent or limit heat stress can be used to improve performance under hot climatic conditions. Although differences in thermal tolerance exist between livestock species (ruminants > monogastrics), there are also large differences between breeds of a species and within each breed. Consequently, the opportunity may exist to improve thermal tolerance of the animals using genetic tools. However, further research is required to quantify the genetic antagonism between adaptation and production traits to evaluate the potential selection response. With the development of molecular biotechnologies, new opportunities are available to characterize gene expression and identify key cellular responses to heat stress. These new tools will enable scientists to improve the accuracy and the efficiency of selection for heat tolerance. Epigenetic regulation of gene expression and thermal imprinting of the genome could also be an efficient method to improve thermal tolerance. Such techniques (e.g. perinatal heat acclimation) are currently being experimented in chicken.

Lactation represents an important element of the life history strategies of all mammals, whether monotreme, marsupial, or eutherian. Milk originated as a glandular skin secretion in synapsids (the lineage ancestral to mammals), perhaps as early as the Pennsylvanian period, that is, approximately 310 million years ago (mya). Early synapsids laid eggs with parchment-like shells intolerant of desiccation and apparently dependent on glandular skin secretions for moisture. Mammary glands probably evolved from apocrine-like glands that combined multiple modes of secretion and developed in association with hair follicles. Comparative analyses of the evolutionary origin of milk constituents support a scenario in which these secretions evolved into a nutrient-rich milk long before mammals arose. A variety of antimicrobial and secretory constituents were co-opted into novel roles related to nutrition of the young. Secretory calcium-binding phosphoproteins may originally have had a role in calcium delivery to eggs; however, by evolving into large, complex casein micelles, they took on an important role in transport of amino acids, calcium and phosphorus. Several proteins involved in immunity, including an ancestral butyrophilin and xanthine oxidoreductase, were incorporated into a novel membrane-bound lipid droplet (the milk fat globule) that became a primary mode of energy transfer. An ancestral c-lysozyme lost its lytic functions in favor of a role as α-lactalbumin, which modifies a galactosyltransferase to recognize glucose as an acceptor, leading to the synthesis of novel milk sugars, of which free oligosaccharides may have predated free lactose. An ancestral lipocalin and an ancestral whey acidic protein four-disulphide core protein apparently lost their original transport and antimicrobial functions when they became the whey proteins β-lactoglobulin and whey acidic protein, which with α-lactalbumin provide limiting sulfur amino acids to the young. By the late Triassic period (ca 210 mya), mammaliaforms (mammalian ancestors) were endothermic (requiring fluid to replace incubatory water losses of eggs), very small in size (making large eggs impossible), and had rapid growth and limited tooth replacement (indicating delayed onset of feeding and reliance on milk). Thus, milk had already supplanted egg yolk as the primary nutrient source, and by the Jurassic period (ca 170 mya) vitellogenin genes were being lost. All primary milk constituents evolved before the appearance of mammals, and some constituents may have origins that predate the split of the synapsids from sauropsids (the lineage leading to ‘reptiles’ and birds). Thus, the modern dairy industry is built upon a very old foundation, the cornerstones of which were laid even before dinosaurs ruled the earth in the Jurassic and Cretaceous periods.

Cattle production faces new challenges regarding sustainability with its three pillars – economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases. (2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

This study set out to demonstrate the feasibility of merging data from different experimental resource dairy populations for joint genetic analyses. Data from four experimental herds located in three different countries (Scotland, Ireland and the Netherlands) were used for this purpose. Animals were first lactation Holstein cows that participated in ongoing or previously completed selection and feeding experiments. Data included a total of 60 058 weekly records from 1630 cows across the four herds; number of cows per herd ranged from 90 to 563. Weekly records were extracted from the individual herd databases and included seven traits: milk, fat and protein yield, milk somatic cell count, liveweight, dry matter intake and energy intake. Missing records were predicted with the use of random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. A total of 23 different lactation traits were derived from these records: total milk, fat and protein yield, average fat and protein percentage, average fat-to-protein ratio, total dry matter and energy intake and average dry matter intake-to-milk yield ratio in lactation weeks 1 to 44 and 1 to 15; average milk somatic cell count in lactation weeks 1 to 15 and 16 to 44; average liveweight in lactation weeks 1 to 44; and average energy balance in lactation weeks 1 to 44 and 1 to 15. Data were subsequently merged across the four herds into a single dataset, which was analysed with mixed linear models. Genetic variance and heritability estimates were greater (P < 0.05) than zero for all traits except for average milk somatic cell count in weeks 16 to 44. Proportion of total phenotypic variance due to genotype-by-environment (sire-by-herd) interaction was not different (P > 0.05) from zero. When estimable, the genetic correlation between herds ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase power of detection in a genetic analysis and augment the potential reference population in genome-wide association studies, especially of difficult-to-record traits.

Eventing competitions in Great Britain (GB) comprise three disciplines, each split into four grades, yielding 12 discipline-grade traits. As there is a demand for tools to estimate (co)variance matrices with a large number of traits, the aim of this work was to investigate different methods to produce large (co)variance matrices using GB eventing data. Data from 1999 to 2008 were used and penalty points were converted to normal scores. A sire model was utilised to estimate fixed effects of gender, age and class, and random effects of sire, horse and rider. Three methods were used to estimate (co)variance matrices. Method 1 used a method based on Gibbs sampling and data augmentation and imputation. Methods 2a and 2b combined sub-matrices from bivariate analyses; one took samples from a multivariate Normal distribution defined by the covariance matrix from each bivariate analysis, then analysed these data in a 12-trait multivariate analysis; the other replaced negative eigenvalues in the matrix with positive values to obtain a positive definite (co)variance matrix. A formal comparison of models could not be conducted; however, estimates from all methods, particularly Methods 2a/2b, were in reasonable agreement. The computational requirements of Method 1 were much less compared with Methods 2a or 2b. Method 2a heritability estimates were as follows: for dressage 7.2% to 9.0%, for show jumping 8.9% to 16.2% and for cross-country 1.3% to 1.4%. Method 1 heritability estimates were higher for the advanced grades, particularly for dressage (17.1%) and show jumping (22.6%). Irrespective of the model, genetic correlations between grades, for dressage and show jumping, were positive, high and significant, ranging from 0.59 to 0.99 for Method 2a and 0.78 to 0.95 for Method 1. For cross-country, using Method 2a, genetic correlations were only significant between novice and pre-novice (0.75); however, using Method 1 estimates were all significant and low to moderate (0.36 to 0.70). Between-discipline correlations were all low and of mixed sign. All methods produced positive definite 12 × 12 (co)variance matrices, suitable for the prediction of breeding values. Method 1 benefits from much reduced computational requirements, and by performing a true multivariate analysis.

In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n = 3835) and Duroc (D; n = 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. = 0.04 to 0.05) for the various traits for Landrace and Duroc were as follows: MG (0.19 and 0.43), FG (0.53 and 0.59), BG (0.37 and 0.58), NCG (0.38 and 0.50), LMP (0.50 and 0.57), ADG1 (0.25 and 0.48), ADG2 (0.41 and 0.42) and FCR (0.29 and 0.42). Genetic correlations for MG with LMP were 0.55 and 0.68, and genetic correlations between MG and ADG2 were −0.06 and 0.07 for Landrace and Duroc, respectively. LMP and ADG2 were clearly unfavourably genetically correlated (L: −0.75 and D: −0.54). 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.

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all <0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.