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Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.

Immediate and short-term changes in diet composition can support individualized, real-time interventions in precision dairy production systems, and might increase feed efficiency (FE) of dairy cattle in the short-term. The objective of this study was to determine immediate and short-term effects of changes in diet composition on production parameters of dairy cattle fed varying amounts of top dressed commodities. A 4 × 4 replicated Latin square design was used to evaluate responses of twenty-four Holstein cows fed either no top dress (Control) or increasing amounts of: corn grain (CG), soybean meal (SBM), or chopped mixed grass hay (GH) top dressed on a total mixed ration (TMR) over four, 9-day periods. Throughout each period, top dressed commodities were incrementally increased, providing 0% to 20% of calculated net energy of lactation (NEL ) intake. Measured production responses were analyzed for each 9-d period using a mixed-effects model considering two different time ranges. Samples collected from d 3 and 4 and from d 7 and 8 of each period were averaged and used to reflect “immediate” vs. “short-term” responses, respectively. In the immediate response time frame, control fed cows had lower milk yield, milk fat yield, and milk true protein yield than CG and SBM supplemented animals but similar responses to GH supplemented animals. Milk fat and protein percentages were not affected by top dress type in the immediate term. In the short-term response time-frame, GH supplemented animals had lower DMI and milk fat yield than all other groups. Control and GH supplemented cows had lower milk yield than CG and SBM fed cows. In the immediate response time frame, FE of SBM supplemented cows was superior to other groups. In the short-term time frame, FE of GH and SBM groups was improved over the control group. Results suggest that lactating dairy cows show rapid performance responses to small (

Background: The back plays a vital role in horse locomotion, where the spine functions as a spring during the stride cycle. A complex interaction between the spine and the muscles of the back contribute to locomotion soundness, gait ability, and performance of riding and racehorses. Conformation is commonly used to select horses for breeding and performance in multiple horse breeds, where the back and croup conformation plays a significant role. The conformation of back and croup plays an important role on riding ability in Icelandic horses. However, the genes behind this trait are still unknown. Therefore, the aim of this study was to identify genomic regions associated with conformation of back and croup in Icelandic horses and to investigate their effects on riding ability. One hundred seventy-seven assessed Icelandic horses were included in the study. A genome-wide association analysis was performed using the 670 K+ Axiom Equine Genotyping Array, and the effects of different haplotypes in the top associated region were estimated for riding ability and additional conformation traits assessed during breeding field tests. Results: A suggestive quantitative trait loci (QTL) for the score of back and croup was detected on Equus caballus (ECA) 22 (p-value = 2.67 × 10− 7 ). Haplotype analysis revealed two opposite haplotypes, which resulted in higher and lower scores of the back and croup, respectively (p-value < 0.001). Horses with the favorable haplotype were more inclined to have a well-balanced backline with an uphill conformation and had, on average, higher scores for the lateral gaits tölt (p-value = 0.02) and pace (p-value = 0.004). This genomic region harbors three genes: C20orf85, ANKRD60 and LOC100056167. ANKRD60 is associated with body height in humans. C20orf85 and ANKRD60 are potentially linked to adolescent idiopathic scoliosis in humans.Conclusions: Our results show that the detected QTL for conformation of back and croup is of importance for quality of lateral gaits in Icelandic horses. These findings could result in a genetic test to aid in the selection of breeding horses, thus they are of major interest for horse breeders. The results may also offer a gateway to comparative functional genomics by potentially linking both motor laterality and back inclination in horses with scoliosis in humans.

Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second (TNB and D11; 0.207). A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11.

The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

Simple Summary Reproductive performance is an important factor, which determines productive life and drives culling decisions in dairy herds. There are strong motives for including reproductive performance in genetic selection programs of dairy cows; however, low heritability estimates reported for reproductive performance measures limit the genetic selection efficiency. More effective genetic selection could be achieved using genomic information. The aim of this study was to identify genomic region(s) associated with the length of the calving interval in Holstein cows. The accuracies of genomic estimated breeding values (GEBVs) with single-step genomic BLUP (ssGBLUP) and the pedigree-based BLUP were compared as well. The results showed that the accuracies of GEBVs using the single-step genomic BLUP were much higher than those estimated using the pedigree-based BLUP. We identified three genomic regions (BTA3, BTA6, and BTA7) associated with the length of the calving interval in Holstein dairy cows. Abstract The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi- and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including ARHGAP29, SEC24D, METTL14, SLC36A2, and SLC36A3 inside the windows associated with the length of the calving interval. The biological process terms including alanine transport, L-alanine transport, proline transport, and glycine transport were identified as the most important terms enriched by the genes inside the identified windows.

The aim of this study was to investigate the sources of variation of milk fatty acids (FA) routinely predicted by mid-infrared spectroscopy in Holstein-Friesian (HF), Brown Swiss (BS), Simmental (SI) and Alpine Grey (AG) cattle breeds. A linear mixed model was used to analyse the data which included 153,801 individual milk samples from 14,301 cows. Fixed effects included in the model were breed, month of sampling, year of sampling, stage of lactation, parity and first order interactions between them, and random effects were herd nested within breed, cow nested within breed and the residual error. Milk of AG cows exhibited the lowest content of saturated FA and the greatest content of unsaturated FA, whereas milk of BS had an opposite pattern. Holstein-Friesian and SI were intermediate between AG and BS breeds for all the FA except for total C18:1, which was the greatest in HF. Saturated FA, C14:0 and C16:0 increased from calving until 120 days in milk, whereas unsaturated FA and C18:1 decreased. First parity cows produced milk with lower concentration of de novo FA than multiparous animals. The greatest content of unsaturated FA and C18:1 was observed in summer, whereas saturated FA, C14:0, C16:0 and C18:0 decreased in summer and increased in winter. The differences in milk FA profile among breeds are useful for genetic selection and for feeding strategies.

Background Infrared spectral analysis of milk is cheap, fast, and accurate. Infrared light interacts with chemical bonds present inside the milk, which means that Fourier transform infrared milk spectra are a reflection of the chemical composition of milk. Heritability of Fourier transform infrared milk spectra has been analysed previously. Further genetic analysis of Fourier transform infrared milk spectra could give us a better insight in the genes underlying milk composition. Breed influences milk composition, yet not much is known about the effect of breed on Fourier transform infrared milk spectra. Improved understanding of the effect of breed on Fourier transform infrared milk spectra could enhance efficient application of Fourier transform infrared milk spectra. The aim of this study is to perform a genome wide association study on a selection of wavenumbers for Danish Holstein and Danish Jersey. This will improve our understanding of the genetics underlying milk composition in these two dairy cattle breeds. Results For each breed separately, fifteen wavenumbers were analysed. Overall, more quantitative trait loci were observed for Danish Jersey compared to Danish Holstein. For both breeds, the majority of the wavenumbers was most strongly associated to a genomic region on BTA 14 harbouring DGAT1. Furthermore, for both breeds most quantitative trait loci were observed for wavenumbers that interact with the chemical bond C-O. For Danish Jersey, wavenumbers that interact with C-H were associated to genes that are involved in fatty acid synthesis, such as AGPAT3, AGPAT6, PPARGC1A, SREBF1, and FADS1. For wavenumbers which interact with –OH, associations were observed to genomic regions that have been linked to alpha-lactalbumin. Conclusions The current study identified many quantitative trait loci that underlie Fourier transform infrared milk spectra, and thus milk composition. Differences were observed between groups of wavenumbers that interact with different chemical bonds. Both overlapping and different QTL were observed for Danish Holstein and Danish Jersey.

The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was car-ried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and β-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week un-til 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no differ-ence in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.

Background Fatty acids (FA) in bovine milk derive through body mobilization, de novo synthesis or from the feed via the blood stream. To be able to digest feedstuff, the cow depends on its rumen microbiome. The relative abundance of the microbes has been shown to differ between cows. To date, there is little information on the impact of the microbiome on the formation of specific milk FA. Therefore, in this study, our aim was to investigate the impact of the rumen bacterial microbiome on milk FA composition. Furthermore, we evaluated the predictive value of the rumen microbiome and the host genetics on the composition of individual FA in milk. Results Our results show that the proportion of variance explained by the rumen bacteria composition (termed microbiability or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{B}^{2}$$\end{document}hB2) was generally smaller than that of the genetic component (heritability), and that rumen bacteria influenced most C15:0, C17:0, C18:2 n-6, C18:3 n-3 and CLA cis-9, trans-11 with estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{B}^{2}$$\end{document}hB2 ranging from 0.26 to 0.42. For C6:0, C8:0, C10:0, C12:0, C16:0, C16:1 cis-9 and C18:1 cis-9, the variance explained by the rumen bacteria component was close to 0. In general, both the rumen microbiome and the host genetics had little value for predicting FA phenotype. Compared to genetic information only, adding rumen bacteria information resulted in a significant improvement of the predictive value for C15:0 from 0.22 to 0.38 (P = 9.50e−07) and C18:3 n-3 from 0 to 0.29 (P = 8.81e−18). Conclusions The rumen microbiome has a pronounced influence on the content of odd chain FA and polyunsaturated C18 FA, and to a lesser extent, on the content of the short- and medium-chain FA in the milk of Holstein cattle. The accuracy of prediction of FA phenotypes in milk based on information from either the animal’s genotypes or rumen bacteria composition was very low. Electronic supplementary material The online version of this article (10.1186/s12711-019-0464-8) contains supplementary material, which is available to authorized users.

Background Large-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are expensive/difficult to measure and for breeds with a small reference population size. An effective method to increase reference population size could be to combine datasets from different populations. Prediction models might also benefit from incorporation of information on the biological underpinnings of quantitative traits. Genome-wide association studies (GWAS) show that genomic regions on Bos taurus chromosomes (BTA) 14, 19 and 26 underlie substantial proportions of the genetic variation in milk FA traits. Genomic prediction models that incorporate such results could enable improved prediction accuracy in spite of limited reference population sizes. In this study, we combine gas chromatography quantified FA samples from the Chinese, Danish and Dutch Holstein populations and implement a genomic feature best linear unbiased prediction (GFBLUP) model that incorporates variants on BTA14, 19 and 26 as genomic features for which random genetic effects are estimated separately. Prediction reliabilities were compared to those estimated with traditional GBLUP models. Results Predictions using a multi-population reference and a traditional GBLUP model resulted in average gains in prediction reliability of 10% points in the Dutch, 8% points in the Danish and 1% point in the Chinese populations compared to predictions based on population-specific references. Compared to the traditional GBLUP, implementation of the GFBLUP model with a multi-population reference led to further increases in prediction reliability of up to 38% points in the Dutch, 23% points in the Danish and 13% points in the Chinese populations. Prediction reliabilities from the GFBLUP model were moderate to high across the FA traits analyzed. Conclusions Our study shows that it is possible to predict genetic merits for milk FA traits with reasonable accuracy by combining related populations of a breed and using models that incorporate GWAS results. Our findings indicate that international collaborations that facilitate access to multi-population datasets could be highly beneficial to the implementation of genomic selection for detailed milk composition traits. Electronic supplementary material The online version of this article (10.1186/s12711-019-0460-z) contains supplementary material, which is available to authorized users.

WOS:000496576600001; International audience; In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood-based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user-defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user-set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (\textgreater200) are genotyped. We have demonstrated that APIS is an easy-to-use and reliable software for parentage assignment, even when up to 50% of sires are missing.

Performing a genome-wide association study (GWAS) might add to a better understanding of the development of claw disorders and the need for trimming. Therefore, the aim of the current study was to perform a GWAS on claw disorders and trimming status and to validate the results for claw disorders based on an independent data set. Data consisted of 20,474 cows with phenotypes for claw disorders and 50,238 cows with phenotypes for trimming status. Recorded claw disorders used in the current study were double sole (DS), interdigital hyperplasia (IH), sole hemorrhage (SH), sole ulcer (SU), white line separation (WLS), a combination of infectious claw disorders consisting of (inter-)digital dermatitis and heel erosion, and a combination of laminitis-related claw disorders (DS, SH, SU, and WLS). Of the cows with phenotypes for claw disorders, 1,771 cows were genotyped and these cow data were used for the GWAS on claw disorders. A SNP was considered significant when the false discovery rate = 0.05 and suggestive when the false discovery rate = 0.20. An independent data set of 185 genotyped bulls having at least 5 daughters with phenotypes (6,824 daughters in total) for claw disorders was used to validate significant and suggestive SNP detected based on the cow data. To analyze the trait “trimming status” (i.e., the need for claw trimming), a data set with 327 genotyped bulls having at least 5 daughters with phenotypes (18,525 daughters in total) was used. Based on the cow data, in total 10 significant and 45 suggestive SNP were detected for claw disorders. The 10 significant SNP were associated with SU, and mainly located on BTA8. The suggestive SNP were associated with DS, IH, SU, and laminitis-related claw disorders. Three of the suggestive SNP were validated in the data set of 185 bulls, and were located on BTA13, BTA14, and BTA17. For infectious claw disorders, SH, and WLS, no significant or suggestive SNP associations were detected. For trimming status, 1 significant and 1 suggestive SNP were detected, both located close to each other on BTA15. Some significant and suggestive SNP were located close to SNP detected in studies on feet and leg conformation traits. Genes with major effects could not be detected and SNP associations were spread across the genome, indicating that many SNP, each explaining a small proportion of the genetic variance, influence claw disorders. Therefore, to reduce the incidence of claw disorders by breeding, genomic selection is a promising approach.

Genetic evaluations in dual-purpose quails (Coturnix coturnix) have demonstrated that overall genetic gains in a breeding program are achieved not only based on a specific trait, but on several. The most common technique to use all this information is the selection index. Another alternative may be the canonical-correlation analysis applied to selection index. There is, however, a lack of studies using canonical correlation in quails. Hence, the objectives of this study were to apply canonical-correlation analysis to estimate the relationship of nine traits and to compare genetic gains obtained by this methodology to desired-gain selection index in three lines of quails. Data for three lines of layer quails consisted of body weight at 28 days (W28), egg weight (EW), age at first egg (APE) and egg production at 30, 60, 90, 120, 150 and 180 days after onset of lay. Two sets of traits were established: the first one contained predictor variables (W28, EW and APE) and the second one contained variables related to egg production. A selection index was constructed using the standardized coefficients of canonical covariates as weighting factors when a given canonical correlation was significant. We constructed two desired-gain selection indices: DG-SI1 and DG-S12. The difference between them is that DG-S12 had a desired gain for body weight set to 0. The estimated canonical correlations were as follows: 0.811, 0.058 and 0.003 for the yellow, 0.821, 0.181 and 0.076 for the red, and 0.825, 0.117 and 0.038 for the blue line. Only the first pair of canonical variates was significant (P

Village- and central nucleus-based schemes were simulated and evaluated for their relative bio-economic efficiencies, using Ethiopia's Menz sheep as example. The schemes were: village-based 2-tier (Scheme-1) and 1-tier (Scheme-2) cooperative village breeding schemes, dispersed village-based nuclei scheme (Scheme-3), conventional 2-tier central nucleus-based scheme (Scheme-4), and schemes linking a central nucleus and village multiplier nuclei with selection in central nucleus (Scheme-5) or in both central and village nuclei (Scheme-6). Among village-based schemes, Scheme-1 gave the highest genetic progress, while Scheme-2 was economically the most efficient with genetic gain in the breeding objective of Birr 5.6 and a profit of Birr 37.2/ewe/year. The central nucleus schemes were more efficient than the village schemes. Scheme-4 was the most efficient with genetic gain in the breeding objective of Birr 13.5 and a profit of Birr 71.2, but is operationally more difficult as it requires a very large central nucleus. The choice between village and central nucleus-based schemes would depend on local conditions (availability of infrastructure, logistics and technical knowhow and support). Linking central nucleus with village-based nuclei (Scheme-6) would be a feasible option to overcome the operational difficulties of the conventional central nucleus scheme. If a village-based breeding program is envisaged as should be the 1st step in most low-input systems, then Scheme-2 is the most efficient. To scale out to an entire Menz breed level, Scheme-3 would be recommended.

The aim of this study was to evaluate the relationship between slaughter age and slaughter-carcass characteristics in 2 quail lines. With this aim, a Japanese quail flock subjected to mass selection to increase BW for 4 generations and a control flock that randomly mated for 4 generations were used. Birds of both lines were slaughtered at 4, 5, 6, 7, and 8 wk of age. Weights of carcass, breast, leg, wing, edible inner organs, and abdominal fat, and their percentages in BW were measured. Short-term mass selection for increased BW resulted in an increase for all slaughter and carcass traits, except edible inner organ percentage. Slaughter age had a significant effect on the studied traits, indicating that the BW and weight of carcass, carcass parts, abdominal fat, edible inner organs, and percentage of abdominal fat increased with increased slaughter age. Conversely, the carcass yield and percentages of carcass parts and edible inner organs were decreased with an increase in slaughter age. The present study showed that deterioration in carcass quality occurred with an increase in slaughter age. Furthermore, the differences between the carcass weights over the different ages ranged between 16.83 to 22.45% in favor of the selection line after a short-term mass selection. Akdeniz University Scientific Research Projects ManagementAkdeniz University [2005.02.0121.005] The research with the project number of 2005.02.0121.005 was supported by the Akdeniz University Scientific Research Projects Management.

A major quantitative trait locus (QTL) for milk fat content and fatty acids in both milk and adipose tissue has been detected on Bos taurus autosome 19 (BTA19) in several cattle breeds. The objective of this study was to refine the location of the QTL on BTA19 for bovine milk fat composition using a denser set of markers. Opportunities for fine mapping were provided by imputation from 50,000 genotyped single nucleotide polymorphisms (SNP) toward a high-density SNP panel with up to 777,000 SNP. The QTL region was narrowed down to a linkage disequilibrium block formed by 22 SNP covering 85,007 bp, from 51,303,322 to 51,388,329 bp on BTA19. This linkage disequilibrium block contained 2 genes: coiled-coil domain containing 57 (CCDC57) and fatty acid synthase (FASN). The gene CCDC57 is minimally characterized and has not been associated with bovine milk fat previously, but is expressed in the mammary gland. The gene FASN has been associated with bovine milk fat and fat in adipose tissue before. This gene is a likely candidate for the QTL on BTA19 because of its involvement in de novo fat synthesis. Future studies using sequence data of both CCDC57 and FASN, and eventually functional studies, will have to be pursued to assign the causal variant(s).

In the era of genome-wide selection (GWS), genotype-by-environment (G×E) interactions can be studied using genomic information, thus enabling the estimation of SNP marker effects and the prediction of genomic estimated breeding values (GEBVs) for young candidates for selection in different environments. Although G×E studies in pigs are scarce, the use of artificial insemination has enabled the distribution of genetic material from sires across multiple environments. Given the relevance of reproductive traits such as the total number born (TNB) and the variation in environmental conditions encountered by commercial dams, understanding G×E interactions can be essential to choose the best sires for different environments. The present work proposes a two-step reaction norm approach for G×E analysis using genomic information. The first step provided estimates of environmental effects (herd-year-season - HYS), and the second step provided estimates of the intercept and slope for the TNB across different HYS levels, obtained from the first step, using a random regression model. In both steps, pedigree (A) and genomic (G) relationship matrices were considered. The genetic parameters (variance components, h2 and genetic correlations) were very similar when estimated using the A and G relationship matrices. The reaction norm graphs showed considerable differences in environmental sensitivity between sires, indicating a reranking of sires in terms of genetic merit across the HYS levels. Based on the G matrix analysis, SNP by environment interactions were observed. For some SNPs, the effects increased at increasing HYS levels, while for others, the effects decreased at increasing HYS levels or showed no changes between HYS levels. Cross-validation analysis demonstrated better performance of the genomic approach with respect to traditional pedigrees for both the G×E and standard models. The genomic reaction norm model resulted in an accuracy of GEBVs for “juvenile” boars varying from 0.14 to 0.44 across different HYS levels, while the accuracy of the standard genomic prediction model, without reaction norms, varied from 0.09 to 0.28. These results show that it is important and feasible to consider G×E interactions in evaluations of sires using genomic prediction models and that genomic information can increase the accuracy of selection across environments.

Osteochondrosis (OC) is a degenerative joint condition developing in a short time frame in young growing gilts that may cause lameness at an older age, affecting welfare and leading to premature culling of breeding sows. Causes of OC are multifactorial including both genetic and environmental factors. Floor type has been suggested to affect OC prevalence and effects might be age dependent during the rearing period. The aim of this study was to investigate possible age-dependent effects of floor type, conventional concrete partially slatted versus wood shavings as deep bedding, on OC prevalence in gilts (Dutch Large White × Dutch Landrace) at slaughter (24 wk of age; 106.5 [14.7 SD] kg of BW). At weaning (4 wk of age; 6.9 [1.3 SD] kg of BW), 212 gilts were subjected to 1 of 4 flooring regimens. Gilts were either subjected to a conventional floor from weaning until slaughter (CC), wood shavings as bedding from weaning until slaughter (WW), a conventional floor from weaning until 10 wk of age after which gilts were switched to wood shavings as bedding (CW), or wood shavings as bedding from weaning until 10 wk of age after which gilts were switched to a conventional floor (WC). After slaughter the elbow, hock, and knee joints were macroscopically examined for OC and scored on a 5 point scale where 0 indicates no OC and 4 indicates the severest form of OC. There was no significant difference (P > 0.4) between treatments on the overall OC prevalence for any joint assessed or at the animal level (all joints combined). At the animal level, however, gilts had greater odds to have OC scores 3 and 4 in the CW treatment (odds ratios [OR] = 2.3; P = 0.05), WC treatment (OR = 2.6; P = 0.02), and WW treatment (OR = 3.7; P

We evaluated the efficiency of selection for body weight in a cooperative village breeding program for Menz sheep of Ethiopia under smallholder farming system. The design of the program involved organizing villagers in a cooperative breeding group to implement selective breeding of their sheep. The program was jump-started through a one-time provision of elite rams from a central nucleus flock, but subsequent replacement rams were selected from within the village flocks. We also evaluated body weight trends in a village where cooperative breeding was not implemented and individual farmers managed their flocks under traditional breeding practices. Under traditional breeding practices, genetic progress over 8 years either stagnated or declined in all the weights recorded. In the cooperative villages, selection differentials of 2.44 and 2.45 kg were achieved in 2010 and 2011 selection seasons, respectively. Birth weight, 3-month weight and 6-month weight increased, respectively, by 0.49, 2.29 and 2.46 kg in the third-generation lambs over the base generation. Improved rams supplied from the central nucleus flock gave an initial genetic lift of 14.4% in the 6-month weight. This was higher than the gain achieved from selection in the village flocks, which was 5.2%. Our results showed that village-based genetic improvement in body weights under smallholder conditions could be feasible if appropriate designs are adopted and that commencing with elite central nucleus rams help jump-start village-based programs.

Summary 1. The rapid advancement in genome sequencing techniques allows the dissection of complex traits of nonmodel organisms of importance in evolutionary biology, conservation genetics, breeding and medicine. This advancement requires new statistical analysis tools that can handle large amount of sequencing data efficiently. 2. We propose an analytic Bayesian implementation of the mixed linear model which allows rapid and robust inferences of heritability. The two main features of the method are (i) breeding values and residual variance component are analytically integrated out of the model and (ii) the parameter space of the variance ratio parameter is discretized so that a Gibbs sampling distribution can be utilized. We propose further two separate methods to infer breeding values that acknowledge uncertainty of the learned heritability. The benefit of the method compared to a standard Markov Chain Monte Carlo (MCMC) method is visualized on public data sets: two simulated data sets and one Wheat (Triticum aestivum L.) pedigree. 3. Results show that while the accuracy of inferred heritability obtained by the proposed and standard methods are almost identical, the computational performance is very encouraging: up to hundred fold speed up and the possibility to make parallel implementation is particularly appealing here, which may further speed up computations. 4. The method allows analysis using a non-invertible relationship matrix so that ad hoc manipulation is avoided which can be important as our results imply. We completely avoid convergence and mixing problems here: this is a well-known problem of MCMC simulation, which sometimes can severely reduce the inferential power. Bayes factors for model comparisons can be conveniently calculated as a by-product of the inference procedure. The source code will be available for download at http://www.rni.helsinki.fi/ � mjs.

The aim of this study was to propose the survival analysis technique as a statistical approach for the analysis of rennet coagulation time (RCT) able to make use of coagulating and noncoagulating (NC) milk information in order to estimate potential sources of variations that affects RCT. A total of 1,025 Italian Holstein-Friesian cows (HF; progeny of 54 sires) and 1,234 Brown Swiss cows (BS; progeny of 58 sires) reared in 34 and 38 herds, respectively, were milk-sampled once. Rennet coagulation time was analyzed with a semiparametric proportional hazard model (i.e., a Cox model), with the NC samples considered as censored records. Furthermore, a different censoring scenario, with a new end point at 18 min, was considered after the rearrangement of the time space originally used for the observation of RCT. The percentage of NC samples was almost 10% for HF and 3.5% for BS cows in in the 31 min set, whereas it increased to 44 and 24.9%, respectively, in the 18 min set. Estimated hazard ratios indicated that the most important factors affecting the coagulation process were herd, days in milk, casein number, and milk acidity (expressed in terms of titratable acidity) for both HF and BS, whereas the SCS was relevant only for BS. The survival model seems to be particularly suitable for this analysis, as it can properly account for censored and uncensored records and appropriately use all available information. Moreover, this methodology allows us to rearrange the time space used for the observation of RCT and to define alternative traits (i.e., RCT with an end point at 18 min). Our restriction of the time space and the increased percentage of censored records did not highlight any substantial differences in terms of the risk of coagulating with respect to the traditional 31 min testing time. Although further research is needed to investigate the effect of these sources of variation on cheese yield, our results indicate that casein number, acidity, and SCS may be used as indicator traits for enhancing the technological properties of bovine milk.

This study aimed to identify chromosomal regions associated with genetic variation in pre-weaning growth traits in Angora goats. A genome-wide scan was performed by genotyping 1042 offspring from 12 half-sib families using 88 microsatellite caprine markers covering 1368cM. Phenotypes were recorded at birth (BW) and weaning (WW) and analysed using GridQTL software. A total of six putative QTL were detected on six different chromosomes, all at chromosome-wide significance level. Four QTL were identified for BW on CHI 4, 8, 17 and 27 and two QTL for WW on CHI 16 and 19. QTL effects ranged from −0.32 to 0.25 in units of residual standard deviation in different families. Some of these QTL correspond to chromosomes where QTL associated with growth have been identified in other species. These chromosomal segments hold potential to influence weight gain in young goats.

Piglet birth weight and litter uniformity were studied in sows of different parities and crossbred lines in relation to 1) the weaning-to-pregnancy interval (WPI); and 2) sow body condition changes (in BW and backfat thickness) during lactation and gestation in sows with a short WPI (= 7d). At the IPG research farm, individual piglet birth weights and sow body condition (BW and backfat thickness at farrowing and weaning) were measured of 949 TOPIGS20 and 889 TOPIGS40 sows with > 4 total born piglets, inseminated between 2003 and 2011. In all analyses, mean piglet birth weight and birth weight SD and CV were corrected for total number born. Total number born was higher in sows with a WPI of 8 to 21d (+ 1.2 piglets; n = 72) and >21d (+ 0.7 piglets; n = 182) compared with sows with a WPI = 7d (P 21d compared with sows with a WPI = 7d (P

The main focus of this study was to identify sow gestation features that affect growth rate (GR) and feed intake (FI) of their offspring during grower–finishing stage. Because the sow provides a specific environment to her offspring during gestation, certain features (e.g., BW of the sow), feed refusals or gestation group, may affect her ability to deliver and feed a healthy litter. Data on 17,743 grower–finishing pigs, coming from 604 sires and 681 crossbred sows, were obtained from the Institute for Pigs Genetics. Sow gestation features were collected during multiple gestations and divided into 3 clusters describing i) sow body condition (i.e., BW, backfat, and gestation length), ii) sow feed refusals (FR), the difference between offered and eaten feed during 3 periods of gestation: 1 to 28, 25 to 50, 45 to 80 d, and iii) sow group features (i.e., number of sows, and average parity). Sow gestation features were added to the base model 1 at a time to study their effect on GR and FI. Significant gestation features (P

Data from 113 Dutch organic farms were analysed to determine the effect of cross-breeding on production and functional traits. In total, data on 33 788 lactations between January 2003 and February 2009 from 15 015 cows were available. Holstein-Friesian pure-bred cows produced most kg of milk in 305 days, but with the lowest percentages of fat and protein of all pure-bred cows in the data set. Cross-breeding Holstein dairy cows with other breeds (Brown Swiss, Dutch Friesian, Groningen White Headed, Jersey, Meuse Rhine Yssel, Montbeliarde or Fleckvieh) decreased milk production, but improved fertility and udder health in most cross-bred animals. In most breeds, heterosis had a significant effect (P < 0.05) on milk (kg in 305 days), fat and protein-corrected milk production (kg in 305 days) and calving interval (CI) in the favourable direction (i.e. more milk, shorter CI), but unfavourably for somatic cell count (higher cell count). Recombination was unfavourable for the milk production traits, but favourable for the functional traits (fertility and udder health). Farm characteristics, like soil type or housing system, affected the regression coefficients on breed components significantly. The effect of the Holstein breed on milk yield was twice as large in cubicle housing as in other housing systems. Jerseys had a negative effect on fertility only on farms on sandy soils. Hence, breed effects differ across farming systems in the organic farming and farmers can use such information to dovetail their farming system with the type of cow they use.

Heat stress is known to adversely affect reproductive performance of sows. However, it is important to know on which days or periods during the reproduction cycle heat stress has the greatest effects for designing appropriate genetic or management strategies. Therefore, this study was conducted to identify days and periods that have greatest effects on farrowing rate and total born of sows using 5 different measures of heat stress. The data consisted of 22,750 records on 5024 Dutch Yorkshire dam line sows from 16 farms in Spain and Portugal. Heat stress on a given day was measured in terms of maximum temperature, diurnal temperature range and heat load. The heat load was estimated using 3 definitions considering different upper critical temperatures. Identification of days during the reproduction cycle that had maximum effect was based on the Pearson correlation between the heat stress variable and the reproduction trait, estimated for each day during the reproduction cycle. Polynomial functions were fitted to describe the trends of these correlations and the days with greatest negative correlation were considered as days with maximum effect. Correlations were greatest for maximum temperature, followed by those for heat load and diurnal temperature range. Correlations for both farrowing rate and total born were stronger in gilts than in sows. This implies that heat stress has a stronger effect on reproductive performance of gilts than of sows. Heat stress during the third week (21 to 14 d) before first insemination had largest effect on farrowing rate. Heat stress during the period between 7 d before successful insemination until 12 d after that had largest effect on total born. Correlations between temperatures on consecutive days during these periods were extremely high ( > 0.9). Therefore, for farrowing rate the maximum temperature on 21 d before first insemination and for total born the maximum temperature at day of successful insemination can be used as predictive measures of heat stress in commercial sow farms. Additionally, differences between daughter groups of sires were identified in response to high temperatures. This might indicate possibilities for genetic selection on heat tolerance.

Osteochondrosis (OC) is one of the main causes of leg weakness causing premature culling in breeding sows and develops in a short time frame in young growing gilts. Dietary restriction may have different effects on OC prevalence depending on the age of the gilts. The aim of this study is to investigate age dependent effects of dietary restriction, ad libitum versus restricted (80% of ad libitum), on the occurrence of OC in gilts at slaughter (26 wk of age). At weaning (4 wk of age), 211 gilts were subjected to one of 4 treatments of feeding regime. Gilts were administered either ad libitum feeding from weaning until slaughter (AA); restricted feeding from weaning until slaughter (RR); ad libitum feeding from weaning until 10 wk of age, after which gilts were switched to restricted feeding (AR); or restricted feeding from weaning until 10 wk of age, after which gilts were switched to ad libitum feeding (RA). At slaughter, the elbow, hock, and knee joints were harvested. Joints were scored macroscopically for articular surface deformations indicative of OC. Gilts in the RA treatment had significantly higher odds to be affected with OC than gilts in the RR and AR treatments in the hock joint (OR = 3.3, P = 0.04 and OR = 8.5, P = 0.002, respectively), and at animal level (OR = 2.5, P = 0.001 and OR = 1.9, P = 0.01, respectively). Gilts in the AA treatment had higher odds to be affected with OC than gilts in the AR treatment in the hock joint (OR = 5.3, P = 0.01). The results indicate a possible pathway to reduce the prevalence of OC in breeding gilts which will have to last several parities. Switching from restricted feeding to ad libitum feeding after 10 wk of age increases OC prevalence as opposed to restricted feeding after 10 wk of age.

Pigs housed together in a group influence each other’s growth. Part of this effect is genetic and can be represented in a social breeding value. It is unknown, however, which traits are associated with social breeding values. The aim of this study was, therefore, to investigate whether personality and response to novelty could be associated with social breeding values for growth in piglets. Female and castrated male piglets from 80 litters, with either an estimated relative positive or negative social breeding value (+SBV or –SBV) for growth, were individually tested in a backtest and novel environment test, and group-wise in a novel object (i.e., a feeder with feed) test and human approach test. All tests were performed during the suckling period. No differences between +SBV and –SBV piglets were found for the frequency and latency of struggling and vocalizing in the backtest (at least, P > 0.30). In the novel object test, piglets with a +SBV for growth touched the feeder faster than piglets with –SBV for growth (P = 0.01) and were more frequently present near the person in the human approach test (P 0.40), but piglets that struggled more in the backtest walked more in this test (P = 0.02). Behavior was affected by gender in each test. Female piglets were faster than castrated male piglets to start struggling in the backtest (P = 0.047). In the novel object test, females were faster than males to touch the feeder and sample the feed. In the human approach test, they were also faster than male piglets to touch a person (all, P

The aim of this study was to estimate breed differences in milk fatty acid (FA) profile among 5 dairy cattle breeds present in the Netherlands: Holstein-Friesian (HF), Meuse-Rhine-Yssel (MRY), Dutch Friesian (DF), Groningen White Headed (GWH), and Jersey (JER). For this purpose, total fat percentage and detailed FA contents in milk (14 individual FA and 14 groups of FA) predicted from mid-infrared spectra were used. Mid-infrared spectrometry profiles were collected during regular milk recording from a range of herds with different combinations of breeds, including both purebred and crossbred cows. The data set used for the analyses contained 41,404 records from a total of 24,445 cows. In total 7,626 cows were crossbreds belonging to the breeds HF, MRY, DF, GWH, and JER; 1,769 purebreds (≥87.5%) belonging to the breeds MRY, DF, GWH, and JER; and the other 15,050 cows were HF. Breed effects were estimated using a single-trait animal model. The content in milk of short-chain FA C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, and C16:0 was higher for JER and the content in milk of C16:0 was lower for GWH compared with the other breeds; when adjusting for breed differences in fat percentage, however, not all breed differences were significant. Breed differences were also found for cis-9 C14:1, cis-9 C16:1, C18:0, and a number of C18 unsaturated FA. In general, differences in fat composition in milk between HF, MRY, and DF were not significant. Jerseys tended to produce more saturated FA, whereas GWH tended to produce relatively less saturated FA. After adjusting for differences in fat percentage, breed differences in detailed fat composition disappeared or became smaller for several short- and medium-chain FA, whereas for several long-chain unsaturated FA, more significant breed differences were found. This indicates that short- and medium-chain FA are for all breeds more related to total fat percentage than long-chain FA. In conclusion, between breed differences were found in detailed FA composition and content of individual FA. Especially, for FA produced through de novo synthesis (short-chain FA, C12:0, C14:0, and partly C16:0) differences were found for JER and GWH, compared with the breeds HF, MRY, and DF.

The Creole goat is a local breed used for meat production in Guadeloupe (French West Indies). As in other tropical countries, improvement of parasite resistance is needed. In this study, we compared predicted selection responses for alternative breeding programs with or without parasites resistance and resilience traits. The overall breeding goal included traits for production, reproduction and parasite resilience and resistance to ensure a balanced selection outcome. The production traits were body weight (BW) and dressing percentage (DP). The reproduction trait was fertility (FER), which was the number of doe kiddings per mating. The resistance trait was worm fecal egg count (FEC), which is a measurement of the number of gastro-intestinal parasite eggs found in the feces. The resilience trait was the packed cell volume (PCV), which is a measurement of the volume of red blood cells in the blood. DP, BW and FEC were measured at 11 mo of age, which is the mating or selling age. FER and PCV were measured on females at each kidding period. The breeding program accounting for the overall breeding goal and a selection index including all traits gave annual selection responses of 800 g for BW, 3.75% for FER, 0.08% for DP, -0.005 ln(eggs/g) for FEC, and 0.28% for PCV. The expected selection responses for BW and DP in this breeding program were reduced by 2% and 6%, respectively, compared to a breeding program not accounting for FEC and PCV. The overall breeding program, proposed for the Creole breed, offers the best breeding strategy in terms of expected selection responses, making it possible to improve all traits together. It offers a good balance between production and adaptation traits and may present some interest for the selection of other goat breeds in the tropics.

Rainbow trout is a globally important fish species for aquaculture. However, fish for most farms worldwide are produced by only a few breeding companies. Selection based solely on fish performance recorded at a nucleus may lead to lower-than-expected genetic gains in other production environments when genotype-by-environment (G × E) interaction exists. The aim was to quantify the magnitude of G × E interaction of growth traits (tagging weight; BWT, harvest weight; BWH, and growth rate; TGC) measured across 4 environments, located in 3 different continents, by estimating genetic correlations between environments. A total of 100 families, of at least 25 in size, were produced from the mating 58 sires and 100 dams. In total, 13,806 offspring were reared at the nucleus (selection environment) in Washington State (NUC) and in 3 other environments: a recirculating aquaculture system in Freshwater Institute (FI), West Virginia; a high-altitude farm in Peru (PE), and a cold-water farm in Germany (GER). To account for selection bias due to selective mortality, a multitrait multienvironment animal mixed model was applied to analyze the performance data in different environments as different traits. Genetic correlation (rg) of a trait measured in different environments and rg of different traits measured in different environments were estimated. The results show that heterogeneity of additive genetic variances was mainly found for BWH measured in FI and PE. Additive genetic coefficient of variation for BWH in NUC, FI, PE, and GER were 7.63, 8.36, 8.64, and 9.75, respectively. Genetic correlations between the same trait in different environments were low, indicating strong reranking (BWT: rg = 0.15 to 0.37, BWH: rg = 0.19 to 0.48, TGC: rg = 0.31 to 0.36) across environments. The rg between BWT in NUC and BWH in both FI (0.31) and GER (0.36) were positive, which was also found between BWT in NUC and TGC in both FI (0.10) and GER (0.20). However, rg were negative between BWT in NUC and both BWH (–0.06) and TGC (–0.20) in PE. Correction for selection bias resulted in higher additive genetic variances. In conclusion, strong G × E interaction was found for BWT, BWH, and TGC. Accounting for G × E interaction in the breeding program, either by using sib information from testing stations or environment-specific breeding programs, would increase genetic gains for environments that differ significantly from NUC.

Background Mortality due to cannibalism causes both economic and welfare problems in laying hens. To limit mortality due to cannibalism, laying hens are often beak-trimmed, which is undesirable for animal welfare reasons. Genetic selection is an alternative strategy to increase survival and is more efficient by taking heritable variation that originates from social interactions into account, which are modelled as the so-called indirect genetic effects (IGE). Despite the considerable heritable variation in survival time due to IGE, genetic improvement of survival time in laying hens is still challenging because the detected heritable variation of the trait with IGE is still limited, ranging from 0.06 to 0.26, and individuals that are still alive at the end of the recording period are censored. Furthermore, survival time records are available late in life and only on females. To cope with these challenges, we tested the hypothesis that genomic prediction increases the accuracy of estimated breeding values (EBV) compared to parental average EBV, and increases response to selection for survival time compared to a traditional breeding scheme. We tested this hypothesis in two lines of brown layers with intact beaks, which show cannibalism, and also the hypothesis that the rate of inbreeding per year is lower for genomic selection than for the traditional breeding scheme. Results and discussion The standard deviation of genomic prediction EBV for survival time was around 22 days for both lines, indicating good prospects for selection against mortality in laying hens with intact beaks. Genomic prediction increased the accuracy of the EBV by 35 and 32 % compared to the parent average EBV for the two lines. At the current reference population size, predicted response to selection was 91 % higher when using genomic selection than with the traditional breeding scheme, as a result of a shorter generation interval in males and greater accuracy of selection in females. The predicted rate of inbreeding per generation with truncation selection was substantially lower for genomic selection than for the traditional breeding scheme for both lines. Conclusions Genomic selection for socially affected traits is a promising tool for the improvement of survival time in laying hens with intact beaks. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0247-4) contains supplementary material, which is available to authorized users.

SUMMARYMilk urea nitrogen (MUN) concentration in dairy cows may serve as an on-farm indicator to guide nutritional strategies and to help reduce emissions of nitrogen (N) to the environment. Excretion of urinary urea nitrogen (UUN) is positively related to MUN, but the relationship is highly variable. The accuracy of MUN as a predictor of UUN may improve when various factors that affect this relationship can be taken into account. The current review discusses the impact of a number of UUN : MUN ratio influencing factors related to: physiological mechanisms in the dairy cow, farm management, differences between individual cows, nutrition and analysis methods for MUN. Factors related to variation in water intake, urine production, dietary protein level, body weight (BW) and time and frequency of feeding and milking are shown to affect MUN and its relationship with UUN. In addition, a number of factors are discussed that are likely to affect this relationship such as biological rhythm, renal reabsorption of urea during periods of protein deficiency and breeding value for MUN. Accounting for these above-mentioned factors in the relationship between MUN and UUN might substantially improve the applicability and accuracy of MUN as a predictor of protein utilization efficiency and UUN.

The aims of this study were, first, to evaluate the effects of climatic variables on daily feed intake of lactating sows and, second, to establish whether the response of sows to variation in temperature on feed intake during lactation was heritable. A total of 82,614 records for daily feed intake during lactation were available for 848 sows with 3,369 litters farrowing from January 2000 to December 2007. Climatic parameters available from the nearest weather station were maximum 24 h outside temperature, day length changes, and humidity. Although ambient room temperature was modified at the animal level in the farrowing shed, these climatic variables still had a significant effect on feed intake during lactation. Regression coefficients temperature and humidity were 0.01385 ± 0.00300 (temperature) - 0.00031 ± 0.00009 (temperature2) and 0.01443 ± 0.00620 (humidity) - 0.00009 ± 0.00004 (humidity2). There was an interaction between temperature and humidity, partly due to the climate control in the farrowing shed. At low temperature, feed intake increased considerably with greater humidity, in contrast to a small reduction in feed intake with greater humidity at high temperature. Day length change was modeled with a cosine function. At the start of autumn (September 21), sows ate 0.36 ± 0.056 kg/d less feed than at the start of spring (March 21). Daily feed intake during lactation was described as a function of days in lactation and as a function of both days in lactation and temperature using random regression models. The average heritability and repeatability summarized over the day in lactation at the mean temperature were 0.21 and 0.69, respectively. Genetic variance of temperature response on feed intake was less than 20% of the day effect. The permanent environmental variance was 2-fold (day) and 4-fold (temperature) greater than the corresponding additive genetic variance. Heritabilities of daily feed intake were greater during the first week of lactation compared with the rest of lactation. The genetic correlation between days decreased as time increased down to about 0.2 between the first and last day in lactation. The genetic correlation between feed intake records at the extreme temperatures decreased to about -0.35. It was concluded that random regression models are useful for research and results may be used to develop simpler models that can be implemented in practical breeding programs. An effect of temperature on lactation feed intake was found even in this climate-controlled environment located in a temperate climate zone. Larger effects are expected in more extreme climatic conditions with less temperature-controlled farrowing sheds.

The sow provides a specific environment to her offspring during gestation and lactation. Certain features in the early life of the sow (sow history features) may affect her ability to deliver and feed a healthy litter. In genetic analyses of grow-finish traits, these effects are estimated as common litter or permanent sow effects. The objective of this research was to identify sow history features that affect the growth rate (GR) and feed intake (FI) of her offspring during the grow-finish stage. Data from 17,743 grow-finish pigs, coming from 604 sires and 681 crossbred sows, were recorded between May 2001 and February 2010 at the experimental farm of the Institute for Pig Genetics (Beilen, the Netherlands). The grow-finish stage was divided into 2 phases (phase 1: 26 to 75 kg; phase 2: 75 to 115 kg). The sow history features were birth litter size, birth year and season, birth farm, weaning age, age of transfer to the experimental farm, and age at first insemination. The sow features were added to the basic model one at a time to study their effect on the grow-finish traits of the pigs. Subsequently, significant sow features (P

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

Conformation and locomotive characteristics (CLC), i.e., leg conformation and gait movement patterns, may be associated with osteochondrosis (OC) in pigs. Osteochondrosis and CLC increase the risk of premature culling. This study investigated whether CLC have an explanatory value, over the previously modelled effects of sex, feeding, and housing conditions, on the occurrence and severity of OC in several joints and at the animal level. At 154 to 156 d of age, 267 pigs were subjectively scored on 9 conformation and 2 locomotive characteristics. Scoring was performed on a 9-point linear grading scale. For conformation characteristics, score 5 indicated normal conformation and scores 1 and 9 indicated severe deviations from normal. For the locomotive characteristics, score 1 indicated normal locomotion and score 9 indicated severe deviation from normal. At 161 to 176 d of age, pigs were slaughtered and joints were dissected for macroscopic evaluation of OC status. Results showed that swaying hindquarters and a stiffer gait were associated with higher scores for OC in, respectively, the femoropatellar (P = 0.018) and tarsocrural joint (P = 0.005); smaller inner claws as compared to the outer claws of the front legs was associated with lower scores for OC than equally sized claws in the femoropatellar joint (P = 0.021) and on animal level (P = 0.010); steep and weak pasterns of the front legs were associated with higher scores for OC in the elbow joint (P = 0.004) and on animal level (P = 0.018); X-shaped hind legs was associated with higher scores for OC on animal level (P = 0.037); and steep and weak pasterns of the hind legs were associated with lower scores for OC than normal conformation in the tarsocrural joint (P = 0.05). This study found several CLC that were associated with OC in several joints and at an animal level. This study showed that certain CLC might be used as indicators of OC and included in the criteria for selection of replacement animals for the breeding herd.

Phenotypic information on individual protein composition of cows is important for many aspects of dairy processing with cheese production as the center of gravity. However, measuring individual protein composition is expensive and time consuming. In this study, we investigated whether protein composition can be predicted based on inexpensive and routinely measured milk Fourier transform infrared (FTIR) spectra. Based on 900 calibration and 900 validation samples that had both capillary zone electrophoresis (CZE)-determined protein composition and FTIR spectra available, low to moderate validation R 2 were reached (from 0.18 for α S1 -casein to 0.56 for β-lactoglobulin). The potential usefulness of this model on the phenotypic level was investigated by means of achieved selection differentials for 25% of the best animals. For α-lactalbumin (R 2 =0.20), the selection differential amounted to 0.18g/100g and for casein index (R 2 =0.50) to 1.24g/100g. We concluded that predictions of protein composition were not accurate enough to enable selection of individual animals. However, for specific purposes when, for example, groups of animals that meet a certain threshold are to be selected, the presented model could be useful in practice on the phenotypic level. The potential usefulness of this model on the genetic level was investigated by means of genetic correlations between CZE-determined and FTIR-predicted protein composition traits. The genetic correlations ranged from 0.62 (β-casein) to 0.97 (whey). Thus, predictions of protein composition, when used as input to estimate breeding values, provide an excellent means for genetic improvement of protein composition. In addition, estimated repeatabilities based on 3 repeated observations of predicted protein composition showed that a considerable amount of prediction error can be removed using repeated observations.

The aim of this study was to detect new polymorphisms in the bovine ß-casein (ß-CN) gene and to evaluate association of (new) ß-CN protein variants with milk production traits and milk protein composition. Screening of the ß-CN gene in genomic DNA from 72 Holstein Friesian (HF) bulls resulted in detection of 19 polymorphisms and revealed the presence of ß-CN protein variant I in the Dutch HF population. Studies of association of ß-CN protein variants with milk composition usually do not discriminate protein variant I from variant A2. Association of ß-CN protein variants with milk composition was studied in 1857 first-lactation HF cows and showed that associations of protein variants A2 and I were quite different for several traits. ß-CN protein variant I was significantly associated with protein percentage and protein yield, and with as1-casein (as1-CN), as2-casein (as2-CN), ¿-casein (¿-CN), a-lactalbumin (a-LA), ß-lactoglobulin (ß-LG), casein index and casein yield. Inferring ß-¿-CN haplotypes showed that ß-CN protein variant I occurred only with ¿-CN variant B. Consequently, associations of ß-¿-CN haplotype IB with protein percentage, ¿-CN, a-LA, ß-LG and casein index are likely resulting from associations of ¿-CN protein variant B, while associations of ß-¿-CN haplotype IB with as1-CN and as2-CN seem to be resulting from associations of ß-CN variant I.

Osteochondrosis (OC) is seen as the main cause of leg weakness in pigs, leading to welfare problems and economic losses. Environmental factors in pig husbandry, such as the housing system and feeding strategy are expected to influence the prevalence of OC. Therefore, this study investigated the effects of housing system and feeding strategy on the prevalence and severity of OC. In the experiment 345 pigs were used. At an age of 69 days intact boars and gilts were separated and assigned to groups of five or six individuals. A two by two factorial design of housing system and feeding strategy was applied. The housing system was either a conventional concrete floor partial slatted, or a deep litter floor with extra space allowance. The feeding strategy was either ad libitum or restricted to 80% of ad libitum. Pigs were slaughtered at the age of 161–176 days. In total, five joints of the left front and hind limbs were macroscopically assessed for OC on a five-point scale, ranged from no OC through (semi-)loose cartilage fragments. The prevalence of OC in the experimental population was 41.4%, and 12.4% of the individuals had severe lesions. The tarsocrural joint was most affected (30.2%) by OC. OC scores between the different joints were not correlated. Medial sections of joints were most affected (63–100%). Boars were more affected than gilts in the elbow joint. Conventionally housed pigs were more affected than deep litter housed pigs. Ad libitum fed pigs had more OC than restrictedly fed pigs. OC was most prevalent with 57.5% in the pigs on the conventional floor with ad libitum feeding. OC was least prevalent with 33.7% in pigs kept in deep litter housing with restricted feeding. The sex, housing system and feeding strategy did not affect OC in the femoropatellar, metacarpophalangeal, and metatarsophalangeal joints. Our results demonstrate that the OC prevalence can be reduced by applying deep litter floors with extra space allowance and/or restricted feeding in fattening pigs.

Sheep internal parasites (nematodes) remain a major health challenge and are costly for pasture-based production systems. Most current breeding programmes for nematode resistance are based on indicator traits such as faecal egg counts (FEC), which are costly and laborious to collect. Hence, genetic markers for resistance would be advantageous. However, although some quantitative trait loci (QTL) have been identified, these QTL are often not consistent across breeds and few breeding strategies for nematode resistance in sheep are currently using molecular information. In this study, QTL for nematode resistance on ovine chromosomes (OAR) 3 and 14, previously identified in the Blackface breed, were explored using commercial Suffolk (n = 336) and Texel lambs (n = 879) sampled from terminal sire breeder flocks in the United Kingdom. FEC were used as the indicator trait for nematode resistance, and these were counted separately for Nematodirus and Strongyles genera. Microsatellite markers were used to map the QTL and the data were analysed using interval mapping regression techniques and variance component analysis. QTL for Nematodirus and Strongyles FEC were found to be segregating on OAR3 at 5% chromosome region-wide significance threshold in both Suffolk and Texel sheep, and Nematodirus FEC QTL were segregating on OAR14 in both breeds. In addition, QTL for growth traits were also found to be segregating at 5% chromosome region-wide on OAR3 and OAR14. The confirmation that FEC QTL segregate in the same position in three widely used breeds widens their potential applicability to purebred Blackface, Suffolk and Texel sheep, with benefits likely to be observed in their commercial crossbred progeny.

A farmer participatory approach was used to define breeding objectives and selection indexes for short-fat-tailed sheep in sheep–barley systems and Black Head Somali sheep in pastoral systems in Ethiopia. Breeding-objective traits were identified based on producers' preferences for traits collected during interviews. The desired gains in the various traits were calculated based on the producers' preferences for traits and were used to weigh traits in the breeding objective using selection-index methodology. This study recognized subsistence producers (producing yearlings) and subsistence + producers (producing and finishing yearlings) within sheep–barley and pastoral systems. Producers' preferences for traits showed that adaptive traits are more important (pastoral system) or as important (sheep–barley system) as production traits. Subsistence producers gave more weight to adaptive traits than did the more market-oriented subsistence + producers. A low correlation (0.31) was found between selection indexes constructed for subsistence and subsistence + producers in the sheep–barley system. This demonstrates that breeding objectives need to be tailored to the specific needs of the different groups of farmers. The results of our study can be used to design sheep breeding programs in Ethiopia and elsewhere with similar production circumstances. We present an approach to incorporate producers' preferred breeding objectives into conventional selection tools.

Social interactions, especially those involving competition among individuals, are important in domesticated livestock and in natural populations. The heritability of traits affected by such interactions has two components, one originating in the individual like that of classical traits (direct effects) and the other originating in other group members (indirect effects). The latter type of trait represents a significant source of ‘hidden heritability’ and it requires population structure and knowledge from relatives in order to access it for selective breeding. When ignored, competitive interactions may increase as an indirect response to direct selection, resulting in diminished yields. We illustrate how population genetic structure affects the response to selection of traits with indirect genetic effects using population genetic and quantitative genetic theory. Population genetic theory permits us to connect our results to the existing body of theory on kin and group selection in natural populations. The quantitative genetic perspective allows us to see how breeders have used knowledge from relatives and family selection in the domestication of plants and animals to improve the welfare and production of livestock by incorporating social genetic effects in the breeding program. We illustrate the central features of these models by reviewing empirical studies from domesticated chickens.

Social interactions between individuals, such as co-operation and competition, are key factors in evolution by natural selection. As a consequence, evolutionary biologists have developed extensive theories to understand the consequences of social interactions for response to natural selection. Current genetic improvement programmes in animal husbandry, in contrast, largely ignore the implications of social interactions for the design of breeding programmes. Recently, we have developed theoretical and empirical tools to quantify the magnitude of heritable social effects, ie the heritable effects that animals have on their group mates' traits, in livestock populations, and to utilise those effects in genetic improvement programmes. Results in commercial populations of pigs and laying hens indicate large heritable social effects, and the potential to substantially increase responses to selection in traits affected by social interactions. In pigs, including social effects into the breeding programme affected aggressive behaviour, both at mixing and in stable groups, indicating changes in the way dominance relationships are established and in aggressiveness. In laying hens, we applied selection between kin-groups to reduce mortality due to cannibalistic pecking. This resulted in a considerable difference in mortality between the low mortality line and the unselected control line in the first generation (20 vs 30%). Furthermore, changes in behavioural and neurobiological responses to stress were detected in the low mortality line, pointing to reduced fearfulness and stress sensitivity. These first results indicate that including social effects into breeding programmes is a promising way to reduce negative social interactions in farm animals, and possibly to also increase positive social interactions, by breeding animals with better social skills

Under commercial conditions, data on egg production in laying hens are usually collected per cage rather than individually. In current breeding programs, genetic evaluations are, however, based on individually recorded egg production. Because commercial flocks are not maintained in single cages, this environmental difference between the breeding and commercial setting may result in a genotype x environment interaction. This study was aimed at estimating genetic parameters and predicting estimated breeding values for early egg production of laying hens by using pooled data (i.e., data from multiple bird cages) from pedigree birds housed in 4-bird cages. Using cage records, we compared 2 different methods of handling pooled data: cage sums and the assignment of cage means to individual birds, referred to as the approximate method. The 2 methods were compared by using cross-validation. Data from 3 purebred White Leghorn layer lines were used. Estimated heritability for early egg production was 0.36 when cage sums were used and 0.30 with the approximate method. The correlation of estimated breeding values between the cage sums method and the approximate method was 0.88. Cross-validation showed that the use of cage sums led to better predictions of missing phenotypes compared with the approximate method. The results of the research demonstrate that pooled data can be used in the genetic evaluation of laying hens and show that using directly pooled records (e.g., cage sums) gives better results than assigning group means to the birds of the group, thus simulating individual records.

In this study, we determined the detailed composition of and seasonal variation in Dutch dairy milk. Raw milk samples representative of the complete Dutch milk supply were collected weekly from February 2005 until February 2006. Large seasonal variation exists in the concentrations of the main components and milk fatty acid composition. Milk lactose concentration was rather constant throughout the season. Milk true protein content was somewhat more responsive to season, with the lowest content in June (3.21 g/100 g) and the highest content in December (3.38 g/100 g). Milk fat concentration increased from a minimum of 4.10 g/100 g in June to a maximum of 4.57 g/100 g in January. The largest (up to 2-fold) seasonal changes in the fatty acid composition were found for trans fatty acids, including conjugated linoleic acid. Milk protein composition was rather constant throughout the season. Milk unsaturation indices, which were used as an indication of desaturase activity, were lowest in spring and highest in autumn. Compared with a previous investigation of Dutch dairy milk in 1992, the fatty acid composition of Dutch raw milk has changed considerably, in particular with a higher content of saturated fatty acids in 2005 milk.

We present the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in long-chain milk fatty acids. Milk-fat composition phenotypes were available on 1,905 Dutch Holstein-Friesian cows. A total of 849 cows and their 7 sires were genotyped for 1,341 single nucleotide polymorphisms across all Bos taurus autosomes (BTA). We detected significant QTL on BTA14, BTA15, and BTA16: for C18:1 cis-9, C18:1 cis-12, C18:2 cis-9,12, CLA cis-9,trans-11, C18:3 cis-9,12,15, the C18 index, the total index, total saturated fatty acids, total unsaturated fatty acids (UFA), and the ratio of saturated fatty acids:unsaturated fatty acids on BTA14; for C18:1 trans fatty acids on BTA15; and for the C18 and CLA indices on BTA16. The QTL explained 3 to 19% of the phenotypic variance. Suggestive QTL were found on 16 other chromosomes. The diacylglycerol acyltransferase 1 (DGAT1) K232A polymorphism on BTA14, which is known to influence fatty acid composition, most likely explains the QTL that was detected on BTA14.

A reaction norm approach was used to estimate trends for days open (DO) with a model that indirectly accounted for heat stress. Data included 3.4 million first-parity records of DO of US Holsteins. A fixed effect model included herd-year, month of calving within region (MOC), age class, and regression on 305-d milk yield. An index calculated from the standardized solutions to MOC derived from the fixed effect model was treated as a proxy for an index on heat stress (SI). The lowest index for any region was set to zero. The highest index was 1.00 for the Southeast, 0.56 for the Northeast, 0.54 for the Midwest, 0.33 for the Northwest, and 0.42 for the Southwest. In all regions except the Northwest, the highest DO and the corresponding highest indices were in March-April. Compared with the fixed model, the reaction norm model also included the effect of an animal and a random regression on the SI; the 2 animal solutions are subsequently referred to as an intercept and a slope. Genetic trends were calculated for cows and sires separately. For cows, the trend for the intercept was - 0.1 d/yr, whereas the trend for the slope was 1 d/yr. For sires, the same trends were - 0.3 and 1.5, respectively. Official proofs were used to characterize the 100 top and 100 bottom bulls with at least 50 daughters for the intercept and the slope. Compared with the top bulls, the bottom bulls for the intercept gave 56 kg more milk and their type performance index was higher by 212 points. For the slope, the same numbers were - 435 kg and - 242 points, respectively. Trends for seasonal changes of days open are unfavorable.