Stature is affected by many polymorphisms of small effect in humans . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

Residual feed intake (RFI) is defined as the difference between the observed ADFI and the ADFI predicted from production and maintenance requirements. The objectives of this study were to evaluate RFI as a selection criterion to improve feed efficiency and its potential to reduce N and P excretion in 4 pig breeds. Data were collected between 2000 and 2009 in French central test stations for 2 dam breeds [French Landrace (LR) and Large White (LWD)], and 2 sire breeds [Large White (LWS) and Piétrain (PP)]. Numbers of recorded pigs were 6407, 10,694, 2342, and 2448 for the LR, LWD, LWS, and PP breeds, respectively. All PP animals were genotyped for the halothane mutation. This data set was used to calculate RFI equations for each of the 4 breeds, and to estimate genetic parameters for RFI together with growth, carcass, and meat quality traits, and N and P excretion during the test period (35 to 110 kg BW). The RFI explained 20.1% in PP, 26.5% in LWS, 27.6% in LWD, and 29.5% in LR of the phenotypic variability of ADFI. The PP breed differed from the others in this respect, probably due to a lower impact of the variation of body composition on ADFI. Heritability estimates of RFI ranged from 0.21 ± 0.03 (LWD) to 0.33 ± 0.06 (PP) depending on the breed. Heritabilities of N and P excretion traits ranged from 0.29 ± 0.06 to 0.40 ± 0.06. The RFI showed positive genetic correlations with feed conversion ratio (FCR) and excretion traits, these correlations being greater in the sire breeds (from 0.57 to 0.86) than in the dam breeds (from 0.38 to 0.53). Compared with FCR, RFI had weaker genetic correlations with carcass composition, growth rate, and excretion traits. Estimates of genetic correlations between FCR and excretion traits were very close to 1 for all breeds. Finally, excretion traits were, at the genetic level, correlated positively with ADFI, negatively with growth rate and carcass leanness, whereas the halothane n mutation in PP was shown to reduce N and P excretion levels. To conclude, new selection indexes including RFI can be envisaged to efficiently disentangle the responses to selection on growth rate and body composition from those on feed efficiency, with favorable impacts on N and P excretions, particularly in sire pig breeds. However, the switch from FCR to RFI in selection indexes should not resolve the genetic antagonism between feed efficiency and meat quality.

BackgroundNumerous quantitative trait loci (QTL) have been detected in pigs over the past 20 years using microsatellite markers. However, due to the low density of these markers, the accuracy of QTL location has generally been poor. Since 2009, the dense genome coverage provided by the Illumina PorcineSNP60 BeadChip has made it possible to more accurately map QTL using genome-wide association studies (GWAS). Our objective was to perform high-density GWAS in order to identify genomic regions and corresponding haplotypes associated with production traits in a French Large White population of pigs.MethodsAnimals (385 Large White pigs from 106 sires) were genotyped using the PorcineSNP60 BeadChip and evaluated for 19 traits related to feed intake, growth, carcass composition and meat quality. Of the 64 432 SNPs on the chip, 44 412 were used for GWAS with an animal mixed model that included a regression coefficient for the tested SNPs and a genomic kinship matrix. SNP haplotype effects in QTL regions were then tested for association with phenotypes following phase reconstruction based on the Sscrofa10.2 pig genome assembly.ResultsTwenty-three QTL regions were identified on autosomes and their effects ranged from 0.25 to 0.75 phenotypic standard deviation units for feed intake and feed efficiency (four QTL), carcass (12 QTL) and meat quality traits (seven QTL). The 10 most significant QTL regions had effects on carcass (chromosomes 7, 10, 16, 17 and 18) and meat quality traits (two regions on chromosome 1 and one region on chromosomes 8, 9 and 13). Thirteen of the 23 QTL regions had not been previously described. A haplotype block of 183 kb on chromosome 1 (six SNPs) was identified and displayed three distinct haplotypes with significant (0.0001 < P < 0.03) associations with all evaluated meat quality traits.ConclusionsGWAS analyses with the PorcineSNP60 BeadChip enabled the detection of 23 QTL regions that affect feed consumption, carcass and meat quality traits in a LW population, of which 13 were novel QTL. The proportionally larger number of QTL found for meat quality traits suggests a specific opportunity for improving these traits in the pig by genomic selection.

Genetic trends for body composition and blood plasma parameters of newborn piglets were estimated through the comparison of two groups of pigs (G77 and G98, respectively) produced by inseminating Large White (LW) sows with semen from LW boars born either in 1977 or in 1998. Random samples of 18 G77 and 19 G98 newborn piglets were used for whole carcass and tissue sampling. Plasma concentrations of glucose, albumin and IGF-1 were determined on 75 G77 and 90 G98 piglets from 18 litters. The G98 piglets had less carcass dry matter, protein and energy (P , 0.01) than their G77 counterparts. When expressed in g/kg birth weight, livers were lighter (P , 0.001) and contained less glycogen (P , 0.01) in G98 piglets, with no difference in the activity of the hepatic glucose-6-phosphatase between G98 and G77 piglets. Concentrations of protein, DNA, RNA in longissimus dorsi muscle were unaffected by selection. Plasma concentrations of glucose (P , 0.05) and IGF-1 (P , 0.01) were lower in G98 than in G77 piglets. On the whole, the results suggest that the improvement in lean growth rate and in sow prolificacy from 1977 to 1998 has resulted in a lower maturity of piglets at birth.

Stochastic simulation was used to compare the efficiency of 3 pig breeding schemes based on either traditional genetic evaluation or genomic evaluation. The simulated population contained 1,050 female and 50 male breeding animals. It was selected for 10 yr for a synthetic breeding goal that included 2 traits with equal economic weights and heritabilities of 0.2 or 0.4. The reference breeding scheme, named BLUP-AM, was based on the phenotyping of all candidates (13,770 animals/yr) for Trait 1 and of relatives from 10% of the litters (270 animals/yr) for Trait 2 and on BLUP-Animal Model genetic evaluations. Under the first alternative scenario, named GE-1TP, selection was based on genomic breeding values (GBV) estimated with one training population (TP) made up of candidate relatives phenotyped for both traits, with a size increasing from 1,000 to 3,430 over time. Under the second alternative scenario, named GE-2TP, the GBV for Trait 2 were estimated using a TP identical to that of GE-1TP, but the GBV for Trait 1 were estimated using a large TP made up of candidates that increased in number from 13,770 to 55,080 over time. Over the simulated period, both genomic breeding schemes generated 39 to 58% more accurate EBV for Trait 2 than the reference scheme, resulting in 78 to 128% (GE-1TP) and 63 to 84% (GE-2TP) greater average annual genetic trends for this trait. For Trait 1, GE-1TP was 18 to 24% less accurate than BLUP-AM, reducing average annual genetic trends by 27 to 44%. By contrast, GE-2TP generated 35 to 43% more accurate EBV and 8 to 22% greater average annual genetic trends for Trait 1 than the reference scheme. Consequently, GE-2TP was 27 to 33% more efficient in improving the global breeding goal than BLUP-AM whereas GE-1TP was globally as efficient as the reference scheme. Both genomic schemes reduced the inbreeding rate, the greatest decrease being observed for GE-2TP (-49 to -60% compared with BLUP-AM). In conclusion, genomic selection could substantially and durably improve the efficiency of pig breeding schemes in terms of reliability, genetic trends, and inbreeding rate without any need to modify their current structure. Even though it only generates a small TP, limited annual phenotyping capacity for traits currently only recorded on relatives would not be prohibitive. A large TP is, however, required to outperform the current schemes for traits recorded on the candidates in the latter.

Background: Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. Results: To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a largescale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10 −8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk. Conclusions: Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.

Predicting phenotypes is a statistical and biotechnical challenge, both in medicine (predicting an illness) and animal breeding (predicting the carcass economical value on a young living animal). High-throughput fine phenotyping is possible using metabolomics, which describes the global metabolic status of an individual, and is the closest to the terminal phenotype. The purpose of this work was to quantify the prediction power of metabolomic profiles for commonly used production phenotypes from a single blood sample from growing pigs. Several statistical approaches were investigated and compared on the basis of cross validation: raw data vs. signal preprocessing (wavelet transformation), with a single-feature selection method. The best results in terms of prediction accuracy were obtained when data were preprocessed using wavelet transformations on the Daubechies basis. The phenotypes related to meat quality were not well predicted because the blood sample was taken some time before slaughter, and slaughter is known to have a strong influence on these traits. By contrast, phenotypes of potential economic interest (e.g., lean meat percentage and ADFI) were well predicted (R(2) = 0.7; P < 0.0001) using metabolomic data.

Genetic trends for growth, feed efficiency, composition, and morphometry of carcasses were estimated in a French Large White (LW) pig population using frozen semen. Two groups of pigs were produced by inseminating LW sows with either stored, frozen semen from 17 LW boars born in 1977 or with semen from 23 LW boars born in 1998. In each group, 15 males and 90 females were randomly chosen and mated to produce approximately 1,000 pigs/group. These pigs were performance tested with individual ADFI and serial BW and backfat thickness measurements, slaughtered at 105 kg of BW, and measured for carcass traits. The data were analyzed using mixed linear animal models, including the fixed effect of the experimental group (offspring of 1977 or 1998 boars), the random effect of the additive genetic value of each animal, and, when significant, the fixed effects of sex, fattening batch, and slaughterhouse, the linear regression on BW, and the random effect of the common environment of birth litter. For each trait, the genetic trend was estimated as twice the difference between the 2 experimental groups. Results showed moderately favorable trends for on-test ADG (3.7 +/- 1.3 g/d per year) and feed conversion ratio (-0.014 +/- 0.005 kg/kg per year) in spite of a tendency toward an increase in ADFI (7.6 +/- 4.7 g/yr). A strong reduction in carcass fatness (-0.35 +/- 0.07 mm/yr for carcass average backfat thickness) and a large improvement in carcass leanness (0.31 +/- 0.10 mm(2)/yr and 0.41 +/- 0.08%/yr for loin eye area and carcass muscle content, respectively) were observed. Carcass shape measurements (back and leg length, back width, muscle thickness of hind limbs) were not affected by selection. Serial measurements of BW and backfat thickness showed that the major part of the genetic gains occurred during late growth and that the reduction in the backfat layer was more pronounced in the rear than in the front part of the carcass. The use of frozen semen appears to be a powerful practice to thoroughly investigate changes attributable to selection.