Uniting tensile ductility with ultrahigh strength via composition undulation.

Metals with nanocrystalline grains have ultrahigh strengths approaching two gigapascals. However, such extreme grain-boundary strengthening results in the loss of almost all tensile ductility, even when the metal has a face-centred-cubic structure-the most ductile of all crystal structures1-3. Here we demonstrate that nanocrystalline nickel-cobalt solid solutions, although still a face-centred-cubic single phase, show tensile strengths of about 2.3 gigapascals with a respectable ductility of about 16 per cent elongation to failure. This unusual combination of tensile strength and ductility is achieved by compositional undulation in a highly concentrated solid solution. The undulation renders the stacking fault energy and the lattice strains spatially varying over length scales in the range of one to ten nanometres, such that the motion of dislocations is thus significantly affected. The motion of dislocations becomes sluggish, promoting their interaction, interlocking and accumulation, despite the severely limited space inside the nanocrystalline grains. As a result, the flow stress is increased, and the dislocation storage is promoted at the same time, which increases the strain hardening and hence the ductility. Meanwhile, the segment detrapping along the dislocation line entails a small activation volume and hence an increased strain-rate sensitivity, which also stabilizes the tensile flow. As such, an undulating landscape resisting dislocation propagation provides a strengthening mechanism that preserves tensile ductility at high flow stresses.

Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

Predictive ability of multi-population genomic prediction methods of phenotypes for reproduction traits in Chinese and Austrian pigs.

BACKGROUND: Multi-population genomic prediction can rapidly expand the size of the reference population and improve genomic prediction ability. Machine learning (ML) algorithms have shown advantages in single-population genomic prediction of phenotypes. However, few studies have explored the effectiveness of ML methods for multi-population genomic prediction. RESULTS: In this study, 3720 Yorkshire pigs from Austria and four breeding farms in China were used, and single-trait genomic best linear unbiased prediction (ST-GBLUP), multitrait GBLUP (MT-GBLUP), Bayesian Horseshoe (BayesHE), and three ML methods (support vector regression (SVR), kernel ridge regression (KRR) and AdaBoost.R2) were compared to explore the optimal method for joint genomic prediction of phenotypes of Chinese and Austrian pigs through 10 replicates of fivefold cross-validation. In this study, we tested the performance of different methods in two scenarios: (i) including only one Austrian population and one Chinese pig population that were genetically linked based on principal component analysis (PCA) (designated as the "two-population scenario") and (ii) adding reference populations that are unrelated based on PCA to the above two populations (designated as the "multi-population scenario"). Our results show that, the use of MT-GBLUP in the two-population scenario resulted in an improvement of 7.1% in predictive ability compared to ST-GBLUP, while the use of SVR and KKR yielded improvements in predictive ability of 4.5 and 5.3%, respectively, compared to MT-GBLUP. SVR and KRR also yielded lower mean square errors (MSE) in most population and trait combinations. In the multi-population scenario, improvements in predictive ability of 29.7, 24.4 and 11.1% were obtained compared to ST-GBLUP when using, respectively, SVR, KRR, and AdaBoost.R2. However, compared to MT-GBLUP, the potential of ML methods to improve predictive ability was not demonstrated. CONCLUSIONS: Our study demonstrates that ML algorithms can achieve better prediction performance than multitrait GBLUP models in multi-population genomic prediction of phenotypes when the populations have similar genetic backgrounds; however, when reference populations that are unrelated based on PCA are added, the ML methods did not show a benefit. When the number of populations increased, only MT-GBLUP improved predictive ability in both validation populations, while the other methods showed improvement in only one population.

DFT-based Machine Learning for Ensemble Effect of Pd@Au Electrocatalysts on CO2 Reduction Reaction.

The ensemble effect due to variation of Pd content in Pd-Au alloys have been widely investigated for several important reactions, including CO2 reduction reaction (CO2 RR), however, identifying the stable Pd arrangements on the alloyed surface and picking out the active sites are still challenging. Here we use a density functional theory (DFT) based machine-learning (ML) approach to efficiently find the low-energy configurations of Pd-Au(111) surface alloys and the potentially active sites for CO2 RR, fully covering the Pd content from 0 to 100 %. The ML model is actively learning process to improve the predicting accuracy for the configuration formation energy and to find the stable Pd-Au(111) alloyed surfaces, respectively. The local surface properties of adsorption sites are classified into two classes by the K-means clustering approach, which are closely related to the Pd content on Au surface. The classification is reflected in the variation of adsorption energy of CO and H: In the low Pd content range (0-60 %) the adsorption energies over the surface alloys can be tuned significantly, and in the medium Pd content (37-68 %), the catalytic activity of surface alloys for CO2 RR can be increased by increase the Pd content and attributed to the meta-stable active site over the surface. Thus, the active site-dependent reaction mechanism is elucidated based on the ensemble effect, which provides new physical insights to understand the surface-related properties of catalysts.

Enhanced Thermoelectric Performance and Low Thermal Conductivity in Cu2GeTe3 with Identified Localized Symmetry Breakdown.

Highly efficient and eco-friendly thermoelectric generators rely on low-cost and nontoxic semiconductors with high symmetry and ultralow lattice thermal conductivity κL. We report the rational synthesis of the novel cubic (Ag, Se)-doped Cu2GeTe3 semiconductors. A localized symmetry breakdown (LSB) was found in the composition of Cu1.9Ag0.1GeTe1.5Se1.5 (i.e., CAGTS15) with an ultralow κL of 0.37 W/mK at 723 K, the lowest value outperforming all Cu2GeCh3 (Ch = S, Se, and Te). A joint investigation of synchrotron X-ray techniques identifies the LSB embedded into the cubic CAGTS15 host matrix. This LSB is an Ångström-scale orthorhombic symmetry unit, characteristic of multiple bond lengths, large anisotropic atomic displacements, and distinct local chemical coordination of anions. Computational results highlight that such an unusual orthorhombic symmetry demonstrates low-frequency phonon modes, which become softer and more predominant with increasing temperatures. This unconventional LSB promotes bond complexity and phonon scattering, highly beneficial for extraordinarily low lattice thermal conductivity.

Morphology evolution of the aluminum surface in a fluorine-containing environment.

The interaction between aluminum (Al) and F and O atoms is essential to understand the etching process of Al and alumina (Al2O3) by fluorine-containing gases. In addition, it also has an influence on the corrosion behavior of Al devices, e.g., the Al collector in lithium-ion batteries operates in fluorine-containing electrolytes. However, the understanding of the structural evolution of the Al surface by fluorination at the atomistic level still remains elusive. Here, the thermodynamic and kinetic behaviors of F adatoms as well as co-adsorbed F and O adatoms on typical Al surfaces have been systematically investigated by combining density functional theory (DFT) calculations, canonical Monte Carlo (CMC) simulations and reactive molecular dynamics (RMD) simulations. The results of DFT calculations indicate that there is a repulsion (about 0.07 eV on Al(111) and Al(110), and 0.7 eV on Al(100)) between the first nearest neighboring (1NN) F adatoms, while an attraction of 0.14 eV on Al(111) exists within a 1NN F-O pair. CMC simulations reveal that the configurations of co-adsorbed F and O adatoms on the Al(111) surface at medium to low temperature (<600 K) and low total coverage (<0.2 monolayer, ML) have F adatoms dispersed in the hexagonal islands of O adatoms due to the attraction within the O-O and F-O pairs and the repulsion between F adatoms. As the coverage increases, the surface undergoes serious deformation. The average 1NN coordination numbers (1st CN) of O-to-O, F-to-O and F-to-F are six, three and zero, respectively. As the temperature increases, the interactions among adsorbates begin to be disrupted: the 1st CNs of O-to-O and F-to-O decrease, while that of F-to-F increases. The O-F hexagonal pattern remains until above the Al melting temperature (>1200 K). For F adatoms, both their migration on the surface and the penetration into the subsurface are easier than those of O adatoms, confirmed by both the DFT and RMD simulations. Our study on the co-adsorbates with opposite lateral interactions is instructive for understanding the thermal etching of Al and Al2O3 by fluorine-containing compounds.

Coverage-dependent adsorption and dissociation of H2O on Al surfaces.

The adsorption and dissociation of H2O on Al surfaces including crystal planes and nanoparticles (ANPs) are systematically investigated by using density functional theory (DFT) calculations. H2O adsorption strength follows the order ANPs > Al(110) > Al(111) > Al(100). Due to the smaller cluster deformation caused by the moderate H2O adsorption, the relative magnitude of H2O adsorption strength on ANPs and crystal planes is opposite to the trend of adatoms like O* and/or N*. The energy barrier for the decomposition of H2O into H* and OH* is larger on ANPs than on crystal planes, and it decreases with the increasing cluster size. Due to the competition between the hydrogen (H) bonding among water molecules and the interaction between the water molecules and the substrate, the adsorption strength of H2O first increases and then decreases with the increase of water coverage. Moreover, each H2O molecule can efficiently form up to two H bonds with two H2O molecules. As a result, H2O molecules tend to aggregate into cyclic structures rather than chains on Al surfaces. Furthermore, the dissociation energy barrier of H2O drops with the increasing water coverage due to the presence of H bonds. Our results provide insight into interactions between water and Al, which can be extended to understand the interaction between water and other metal surfaces.

Unusual activated processes controlling dislocation motion in body-centered-cubic high-entropy alloys.

Atomistic simulations of dislocation mobility reveal that body-centered cubic (BCC) high-entropy alloys (HEAs) are distinctly different from traditional BCC metals. HEAs are concentrated solutions in which composition fluctuation is almost inevitable. The resultant inhomogeneities, while locally promoting kink nucleation on screw dislocations, trap them against propagation with an appreciable energy barrier, replacing kink nucleation as the rate-limiting mechanism. Edge dislocations encounter a similar activated process of nanoscale segment detrapping, with comparable activation barrier. As a result, the mobility of edge dislocations, and hence their contribution to strength, becomes comparable to screw dislocations.

Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait.

BACKGROUND: Average backfat thickness (BFT) is a critical complex trait in pig and an important indicator for fat deposition and lean rate. Usually, genome-wide association study (GWAS) was used to discover quantitative trait loci (QTLs) of BFT in a single population. However, the power of GWAS is limited by sample size in a single population. Alternatively, meta-analysis of GWAS (metaGWAS) is an attractive method to increase the statistical power by integrating data from multiple breeds and populations. The aim of this study is to identify shared genetic characterization of BFT across breeds in pigs via metaGWAS.  RESULTS: In this study, we performed metaGWAS on BFT using 15,353 pigs (5,143 Duroc, 7,275 Yorkshire, and 2,935 Landrace) from 19 populations. We detected 40 genome-wide significant SNPs (Bonferroni corrected P < 0.05) and defined five breed-shared QTLs in across-breed metaGWAS. Markers within the five QTL regions explained 7 ~ 9% additive genetic variance and showed strong heritability enrichment. Furthermore, by integrating information from multiple bioinformatics databases, we annotated 46 candidate genes located in the five QTLs. Among them, three important (MC4R, PPARD, and SLC27A1) and seven suggestive candidate genes (PHLPP1, NUDT3, ILRUN, RELCH, KCNQ5, ITPR3, and U3) were identified. CONCLUSION: QTLs and candidate genes underlying BFT across breeds were identified via metaGWAS from multiple populations. Our findings contribute to the understanding of the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.

Switch of Thermal Expansions Triggered by Itinerant Electrons in Isostructural Metal Trifluorides.

Manageable thermal expansion (MTE) of metal trifluorides can be achieved by introducing local structure distortion (LSD) in the negative thermal expansion ScF3. However, an open issue is why isostructural TiF3, free of LSD, exhibits positive thermal expansion. Herein, a combined analysis of synchrotron X-ray diffraction, X-ray pair distribution function, and rigorous first-principles calculations was performed to reveal the important role of itinerant electrons in mediating soft phonons and lattice dynamics. Metallic TiF3 demonstrates itinerant electrons and a suppressed Grüneisen parameter γ ≈ -20, while insulating ScF3 absence of itinerant electrons has a considerable γ ≈ -120. With increasing electron doping concentrations in ScF3, soft phonons become hardened and the γ is repressed significantly, identical to TiF3. The presented results update the thermal expansion transition mechanism in framework structure analogues and provide a practical approach to obtaining MTE without inducing sizable structure distortion.

Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation.

BACKGROUND: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS: The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS: Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by - 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS: Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.

The evolving quality of frictional contact with graphene.

Graphite and other lamellar materials are used as dry lubricants for macroscale metallic sliding components and high-pressure contacts. It has been shown experimentally that monolayer graphene exhibits higher friction than multilayer graphene and graphite, and that this friction increases with continued sliding, but the mechanism behind this remains subject to debate. It has long been conjectured that the true contact area between two rough bodies controls interfacial friction. The true contact area, defined for example by the number of atoms within the range of interatomic forces, is difficult to visualize directly but characterizes the quantity of contact. However, there is emerging evidence that, for a given pair of materials, the quality of the contact can change, and that this can also strongly affect interfacial friction. Recently, it has been found that the frictional behaviour of two-dimensional materials exhibits traits unlike those of conventional bulk materials. This includes the abovementioned finding that for few-layer two-dimensional materials the static friction force gradually strengthens for a few initial atomic periods before reaching a constant value. Such transient behaviour, and the associated enhancement of steady-state friction, diminishes as the number of two-dimensional layers increases, and was observed only when the two-dimensional material was loosely adhering to a substrate. This layer-dependent transient phenomenon has not been captured by any simulations. Here, using atomistic simulations, we reproduce the experimental observations of layer-dependent friction and transient frictional strengthening on graphene. Atomic force analysis reveals that the evolution of static friction is a manifestation of the natural tendency for thinner and less-constrained graphene to re-adjust its configuration as a direct consequence of its greater flexibility. That is, the tip atoms become more strongly pinned, and show greater synchrony in their stick-slip behaviour. While the quantity of atomic-scale contacts (true contact area) evolves, the quality (in this case, the local pinning state of individual atoms and the overall commensurability) also evolves in frictional sliding on graphene. Moreover, the effects can be tuned by pre-wrinkling. The evolving contact quality is critical for explaining the time-dependent friction of configurationally flexible interfaces.

The theory on and software simulating large-scale genomic data for genotype-by-environment interactions.

BACKGROUND: With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. RESULTS: We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. CONCLUSIONS: This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

2.4 kW 1045 nm narrow-spectral-width monolithic single-mode CW fiber laser by using an FBG-based MOPA configuration.

A 24 kW narrow-spectral-width near-diffraction-limited monolithic fiber laser system at ${\sim}{1045.2}\;{\rm{nm}}$ in a fiber Bragg grating (FBG)-based master oscillator power amplifier (MOPA) configuration is demonstrated in this paper. The near-diffraction-limited beam quality (${{\rm{M}}^2}\sim{1.2}$) and a spectral width of 0.35 nm (${\sim}{{96}}\;{\rm{GHz}}$) are achieved. The stimulated Raman scattering (SRS) is theoretically and experimentally investigated. The SRS has been suppressed by carefully optimizing the length of the Yb-doped fiber and the pumping scheme, and a signal-to-noise ratio of ${\sim}{{33}}\;{\rm{dB}}$ between the laser signal and the Raman Stokes component is achieved. The stimulate Brillouin scattering and the transverse mode instability are not observed. To our best knowledge, this is the highest-output power for ${{104}} \times {\rm{nm}}$ single-mode fiber laser with ${\sim}{{96}}\;{\rm{GHz}}$ spectral width by using an FBG-based MOPA configuration.

Integrative analysis of genome-wide DNA methylation and gene expression profiles reveals important epigenetic genes related to milk production traits in dairy cattle.

Epigenetic modification plays a critical role in establishing and maintaining cell differentiation, embryo development, tumorigenesis and many complex diseases. However, little is known about the epigenetic regulatory mechanisms for milk production in dairy cattle. Here, we conducted an epigenome-wide study, together with gene expression profiles to identify important epigenetic candidate genes related to the milk production traits in dairy cattle. Whole-genome bisulphite sequencing and RNA sequencing were employed to detect differentially methylated genes (DMG) and differentially expressed genes (DEG) in blood samples in dry period and lactation period between two groups of cows with extremely high and low milk production performance. A total of 10,877 and 6,617 differentially methylated regions were identified between the two groups in the two periods, which corresponded to 3,601 and 2,802 DMGs, respectively. Furthermore, 156 DEGs overlap with DMGs in comparison of the two groups, and 131 DEGs overlap with DMGs in comparison of the two periods. By integrating methylome, transcriptome and GWAS data, some potential candidate genes for milk production traits in dairy cattle were suggested, such as DOCK1, PTK2 and PIK3R1. Our studies may contribute to a better understanding of epigenetic modification on milk production traits of dairy cattle.

A mutation in MAP2 is associated with prenatal hair follicle density.

Hairlessness is usually a rare trait in pigs; however, in this study, we found hairless (HR) pigs at a relatively high frequency in 1 pig herd. We observed that, the lower hair shaft density of HR pigs could be mainly attributed to the lower hair follicle density, and during the embryonic period, d 39-45 were a critical stage for the formation of the hair follicle. In this regard, d 41 during gestation was a particularly important point. Hair follicle morphogenesis occurring at an early stage of embryo development is similar to humans and mice. Further analyses of association studies based on single-nucleotide polymorphism chip as well as sequence data, mRNA sequencing, immunohistochemistry, and comparative genomics demonstrated that microtubule-associated protein 2 (MAP2) is a key gene responsible for hair follicle density and 1 missense mutation of A-to-G at rs328005415 in MAP2, causing a valine-to-methionine substitution leads to the HR phenotype. Considering the high homology between pigs and humans, our research has some significance for the study of the mechanisms of skin development, hair morphogenesis, and hair loss in humans by showing that the pig may be a more appropriate model in which to study these processes.-Jiang, Y., Jiang, Y., Zhang, H., Mei, M., Song, H., Ma, X., Jiang, L., Yu, Z., Zhang, Q., Ding, X. A mutation in MAP2 is associated with prenatal hair follicle density.

Optimizing genomic prediction model given causal genes in a dairy cattle population.

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge. Although several plausible results have been obtained from model modification or strategy optimization, most of them were validated in a specific empirical population with a limited variety of genetic architecture for complex traits. An alternative approach is to use simulated genetic architecture with known causal genes (e.g., simulated causative SNP) to evaluate different GP models with given causal genes. Our objectives were to (1) evaluate the performance of GP under a variety of genetic architectures with a subset of known causal genes and (2) compare different GP models modified by highlighting causal genes and different strategies to weight causal genes. In this study, we simulated pseudo-phenotypes under a variety of genetic architectures based on the real genotypes and phenotypes of a dairy cattle population. Besides classical genomic best linear unbiased prediction, we evaluated 3 modified GP models that highlight causal genes as follows: (1) by treating them as fixed effects, (2) by treating them as a separate random component, and (3) by combining them into the genomic relationship matrix as random effects. Our results showed that highlighting the known causal genes, which explained a considerable proportion of genetic variance in the GP models, increased the predictive accuracy. Combining all given causal genes into the genomic relationship matrix was the optimal strategy under all the scenarios validated, and treating causal genes as a separate random component is also recommended, when more than 20% of genetic variance was explained by known causal genes. Moreover, assigning differential weights to each causal gene further improved the predictive accuracy.

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model.

Economically important traits are usually complex traits influenced by genes, environment and genotype-by-environment (G × E) interactions. Ignoring G × E interaction could lead to bias in the estimation of breeding values and selection decisions. A total of 1,778 pigs were genotyped using the PorcineSNP80 BeadChip. The existence of G × E interactions was investigated using a single-step reaction norm model for growth traits of days to 100 kg (AGE) and backfat thickness adjusted to 100 kg (BFT), based on a pedigree-based relationship matrix (A) or a genomic-pedigree joint relationship matrix (H). In the reaction norm model, the herd-year-season effect was measured as the environmental variable (EV). Our results showed no G × E interactions for AGE, but for BFT. For both AGE and BFT, the genomic reaction norm model (H) produced more accurate predictions than the conventional reaction norm model (A). For BFT, the accuracies were greater based on the reaction norm model than those based on the reduced model without exploiting G × E interaction, with EV ranging from 0.5 to 1, and accuracy increasing by 3.9% and 4.6% in the reaction norm model based on A and H matrices, respectively, while reaction norm model yielded approximately 8.4% and 7.9% lower accuracy for EVs ranging from 0 to 0.4, based on A and H matrices, respectively. In addition, for BFT, the highest accuracy was obtained in the BJLM6 farm for realizing directional selection. This study will help to apply G × E interactions to practical genomic selection.

Estimation of genetic parameters for reproductive traits in connectedness groups of Duroc, Landrace and Yorkshire pigs in China.

The objective of this study was to estimate group- and breed-specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five-trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from .01 (for FI in Yorkshire) to .30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from .14 to .20 and were consistent for each breed, and for FI, the estimates varied from .01 to .11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were -.25, -.27, -.18, -.04, -.10, -.02, and .28 for FI-TNB, FI-NBA, FI-LWF, AFF-TNB, AFF-NBA, AFF-LWF and FI-AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non-genetic factors such as management and measurement.

A genome-wide association study of reproduction traits in four pig populations with different genetic backgrounds.

OBJECTIVE: Genome-wide association study and two meta-analysis based on GWAS performed to explore the genetic mechanism underlying variation in pig number born alive (NBA) and total number born (TNB). METHODS: Single trait GWAS and two meta-analysis (single-trait meta analysis and multitrait meta analysis) were used in our study for NBA and TNB on 3,121 Yorkshires from 4 populations, including three different American Yorkshire populations (n = 2,247) and one British Yorkshire populations (n = 874). RESULTS: The result of single trait GWAS showed that no significant associated single nucleotide polymorphisms (SNPs) were identified. Using single-trait meta analysis and multi-trait meta analysis within populations, 11 significant loci were identified associated with target traits. Spindlin 1, vascular endothelial growth factor A, forkhead box Q1, msh homeobox 1, and LHFPL tetraspan submily member 3 are five functionally plausible candidate genes for NBA and TNB. Compared to the single population GWAS, single-trait Meta analysis can improve the detection power to identify SNPs by integrating information of multiple populations. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits. CONCLUSION: In total, our findings identified novel genes to be validated as candidates for NBA and TNB in pigs. Also, it enabled us to enlarge population size by including multiple populations with different genetic backgrounds and increase the power of GWAS by using meta analysis.