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ABSTRACT: Disease-related milk losses directly affect dairy herds' profitability and the production efficiency of the dairy industry. Therefore, this study aimed to quantify phenotypic variability in milk fluctuation periods related to diseases and to explore milk fluctuation traits as indicators of disease resilience. By combining high-frequency daily milk yield data with disease records of cows that were treated and recovered from the disease, we estimated milk variability trends within a fixed period around the treatment day of each record for 5 diseases: udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health. The average milk yield decreased rapidly from 6 to 8 d before the treatment day for all diseases, with the largest milk reduction observed on the treatment day. Additionally, we assessed the significance of milk fluctuation periods highly related to diseases by defining milk fluctuations as a period of at least 10 consecutive days in which milk yield fell below 90% of the expected milk production values at least once. We defined the development and recovery phases of milk fluctuations using 3,847 milk fluctuation periods related to disease incidences, and estimated genetic parameters of milk fluctuation traits, including milk losses, duration of the fluctuation, variation rate in daily milk yield, and standard deviation of milk deviations for each phase and their genetic correlation with several important traits. In general, the disease-related milk fluctuation periods lasted 21.19 ± 10.36 d with a milk loss of 115.54 ± 92.49 kg per lactation. Compared with the development phase, the recovery phase lasted an average of 3.3 d longer, in which cows produced 11.04 kg less milk and exhibited a slower variation rate in daily milk yield of 0.35 kg/d. There were notable differences in milk fluctuation traits depending on the disease, and greater milk losses were observed when multiple diseases occurred simultaneously. All milk fluctuation traits evaluated were heritable with heritability estimates ranging from 0.01 to 0.10, and moderate to high genetic correlations with milk yield (0.34 to 0.64), milk loss throughout the lactation (0.22 to 0.97), and resilience indicator (0.39 to 0.95). These results indicate that cows with lower milk losses and higher resilience tend to have more stable milk fluctuations, which supports the potential for breeding for more disease-resilient cows based on milk fluctuation traits. Overall, this study confirms the high effect of diseases on milk yield variability and provides insightful information about their relationship with relevant traits in Holstein cattle. Furthermore, this study shows the potential of using high-frequency automatic monitoring of milk yield to assist on breeding practices and health management in dairy cows.

Abstract Background Eurasian pigs have undergone lineage admixture throughout history. It has been confirmed that the genes of indigenous pig breeds in China have been introduced into Western commercial pigs, providing genetic materials for breeding Western pigs. Pigs in Taihu Lake region (TL), such as the Meishan pig and Erhualian pig, serve as typical representatives of indigenous pig breeds in China due to their high reproductive performances. These pigs have also been imported into European countries in 1970 and 1980 s. They have played a positive role in improving the reproductive performances in European commercial pigs such as French Large White pigs (FLW). However, it is currently unclear if the lineage of TL pigs have been introgressed into the Danish Large White pigs (DLW), which are also known for their high reproductive performances in European pigs. To systematically identify genomic regions in which TL pigs have introgressed into DLW pigs and their physiological functions, we collected the re-sequencing data from 304 Eurasian pigs, to identify shared haplotypes between DLW and TL pigs. Results The findings revealed the presence of introgressed genomic regions from TL pigs in the genome of DLW pigs indeed. The genes annotated within these regions were found to be mainly enriched in neurodevelopmental pathways. Furthermore, we found that the 115 kb region located in SSC16 exhibited highly shared haplotypes between TL and DLW pigs. The major haplotype of TL pigs in this region could significantly improve reproductive performances in various pig populations. Around this genomic region, NDUFS4 gene was highly expressed and showed differential expression in multiple reproductive tissues between extremely high and low farrowing Erhualian pigs. This suggested that NDUFS4 gene could be an important candidate causal gene responsible for affecting the reproductive performances of DLW pigs. Conclusions Our study has furthered our knowledge of the pattern of introgression from TL into DLW pigs and the potential effects on the fertility of DLW pigs.

Litter size is an important trait in pig production. But selection accuracy for this trait is relatively low, compared with production traits. This study, for the first time, investigated the improvement of genetic evaluation of reproduction traits such as litter size in pigs using data of production traits as an additional information source. The data of number of piglets born alive per litter (NBA), age at 100 kg of body weight (Age100), and lean meet percentage (LMP) in a Yorkshire population were analyzed, using either a single-trait model or the multitrait model that allows us to account for environmental correlation between reproduction and production traits in the situation that one individual has only one record for a production trait while multiple records for a reproduction trait. Accuracy of genetic evaluation using single-trait and multitrait models were assessed by model-based accuracy (Rm) and validation accuracy (Rv). Two validation scenarios were considered. One scenario (Valid_r1) was that the individuals did not have a record of NBA, but Age100 and LMP. The other (Valid_r2) was that the individuals did not have a record for all the three traits. The estimate of heritability was 0.279 for Age100, 0.371 for LMP, and 0.076 for NBA. Genetic correlation was 0.308 between Age100 and LMP, 0.369 between Age100 and NBA, and 0.022 between LMP and NBA. Compared with the single-trait model, the multitrait model including Age100 increased prediction accuracy for NBA by 3.6 percentage points in Rm and 5.9 percentage points in Rv for the scenario of Valid_r1. The increase was 1.8 percentage points in Rm and 3.8 percentage points in Rv for the scenario of Valid_r2. Age100 also gained in the multitrait model but was smaller than NBA. However, LMP did not benefit from a multitrait model and did not have a positive contribution to genetic evaluation for NBA. In addition, the multitrait model, in general, slightly reduced level bias but not dispersion bias of genetic evaluation. According to these results, it is recommended to predict breeding values using a multitrait model including growth and reproduction traits.

Abstract Background The breeding value of a crossbred individual can be expressed as the sum of the contributions from each of the contributing pure breeds. In theory, the breeding value should account for segregation between breeds, which results from the difference in the mean contribution of loci between breeds, which in turn is caused by differences in allele frequencies between breeds. However, with multiple generations of crossbreeding, how to account for breed segregation in genomic models that split the breeding value of crossbreds based on breed origin of alleles (BOA) is not known. Furthermore, local breed proportions (LBP) have been modelled based on BOA and is a concept related to breed segregation. The objectives of this study were to explore the theoretical background of the effect of LBP and how it relates to breed segregation and to investigate how to incorporate breed segregation (co)variance in genomic BOA models. Results We showed that LBP effects result from the difference in the mean contribution of loci between breeds in an additive genetic model, i.e. breed segregation effects. We found that the (co)variance structure for BS effects in genomic BOA models does not lead to relationship matrices that are positive semi-definite in all cases. However, by setting one breed as a reference breed, a valid (co)variance structure can be constructed by including LBP effects for all other breeds and assuming them to be correlated. We successfully estimated variance components for a genomic BOA model with LBP effects in a simulated example. Conclusions Breed segregation effects and LBP effects are two alternative ways to account for the contribution of differences in the mean effects of loci between breeds. When the covariance between LBP effects across breeds is included in the model, a valid (co)variance structure for LBP effects can be constructed by setting one breed as reference breed and fitting an LBP effect for each of the other breeds.

The elite bull plays an extremely important role in the genetic progression of the dairy cow population. The previous results indicated the potential positive relationship of large scrotal circumference (SC) with improved semen volume, concentration, and motility. In order to improve bull’s semen quantity and quality by selection, it is necessary to estimate the genetic parameters of semen traits and their correlations with other conformation traits such as SC that could be used for an indirect selection. In this study, the genetic parameters of seven semen traits (n = 66,260) and nine conformation traits (n = 3,642) of Holstein bulls (n = 453) were estimated by using the bivariate repeatability animal model with the average information-restricted maximum likelihood (AI-REML) approach. The results showed that the estimated heritabilities of semen traits ranged from 0.06 (total number of motile sperm, TNMS) to 0.37 (percentage of abnormal sperm, PAS) and conformation traits ranged from 0.23 (pin width, PW) to 0.69 (hip height, HH). The highest genetic correlations were found between semen volume per ejaculation (SVPE), semen concentration per ejaculation (SCPE), total number of sperm (TNS), and TNMS traits that were 0.97, 0.98, 1.00, and 0.99, respectively. Phenotypic correlations between SC and SVPE, SCPE, TNS, and TNMS were 0.35, 0.35, 0.48, and 0.42, respectively. In summary, the moderate or high heritability of semen traits indicates that genetic improvement of semen quality by selection is feasible, where SC could be a useful trait for indirect selection or as correlated information to improve semen quantity and production in the practical bull breeding programs.

Abstract Background Survival from birth to slaughter is an important economic trait in commercial pig productions. Increasing survival can improve both economic efficiency and animal welfare. The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter. Results We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model, a logit model, and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes (0, 1). The results show that in the case of only alive animals having genotype data, unbiased genomic predictions can be achieved when using variances estimated from pedigree-based model. Models using genomic information achieved up to 59.2% higher accuracy of estimated breeding value compared to pedigree-based model, dependent on genotyping scenarios. The scenario of genotyping all individuals, both dead and alive individuals, obtained the highest accuracy. When an equal number of individuals (80%) were genotyped, random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes. The linear model, logit model and probit model achieved similar accuracy. Conclusions Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes, but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06% to 6.04%.

Abstract Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon (PRKCE) and myosin light chain 2 (MYL2). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improvement (0.006), adding significant iWGS SNPs led to an improvement of reliability by 0.050 when using a one‐component GBLUP, a further increase of 0.033 when using a two‐component GBLUP model. For BayesMix models, compared with using 80 K SNPs alone, adding additional significant iWGS SNPs into one‐ or two‐component BayesMix models led to improvements of reliabilities for IFC by 0.040 and 0.089, respectively. Our results may facilitate further identification of causal genes for IFC and may be beneficial for the improvement of IFC in pig breeding programs.

ABSTRACT: For genomic prediction of crossbred animals, models that account for the breed origin of alleles (BOA) in marker genotypes can allow the effects of marker alleles to differ depending on their ancestral breed. Previous studies have shown that genomic estimated breeding values for crossbred cows can be calculated using the marker effects that are estimated in the contributing pure breeds and combined based on estimated BOA in the genotypes of the crossbred cows. In the presented study, we further exploit the BOA information for improving the prediction of genomic breeding values of crossbred dairy cows. We investigated 2 types of BOA-derived breed proportions: global breed proportions, defined as the proportion of marker alleles assigned to each breed across the whole genome; and local breed proportions (LBP), defined as the proportions of alleles on chromosome segments which were assigned to each breed. Further, we investigated 2 BOA-derived measures of heterozygosity for the prediction of total genetic value. First, global breed heterozygosity, defined as the proportion of marker loci that have alleles originating in 2 different breeds over the whole genome. Second, local breed heterozygosity (LBH), defined as proportions of marker loci on chromosome segments that had alleles originating in 2 different breeds. We estimated variance related to LBP and LBH on the remaining variation after accounting for prediction with solutions from the genomic evaluations of the pure breeds and validated alternative models for production traits in 5,214 Danish crossbred dairy cows. The estimated LBP variances were 0.9, 1.2, and 1.0% of phenotypic variance for milk, fat, and protein yield, respectively. We observed no clear LBH effect. Cross-validation showed that models with LBP effects had a numerically small but statistically significantly higher predictive ability than models only including global breed proportions. We observed similar improvement in accuracy by the model having an across crossbred residual additive genetic effect, accounting for the additive genetic variation that was not accounted for by the solutions from purebred. For genomic predictions of crossbred animals, estimated BOA can give useful information on breed proportions, both globally in the genome and locally in genome regions, and on breed heterozygosity.

A fine-grained WC-15wt%Al0.5CoCrFeNi cemented carbide was prepared through a vacuum and gas pressure sintering. For achieving high surface integrity, diamond wheel grinding serves as the primary molding process for the machining of WC cemented carbide. To reveal the influence of grinding on the surface integrity of fine-grained WC-HEA cemented carbide, studies were conducted on grinding force, surface microstructure, surface roughness, residual stress, microhardness, and bending strength. The morphological analysis of the ground surface indicated a transition in the material removal mechanism of WC-HEA cemented carbide from ductile removal to brittle removal, with brittle removal becoming predominant as the depth of grinding increases. With the increasing depth of grinding, the grinding force increases, and the grinding force increases while the surface roughness decreases. Correspondingly, there is an improvement in both hardness and bending strength. Additionally, grinding induces high residual compressive stress on the surface, with a maximum compressive stress of 1795 MPa. The bending strength of the material is found to be dependent on the residual stress.

ABSTRACT: High mortality and involuntary culling rates cause great economic losses to the worldwide dairy cattle industry. However, there is low emphasis on wellness traits in replacement animals (dairy calves and replacement heifers) during their development stages in modern dairy cattle breeding programs. Therefore, the main objectives of this study were to estimate genetic parameters of wellness traits in replacement cattle (replacement wellness traits) and obtain their genetic correlations with 12 cow health and longevity traits in the Chinese Holstein population. Seven replacement wellness traits were analyzed, including birth weight, survival from 3 to 60 d (Sur1), survival from 61 to 365 d (Sur2), survival from 366 d to the first calving (Sur3), calf diarrhea, calf pneumonia, and calf serum total protein (STP). Single and bivariate animal models were employed to estimate (co)variance components using the data from 189,980 Holstein cattle. The genetic correlations between replacement wellness traits and cow longevity, health traits were calculated by employing bivariate models, including 6 longevity traits and 6 health traits (clinical mastitis, metritis, ketosis, displaced abomasum, milk fever, and hoof health or hoof disease). The estimated heritabilities (± SE) were 0.335 (± 0.008), 0.088 (± 0.005), 0.166 (± 0.006), 0.102 (±0 .006), 0.048 (± 0.003), 0.063 (± 0.004), and 0.170 (± 0.019) for birth weight, Sur1, Sur2, Sur3, pneumonia, diarrhea, and STP, respectively. The majority of the genetic correlations among the 7 replacement wellness traits were negligible. The genetic correlations among Sur1, Sur2, and Sur3 ranged from 0.112 (Sur1 and Sur3) to 0.445 (Sur1 and Sur2) when fitting a linear model (estimates in the observed scale), and from 0.560 (Sur1 and Sur3) to 0.773 (Sur1 and Sur2) when fitting a threshold model (estimates in the liability scale). The genetic correlations between replacement wellness and cow longevity were low (absolute value lower than 0.30), but some of them were significantly different from zero. Compared with other replacement wellness traits, Sur3 and STP had relatively high genetic correlations with cow longevity. Replacement wellness traits are heritable and can be improved through direct genetic and genomic selection. The results from the current study will contribute for better balancing dairy cattle breeding goals to genetically improve dairy cattle wellness in the period from birth to first calving.

ABSTRACT: Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

ABSTRACT: This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co)variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.

Abstract Background In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds. Application of BOA models in rotational crossbreeding requires methods for detecting BOA, but the existing methods have not been developed for rotational crossbreeding. Therefore, the aims of this study were to develop and test methods for detecting BOA in a rotational crossbreeding system, and to investigate methods for calculating GEBV for crossbred cows using estimated marker effects from purebreds. Results For detecting BOA in crossbred cows from rotational crossbreeding for which pedigree is recorded, we developed the AllOr method based on the comparison of haplotypes in overlapping windows. To calculate the GEBV of crossbred cows, two models were compared: a BOA model where marker effects estimated from purebreds are combined based on the detected BOA; and a breed proportion model where marker effects are combined based on estimated breed proportions. The methods were tested on simulated data that mimic the first four generations of rotational crossbreeding between Holstein, Jersey and Red Dairy Cattle. The AllOr method detected BOA correctly for 99.6% of the marker alleles across the four crossbred generations. The reliability of GEBV was higher with the BOA model than with the breed proportion model for the four generations of crossbreeding, with the largest difference observed in the first generation. Conclusions In rotational crossbreeding for which pedigree is recorded, BOA can be accurately detected using the AllOr method. Combining marker effects estimated from purebreds to predict the breeding value of crossbreds based on BOA is a promising approach to provide GEBV for crossbred dairy cows.

As an emerging grinding equipment, roller presses are widely used in Cement industry. The current problem with roller press is that the rolls surface is prone to wear and needs to be replaced regularly. This greatly reduces the service life of the roller press and affects the development of the roller press. Therefore, how to reduce the wear on the surface of the roller press and increase the service life of the roller press is an urgent problem that needs to be solved for the current roller press. Current research mainly focuses on the mechanism research of roller press wear and the optimization of rolls surface structure. In this paper, the extrusion force between the stud-lining and the material is calculated by analyzing the stress of the studded rolls under actual working conditions and the compression and rebound characteristics of material layer. The failure modes of studded rolls are mainly divided into two parts. On the one hand, it is fatigue cracking of the roller shaft and stud-lining. On the other hand, it is cracking at the contact between the stud-lining and the stud, and the fracture of the stud. Because the failure modes of the studded rolls are divided into two parts, the studded rolls are divided into two parts for simulation by using ANSYS 18.0. Firstly, the static analysis is carried out on the roller shaft and the stud-lining, and the distribution cloud diagram of the stress and contact pressure of the roller shaft and the stud-lining is obtained, and the stress concentration area is optimized. After optimization, the contact pressure between the roller shaft and the stud-lining is reduced by about 50%. The maximum equivalent stress of the roller shaft and stud-lining has also been reduced. Secondly, a static analysis was conducted on the stud-lining and studs. Since the stud-lining of the studded rolls are composed of stud holes arranged in a certain order. Therefore, stress concentration is prone to occur around the stud hole. The simulation experiment was carried out by changing the optimization schemes such as the assembly method of the stud and the stud-lining, the distance between the studs, and the length of the stud. To reduce the stress concentration of stud and stud-lining. After optimizing the model of the stud and stud-lining, the maximum equivalent stress of the stud-lining and stud decreased by about 10% and 25% respectively. Through the optimized design of roller shafts, stud-lining and studs. The service life of the studded rolls can be effectively improved and the production cost can be reduced. This can provide a reference for the design of the studded rolls.

Annotating functional elements of the genome helps the interpretation of genetic variation. Here, the authors compile functional genomics data for the pig genome over 14 tissues with 15 different chromatin states, integrate the data with WGS and GWAS data, and compare conservation of regulatory elements across mouse and human tissues.

Abstract Background In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip. Results For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds. Conclusions Combining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.

Abstract Background In breeding programs, recording large-scale feed intake (FI) data routinely at the individual level is costly and difficult compared with other production traits. An alternative approach could be to record FI at the group level since animals such as pigs are normally housed in groups and fed by a shared feeder. However, to date there have been few investigations about the difference between group- and individual-level FI recorded in different environments. We hypothesized that group- and individual-level FI are genetically correlated but different traits. This study, based on the experiment undertaken in purebred DanBred Landrace (L) boars, was set out to estimate the genetic variances and correlations between group- and individual-level FI using a bivariate random regression model, and to examine to what extent prediction accuracy can be improved by adding information of individual-level FI to group-level FI for animals recorded in groups. For both bivariate and univariate models, single-step genomic best linear unbiased prediction (ssGBLUP) and pedigree-based BLUP (PBLUP) were implemented and compared. Results The variance components from group-level records and from individual-level records were similar. Heritabilities estimated from group-level FI were lower than those from individual-level FI over the test period. The estimated genetic correlations between group- and individual-level FI based on each test day were on average equal to 0.32 (SD = 0.07), and the estimated genetic correlation for the whole test period was equal to 0.23. Our results demonstrate that by adding information from individual-level FI records to group-level FI records, prediction accuracy increased by 0.018 and 0.032 compared with using group-level FI records only (bivariate vs. univariate model) for PBLUP and ssGBLUP, respectively. Conclusions Based on the current dataset, our findings support the hypothesis that group- and individual-level FI are different traits. Thus, the differences in FI traits under these two feeding systems need to be taken into consideration in pig breeding programs. Overall, adding information from individual records can improve prediction accuracy for animals with group records.

Abstract Background Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981‬ non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population. Results With basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models. Conclusions In summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP.

A roller tip was utilized in a set of self-fabricated ultrasonic burnishing equipment. Ultrasonic roller burnishing with/without heat treatment was proposed and investigated in the surface strengthening of TC11 titanium alloy material. Experimental research together with finite element analysis were carried out to perform the study. Three treatments, including conventional turning, ultrasonic roller burnishing at ambient temperature (URB) and ultrasonic roller burnishing at medium temperature (URB/HT), were comparatively evaluated in term of surface integrity and corrosion resistance of the treated sample. As a result, compared with URB, the surface of TC11 alloy treated by URB/HT had better surface finishing, which significantly improved corrosion resistance, and thus the surface of TC11 alloy was strengthened. The good performance of URB/HT was attributed to the combined effect of the high frequency dynamic impact from URB and the thermoplastic effect of the heat treatment. Owing to the excitation of the ultrasonic vibration during URB, an oscillating stress wave formed and propagated deep inside the being treated material, and the heat treatment in URB/HT case was supposed to accelerate this sort of stress propagation. Meanwhile, both the heat treatment and the dynamic oscillating stress wave facilitated the deformation of the surface layer.

An amendment to this paper has been published and can be accessed via the original article.

To investigate the formation mechanism of the white layer on the machined surface during high-speed milling of nickel-based superalloy GH4169, several cutting parameters were selected for milling experiments. Energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and electron backscattered diffraction (EBSD) were employed to characterize element distribution, phase transformation, and microstructure changes in the machined surface of the superalloy and then reveal the formation mechanism of the white layer on the machined surface. The results show that the white layer appears on the machined surface of GH4169, which is dense and has no obvious structural features. The total amount of elements in the white layer remains unchanged, but the distribution of elements such as C, N, O, Fe, and Ni changes due to phase change. The formation mechanism of the white layer is due to the dynamic recovery and dynamic recrystallization caused by the heat–force coupling effect, which leads to the grain refinement of the material and thus forms the white layer. This investigation can provide theoretical support to improve the service life of the parts in actual machining.

Due to the strong plasticity of Inconel 718 and the significant size effect of micromachining, a large number of burrs will be produced in traditional processing. The addition of ultrasonic vibration during machining can reduce the burr problem. The mechanism of burr generation in traditional micromilling (TMM) and ultrasonic vibration-assisted micromilling (UVAMM) was analyzed by simulation, and verified by corresponding experiments. It is found that applying high-frequency ultrasonic vibration in the milling feed direction can reduce cutting temperature and cutting force, improve chip breaking ability, and reduce burr formation. When the cutting thickness will reach the minimum cutting thickness hmin, the chip will start to form. When A/ƒz > 1/2, the tracks of the two tool heads start to cut, and the chips are not continuous. Some of the best burr suppression effects were achieved under conditions of low cutting speed (Vc), feed per tooth (ƒz), and large amplitude (A). When A is 6 μm, the size and quantity of burr is the smallest. When ƒz reaches 6 μm, large continuous burrs appear at the top of the groove. The experimental results further confirm the accuracy of the simulation results and provide parameter reference.

The size of the reference population is critical in order to improve the accuracy of genomic prediction. Indeed, improving genomic prediction accuracy by combining multinational reference populations has proven to be effective. In this study, we investigated the improvement of genomic prediction accuracy in seven complex traits (i.e., milk yield; fat yield; protein yield; somatic cell count; body conformation; feet and legs; and mammary system conformation) by combining the Chinese and Nordic Holstein reference populations. The estimated genetic correlations between the Chinese and Nordic Holstein populations are high with respect to protein yield, fat yield, and milk yield—whereby these correlations range from 0.621 to 0.720—and are moderate with respect to somatic cell count (0.449), but low for the three conformation traits (which range from 0.144 to 0.236). When utilizing the joint reference data and a two-trait GBLUP model, the genomic prediction accuracy in the Chinese Holsteins improves considerably with respect to the traits with moderate-to-high genetic correlations, whereas the improvement in Nordic Holsteins is small. When compared with the single population analysis, using the joint reference population for genomic prediction in younger animals, results in a 2.3 to 8.1 percent improvement in accuracy. Meanwhile, 10 replications of five-fold cross-validation were also implemented in order to evaluate the performance of joint genomic prediction, thereby resulting in a 1.6 to 5.2 percent increase in accuracy. With respect to joint genomic prediction, the bias was found to be quite low. However, for traits with low genetic correlations, the joint reference data do not improve the prediction accuracy substantially for either population.

Accurately estimating the genetic parameters and revealing more genetic variants underlying milk production and quality are conducive to the genetic improvement of dairy cows. In this study, we estimate the genetic parameters of five milk-related traits of cows—namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)—based on a random regression test-day model. A total of 95,375 test-day records of 9,834 cows in the lower reaches of the Yangtze River were used for the estimation. In addition, genome-wide association studies (GWASs) for these traits were conducted, based on adjusted phenotypes. The heritability, as well as the standard errors, of MY, MFP, MFY, MPP, and MPY during lactation ranged from 0.22 ± 0.02 to 0.31 ± 0.04, 0.06 ± 0.02 to 0.15 ± 0.03, 0.09 ± 0.02 to 0.28 ± 0.04, 0.07 ± 0.01 to 0.16 ± 0.03, and 0.14 ± 0.02 to 0.27 ± 0.03, respectively, and the genetic correlations between different days in milk (DIM) within lactations decreased as the time interval increased. Two, six, four, six, and three single nucleotide polymorphisms (SNPs) were detected, which explained 5.44, 12.39, 8.89, 10.65, and 7.09% of the phenotypic variation in MY, MFP, MFY, MPP, and MPY, respectively. Ten Kyoto Encyclopedia of Genes and Genomes pathways and 25 Gene Ontology terms were enriched by analyzing the nearest genes and genes within 200 kb of the detected SNPs. Moreover, 17 genes in the enrichment results that may play roles in milk production and quality were selected as candidates, including CAMK2G, WNT3A, WNT9A, PLCB4, SMAD9, PLA2G4A, ARF1, OPLAH, MGST1, CLIP1, DGAT1, PRMT6, VPS28, HSF1, MAF1, TMEM98, and F7. We hope that this study will provide useful information for in-depth understanding of the genetic architecture of milk production and quality traits, as well as contribute to the genomic selection work of dairy cows in the lower reaches of the Yangtze River.

Nodular cast iron QT700-2 is extensively used in automobile engine crankshaft parts due to its prime mechanical properties. The journal of a crankshaft is a curved surface, and traditional wheel grinding easily causes grinding burn and surface and subsurface damage. Shape adaptive grinding (SAG) is a flexible grinding technology, which has the advantages of low grinding force and temperature, and good grinding quality. It is suitable for machining curved surface parts with complex shapes. Therefore, the SAG surface integrity of nodular cast iron QT700-2 was experimentally investigated. The influence of grinding parameters on grinding force, material removal rate, grinding temperature, and surface integrity was studied, and the machining performance of SAG tools was evaluated. It was concluded that the grain size in SAG is the most important factor affecting the grinding force, material removal rate, and surface roughness; the influence of SAG grinding is very weak, mainly removing the workpiece material. Then, the influence law of SAG technology on the surface integrity of nodular cast iron QT700-2 was summarized, and the optimal grinding parameters were obtained, providing a reference for the curved surface grinding of nodular cast iron QT700-2 in the future.

Abstract Background Genotyping by sequencing (GBS) still has problems with missing genotypes. Imputation is important for using GBS for genomic predictions, especially for low depths, due to the large number of missing genotypes. Minor allele frequency (MAF) is widely used as a marker data editing criteria for genomic predictions. In this study, three imputation methods (Beagle, IMPUTE2 and FImpute software) based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions, based on simulated data of livestock population. Results Four MAFs (no MAF limit, MAF ≥ 0.001, MAF ≥ 0.01 and MAF ≥ 0.03) were used for editing marker data before imputation. Beagle, IMPUTE2 and FImpute software were applied to impute the original GBS. Additionally, IMPUTE2 also imputed the expected genotype dosage after genotype correction (GcIM). The reliability of genomic predictions was calculated using GBS and imputed GBS data. The results showed that imputation accuracies were the same for the three imputation methods, except for the data of sequencing read depth (depth) = 2, where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2. GcIM was observed to be the best for all of the imputations at depth = 4, 5 and 10, but the worst for depth = 2. For genomic prediction, retaining more SNPs with no MAF limit resulted in higher reliability. As the depth increased to 10, the prediction reliabilities approached those using true genotypes in the GBS loci. Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points, and FImpute gained 3 percentage points at depth = 2. The best prediction was observed at depth = 4, 5 and 10 using GcIM, but the worst prediction was also observed using GcIM at depth = 2. Conclusions The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths. Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths. These results suggest that the application of IMPUTE2, based on a corrected GBS (GcIM) to improve genomic predictions for higher depths, and FImpute software could be a good alternative for routine imputation.

The efficiency of feed utilization plays an important role in animal breeding. However, measuring feed intake (FI) is costly on an individual basis under practical conditions. Using group measurements to model FI could be practically feasible and cost-effective. The objectives of this study were to develop a random regression model based on repeated group measurements with consideration of missing phenotypes caused by drop out animals. Focus is on variance components (VC) estimation and genetic evaluation, and to investigate the effect of group composition on VC estimation and genetic evaluation using simulated datasets. Data were simulated based on individual FI in a pig population. Each individual had measurement on FI at 6 different time points, reflecting 6 different weeks during the test period. The simulated phenotypes consisted of additive genetic, permanent environment, and random residual effects. Additive genetic and permanent environmental effects were both simulated and modeled by first order Legendre polynomials. Three grouping scenarios based on genetic relationships among the group members were investigated: (1) medium within and across pen genetic relationship; (2) high within group relationship; (3) low within group relationship. To investigate the effect of the drop out animals during test period, a proportion (15%) of animals with individual phenotypes was set as the drop out animals, and two drop out scenarios within each grouping scenario were assessed: (1) animals were randomly dropped out; (2) animals with lower phenotypes were dropped out based on the ranking at each time point. The results show that using group measurements yielded similar VCs estimates but with larger SDs compared with the corresponding scenario of using individual measurements. Compared to scenarios without drop out, similar VC estimates were observed when animals were dropped out randomly, whereas reduced VC estimates were observed when animals were dropped out by the ranking of phenotypes. Different grouping scenarios produced similar VC estimates. Compared to scenarios without drop out, there were no loss of accuracies of genetic evaluation for drop out scenarios. However, dropping out animals by the ranking of phenotypes produced larger bias of estimated breeding values compared to the scenario without dropped out animals and scenario of dropping out animals by random. In conclusion, with an optimized group structure, the developed model can properly handle group measurements with drop out animals, and can achieve comparable accuracy of genetic evaluation for traits measured at the group level.

Abstract Background Crossbreeding is widely used in pig production because of the benefits of heterosis effects and breed complementarity. Commonly, sire lines are bred for traits such as feed efficiency, growth and meat content, whereas maternal lines are also bred for reproduction and longevity traits, and the resulting three-way crossbred pigs are used for production of meat. The most important genetic basis for heterosis is dominance effects, e.g. removal of inbreeding depression. The aims of this study were to (1) present a modification of a previously developed model with additive, dominance and inbreeding depression genetic effects for analysis of data from a purebred sire line and three-way crossbred pigs; (2) based on this model, present equations for additive genetic variances, additive genetic covariance, and estimated breeding values (EBV) with associated accuracies for purebred and crossbred performances; (3) use the model to analyse four production traits, i.e. ultra-sound recorded backfat thickness (BF), conformation score (CONF), average daily gain (ADG), and feed conversion ratio (FCR), recorded on Danbred Duroc and Danbred Duroc-Landrace–Yorkshire crossbred pigs reared in the same environment; and (4) obtain estimates of genetic parameters, additive genetic correlations between purebred and crossbred performances, and EBV with associated accuracies for purebred and crossbred performances for this data set. Results Additive genetic correlations (with associated standard errors) between purebred and crossbred performances were equal to 0.96 (0.07), 0.83 (0.16), 0.75 (0.17), and 0.87 (0.18) for BF, CONF, ADG, and FCR, respectively. For BF, ADG, and FCR, the additive genetic variance was smaller for purebred performance than for crossbred performance, but for CONF the reverse was observed. EBV on Duroc boars were more accurate for purebred performance than for crossbred performance for BF, CONF and FCR, but not for ADG. Conclusions Methodological developments led to equations for genetic (co)variances and EBV with associated accuracies for purebred and crossbred performances in a three-way crossbreeding system. As illustrated by the data analysis, these equations may be useful for implementation of genomic selection in this system.

Abstract Background We tested the premise that optimum-contribution selection with pedigree relationships to control inbreeding (POCS) realises at least as much true genetic gain as optimum-contribution selection with genomic relationships (GOCS) at the same rate of true inbreeding. Methods We used stochastic simulation to estimate rates of true genetic gain realised by POCS and GOCS at a 0.01 rate of true inbreeding in three breeding schemes with best linear unbiased predictions of breeding values based on pedigree (PBLUP) and genomic (GBLUP) information. The three breeding schemes differed in number of matings and litter size. Selection was for a single trait with a heritability of 0.2. The trait was controlled by 7702 biallelic quantitative-trait loci (QTL) that were distributed across a 30-M genome. The genome contained 54,218 biallelic markers that were used in GOCS and GBLUP. A total of 6012 identity-by-descent loci were placed across the genome in base populations. Unique alleles at these loci were used to calculate rates of true inbreeding. Breeding schemes were run for 10 discrete generations. Selection candidates were genotyped and phenotyped before selection. Results POCS realised more true genetic gain than GOCS at a 0.01 rate of true inbreeding in all combinations of breeding scheme and prediction method. POCS realised 14 to 33% more true genetic gain than GOCS with PBLUP in the three breeding schemes. It realised 1.5 to 5.7% more true genetic gain than GOCS with GBLUP. Conclusions POCS realised more true genetic gain than GOCS because it managed expected genetic drift without restricting selection at QTL. By contrast, GOCS penalised changes in allele frequencies at markers that were generated by genetic drift and selection. Because these marker alleles were in linkage disequilibrium with QTL alleles, GOCS restricted changes in allele frequencies at QTL. This provides little incentive to use GOCS and highlights that we have more to learn before we can control inbreeding using genomic relationships in selective-breeding schemes. Until we can do so, POCS remains a worthy method of optimum-contribution selection because it realises more true genetic gain than GOCS at the same rate of true inbreeding.

Abstract The distribution of Asian ancestry in the genome of Danish Duroc pigs was investigated using whole‐genome sequencing data from European wild boars, Danish Duroc, Chinese Meishan and Bamaxiang pigs. Asian haplotypes deriving from Meishan and Bamaxiang occur widely across the genome. Signatures of selection on Asian haplotypes are common in the genome, but few of these haplotypes have been fixed. By defining 50‐kb windows with more than 50% Chinese ancestry, which did not exhibit extreme genetic differentiation between Meishan and Bamaxiang as candidate regions, the enrichment of quantitative trait loci in candidate regions supports that Asian haplotypes under selection play an important role in contributing genetic variation underlying production, reproduction, meat and carcass, and exterior traits. Gene annotation of regions with the highest proportion of Chinese ancestry revealed genes of biological interest, such as NR6A1. Further haplotype clustering analysis suggested that a haplotype of Chinese origin around the NR6A1 gene was introduced to Europe and then underwent a selective sweep in European pigs. Besides, functional genes in candidate regions, such as AHR and PGRMC2, associated with fertility, and SAL1, associated with meat quality, were identified. Our results demonstrate the contribution of Asian haplotypes to the genomes of European pigs. Findings herein facilitate further genomic studies such as genomewide association study and genomic prediction by providing ancestry information of variants.

With the in-depth research on high-entropy alloys (HEAs), most of the current research uses experimental methods to verify the effects of the main elements of HEAs on the mechanical properties of the alloys. However, this is limited by the long experimental period and the influence of many external factors. The computer simulation method can not only effectively save costs and shorten the test cycle, but also help to discover new materials and broaden the field of materials. Therefore, in this paper, the physical properties (such as lattice constant, density and elastic constant) of AlCoxCrFeNi (x = 0, 0.25, 0.5, 0.75, 1) HEAs were calculated based on the first-principles calculation method and virtual crystal approximate modeling method. It is found that AlCoxCrFeNi HEAs have the best hardness and toughness properties, with a Co content of 0.5~0.7. The research results can provide theoretical guidance for the preparation of HEAs with optimal mechanical properties.

This study investigated effects of integrating single-nucleotide polymorphisms (SNPs) selected based on previous genome-wide association studies (GWASs), from imputed whole-genome sequencing (WGS) data, in the conventional 54K chip on genomic prediction reliability of young stock survival (YSS) traits in dairy cattle. The WGS SNPs included two groups of SNP sets that were selected based on GWAS in the Danish Holstein for YSS index (YSS_SNPs, n = 98) and SNPs chosen as peaks of quantitative trait loci for the traits of Nordic total merit index in Denmark–Finland–Sweden dairy cattle populations (DFS_SNPs, n = 1,541). Additionally, the study also investigated the possibility of improving genomic prediction reliability for survival traits by modeling the SNPs within recessive lethal haplotypes (LET_SNP, n = 130) detected from the 54K chip in the Nordic Holstein. De-regressed proofs (DRPs) were obtained from 6,558 Danish Holstein bulls genotyped with either 54K chip or customized LD chip that includes SNPs in the standard LD chip and some of the selected WGS SNPs. The chip data were subsequently imputed to 54K SNP together with the selected WGS SNPs. Genomic best linear unbiased prediction (GBLUP) models were implemented to predict breeding values through either pooling the 54K and selected WGS SNPs together as one genetic component (a one-component model) or considering 54K SNPs and selected WGS SNPs as two separate genetic components (a two-component model). Across all the traits, inclusion of each of the selected WGS SNP sets led to negligible improvements in prediction accuracies (0.17 percentage points on average) compared to prediction using only 54K. Similarly, marginal improvement in prediction reliability was obtained when all the selected WGS SNPs were included (0.22 percentage points). No further improvement in prediction reliability was observed when considering random regression on genotype code of recessive lethal alleles in the model including both groups of the WGS SNPs. Additionally, there was no difference in prediction reliability from integrating the selected WGS SNP sets through the two-component model compared to the one-component GBLUP.

Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λk, which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λk, after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global–local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global–local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the “U” type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global–local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.

Due to the complexity of longevity trait in dairy cattle, two groups of trait definitions are widely used to measure longevity, either covering the full lifespan or representing only a part of it to achieve an early selection. Usually, only one group of longevity definition is used in breeding program for one population, and genetic studies on the comparisons of two groups of trait definitions are scarce. Based on the data of eight traits well representing the both groups of trait definitions, the current study investigated genetic parameters and genetic architectures of longevity in Holsteins. Heritabilities and correlations of eight longevity traits were estimated using single-trait and multi-trait animal models, with the data from 103,479 cows. Among the cows with phenotypes, 2,630 cows were genotyped with the 150K-SNP panel. A single-trait fixed and random Circuitous Probability Unification model was performed to detect candidate genes for eight longevity traits. Generally, all eight longevity traits had low heritabilities, ranging from 0.038 for total productive life and herd life to 0.090 for days from the first calving to the end of first lactation or culling. High genetic correlations were observed among the traits within the same definition group: from 0.946 to 0.997 for three traits reflecting full lifespan and from 0.666 to 0.997 for five traits reflecting partial productive life. Genetic correlations between two groups of traits ranged from 0.648 to 0.963, and increased gradually with the extension of lactations number regarding the partial productive life traits. A total of 55 SNPs located on 25 chromosomes were found genome-wide significantly associated with longevity, in which 12 SNPs were associated with more than one trait, even across traits of different definition groups. This is the first study to investigate the genetic architecture of longevity representing both full and the partial lifespan simultaneously, which will assist the selection of an appropriate trait definition for genetic improvement of longevity. Because of high genetic correlations with the full lifespan traits and higher heritability, the partial productive life trait measured as the days from the first calving to the end of the third lactation or culling could be a good alternative for early selection on longevity. The candidate genes identified by this study, such as RPRM, GRIA3, GTF2H5, CA5A, CACNA2D1, FGF10, and DNAJA3, could be used to pinpoint causative mutations for longevity and further benefit the genomic improvement of longevity in dairy cattle.

The random regression test-day model has become the most commonly adopted model for routine genetic evaluations in dairy populations, which allows accurately accounting for genetic and environmental effects over lactation. The objective of this study was to explore appropriate random regression test-day models for genetic evaluation of milk yield in a Holstein population with a relatively small size, which is the common situation in regional or independent breeding companies to preform genetic evaluation. Data included 419,567 test-day records from 54,417 cows from the first lactation. Variance components and breeding values were estimated using a random regression test-day model with different orders (from first to fifth) of Legendre polynomials (LP) and accounted for homogeneous or heterogeneous residual variance across the lactation. Models were compared based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and predictive ability. In general, models with a higher order of LP showed better goodness of fit based on AIC and BIC values. However, models with third, fourth, and fifth order of LP led to similar estimates of genetic parameters and predictive ability. Models with assumption of heterogeneous residual variances achieved better goodness of fit but yielded similar predictive ability compared with those with assumption of homogeneous residual variances. Therefore, a random regression model with third order of LP is suggested to be an appropriate model for genetic evaluation of milk yield in local Chinese Holstein populations.

Selective genotyping of phenotypically superior animals may lead to bias and less accurate genomic breeding values (GEBV). Performing selective genotyping based on phenotypes measured in the breeding environment (B) is not necessarily a good strategy when the aim of a breeding program is to improve animals’ performance in the commercial environment (C). Our simulation study compared different genotyping strategies for selection candidates and for fish in C in a breeding program for rainbow trout in the presence of genotype-by-environment interactions when the program had limited genotyping resources and unregistered pedigrees of individuals. For the reference population, selective genotyping of top and bottom individuals in C based on phenotypes measured in C led to the highest genetic gains, followed by random genotyping and then selective genotyping of top individuals in C. For selection candidates, selective genotyping of top individuals in B based on phenotypes measured in B led to the highest genetic gains, followed by selective genotyping of top and bottom individuals and then random genotyping. Selective genotyping led to bias in predicting GEBV. However, in scenarios that used selective genotyping of top fish in B and random genotyping of fish in C, predictions of GEBV were unbiased, with genetic correlations of 0.2 and 0.5 between traits measured in B and C. Estimates of variance components were sensitive to genotyping strategy, with an overestimation of the variance with selective genotyping of top and bottom fish and an underestimation of the variance with selective genotyping of top fish. Unbiased estimates of variance components were obtained when fish in B and C were genotyped at random. In conclusion, we recommend phenotypic genotyping of top and bottom fish in C and top fish in B for the purpose of selecting breeding animals and random genotyping of individuals in B and C for the purpose of estimating variance components when a genomic breeding program for rainbow trout aims to improve animals’ performance in C.

Abstract Background Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations. Results Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci (GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively. Conclusions The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.

Large curvature surfaces are often milled by straight-line interpolation method. However the misuse of the cutting parameters may lead to checkerboard texture on the machined surface. In this paper, the effect of interpolating straight-line length and tool path on the checkerboard texture on the milled surface is studied. The checkerboard texture is measured and characterized in terms of surface micromorphology and profile shape. Influences of the interpolation line length and milling path on the appearance of the checkerboard texture are analyzed. The influences of feed per tooth and pick feed on the checkerboard texture are discussed. Results show that linear interpolation method is readily to cause formation of the checkerboard texture on the milled surface of a workpiece. With the increase of the length of the interpolation line the present of checkerboard texture on the milled surface becomes more obvious, and vice versa. The bright and dark bands of the checkerboard texture are always perpendicular to the curvature plane of the milled surface. The feed per tooth and pick feed may influence the appearance of checkerboard texture as well.

The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12th week (BW) and feed conversion ratio (FCR) in a local broiler population. A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089–0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations.

Abstract Background Availability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in dairy cattle. The objective of this study was to examine the impact of including RLFV that are within genes and selected from whole-genome sequence variants, on the reliability of genomic prediction for fertility, health and longevity in dairy cattle. Results All genic RLFV with a minor allele frequency lower than 0.05 were extracted from imputed sequence data and subsets were created using different strategies. These subsets were subsequently combined with Illumina 50 k single nucleotide polymorphism (SNP) data and used for genomic prediction. Reliability of prediction obtained by using 50 k SNP data alone was used as reference value and absolute changes in reliabilities are referred to as changes in percentage points. Adding a component that included either all the genic or a subset of selected RLFV into the model in addition to the 50 k component changed the reliability of predictions by − 2.2 to 1.1%, i.e. hardly no change in reliability of prediction was found, regardless of how the RLFV were selected. In addition to these empirical analyses, a simulation study was performed to evaluate the potential impact of adding RLFV in the model on the reliability of prediction. Three sets of causal RLFV (containing 21,468, 1348 and 235 RLFV) that were randomly selected from different numbers of genes were generated and accounted for 10% additional genetic variance of the estimated variance explained by the 50 k SNPs. When genic RLFV based on mapping results were included in the prediction model, reliabilities improved by up to 4.0% and when the causal RLFV were included they improved by up to 6.8%. Conclusions Using selected RLFV from whole-genome sequence data had only a small impact on the empirical reliability of genomic prediction in dairy cattle. Our simulations revealed that for sequence data to bring a benefit, the key is to identify causal RLFV.

Implicit assumption of common (co)variance for all loci in multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) results in a genomic relationship matrix (G) that is common to all traits. When this assumption is violated, Bayesian whole genome regression methods may be superior to GBLUP by accounting for unequal (co)variance for all loci or genome regions. This study aimed to develop a strategy to improve the accuracy of GBLUP for multi-trait genomic prediction, using (co)variance estimates of SNP effects from Bayesian whole genome regression methods. Five generations (G1-G5, test populations) of genotype data were available by simulations based on data of 2,200 Danish Holstein cows (G0, reference population). Two correlated traits with heritabilities of 0.1 or 0.4, and a genetic correlation of 0.45 were generated. First, SNP effects and breeding values were estimated using BayesAS method, assuming (co)variance was the same for SNPs within a genome region, and different between regions. Region size was set as one SNP, 100 SNPs, a whole chromosome or whole genome. Second, posterior (co)variances of SNP effects were used to weight SNPs in construction of G matrices. In general, region size of 100 SNPs led to highest prediction accuracies using BayesAS, and wGBLUP outperformed GBLUP at this region size. Our results suggest that when genetic architectures of traits favor Bayesian methods, the accuracy of multi-trait GBLUP can be as high as the Bayesian method if SNPs are weighted by the Bayesian posterior (co)variances.

Abstract Background Records on groups of individuals rather than on single individuals could be valuable for predicting breeding values (BV) of the traits that are difficult or costly to measure individually, such as feed intake in pigs or beef cattle. Here, we present a model, which handles group records from varying group sizes and involves multiple fixed and random effects, for estimating variance components and predicting BV. Moreover, using simulation, we investigated the efficiency of group records for predicting BV in situations with various group sizes and structures, and factors that affect the trait. Results The results show that the presented model for group records worked well and that variances estimated from group records with varying group sizes were consistent with those estimated from individual records, but with larger standard errors. Ignoring litter and pen effects had very little or no influence on the accuracy of estimated BV (EBV) obtained from group records. However, ignoring litter effects resulted in biased estimates of additive genetic variance and EBV. The presence of litter and pen effects on phenotypes decreased the accuracy of EBV although the prediction model fitted both effects. Having more littermates in the same pen led to a higher accuracy of EBV. The decay of EBV accuracy with increasing group size was more marked for scenarios with litter and pen effects than without. When litters of six individuals were divided into two pens, accuracies of EBV obtained from group records with a size up to 12 (average 9.6) and up to 24 (average 19.2) were 66.6 and 57.6% of those estimated from individual records in the scenario with litter and pen effects on phenotypes. These percentages reached 77.0 and 68.4% in the scenario without litter and pen effects on phenotypes. Conclusions Our results indicate that the model works appropriately for the analysis of group records from varying group sizes. Using group records for genetic evaluation of traits such as feed intake in pig is feasible and the efficiency of the resulting estimates depends on the size and structure of the groups and on the magnitude of the variances for litter and pen effects.

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

The contact stress and heating effect between the cutting tool and workpiece in metal machining is symmetrical. However, the symmetry may be destroyed by changes in the workpiece material mechanical properties, such as ductility. The goal of this study is to reveal the wear characteristics of the cutting tool in machining a ductile metal with the cutting speed at which the metal is embrittled by the high-strain-rate-embrittle effect (HSREE). Orthogonal high-speed turning experiments were carried out. Pure iron type DT8 was cut at different cutting speeds, ranging from 1000 m/min to 9000 m/min. The shape and morphology of the chips obtained in the experiment were observed and analyzed by optical microscope and scanning electron microscope (SEM). Tool wear characteristics at different cutting speeds were observed. It shows that the pure iron becomes completely brittle when the cutting speed is higher than 8000 m/min. On the rake face, the coating of the cutting tool bursts apart and peels off. A matrix crack originates in the cutting edge or rake face and extends to the flank face of the cutting tool. The effects of HSREE on the tool wear is discussed. The findings of this study are helpful for choosing a suitable tool for brittle cutting of the ductile metal pure iron with very high cutting speed and solving the problems in machining due to its high ductility and high stickiness.

Abstract Reduced female fertility could cause considerable economic loss and has become a worldwide problem in the modern dairy industry. The objective of this study was to detect quantitative trait loci (QTL) for female fertility traits in Chinese and Nordic Holsteins using various strategies. First, single-trait association analyses were performed for female fertility traits in Chinese and Nordic Holsteins. Second, the SNPs with P-value

Abstract Background A quantitative trait is controlled both by major variants with large genetic effects and by minor variants with small effects. Genome-wide association studies (GWAS) are an efficient approach to identify quantitative trait loci (QTL), and genomic selection (GS) with high-density single nucleotide polymorphisms (SNPs) can achieve higher accuracy of estimated breeding values than conventional best linear unbiased prediction (BLUP). GWAS and GS address different aspects of quantitative traits, but, as statistical models, they are quite similar in their description of the genetic mechanisms that underlie quantitative traits. Methods Here, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. First, the variance components of the genomic-BLUP (GBLUP) model are estimated. Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected and included into the evaluation model. In the SNP dropping step, the SNPs in the evaluation model are tested according to the standard errors of their estimated effects. If non-significant SNPs are present, the least significant one is dropped from the model and variance components are re-estimated. We used extended Bayesian information criteria (eBIC) to evaluate the model optimization, i.e. the model with the smallest eBIC is the final one and includes only significant SNPs. Results We simulated scenarios with different heritabilities with 100 QTL. StepLMM estimated heritability accurately and mapped QTL precisely. Genomic prediction accuracy was much higher with StepLMM than with GBLUP. The comparison of StepLMM with other GWAS and GS methods based on a dataset from the 16th QTLMAS Workshop showed that StepLMM had medium mapping power, the lowest rate of false positives for QTL mapping, and the highest accuracy for genomic prediction. Conclusions StepLMM is a combination of GWAS and GBLUP. GWAS and GBLUP are beneficial to each other in a single statistical model, GWAS improves genomic prediction accuracy, while GBLUP increases mapping precision and decreases the rate of false positives of GWAS. StepLMM has a high performance in both GWAS and GS and is feasible for agricultural breeding programs and human genetic studies.

Abstract Background A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of “Gene Ontology” (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Results Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Conclusions Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

Abstract Background The objective of the present study was to estimate (co)variance components of female fertility traits in Chinese Holsteins, considering fertility traits in different parities as different traits. Data on 88,647 females with 215,632 records (parities) were collected during 2000 to 2014 from 32 herds in the Sanyuan Lvhe Dairy Cattle Center, Beijing, China. The analyzed female fertility traits included interval from calving to first insemination, interval from first to last insemination, days open, conception rate at first insemination, number of inseminations per conception and non-return rates within 56 days after first insemination. Results The descriptive statistics showed that the average fertility of heifers was superior to that of cows. Moreover, the genetic correlations between the performances of a trait in heifers and in cows were all moderate to high but far from one, which suggested that the performances of a trait in heifers and cows should be considered as different but genetically correlated traits in genetic evaluations. On the other hand, genetic correlations between performances of a trait in different parities of cows were greater than 0.87, with only a few exceptions, but variances were not homogeneous across parities for some traits. The estimated heritabilities of female fertility traits were low; all were below 0.049 (except for interval from calving to first insemination). Additionally, the heritabilities of the heifer interval traits were lower than those of the corresponding cow interval traits. Moreover, the heritabilities of the interval traits were higher than those of the threshold traits when measuring similar fertility functions. In general, estimated genetic correlations between traits were highly consistent with the biological categories of the female fertility traits. Conclusions Interval from calving to first insemination, interval from first to last insemination and non-return rates within 56 days after first insemination are recommended to be included in the selection index of the Chinese Holstein population. The parameters estimated in the present study will facilitate the development of a genetic evaluation system for female fertility traits to improve the reproduction efficiency of Chinese Holsteins.

Abstract Background A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. Results We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P