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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., (© 2020 Wiley-VCH GmbH.)

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., (© 2019 Blackwell Verlag GmbH.)

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.

Background: As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms., Results: Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling., Conclusions: Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.

Background: For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs., Results: In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations., Conclusions: The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Identifying signatures of selection can provide a straightforward insight into the mechanism of artificial selection and further uncover the causal genes related to the phenotypic variation. Based on Illumina Porcine60KSNP chip data, four complementary methods, Long-Range Haplotype (LRH), Tajima's D, Cross Population Extend Haplotype Homozygosity Test (XPEHH) and FST, were implemented in this study to detect the selection signatures in the whole genome of one typical Chinese indigenous breed, Rongchang, one Chinese cultivated breed, Songliao, and two western breeds, Landrace and Yorkshire. False Discovery Rate (FDR) was implemented to control the false positive rates. In our study, a total of 159, 127, 179 and 159 candidate selection regions with average length of 0.80 Mb, 0.73 Mb, 0.78 Mb and 0.73 Mb were identified in Landrace, Rongchang, Songliao and Yorkshire, respectively, that span approximately 128.00 Mb, 92.38 Mb, 130.30 Mb and 115.40 Mb and account for approximately 3.74-5.33% of genome across all autosomes. The selection regions of 11.52 Mb shared by Landrace and Yorkshire were the longest when chosen pairs from the pool of the four breeds were examined. The overlaps between Yorkshire and Songliao, approximately 9.20 Mb, were greater than those of Yorkshire and Rongchang. Meanwhile, the overlaps between Landrace and Songliao were greater than those of Landrace and Rongchang but less than those of Songliao and Ronchang. Bioinformatics analysis showed that the genes/QTLs relevant to fertility, coat color, and ear morphology were found in candidate selection regions. Some genes, such as LEMD3, MC1R, KIT, TRHR etc. that were reported under selection, were confirmed in our study, and this analysis also demonstrated the diversity of breeds.

Identifying footprints of selection can provide a straightforward insight into the mechanism of artificial selection and further dig out the causal genes related to important traits. In this study, three between-population and two within-population approaches, the Cross Population Extend Haplotype Homozygosity Test (XPEHH), the Cross Population Composite Likelihood Ratio (XPCLR), the F-statistics (Fst), the Integrated Haplotype Score (iHS) and the Tajima's D, were implemented to detect the selection footprints on the X chromosome in three pig breeds using Illumina Porcine60K SNP chip. In the detection of selection footprints using between-population methods, 11, 11 and 7 potential selection regions with length of 15.62 Mb, 12.32 Mb and 9.38 Mb were identified in Landrace, Chinese Songliao and Yorkshire by XPEHH, respectively, and 16, 13 and 17 potential selection regions with length of 15.20 Mb, 13.00 Mb and 19.21 Mb by XPCLR, 4, 2 and 4 potential selection regions with length of 3.20 Mb, 1.60 Mb and 3.20 Mb by Fst. For within-population methods, 7, 10 and 9 potential selection regions with length of 8.12 Mb, 8.40 Mb and 9.99 Mb were identified in Landrace, Chinese Songliao and Yorkshire by iHS, and 4, 3 and 2 potential selection regions with length of 3.20 Mb, 2.40 Mb and 1.60 Mb by Tajima's D. Moreover, the selection regions from different methods were partly overlapped, especially the regions around 22∼25 Mb were detected under selection in Landrace and Yorkshire while no selection in Chinese Songliao by all three between-population methods. Only quite few overlap of selection regions identified by between-population and within-population methods were found. Bioinformatics analysis showed that the genes relevant with meat quality, reproduction and immune were found in potential selection regions. In addition, three out of five significant SNPs associated with hematological traits reported in our genome-wide association study were harbored in potential selection regions.

Increased disease resistance through improved immune capacity would be beneficial for the welfare and productivity of farm animals. To identify genomic regions responsible for immune capacity traits in swine, a genome-wide association study was conducted. In total, 675 pigs were included. At 21 days of age, all piglets were vaccinated with modified live classical swine fever vaccine. Blood samples were sampled when the piglets were 20 and 35 days of age, respectively. Four traits, including Interferon-gamma (IFN-γ) and Interleukin 10 (IL-10) levels, the ratio of IFN-γ to IL-10 and Immunoglobulin G (IgG) blocking percentage to CSFV in serum were measured. All the samples were genotyped for 62,163 single nucleotide polymorphisms (SNP) using the Illumina porcineSNP60k BeadChip. After quality control, 46,079 SNPs were selected for association tests based on a single-locus regression model. To tackle the issue of multiple testing, 10,000 permutations were performed to determine the chromosome-wise and genome-wise significance level. In total, 32 SNPs with chromosome-wise significance level (including 4 SNPs with genome-wise significance level) were identified. These SNPs account for 3.23% to 13.81% of the total phenotypic variance individually. For the four traits, the numbers of significant SNPs range from 5 to 15, which jointly account for 37.52%, 82.94%, 26.74% and 24.16% of the total phenotypic variance of IFN-γ, IL-10, IFN-γ/IL-10, and IgG, respectively. Several significant SNPs are located within the QTL regions reported in previous studies. Furthermore, several significant SNPs fall into the regions which harbour a number of known immunity-related genes. Results herein lay a preliminary foundation for further identifying the causal mutations affecting swine immune capacity in follow-up studies.

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

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. [ABSTRACT FROM AUTHOR]

The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin‐related diseases in humans. Here, single‐cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross‐species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome‐wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin‐related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor‐like OGN+/UCHL1+ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in‐depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Identifying epigenetic changes is essential for an in-depth understanding of phenotypic diversity and pigs as the human medical model for anatomizing complex diseases. Abnormal sperm DNA methylation can lead to male infertility, fetal development failure, and affect the phenotypic traits of offspring. However, the whole genome epigenome map in pig sperm is lacking to date. In this study, we profiled methylation levels of cytosine in three commercial pig breeds, Landrace, Duroc, and Large White using whole-genome bisulfite sequencing (WGBS). The results showed that the correlation of methylation levels between Landrace and Large White pigs was higher. We found that 1,040–1,666 breed-specific hypomethylated regions (HMRs) were associated with embryonic developmental and economically complex traits for each breed. By integrating reduced representation bisulfite sequencing (RRBS) public data of pig testis, 1743 conservated HMRs between sperm and testis were defined, which may play a role in spermatogenesis. In addition, we found that the DNA methylation patterns of human and pig sperm showed high similarity by integrating public data from WGBS and chromatin immunoprecipitation sequencing (ChIP-seq) in other mammals, such as human and mouse. We identified 2,733 conserved HMRs between human and pig involved in organ development and brain-related traits, such as NLGN1 (neuroligin 1) containing a conserved-HMR between human and pig. Our results revealed the similarities and diversity of sperm methylation patterns among three commercial pig breeds and between human and pig. These findings are beneficial for elucidating the mechanism of male fertility, and the changes in commercial traits that undergo strong selection. [ABSTRACT FROM AUTHOR]

Feed efficiency (FE) traits in pigs are of utmost economic importance. Genetic improvement of FE related traits in pigs might significantly reduce production cost and energy consumption. Hence, our study aimed at identifying SNPs and candidate genes associated with FE related traits, including feed conversion ratio (FCR), average daily gain (ADG), average daily feed intake (ADFI), and residual feed intake (RFI). A genome-wide association study (GWAS) was performed for the four FE related traits in 296 Landrace pigs genotyped with PorcineSNP50 BeadChip. Two different single-trait methods, single SNP linear model GWAS (LM-GWAS) and single-step GWAS (ssGWAS), were implemented. Our results showed that the two methods showed high consistency with respect to SNP identification. A total of 32 common significant SNPs associated with the four FE related traits were identified. Bioinformatics analysis revealed eight common QTL regions, of which three QTL regions related to ADFI and RFI traits were overlapped. Gene ontology analysis revealed six common candidate genes (PRELID2, GPER1, PDX1, TEX2, PLCL2, ICAM2) relevant for the four FE related traits. These genes are involved in the processes of fat synthesis and decomposition, lipid transport process, insulin metabolism, among others. Our results provide, new insights into the genetic mechanisms and candidate function genes of FE related traits in pigs. However, further investigations to validate these results are warranted. [ABSTRACT FROM AUTHOR]

Background: Different production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions. Results: In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation. Our results regarding between-environment genetic correlations of growth and reproductive traits (ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions, yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively, compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population. Conclusions: In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions. [ABSTRACT FROM AUTHOR]

Automatic non-contact estimation of pig weight can avoid porcine stress and prevent the spread of swine fever. Many recent relevant works employ convolutional neural networks to extract deeply learned features for regressing pig weight based on single modality, either RGB images or depth images. However, utilizing only one modality may not be sufficient for pig-weight estimation, since both modalities are complementary for representing the spatial body information of pigs. In this paper, we propose a two-stream cross-attention vision Transformer for regressing pig weight based on both RGB and depth images. Specifically, we employ two separate Swin Transformer to extract texture appearance information and spatial structure information from RGB and depth images, respectively. Meanwhile, we design the cross-attention blocks to learn mutual-modal representations from both modalities. Finally, we construct a feature fusion layer to combine the features from both streams for regressing pig weight. In the experiments, we collect a new dataset of paired RGB-D pig images, which contains 10,263 RGB-D pairs for training and 5203 RGB-D pairs for testing. Comprehensive comparative experimental results show that the proposed method yields the best performance on this dataset, where the mean absolute error is 3.237. • A two-stream vision Transformer based on RGB-D images for estimating pig weight. • Cross-attention blocks learn mutual-modal representations by feature interaction. • The proposed method achieves very promising performance for pig weight estimation. [ABSTRACT FROM AUTHOR]