The imprinted gene DIO3 is a candidate gene for litter size in pigs

Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability (h mi 2 $$ {h}_{\mathrm{mi}}^2 $$ ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability (h a 2 $$ {h}_{\mathrm{a}}^2 $$ ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Effect of SNPs on Litter Size in Swine

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein-protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows' non-pregnancy period as much as possible.

A multivariate gametic model for the analysis of purebred and crossbred data. An example between two populations of Iberian pigs

Crossbreeding plays a pivotal role within pig breeding programmes, aiming to maximize heterosis and improve reproductive traits in crossbred maternal lines. Nevertheless, there is evidence indicating that the performance of reciprocal crosses between two genetic lines might exhibit variability. These variations in performance can be attributed to differences in the correlations between gametic effects, acting as either sire or dam, within purebred and crossbred populations. To address this issue, we propose a multivariate gametic model that incorporates up to four correlated gametic effects for each parental population. The model is employed on a data set comprising litter size data (total number of piglets born-TNB- and number of piglets born alive-NBA-) derived from a reciprocal cross involving two Iberian pig populations: Entrepelado and Retinto. The data set comprises 6933 records from 1564 purebred Entrepelado (EE) sows, 4995 records from 1015 Entrepelado × Retinto (ER) crosses, 2977 records from 756 Retinto × Entrepelado (RE) crosses and 7497 records from 1577 purebred Retinto (RR) sows. The data set is further supplemented by a pedigree encompassing 6007 individual-sire-dam entries. The statistical model also included the order of parity (with six levels), the breed of the service sire (five levels) and the herd-year-season effects (141 levels). Additionally, the model integrates random dominant and permanent environmental sow effects. The analysis employed a Bayesian approach, and the results revealed all the posterior estimates of the gametic correlations to be positive. The range of the posterior mean estimates of the correlations varied across different gametic effects and traits, with a range between 0.04 (gametic correlation between the paternal effects for purebred and the maternal for crossbred in Retinto) and 0.53 (gametic correlation between the paternal effects for purebred and the paternal for crossbred in Entrepelado). Furthermore, the posterior mean variance estimates of the maternal gametic effects were consistently surpassed those for paternal effects within all four populations. The results suggest the possible influence of imprinting effects on the genetic control of litter size, and underscore the importance of incorporating crossbred data into the breeding value predictions for purebred individuals.

Effect of NLR family pyrin domain containing 9 gene polymorphism on litter size in large white pigs

NLR family pyrin domain containing 9 (NLRP9) is a mammalian reproduction-related gene. In this study, we researched the associations between polymorphisms located in the coding sequence (CDS) of the NLRP9 gene, and both the total number of piglets born per litter (TNB) and the number of piglets born alive per litter (NBA) in Canada Large White pigs (CLW). We detected a single nucleotide polymorphism (SNP) within exon 3 (g.10910C > T). The allele frequencies at the NLRP9 locus were 0.474 for the C allele and 0.526 for the T allele. Three genotypes, CC, CT, and TT, occurred with frequencies of 0.216, 0.515, and 0.269, respectively. Sows with the CC genotype had the largest TNB and NBA, sows with TT had the smallest, and those with CT were in-between. This difference was statistically significant (p < 0.05). Furthermore, CC females grew faster than CT or TT females, and there was a significant relationship between NLRP9 polymorphism and the average daily gain (p < 0.05). Here, we provide the first evidence for a novel SNP in NLRP9 associated with litter size in CLW sows, which could be used as a genetic marker to improve litter size in pig breeding and production.

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

A Bayesian Multivariate Gametic Model in a Reciprocal Cross with Genomic Information: An Example with Two Iberian Varieties

INGA FOOD, S.A. initiated a crossbreeding program between two Iberian pig varieties, Retinto (R) and Entrepelado (E), with the goal of producing a hybrid sow (F1). Several studies have been conducted to evaluate its productive performance, and these studies have revealed differences in litter size between the two reciprocal crosses, suggesting the presence of genomic imprinting effects. To further investigate these effects, this study introduces a multivariate gametic model designed to estimate gametic correlations between paternal and maternal effects originating from both genetic backgrounds involved in the reciprocal crosses. The dataset consisted of 1258 records (the total number born-TNB and the number born alive-NBA) from 203 crossbred dams for the Entrepelado (sire) × Retinto (dam) cross and 700 records from 125 crossbred dams for the Retinto (sire) × Entrepelado (dam) cross. All animals were genotyped using the GeneSeek^® GPP Porcine 70 K HDchip (Illumina Inc., San Diego, CA, USA). The results indicated that the posterior distribution of the gametic correlation between paternal and maternal effects was distinctly different between the two populations. Specifically, in the Retinto population, the gametic correlation showed a positive skew with posterior probabilities of 0.78 for the TNB and 0.80 for the NBA. On the other hand, the Entrepelado population showed a posterior probability of a positive gametic correlation between paternal and maternal effects of approximately 0.50. The differences in the shape of the posterior distribution of the gametic correlations between paternal and maternal effects observed in the two varieties may account for the distinct performance outcomes observed in the reciprocal crosses.

Allelic to genome wide perspectives of swine genetic variation to litter size and its component traits

Litter size is a complex and sex limited trait that depends on various biological, managemental and environmental factors. Owing to its low heritability it is inefficaciously selected by traditional methods. However, due to higher heritability of ovulation rate and embryo survival, selection based on component traits of litter size is advocated. QTL analysis and candidate gene approach are among the various supplementary/alternate strategies for selection of litter size. QTL analysis is aimed at identifying genomic regions affecting trait of interest significantly. Candidate gene approach necessitates identification of genes potentially affecting the trait. There are various genes that significantly affect litter size and its component traits viz. ESR, LEP, BF, IGFBP, RBP4, PRLR, CTNNAL1, WNT10B, TCF12, DAZ, and RNF4. These genes affect litter size in a complex interacting manner. Lately, genome wide association study (GWAS) have been utilized to unveil the genetic and biological background of litter traits, and elucidate the genes governing litter size. Favorable SNPs in these genes have been identified and offers a scope for inclusion in selection programs thereby increasing breeding efficiency and profit in pigs. The review provides a comprehensive coverage of investigations carried out globally to unravel the genetic variation in litter size and its component traits in pigs, both at allelic and genome wide level. It offers a current perspective on different strategies including the profiling of candidate genes, QTLs, and genome wide association studies as an aid to efficient selection for litter size and its component traits.

Identification of the porcine IG-DMR and abnormal imprinting of DLK1-DIO3 in cloned pigs

Correct reprogramming of the DLK1-DIO3 imprinted region is critical for the development of cloned animals. However, in pigs, the imprinting and regulation of the DLK1-DIO3 region has not been systematically analyzed. The objective of this study was to investigate the imprinting status and methylation regulation of the DLK1-DIO3 region in wild-type and cloned neonatal pigs. We mapped the imprinting control region, IG-DMR, by homologous alignment and validated it in sperm, oocytes, fibroblasts, and parthenogenetic embryos. Subsequently, single nucleotide polymorphism-based sequencing and bisulfite sequencing polymerase chain reaction were conducted to analyze imprinting and methylation in different types of fibroblasts, as well as wild-type and cloned neonatal pigs. The results showed that Somatic cell nuclear transfer (SCNT) resulted in hypermethylation of the IG-DMR and aberrant gene expression in the DLK1-DIO3 region. Similar to wild-type pigs, imprinted expression and methylation were observed in the surviving cloned pigs, whereas in dead cloned pigs, the IG-DMR was hypermethylated and the expression of GTL2 was nearly undetectable. Our study reveals that abnormal imprinting of the DLK1-DIO3 region occurs in cloned pigs, which provides a theoretical basis for improving the cloning efficiency by gene editing to correct abnormal imprinting.

Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits

BACKGROUND

Long-term selection experiments are a powerful tool to understand the genetic background of complex traits. The longest of such experiments has been conducted in the Research Institute for Farm Animal Biology (FBN), generating extreme mouse lines with increased fertility, body mass, protein mass and endurance. For >140 generations, these lines have been maintained alongside an unselected control line, representing a valuable resource for understanding the genetic basis of polygenic traits. However, their history and genomes have not been reported in a comprehensive manner yet. Therefore, the aim of this study is to provide a summary of the breeding history and phenotypic traits of these lines along with their genomic characteristics. We further attempt to decipher the effects of the observed line-specific patterns of genetic variation on each of the selected traits.

RESULTS

Over the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line. Whole genome sequencing analysis on 25 animals per line revealed line-specific patterns of genetic variation among lines, as well as high levels of homozygosity within lines. This high degree of distinctiveness results from the combined effects of long-term continuous selection, genetic drift, population bottleneck and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits.

CONCLUSIONS

The genomes of the Dummerstorf trait-selected mouse lines display distinct patterns of genomic variation harbouring multiple trait-relevant genes. Low levels of within-line genetic diversity indicate that many of the beneficial alleles have arrived to fixation alongside with neutral alleles. This study represents the first step in deciphering the influence of selection and neutral evolutionary forces on the genomes of these extreme mouse lines and depicts the genetic complexity underlying polygenic traits.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

BACKGROUND

The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM.

RESULTS

Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar.

CONCLUSIONS

The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BACKGROUND

There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.

RESULTS

The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.

CONCLUSIONS

We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

Genome-Wide Analyses Reveal Genetic Convergence of Prolificacy between Goats and Sheep

The litter size of domestic goats and sheep is an economically important trait that shows variation within breeds. Strenuous efforts have been made to understand the genetic mechanisms underlying prolificacy in goats and sheep. However, there has been a paucity of research on the genetic convergence of prolificacy between goats and sheep, which likely arose because of similar natural and artificial selection forces. Here, we performed comparative genomic and transcriptomic analyses to identify the genetic convergence of prolificacy between goats and sheep. By combining genomic and transcriptomic data for the first time, we identified this genetic convergence in (1) positively selected genes (CHST11 and SDCCAG8), (2) differentially expressed genes (SERPINA14, RSAD2, and PPIG at follicular phase, and IGF1, GPRIN3, LIPG, SLC7A11, and CHST15 at luteal phase), and (3) biological pathways (genomic level: osteoclast differentiation, ErbB signaling pathway, and relaxin signaling pathway; transcriptomic level: the regulation of viral genome replication at follicular phase, and protein kinase B signaling and antigen processing and presentation at luteal phase). These results indicated the potential physiological convergence and enhanced our understanding of the overlapping genetic makeup underlying litter size in goats and sheep.

Combined approaches to reveal genes associated with litter size in Yunshang black goats

Intensive artificial selection has been imposed in Yunshang black goats, the first black specialist mutton goat breed in China, with a breeding object of improving reproductive performance, which has contributed to reshaping of the genome including the characterization of SNP, ROH and haplotype. However, variation in reproductive ability exists in the present population. A WGS was implemented in two subpopulations (polytocous group, PG, and monotocous group, MG) with evident differences of litter size. Following the mapping to reference genome, and SNP calling and pruning, three approaches - GWAS, ROH analysis and detection of signatures of selection - were employed to unveil candidate genes responsible for litter size. Consequently, 12 candidate genes containing OSBPL8 with the minimum P-value were uncovered by GWAS. Differences were observed in the pattern of ROH between two subpopulations that shared similar low inbreeding coefficients. Two ROH hotspots and 12 corresponding genes emerged from ROH pool association analysis. Based on the nS_L statistic, 15 and 61 promising genes were disclosed under selection for MG and PG respectively. Of them, some promising genes participate in ovarian function (PPP2R5C, CDC25A, ESR1, RPS26 and SERPINBs), seasonal reproduction (DIO3, BTG1 and CRYM) and metabolism (OSBPL8, SLC39A5 and SERPINBs). Our study pinpointed some novel promising genes influencing litter size, provided a comprehensive insight into genetic makeup of litter size and might facilitate selective breeding in goats.

Survey of SNPs Associated with Total Number Born and Total Number Born Alive in Pig

Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as "Genome map association". Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming.

Detection and analysis of genome-wide copy number variation in the pig genome using an 80 K SNP Beadchip

Copy number variation (CNV) is an important source of genetic variability in human or animal genomes and play key roles in phenotypic diversity and disease susceptibility. In the present study, we performed a genome-wide analysis for CNV detection using SNP genotyping data of 857 Large White pigs. A total of 312 CNV regions (CNVRs) were detected with the PennCNV algorithm, which covered 57.76 Mb of the pig genome and correspond to 2.36% of the genome sequence. The length of the CNVRs on autosomes ranged from 1.77 Kb to 1.76 Mb with an average of 185.11 Kb. Of these, 220 completely or partially overlapped with 1,092 annotated genes, which enriched a wide variety of biological processes. Comparisons with previously reported pig CNVR revealed 92 (29.49%) novel CNVRs. Experimentally, 80% of CNVRs selected randomly were validated by quantitative PCR (qPCR). We also performed an association analysis between some of the CNVRs and reproductive traits, with results demonstrating the potential importance of CNVR61 and CNVR283 associated with litter sizes. Notably, the GPER1 gene located in CNVR61 plays a key role in reproduction. Our study is an important complement to the CNV map in the pig genome and provides valuable information for investigating the association between genomic variation and economic traits.

Genetic parameters and direct, maternal and heterosis effects on litter size in a diallel cross among three commercial varieties of Iberian pig

The Iberian pig is one of the pig breeds that has the highest meat quality. Traditionally, producers have bred one of the available varieties, exclusively, and have not used crosses between them, which has contrasted sharply with other populations of commercial pigs for which crossbreeding has been a standard procedure. The objective of this study was to perform an experiment under full diallel design among three contemporary commercial varieties of Iberian pig and estimate the additive genetic variation and the crossbreeding effects (direct, maternal and heterosis) for prolificacy. The data set comprised 18 193 records for total number born and number born alive from 3800 sows of three varieties of the Iberian breed (Retinto, Torbiscal and Entrepelado) and their reciprocal crosses (Retinto × Torbiscal, Torbiscal × Retinto, Retinto × Entrepelado, Entrepelado × Retinto, Torbiscal × Entrepelado and Entrepelado × Torbiscal), and a pedigree of 4609 individuals. The analysis was based on a multiple population repeatability model, and we developed a model comparison test that indicated the presence of direct line, maternal and heterosis effects. The results indicated the superiorities of the direct line effect of the Retinto and the maternal effect of the Entrepelado populations. All of the potential crosses produced significant heterosis, and additive genetic variation was higher in the Entrepelado than it was in the other two populations. The recommended cross for the highest yield in prolificacy is a Retinto father and an Entrepelado mother to generate a hybrid commercial sow.

Hypomethylation in the promoter region of ZPBP as a potential litter size indicator in Berkshire pigs

In pigs, litter size is typically defined as the total number of piglets born (TNB) or the number of piglets born alive (NBA). Increasing pig litter size is of great economic interest as a means to increase productivity. The capacity of the uterus is a critical component of litter size and may play a central role in prolificacy. In this study, we investigated litter-size-related epigenetic markers in uterine tissue from Berkshire pigs with smaller litter size groups (SLGs) and larger litter size groups (LLGs) using genome-wide bisulfite sequencing (GWBS). A total of 3269 differentially methylated regions (DMRs) were identified: 1566 were hypermethylated and 1703 hypomethylated in LLG compared to SLG. The zona pellucida binding protein (ZPBP) gene was significantly hypomethylated in the LLG promoter region, and its expression was significantly upregulated in uterine tissue. Thus, the methylation status of ZPBP gene was identified as a potential indicator of litter size. Furthermore, we verified its negative correlation with litter size traits (TNB and NBA) in whole blood samples from 172 Berkshire sows as a blood-based biomarker by a porcine methylation-specific restriction enzyme polymerase chain reaction (PMP) assay. The results suggest that the methylation status of the ZPBP gene can serve as a valuable epigenetic biomarker for hyperprolific sows.

Influence of swine leukocyte antigen haplotype on serum antibody titers against swine erysipelas vaccine and reproductive and meat production traits of SLA-defined selectively bred Duroc pigs

We investigated possible associations of SLA class II haplotypes with serum antibody titers against a swine erysipelas vaccine, reproductive and meat production traits using a population of selective breeding Duroc pigs. In the selective breeding Duroc pigs, four SLA class II-DRB1 and -DQB1 alleles were assigned by using PCR-sequence specific primer technique. Low-resolution haplotype (Lr)-0.30 and/or Lr-0.13 were deduced from the SLA class II alleles in the population of SLA-defined Duroc pigs. SLA-homozygous piglets with the Lr-0.30 haplotype had relatively lower serum antibody titers against the vaccine compared to those with Lr-0.13. In contrast, there were no statistically significant differences in reproductive performance between the SLA-defined pigs with two SLA class II haplotypes. Weaning and rearing rates until the body weight of 105 kg was reached in homozygous piglets with Lr-0.30 were significantly lower than those in homozygous piglets with Lr-0.13. The SLA-defined pigs had lower birth and weaning weights, body weights at 60 days of age, and daily weight gains than non-selective breeding Duroc pigs. Furthermore, the SLA-defined pigs had slightly lower back fat thickness compared to the non-selective breeding pigs. The rib eye areas of homozygous or heterozygous pigs with Lr-0.13 were larger than those of homozygous pigs with Lr-0.30 and non-selective breeding pigs. These data suggested that SLA haplotypes had the potential as useful genetic markers for selective breeding in the population of SLA-defined Duroc pigs.

Association of single-nucleotide polymorphisms in NAT9 and MAP3K3 genes with litter size traits in Berkshire pigs

Litter size is an economically important trait in the pig industry. We aimed to identify genetic markers associated with litter size, which can be used in breeding programs for improving reproductive traits. Single-nucleotide polymorphisms (SNPs) of Berkshire pigs in the N-acetyltransferase 9 (NAT9) and Mitogen-activated protein kinase kinase kinase 3 (MAP3K3) genes were from RNA sequencing results, and already exist in the databank (NCBI), and were confirmed by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). A total of 272 Berkshire sows were used to examine the genotype, and their association with litter size traits was analyzed. The NAT9 SNP was located in chromosome 12 exon 640 mRNA (A > G) and the MAP3K3 SNP was located in chromosome 12 intron 11 (80, C > T). Association analysis indicated that the GG genotype of NAT9 and the CT genotype of MAP3K3 had the highest values for litter size traits. The GG genotype expressed higher levels of NAT9 mRNA in the endometrium than the other genotypes did, and a positive correlation was found between litter size traits and NAT9, but not MAP3K3 expression level. These results indicate that the NAT9 and MAP3K3 can be used as candidate genes applicable in breeding program for the improvement of litter size traits in Berkshire pigs.

Single step genome-wide association studies based on genotyping by sequence data reveals novel loci for the litter traits of domestic pigs

In this study, data genotyping by sequence (GBS) was used to perform single step GWAS (ssGWAS) to identify SNPs associated with the litter traits in domestic pigs and search for candidate genes in the region of significant SNPs. After quality control, 167,355 high-quality SNPs from 532 pigs were obtained. Phenotypic traits on 2112 gilt litters from 532 pigs were recorded including total number born (TNB), number born alive (NBA), and litter weight born alive (LWB). A single-step genomic BLUP approach (ssGBLUP) was used to implement the genome-wide association analysis at a 5% genome-wide significance level. A total of 8, 23 and 20 significant SNPs were associated with TNB, NBA, and LWB, respectively, and these significant SNPs accounted for 62.78%, 79.75%, and 58.79% of genetic variance. Furthermore, 1 (SSC14: 16314857), 4 (SSC1: 81986236, SSC1: 66599775, SSC1: 161999013, and SSC1: 267883107), and 5 (SSC9: 29030061, SSC2: 32368561, SSC5: 110375350, SSC13: 45619882 and SSC13: 45647829) significant SNPs for TNB, NBA, and LWB were inferred to be novel loci. At SSC1, the AIM1 and FOXO3 genes were found to be associated with NBA; these genes increase ovarian reproductive capacity and follicle number and decrease gonadotropin levels. The genes SLC36A4 and INTU are involved in cell growth, cytogenesis and development were found to be associated with LWB. These significant SNPs can be used as an indication for regions in the Sus scrofa genome for variability in litter traits, but further studies are expected to confirm causative mutations.

DNA methylation patterns and gene expression associated with litter size in Berkshire pig placenta

Increasing litter size is of great interest to the pig industry. DNA methylation is an important epigenetic modification that regulates gene expression, resulting in livestock phenotypes such as disease resistance, milk production, and reproduction. We classified Berkshire pigs into two groups according to litter size and estimated breeding value: smaller (SLG) and larger (LLG) litter size groups. Genome-wide DNA methylation and gene expression were analyzed using placenta genomic DNA and RNA to identify differentially methylated regions (DMRs) and differentially expressed genes (DEGs) associated with litter size. The methylation levels of CpG dinucleotides in different genomic regions were noticeably different between the groups, while global methylation pattern was similar, and excluding intergenic regions they were found the most frequently in gene body regions. Next, we analyzed RNA-Seq data to identify DEGs between the SLG and LLG groups. A total of 1591 DEGs were identified: 567 were downregulated and 1024 were upregulated in LLG compared to SLG. To identify genes that simultaneously exhibited changes in DNA methylation and mRNA expression, we integrated and analyzed the data from bisulfite-Seq and RNA-Seq. Nine DEGs positioned in DMRs were found. The expression of only three of these genes (PRKG2, CLCA4, and PCK1) was verified by RT-qPCR. Furthermore, we observed the same methylation patterns in blood samples as in the placental tissues by PCR-based methylation analysis. Together, these results provide useful data regarding potential epigenetic markers for selecting hyperprolific sows.

The placental imprinted DLK1-DIO3 domain: a new link to prenatal and postnatal growth in humans

BACKGROUND

The developmentally important DLK1-DIO3 imprinted domain on human chromosome 14 is regulated by 2 differentially methylated regions, the intergenic differentially methylated region and the MEG3 differentially methylated region.

OBJECTIVE

The aim was to determine the natural variation in DNA methylation at these differentially methylated regions in human placentas, and to determine its link to gene expression levels at the domain. The second goal was to explore whether the domain's methylation and gene expression correlate with prenatal and early postnatal growth of the conceptus.

STUDY DESIGN

Using pyrosequencing, we determined methylation levels at CpG dinucleotides across the 2 regulatory differentially methylated regions in placentas from 91 healthy mothers. At birth, placentas and infants were weighed (gestational age 39 ± 1 weeks; birthweight SD score 0.1 ± 0.8) and placental biopsies were collected. RNA expression was quantitated by real-time polymerase chain reaction. Infants' weights and lengths were followed up monthly during the first year.

RESULTS

Methylation levels at the 2 regulatory differentially methylated regions were linked and varied considerably between placentas. MEG3 promoter differentially methylated region methylation correlated negatively with weight increase (β = -0.406, P = .001, R² = 0.206) and length increase (β = -0.363, P = .002, R² = 0.230) during the first postnatal year. The methylation level of the intergenic differentially methylated region correlated with DIO3 expression (β = 0.313, P = .032, R² = 0.152). Furthermore, the expression of both DIO3 and RTL1 (both imprinted genes within the DLK1-DIO3 domain) was negatively associated with birthweight (β = -0.331, P = .002, R² = 0.165; and β = -0.307, P = .005, R² = 0.159, respectively). RTL1 expression, in addition, was negatively linked to birth length (β = -0.306, P = .007, R² = 0.162).

CONCLUSION

Our combined findings strongly suggest that placental DNA methylation at the DLK1-DIO3 domain's intergenic differentially methylated region and MEG3 promoter differentially methylated region relates to measures of early human growth, and may thus contribute to its control.

Use of haplotypes to identify regions harbouring lethal recessive variants in pigs

Background

Lethal recessive genetic variants are maintained at relatively low frequencies in a population in the heterozygous state, but by definition are fatal and therefore unobserved in the homozygous state. Since haplotypes allow the tagging of rare and untyped genetic variants, they have potential for studying lethal recessive variants. In this study, we used a large commercial population to identify putative lethal recessive haplotypes that impact either the total number born (TNB) or the number born alive (NBA) as a proportion of the total number born (NBA/TNB). We also compared the use of haplotypes with a single nucleotide polymorphism (SNP)-by-SNP approach and examined the benefits of using additional haplotypes imputed from low-density genotype data for the detection of lethal recessive variants. Candidate haplotypes were identified using population-wide haplotype frequencies and within-family analyses. These candidate haplotypes were subsequently assessed for putative lethal recessive effects on TNB and NBA/TNB by comparing carrier-to-carrier matings with carrier-to-non-carrier matings.

Results

Using both medium-density and imputed low-density genotype data six regions were identified as containing putative lethal recessive haplotypes that had an effect on TNB. It is likely that these regions were related to at least four putative lethal recessive variants, each located on a different chromosome. Evidence for putative lethal recessive effects on TNB was found on chromosomes 1, 6, 10 and 14 using haplotypes. Using haplotypes from individuals genotyped only at medium-density or a SNP-by-SNP approach did not detect any lethal recessive effects. No lethal recessive haplotypes or SNPs were detected that had an effect on NBA/TNB.

Conclusions

We show that the use of haplotypes from combining medium-density and imputed low-density genotype data is superior for the identification of lethal recessive variants compared to both a SNP-by-SNP approach and to the use of only medium-density data. We developed a formal statistical framework that provided sufficient power to detect lethal recessive variants in species, which produce large full-sib families, while reducing false positive or type I errors. Applying this framework results in improvements in reproductive performance by purging lethal recessive alleles from a population in a timely and cost-effective manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0332-3) contains supplementary material, which is available to authorized users.

Genomic prediction using models with dominance and imprinting effects for backfat thickness and average daily gain in Danish Duroc pigs

BACKGROUND

Dominance and imprinting genetic effects have been shown to contribute to genetic variance for certain traits but are usually ignored in genomic prediction of complex traits in livestock. The objectives of this study were to estimate variances of additive, dominance and imprinting genetic effects and to evaluate predictions of genetic merit based on genomic data for average daily gain (DG) and backfat thickness (BF) in Danish Duroc pigs.

METHODS

Corrected phenotypes of 8113 genotyped pigs from breeding and multiplier herds were used. Four Bayesian mixture models that differed in the type of genetic effects included: (A) additive genetic effects, (AD) additive and dominance genetic effects, (AI) additive and imprinting genetic effects, and (ADI) additive, dominance and imprinting genetic effects were compared using Bayes factors. The ability of the models to predict genetic merit was compared with regard to prediction reliability and bias.

RESULTS

Based on model ADI, narrow-sense heritabilities of 0.18 and 0.31 were estimated for DG and BF, respectively. Dominance and imprinting genetic effects accounted for 4.0 to 4.6 and 1.3 to 1.4 % of phenotypic variance, respectively, which were statistically significant. Across the four models, reliabilities of the predicted total genetic values (GTV, sum of all genetic effects) ranged from 16.1 (AI) to 18.4 % (AD) for DG and from 30.1 (AI) to 31.4 % (ADI) for BF. The least biased predictions of GTV were obtained with model AD, with regression coefficients of corrected phenotypes on GTV equal to 0.824 (DG) and 0.738 (BF). Reliabilities of genomic estimated breeding values (GBV, additive genetic effects) did not differ significantly among models for DG (between 16.5 and 16.7 %); however, for BF, model AD provided a significantly higher reliability (31.3 %) than model A (30.7 %). The least biased predictions of GBV were obtained with model AD with regression coefficients of 0.872 for DG and 0.764 for BF.

CONCLUSIONS

Dominance and genomic imprinting effects contribute significantly to the genetic variation of BF and DG in Danish Duroc pigs. Genomic prediction models that include dominance genetic effects can improve accuracy and reduce bias of genomic predictions of genetic merit.

Genome-wide association analyses using a Bayesian approach for litter size and piglet mortality in Danish Landrace and Yorkshire pigs

BACKGROUND

Litter size and piglet mortality are important traits in pig production. The study aimed to identify quantitative trait loci (QTL) for litter size and mortality traits, including total number of piglets born (TNB), litter size at day 5 (LS5) and mortality rate before day 5 (MORT) in Danish Landrace and Yorkshire pigs by genome-wide association studies (GWAS).

METHODS

The phenotypic records and genotypes were available in 5,977 Landrace pigs and 6,000 Yorkshire pigs born from 1998 to 2014. A linear mixed model (LM) with a single SNP regression and a Bayesian mixture model (BM) including effects of all SNPs simultaneously were used for GWAS to detect significant QTL association. The response variable used in the GWAS was corrected phenotypic value which was obtained by adjusting original observations for non-genetic effects. For BM, the QTL region was determined by using a novel post-Gibbs analysis based on the posterior mixture probability.

RESULTS

The detected association patterns from LM and BM models were generally similar. However, BM gave more distinct detection signals than LM. The clearer peaks from BM indicated that the BM model has an advantage in respect of identifying and distinguishing regions of putative QTL. Using BM and QTL region analysis, for the three traits and two breeds a total of 15 QTL regions were identified on SSC1, 2, 3, 6, 7, 9, 13 and 14. Among these QTL regions, 6 regions located on SSC2, 3, 6, 7 and 13 were associated with more than one trait.

CONCLUSION

This study detected QTL regions associated with litter size and piglet mortality traits in Danish pigs using a novel approach of post-Gibbs analysis based on posterior mixture probability. All of the detected QTL regions overlapped with regions previously reported for reproduction traits. The regions commonly detected in different traits and breeds could be resources for multi-trait and across-bred selection. The proposed novel QTL region analysis method would be a good alternative to detect and define QTL regions.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits

Background

Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results

The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions

The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Background

Genomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model. Analytical deductions from the proposed imprinting model indicated a non-negligible contribution of imprinting to genetic variation of complex traits. Also, we performed a whole-genome scan analysis on mouse body mass index (BMI) aiming at revealing potential consequences when existing imprinting effects are ignored in genetic analysis.

Results

10,021 SNP markers were used to perform a whole-genome single marker regression on mouse BMI using an additive and an imprinting model. Markers significant for imprinting indicated that BMI is subject to imprinting. Marked variance changed from 1.218 ×10⁻⁴ to 1.842 ×10⁻⁴ when imprinting was considered in the analysis, implying that one third of marked variance would be lost if existing imprinting effects were not accounted for. When both marker and pedigree information were used, estimated heritability increased from 0.176 to 0.195 when imprinting was considered.

Conclusions

When a complex trait is subject to imprinting, using an additive model that ignores this phenomenon may result in an underestimate of additive variability, potentially leading to wrong inferences about the underlying genetic architecture of that trait. This could be a possible factor explaining part of the missing heritability commonly observed in genome-wide association studies (GWAS).

Genomic imprinting effects on complex traits in domesticated animal species

Monoallelically expressed genes that exert their phenotypic effect in a parent-of-origin specific manner are considered to be subject to genomic imprinting, the most well understood form of epigenetic regulation of gene expression in mammals. The observed differences in allele specific gene expression for imprinted genes are not attributable to differences in DNA sequence information, but to specific chemical modifications of DNA and chromatin proteins. Since the discovery of genomic imprinting some three decades ago, over 100 imprinted mammalian genes have been identified and considerable advances have been made in uncovering the molecular mechanisms regulating imprinted gene expression. While most genomic imprinting studies have focused on mouse models and human biomedical disorders, recent work has highlighted the contributions of imprinted genes to complex trait variation in domestic livestock species. Consequently, greater understanding of genomic imprinting and its effect on agriculturally important traits is predicted to have major implications for the future of animal breeding and husbandry. In this review, we discuss genomic imprinting in mammals with particular emphasis on domestic livestock species and consider how this information can be used in animal breeding research and genetic improvement programs.

A genome-wide association study reveals dominance effects on number of teats in pigs

Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive (SSC6, 7 and 12) and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (−0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene: VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT.

Genomewide association and identification of candidate genes for ovulation rate in swine

Reproductive efficiency has a great impact on the economic success of pork production. Ovulation rate is an early component of reproduction efficiency and contributes to the number of pigs born in a litter. To better understand the underlying genetics of ovulation rate, a genomewide association study was undertaken. Samples of DNA were collected and tested using the Illumina Porcine SNP60 BeadChip from 1,180 females with ovulation measurements ranging from never farrowed to measurements taken after parity 2. A total of 41,848 SNP were tested using the Bayes C option of GenSel. After the Bayes C analysis, SNP were assigned to sliding windows of 5 consecutive SNP by chromosome-position order beginning with the first 5 SNP on SSC1 and ending with the last 5 SNP on SSCX. The 5-SNP windows were analyzed using the Predict option of GenSel. From the Predict analysis, putative QTL were selected having no overlap with other 5-SNP window groups, no overlap across chromosomes, and the highest genetic variation. These putative QTL were submitted to statistical testing using the bootstrap option of GenSel. Of the putative QTL tested, 80 were found to be statistically significant (P < 0.01). Ten QTL were found on SSC1, 12 on SSC2, 4 on SSC3, 8 on SSC4, 3 on SSC5, 3 on SSC6, 3 on SSC7, 4 on SSC8, 2 on SSC9, 4 on SSC10, 1 on SSC12, 4 on SSC13, 2 on SSC14, 4 on SSC15, 4 on SSC16, 6 on SSC17, 4 on SSC18, and 1 on SSCX. Sixteen QTL were found to be statistically significant at the P < 0.001 level. Six additional QTL were significant at the P = 0.001 level. These 22 QTL accounted for 71.10% of the total genetic variance. The most compelling candidate genes in these regions include Estrogen receptor 1, growth differentiation factor 9, and inhibin βA. These QTL, when combined with information on genes found in the same regions, should provide useful information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.

Relationship between pregnancy rate and serum progesterone concentration in cases of porcine embryo transfer

The level of P4 at the time of embryo transfer (ET) is important. P4 concentrations and numbers of corpora lutea for 126 recipients were evaluated. Nuclear transfer embryos were transferred into 126 surrogates. 11 maintained their pregnancy until full-term delivery, 17 miscarried, and implantation failed in 98 animals. P4 levels in the full-term group were significantly different from those of the pigs that aborted or in which implantation failed (p < 0.05). However, the numbers of corpora lutea were not significantly different. These findings indicate that the concentration of progesterone can be an important factor for successful ET in pigs.