Genome-wide and local pattern of linkage disequilibrium and persistence of phase for 3 Danish pig breeds

Evaluation of the genetic diversity and population structure of 5 pheasant breeds in Shanghai

The genetics of pheasant breeds in Chinese farms has not been investigated yet. Understanding their genetic diversity and population structure is important for future advancements in pheasant breeding. In this study, the whole-genome resequencing was used to analyze a total of 352 samples from 5 pheasant species (American pheasant, White pheasant, Green pheasant, Shenhong pheasant, and Fengxian blue pheasant). The average effective population size (Ne) was 45.82. The average of expected heterozygosity (He) and observed heterozygosity (Ho) was 0.28514 and 0.27938, respectively. The Green pheasant had the lowest values of He (0.2730) and Ho (0.2692), whereas Fengxian blue pheasant had the highest values of He (0.2885) and Ho (0.2937), respectively. In addition, the 5 pheasant breeds could be divided into four different genetic populations. A similar genetic structure was also observed between American pheasant and Shenhong pheasant, whereas the other three pheasant breeds (White pheasant, Green pheasant, and Fengxian blue pheasant) exhibited obviously different genetic structures. Further analysis of population structure showed that some individuals among all 5 pheasant breeds had a high genetic distance and weak genetic relationships. A certain degree of inbreeding might exist in the population of White pheasant. Thus, effective breeding and conservation plans should be conducted to retain the genetic distinctiveness for White pheasant. Our data is of great significance for promoting the conservation and development of pheasant genetic resources.

Signatures of selection analyses reveal genomic differences among three heavy pig breeds that constitute the genetic backbone of a dry-cured ham production system

The Italian pig farming industry is unique in its focus on raising heavy pigs primarily for the production of high-quality dry-cured hams. These products require pigs to be slaughtered at a live weight of around 170 kg at 9 months of age. The primary breeds used in this system are Italian Duroc, Italian Landrace, and Italian Large White which are crossed to produce lines that meet standard requirements. Over the past four decades, selection and breeding programmes for these breeds have been subjected to distinct selective pressures to highlight the characteristics of each breed. In this study, we investigated the genome of these breeds by analysing high-density single nucleotide polymorphism data from over 9 000 pigs to scan for signatures of selection using four different methods, two within breeds and two across breeds. This allowed to identify the genomic regions that differentiate these breeds as well as any relevant genes and biological terms. On a global scale, we found that the Italian Duroc breed exhibited a higher genetic differentiation from the Italian Landrace and Italian Large White breeds, with a pairwise FST value of 0.20 compared with the 0.13 between Italian Landrace and Italian Large White. This may reflect either their different origins or the different breeding goals, which are more similar for the Italian Landrace and Italian Large White breeds. Despite these genetic differences at a global level, few signatures of selection regions reached complete fixation, possibly due to challenges in detecting selection linked to quantitative polygenic traits. The differences among the three breeds are confirmed by the low level of overlap in the regions detected. Genetic enrichment analyses of the three breeds revealed pathways and genes related to various productive traits associated with growth and fat deposition. This may indicate a common selection direction aimed at enhancing specific production traits, though different biological mechanisms are likely targeted by the same directional selection in these three breeds. Therefore, these genes may play a critical role in determining the distinctive characteristics of Italian Duroc, Italian Landrace, and Italian Large White, and potentially influence the traits in crossbred pigs derived from them. Overall, the insights gained from this study will contribute to understanding how directional selection has shaped the genome of these heavy pig breeds and to better address selection strategies aimed at enhancing the meat processing industry linked with dry-cured ham production chains.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

BACKGROUND

In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).

METHODS

Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.

RESULTS

Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.

CONCLUSIONS

Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs

BACKGROUND

Long-term natural and artificial selection has resulted in many genetic footprints within the genomes of pig breeds across distinct agroecological zones. Nevertheless, the mechanisms by which these signatures contribute to phenotypic diversity and facilitate environmental adaptation remain unclear.

RESULTS

Here, we leveraged whole-genome sequencing data from 82 individuals from 6 domestic pig breeds originating in tropical, high-altitude, and frigid regions. Population genetic analysis suggested that habitat isolation significantly shaped the genetic diversity and contributed to population stratification in local Chinese pig breeds. Analysis of selection signals revealed regions under selection for adaptation in tropical (55.5 Mb), high-altitude (43.6 Mb), and frigid (17.72 Mb) regions. The potential functions of the selective sweep regions were linked to certain complex traits that might play critical roles in different geographic environments, including fat coverage in frigid environments and blood indicators in tropical and high-altitude environments. Candidate genes under selection were significantly enriched in biological pathways involved in environmental adaptation. These pathways included blood circulation, protein degradation, and inflammation for adaptation to tropical environments; heart and lung development, hypoxia response, and DNA damage repair for high-altitude adaptation; and thermogenesis, cold-induced vasodilation (CIVD), and the cell cycle for adaptation to frigid environments. By examining the chromatin state of the selection signatures, we identified the lung and ileum as two candidate functional tissues for environmental adaptation. Finally, we identified a mutation (chr1: G246,175,129A) in the cis-regulatory region of ABCA1 as a plausible promising variant for adaptation to tropical environments.

CONCLUSIONS

In this study, we conducted a genome-wide exploration of the genetic mechanisms underlying the adaptability of local Chinese pig breeds to tropical, high-altitude, and frigid environments. Our findings shed light on the prominent role of cis-regulatory elements in environmental adaptation in pigs and may serve as a valuable biological model of human plateau-related disorders and cardiovascular diseases.

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations

BACKGROUND

Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.

RESULTS

Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95-69.59% of the total genetic variance.

CONCLUSIONS

Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Integrated model for genomic prediction under additive and non-additive genetic architecture

Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Background

Runs of homozygosity (ROH) are continuous homozygous regions typically located in the DNA sequence of diploid organisms. Identifications of ROH that lead to reduced performance can provide valuable insight into the genetic architecture of complex traits. Here, we systematically investigated the population genetic structure of five Anhui indigenous pig breeds (AHIPs), and compared them to those of five Western commercial pig breeds (WECPs). Furthermore, we examined the occurrence and distribution of ROHs in the five AHIPs and estimated the inbreeding coefficients based on the ROHs (F_ROH) and homozygosity (F_HOM). Finally, we identified genomic regions with high frequencies of ROHs and annotated candidate genes contained therein.

Results

The WECPs and AHIPs were clearly differentiated into two separate clades consistent with their geographical origins, as revealed by the population structure and principal component analysis. We identified 13,530 ROHs across all individuals, of which 4,555 and 8,975 ROHs were unique to AHIPs and WECPs, respectively. Most ROHs identified in our study were short (< 10 Mb) or medium (10–20 Mb) in length. WECPs had significantly higher numbers of short ROHs, and AHIPs generally had longer ROHs. F_ROH values were significantly lower in AHIPs than in WECPs, indicating that breed improvement and conservation programmes were successful in AHIPs. On average, F_ROH and F_HOM values were highly correlated (0.952–0.991) in AHIPs and WECPs. A total of 27 regions had a high frequency of ROHs and contained 17 key candidate genes associated with economically important traits in pigs. Among these, nine candidate genes (CCNT2, EGR2, MYL3, CDH13, PROX1, FLVCR1, SETD2, FGF18, and FGF20) found in WECPs were related to muscular and skeletal development, whereas eight candidate genes (CSN1S1, SULT1E1, TJP1, ZNF366, LIPC, MCEE, STAP1, and DUSP) found in AHIPs were associated with health, reproduction, and fatness traits.

Conclusion

Our findings provide a useful reference for the selection and assortative mating of pig breeds, laying the groundwork for future research on the population genetic structures of AHIPs, ultimately helping protect these local varieties.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08583-9.

Analysis of the Genetic Diversity and Family Structure of the Licha Black Pig Population on Jiaodong Peninsula, Shandong Province, China

Simple Summary

This study investigated the current conservation status, including the genetic diversity, the family structure, and inbreeding, of the Licha black pig population on Jiaodong Peninsula (Shandong Province, China). The results provide insights into the management and conservation of a local pig breed. Breeders are encouraged to utilize genomic data to improve mating schemes based on the family information obtained in this study, such as keeping an equivalent number of boars and sows in each family and selecting individuals with a kinship coefficient of less than 0.1 for mating.

Abstract

The Licha black pig, a popular indigenous Chinese pig breed, is known for its multi-vertebral trait and higher lean meat rate. Understanding the current conservation status, family structure, and degree of inbreeding of the Licha black pig population will be useful to maintain a sufficient level of genetic diversity in these animal resources. In the present study, the genetic diversity, population structure, and inbreeding coefficient of this conserved population were analyzed using SNP genotyping data from 209 Licha black pigs. Based on the genomic information, this population was divided into eight different families with boars. The effective population size (N_e), polymorphic marker ratio (P_N), expected heterozygosity (H_e), and observed heterozygosity (H_o) of this population were 8.7, 0.827, 0.3576, and 0.3512, respectively. In addition, a total of 5976 runs of homozygosity (ROHs) were identified, and most of the ROHs (54.9%) were greater than 5 Mb. The genomic inbreeding coefficient of each individual was estimated based on ROHs (F_ROH) with an average inbreeding coefficient of 0.11 for the population. Five statistics (N_e, P_N, H_o, H_e, and F_ROH) showed a decrease in the level of genetic diversity and a high degree of inbreeding in this population. Thus, special preservation programs need to be implemented in the future, such as introducing new individuals or improving the mating plan. Altogether, our study provides the first genomic overview of the genetic diversity and population structure of Licha black pigs, which will be useful for the management and long-term preservation of this breed.

Estimates of Effective Population Size in Commercial and Hatchery Strains of Coho Salmon (Oncorhynchus kisutch (Walbaum, 1792))

Simple Summary

Several populations of Coho salmon have been maintained in aquaculture, but the extent of the genetic diversity in these strains is unknown. This paper describes the genetic status of several aquaculture strains of Coho salmon from North America, Chile, and Japan and a wild-type hatchery strain from the Pacific Northwest of North America. The Chilean strains in particular have been subject to changes in population sizes attributable to their establishment, reductions due to disease outbreaks, and maintenance of small population sizes in culture. An assumption-free method for estimating the changes in genetic diversity showed that many aquaculture strains had reduced variability. These results highlight the importance of monitoring the genetic diversity of aquaculture species from the start of breeding programs to secure their future genetic variation, particularly in challenging environments such as those expected from climate change.

Abstract

Understanding the genetic status of aquaculture strains is essential for developing management guidelines aimed at sustaining the rates of genetic gain for economically important traits, as well as securing populations that will be robust to climate change. Coho salmon was the first salmonid introduced to Chile for commercial purposes and now comprises an essential component of the country’s aquaculture industry. Several events, such as admixture, genetic bottlenecks, and rapid domestication, appear to be determinants in shaping the genome of commercial strains representing this species. To determine the impact of such events on the genetic diversity of these strains, we sought to estimate the effective population size (Ne) of several of these strains using genome-wide approaches. We compared these estimates to commercial strains from North America and Japan, as well as a hatchery strain used for supportive breeding of wild populations. The estimates of Ne were based on a method robust to assumptions about changes in population history, and ranged from low (Ne = 34) to relatively high (Ne = 80) in the Chilean strains. These estimates were higher than those obtained from the commercial North American strain but lower than those observed in the hatchery population and the Japanese strain (with Ne over 150). Our results suggest that some populations require measures to control the rates of inbreeding, possibly by using genomic information and incorporating new genetic material to ensure the long-term sustainability of these populations.

Linkage Disequilibrium and Effective Population Size of Buffalo Populations of Iran, Turkey, Pakistan, and Egypt Using a Medium Density SNP Array

Linkage disequilibrium (LD) across the genome provides information to identify the genes and variations related to quantitative traits in genome-wide association studies (GWAS) and for the implementation of genomic selection (GS). LD can also be used to evaluate genetic diversity and population structure and reveal genomic regions affected by selection. LD structure and Ne were assessed in a set of 83 water buffaloes, comprising Azeri (AZI), Khuzestani (KHU), and Mazandarani (MAZ) breeds from Iran, Kundi (KUN) and Nili-Ravi (NIL) from Pakistan, Anatolian (ANA) buffalo from Turkey, and buffalo from Egypt (EGY). The values of corrected r² (defined as the correlation between two loci) of adjacent SNPs for three pooled Iranian breeds (IRI), ANA, EGY, and two pooled Pakistani breeds (PAK) populations were 0.24, 0.28, 0.27, and 0.22, respectively. The corrected r² between SNPs decreased with increasing physical distance from 100 Kb to 1 Mb. The LD values for IRI, ANA, EGY, and PAK populations were 0.16, 0.23, 0.24, and 0.21 for less than 100Kb, respectively, which reduced rapidly to 0.018, 0.042, 0.059, and 0.024, for a distance of 1 Mb. In all the populations, the decay rate was low for distances greater than 2Mb, up to the longest studied distance (15 Mb). The r² values for adjacent SNPs in unrelated samples indicated that the Affymetrix Axiom 90 K SNP genomic array was suitable for GWAS and GS in these populations. The persistency of LD phase (PLDP) between populations was assessed, and results showed that PLPD values between the populations were more than 0.9 for distances of less than 100 Kb. The Ne in the recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results.

Analysis of Homozygous-by-Descent (HBD) Segments for Purebred and Crossbred Pigs in Russia

Intensive selection raises the efficiency of pig farming considerably, but it also promotes the accumulation of homozygosity, which can lead to an increase in inbreeding and the accumulation of deleterious variation. The analysis of segments homozygous-by-descent (HBD) and non-HBD segments in purebred and crossbred pigs is of great interest. Research was carried out on 657 pigs, of which there were Large White (LW, n = 280), Landrace (LR, n = 218) and F1 female (♂LR × ♀LW) (F1, n = 159). Genotyping was performed using the GeneSeek^® GGP Porcine HD Genomic Profiler v1 (Illumina Inc., USA). To identify HBD segments and estimate autozygosity (inbreeding coefficient), we used the multiple HBD classes model. LW pigs exhibited 50,420 HBD segments, an average of 180 per animal; LR pigs exhibited 33,586 HBD segments, an average of 154 per animal; F1 pigs exhibited 21,068 HBD segments, an average of 132 per animal. The longest HBD segments in LW were presented in SSC1, SSC13 and SSC15; in LR, in SSC1; and in F1, in SSC15. In these segments, 3898 SNPs localized in 1252 genes were identified. These areas overlap with 441 QTLs (SSC1—238 QTLs; SSC13—101 QTLs; and SSC15—102 QTLs), including 174 QTLs for meat and carcass traits (84 QTLs—fatness), 127 QTLs for reproduction traits (100 QTLs—litter traits), 101 for production traits (69 QTLs—growth and 30 QTLs—feed intake), 21 QTLs for exterior traits (9 QTLs—conformation) and 18 QTLs for health traits (13 QTLs—blood parameters). Thirty SNPs were missense variants. Whilst estimating the potential for deleterious variation, six SNPs localized in the NEDD4, SEC11C, DCP1A, CCT8, PKP4 and TENM3 genes were identified, which may show deleterious variation. A high frequency of potential deleterious variation was noted for LR in DCP1A, and for LW in TENM3 and PKP4. In all cases, the genotype frequencies in F1 were intermediate between LR and LW. The findings presented in our work show the promise of genome scanning for HBD as a strategy for studying population history, identifying genomic regions and genes associated with important economic traits, as well as deleterious variation.

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

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.

Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds

Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.

Population structure and genetic diversity in red clover (Trifolium pratense L.) germplasm

Red clover (Trifolium pratense L.) is a highly adaptable forage crop for temperate livestock agriculture. Genetic variation can be identified, via molecular techniques, and used to assess diversity among populations that may otherwise be indistinguishable. Here we have used genotyping by sequencing (GBS) to determine the genetic variation and population structure in red clover natural populations from Europe and Asia, and varieties or synthetic populations. Cluster analysis differentiated the collection into four large regional groups: Asia, Iberia, UK, and Central Europe. The five varieties clustered with the geographical area from which they were derived. Two methods (BayeScan and Samβada) were used to search for outlier loci indicating signatures of selection. A total of 60 loci were identified by both methods, but no specific genomic region was highlighted. The rate of decay in linkage disequilibrium was fast, and no significant evidence of any bottlenecks was found. Phenotypic analysis showed that a more prostrate and spreading growth habit was predominantly found among populations from Iberia and the UK. A genome wide association study identified a single nucleotide polymorphism (SNP) located in a homologue of the VEG2 gene from pea, associated with flowering time. The identification of genetic variation within the natural populations is likely to be useful for enhancing the breeding of red clover in the future.

Single nucleotide polymorphism-based analysis of the genetic structure of Liangshan pig population

Objective

To conserve and utilize the genetic resources of a traditional Chinese indigenous pig breed, Liangshan pig, we assessed the genetic diversity, genetic structure, and genetic distance in this study.

Methods

We used 50K single nucleotide polymorphism (SNP) chip for SNP detection of 139 individuals in the Liangshan Pig Conservation Farm.

Results

The genetically closed conserved population consisted of five overlapping generations, and the total effective content of the population (Ne) was 15. The whole population was divided into five boar families and one non-boar family. Among them, the effective size of each generation subpopulation continuously decreased. However, the proportion of polymorphic markers (P_N) first decreased and then increased. The average genetic distance of these 139 Liangshan pigs was 0.2823±0.0259, and the average genetic distance of the 14 boars was 0.2723±0.0384. Thus, it can be deduced that the genetic distance changed from generation to generation. In the conserved population, 983 runs of homozygosity (ROH) were detected, and the majority of ROH (80%) were within 100 Mb. The inbreeding coefficient calculated based on ROH showed an average value of 0.026 for the whole population. In addition, the inbreeding coefficient of each generation subpopulation initially increased and then decreased. In the pedigree of the whole conserved population, the error rate of paternal information was more than 11.35% while the maternal information was more than 2.13%.

Conclusion

This molecular study of the population genetic structure of Liangshan pig showed loss of genetic diversity during the closed cross-generation reproduction process. It is necessary to improve the mating plan or introduce new outside blood to ensure long-term preservation of Liangshan pig.

Estimation of inbreeding and identification of regions under heavy selection based on runs of homozygosity in a Large White pig population

Background

Runs of homozygosity (ROHs) are homozygous segments of the genome where the two haplotypes inherited from the parents are identical. The current availability of genotypes for a very large number of single nucleotide polymorphisms (SNPs) is leading to more accurate characterization of ROHs in the whole genome. Here, we investigated the occurrence and distribution of ROHs in 3,692 Large White pigs and compared estimates of inbreeding coefficients calculated based on ROHs (F_ROH), homozygosity (F_HOM), genomic relationship matrix (F_GRM) and pedigree (F_PED). Furthermore, we identified genomic regions with high ROH frequencies and annotated their candidate genes.

Results

In total, 176,182 ROHs were identified from 3,569 animals, and all individuals displayed at least one ROH longer than 1 Mb. The ROHs identified were unevenly distributed on the autosomes. The highest and lowest coverages of Sus scrofa chromosomes (SSC) by ROH were on SSC14 and SSC13, respectively. The highest pairwise correlation among the different inbreeding coefficient estimates was 0.95 between F_{ROH_total} and F_HOM, while the lowest was − 0.083 between F_GRM and F_PED. The correlations between F_PED and F_ROH using four classes of ROH lengths ranged from 0.18 to 0.37 and increased with increasing ROH length, except for ROH > 10 Mb. Twelve ROH islands were located on four chromosomes (SSC1, 4, 6 and 14). These ROH islands harboured genes associated with reproduction, muscular development, fat deposition and adaptation, such as SIRT1, MYPN, SETDB1 and PSMD4.

Conclusion

F_ROH can be used to accurately assess individual inbreeding levels compared to other inbreeding coefficient estimators. In the absence of pedigree records, F_ROH can provide an alternative to inbreeding estimates. Our findings can be used not only to effectively increase the response to selection by appropriately managing the rate of inbreeding and minimizing the negative effects of inbreeding depression but also to help detect genomic regions with an effect on traits under selection.

Controlling Coancestry and Thereby Future Inbreeding by Optimum-Contribution Selection Using Alternative Genomic-Relationship Matrices

We tested the consequences of using alternative genomic relationship matrices to predict genomic breeding values (GEBVs) and control of coancestry in optimum contribution selection, where the relationship matrix used to calculate GEBVs was not necessarily the same as that used to control coancestry. A stochastic simulation study was carried out to investigate genetic gain and true genomic inbreeding in breeding schemes that applied genomic optimum contribution selection (GOCS) with different genomic relationship matrices. Three genomic-relationship matrices were used to predict the GEBVs based on three information sources: markers (G_M), QTL (G_Q), and markers and QTL (G_A). Strictly, G_Q is not possible to implement in practice since we do not know the quantitative trait loci (QTL) positions, but more and more information is becoming available especially about the largest QTL. Two genomic-relationship matrices were used to control coancestry: G_M and G_A. Three genetic architectures were simulated: with 7702, 1000, and 500 QTLs together with 54,218 markers. Selection was for a single trait with heritability 0.2. All selection candidates were phenotyped and genotyped before selection. With 7702 QTL, there were no significant differences in rates of genetic gain at the same rate of true inbreeding using different genomic relationship matrices in GOCS. However, as the number of QTLs was reduced to 1000, prediction of GEBVs using a genomic relationship matrix constructed based on G_Q and control of coancestry using G_M realized 29.7% higher genetic gain than using G_M for both prediction and control of coancestry. Forty-three percent of this increased rate of genetic gain was due to increased accuracies of GEBVs. These findings indicate that with large numbers of QTL, it is not critical what information, i.e., markers or QTL, is used to construct genomic-relationship matrices. However, it becomes critical with small numbers of QTL. This highlights the importance of using genomic-relationship matrices that focus on QTL regions for GEBV estimation when the number of QTL is small in GOCS. Relationships used to control coancestry are preferably based on marker data.

Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome

Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method (BayesN0), which is based on grouping a number of predefined SNPs to account for heterogeneous (co)variance structure across the genome. This model was also implemented in single-step Bayesian regression (ssBayesN0). For practical implementation, we considered multi-trait single-step SNPBLUP models, using (co)variance estimates from BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes on first five chromosomes of 2200 Danish Holstein cattle, and phenotypes were simulated for two traits with heritabilities 0.1 or 0.4, assuming 200 quantitative trait loci (QTL). We compared prediction accuracy from different prediction models and different region sizes (one SNP, 100 SNPs, one chromosome or whole genome). In general, highest accuracies were obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using (co)variance estimates from ssBayesN0 generally yielded higher accuracies than using (co)variance estimates from BayesN0, for the 100 SNPs region size. Our results suggest that it could be a good strategy to estimate (co)variance components from ssBayesN0, and then to use those estimates in genomic prediction using multi-trait single-step SNPBLUP, in routine genomic evaluations.

Exome sequences and multi-environment field trials elucidate the genetic basis of adaptation in barley

Broadening the genetic base of crops is crucial for developing varieties to respond to global agricultural challenges such as climate change. Here, we analysed a diverse panel of 371 domesticated lines of the model crop barley to explore the genetics of crop adaptation. We first collected exome sequence data and phenotypes of key life history traits from contrasting multi-environment common garden trials. Then we applied refined statistical methods, including some based on exomic haplotype states, for genotype-by-environment (G×E) modelling. Sub-populations defined from exomic profiles were coincident with barley's biology, geography and history, and explained a high proportion of trial phenotypic variance. Clear G×E interactions indicated adaptation profiles that varied for landraces and cultivars. Exploration of circadian clock-related genes, associated with the environmentally adaptive days to heading trait (crucial for the crop's spread from the Fertile Crescent), illustrated complexities in G×E effect directions, and the importance of latitudinally based genic context in the expression of large-effect alleles. Our analysis supports a gene-level scientific understanding of crop adaption and leads to practical opportunities for crop improvement, allowing the prioritisation of genomic regions and particular sets of lines for breeding efforts seeking to cope with climate change and other stresses.

Pedigree relationships to control inbreeding in optimum-contribution selection realise more genetic gain than genomic relationships

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.

Genome-wide estimates of genetic diversity, inbreeding and effective size of experimental and commercial rainbow trout lines undergoing selective breeding

BACKGROUND

Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several generations, should be assessed. Indeed, a significant decrease in genetic variability due to high selection intensity could have occurred, potentially jeopardizing the long-term genetic progress as well as the adaptive capacities of populations facing change(s) in the environment. Thus, it is important to evaluate the impact of selection practices on genetic diversity to limit future inbreeding. The current study presents an analysis of genetic diversity within and between six French rainbow trout (Oncorhynchus mykiss) experimental or commercial lines based on a medium-density single nucleotide polymorphism (SNP) chip and various molecular genetic indicators: fixation index (FST), linkage disequilibrium (LD), effective population size (Ne) and inbreeding coefficient derived from runs of homozygosity (ROH).

RESULTS

Our results showed a moderate level of genetic differentiation between selected lines (FST ranging from 0.08 to 0.15). LD declined rapidly over the first 100 kb, but then remained quite high at long distances, leading to low estimates of Ne in the last generation ranging from 24 to 68 depending on the line and methodology considered. These results were consistent with inbreeding estimates that varied from 10.0% in an unselected experimental line to 19.5% in a commercial line, and which are clearly higher than corresponding estimates in ruminants or pigs. In addition, strong variations in LD and inbreeding were observed along the genome that may be due to differences in local rates of recombination or due to key genes that tended to have fixed favorable alleles for domestication or production.

CONCLUSIONS

This is the first report on ROH for any aquaculture species. Inbreeding appeared to be moderate to high in the six French rainbow trout lines, due to founder effects at the start of the breeding programs, but also likely to sweepstakes reproductive success in addition to selection for the selected lines. Efficient management of inbreeding is a major goal in breeding programs to ensure that populations can adapt to future breeding objectives and SNP information can be used to manage the rate at which inbreeding builds up in the fish genome.

Genomic structure of a crossbred Landrace pig population

Single nucleotide polymorphism (SNP) markers are used to study population structure and conservation genetics, which permits assessing similarities regarding the linkage disequilibrium and information about the relationship among individuals. To investigate the population genomic structure of 300 females and 25 males from a commercial maternal pig line we analyzed linkage disequilibrium extent, inbreeding coefficients using genomic and conventional pedigree data, and population stratification. The average linkage disequilibrium (r²) was 0.291 ± 0.312 for all adjacent SNPs, distancing less than 100 Kb (kilobase) between markers. The average inbreeding coefficients obtained from runs of homozygosity (ROH) and pedigree analyses were 0.119 and 0.0001, respectively. Low correlation was observed between the inbreeding coefficients possibly as a result of genetic recombination effect accounted for the ROH estimates or caused by pedigree identification errors. A large number of long ROHs might indicate recent inbreeding events in the studied population. A total of 36 homozygous segments were found in more than 30% of the population and these ROH harbor genes associated with reproductive traits. The population stratification analysis indicated that this population was possibly originated from two distinct populations, which is a result from crossings between the eastern and western breeds used in the formation of the line. Our findings provide support to understand the genetic structure of swine populations and may assist breeding companies to avoid a high level of inbreeding coefficients to maintain genetic diversity, showing the effectiveness of using genome-wide SNP information for quantifying inbreeding when the pedigree was incomplete or incorrect.

Extent of linkage disequilibrium and effective population size of Korean Yorkshire swine

Objective

We aimed to characterize linkage disequilibrium (LD) and effective population size (N_e) in a Korean Yorkshire population using genomic data from thousands of individuals.

Methods

We genotyped 2,470 Yorkshire individuals from four major Grand-Grand-Parent farms in Korea using the Illumina PorcineSNP60 version2 BeadChip, which covers >61,565 single nucleotide polymorphisms (SNPs) located across all chromosomes and mitochondria. We estimated the expected LD and inferred current N_e as well as ancestral N_e.

Results

We identified 61,565 SNP from autosomes, mitochondria, and sex chromosomes and characterized the LD of the Yorkshire population, which was relatively high between closely linked markers (>0.55 at 50 kb) and declined with increasing genetic distance. The current N_e of this Korean Yorkshire population was 122.87 (106.90; 138.84), while the historical N_e of Yorkshire pigs suggests that the ancestor N_e has decreased by 99.6% over the last 10,000 generations.

Conclusion

To maintain genetic diversity of a domesticated animal population, we must carefully consider appropriate breed management methods to avoid inbreeding. Although attenuated selection can affect short-term genetic gain, it is essential for maintaining the long-term genetic variability of the Korean Yorkshire population. Continuous and long-term monitoring would also be needed to maintain the pig population to avoid an unintended reduction of N_e. The best way to preserve a sustainable population is to maintain a sufficient N_e.

Genomic Model with Correlation Between Additive and Dominance Effects

Dominance genetic effects are rarely included in pedigree-based genetic evaluation. With the availability of single nucleotide polymorphism markers and the development of genomic evaluation, estimates of dominance genetic effects have become feasible using genomic best linear unbiased prediction (GBLUP). Usually, studies involving additive and dominance genetic effects ignore possible relationships between them. It has been often suggested that the magnitude of functional additive and dominance effects at the quantitative trait loci are related, but there is no existing GBLUP-like approach accounting for such correlation. Wellmann and Bennewitz (2012) showed two ways of considering directional relationships between additive and dominance effects, which they estimated in a Bayesian framework. However, these relationships cannot be fitted at the level of individuals instead of loci in a mixed model, and are not compatible with standard animal or plant breeding software. This comes from a fundamental ambiguity in assigning the reference allele at a given locus. We show that, if there has been selection, assigning the most frequent as the reference allele orients the correlation between functional additive and dominance effects. As a consequence, the most frequent reference allele is expected to have a positive value. We also demonstrate that selection creates negative covariance between genotypic additive and dominance genetic values. For parameter estimation, it is possible to use a combined additive and dominance relationship matrix computed from marker genotypes, and to use standard restricted maximum likelihood algorithms based on an equivalent model. Through a simulation study, we show that such correlations can easily be estimated by mixed model software and that the accuracy of prediction for genetic values is slightly improved if such correlations are used in GBLUP. However, a model assuming uncorrelated effects and fitting orthogonal breeding values and dominant deviations performed similarly for prediction.

Systems genomics study reveals expression quantitative trait loci, regulator genes and pathways associated with boar taint in pigs

Boar taint is an offensive odour and/or taste from a proportion of non-castrated male pigs caused by skatole and androstenone accumulation during sexual maturity. Castration is widely used to avoid boar taint but is currently under debate because of animal welfare concerns. This study aimed to identify expression quantitative trait loci (eQTLs) with potential effects on boar taint compounds to improve breeding possibilities for reduced boar taint. Danish Landrace male boars with low, medium and high genetic merit for skatole and human nose score (HNS) were slaughtered at ~100 kg. Gene expression profiles were obtained by RNA-Seq, and genotype data were obtained by an Illumina 60K Porcine SNP chip. Following quality control and filtering, 10,545 and 12,731 genes from liver and testis were included in the eQTL analysis, together with 20,827 SNP variants. A total of 205 and 109 single-tissue eQTLs associated with 102 and 58 unique genes were identified in liver and testis, respectively. By employing a multivariate Bayesian hierarchical model, 26 eQTLs were identified as significant multi-tissue eQTLs. The highest densities of eQTLs were found on pig chromosomes SSC12, SSC1, SSC13, SSC9 and SSC14. Functional characterisation of eQTLs revealed functions within regulation of androgen and the intracellular steroid hormone receptor signalling pathway and of xenobiotic metabolism by cytochrome P450 system and cellular response to oestradiol. A QTL enrichment test revealed 89 QTL traits curated by the Animal Genome PigQTL database to be significantly overlapped by the genomic coordinates of cis-acting eQTLs. Finally, a subset of 35 cis-acting eQTLs overlapped with known boar taint QTL traits. These eQTLs could be useful in the development of a DNA test for boar taint but careful monitoring of other overlapping QTL traits should be performed to avoid any negative consequences of selection.

Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders

A single-step genomic BLUP method (ssGBLUP) has been successfully developed and applied for purebred and crossbred performance in pigs. However, it requires phasing the genotypes and inferring the breed origin of alleles in crossbred animals, which is somewhat inconvenient. Recently, a new concept of metafounders that considers the relationship within and across base populations was developed. With this concept of metafounders, regular methods to build and invert the pedigree relationships matrix can be used with only minor modifications and, moreover, genomic relationships and pedigree-based relationships are automatically compatible in the ssGBLUP. In this study, data for the total number of piglets born in Danish Landrace, Yorkshire, and 2-way crossbred pigs and models for purebred and crossbred performance were revisited by use of ssGBLUP with 2 metafounders. Genetic variances and genetic correlations between purebred and crossbred performances were first reestimated. Then, model-based reliabilities of purebred boars for their crossbred performance and predictive abilities for crossbred animals were compared in different scenarios. Results in this study were compared to those in a previous study with identical data but with models that required known breed origin of crossbred genotypes. Results show that relationships for base individuals within Landrace and within Yorkshire are similar and that the ancestor populations for Landrace and Yorkshire are related. In terms of model-based reliabilities and predictive abilities, ssGBLUP with metafounders performs at least as well as the single-step method requiring phasing at a lower complexity.

Genome-wide association study for conformation traits in three Danish pig breeds

Background

Selection for sound conformation has been widely used as a primary approach to reduce lameness and leg weakness in pigs. Identification of genomic regions that affect conformation traits would help to improve selection accuracy for these lowly to moderately heritable traits. Our objective was to identify genetic factors that underlie leg and back conformation traits in three Danish pig breeds by performing a genome-wide association study followed by meta-analyses.

Methods

Data on four conformation traits (front leg, back, hind leg and overall conformation) for three Danish pig breeds (23,898 Landrace, 24,130 Yorkshire and 16,524 Duroc pigs) were used for association analyses. Estimated effects of single nucleotide polymorphisms (SNPs) from single-trait association analyses were combined in two meta-analyses: (1) a within-breed meta-analysis for multiple traits to examine if there are pleiotropic genetic variants within a breed; and (2) an across-breed meta-analysis for a single trait to examine if the same quantitative trait loci (QTL) segregate across breeds. SNP annotation was implemented through Sus scrofa Build 10.2 on Ensembl to search for candidate genes.

Results

Among the 14, 12 and 13 QTL that were detected in the single-trait association analyses for the three breeds, the most significant SNPs explained 2, 2.3 and 11.4% of genetic variance for back quality in Landrace, overall conformation in Yorkshire and back quality in Duroc, respectively. Several candidate genes for these QTL were also identified, i.e. LRPPRC, WRAP73, VRTN and PPARD likely control conformation traits through the regulation of bone and muscle development, and IGF2BP2, GH1, CCND2 and MSH2 can have an influence through growth-related processes. Meta-analyses not only confirmed many significant SNPs from single-trait analyses with higher significance levels, but also detected several additional associated SNPs and suggested QTL with possible pleiotropic effects.

Conclusions

Our results imply that conformation traits are complex and may be partly controlled by genes that are involved in bone and skeleton development, muscle and fat metabolism, and growth processes. A reliable list of QTL and candidate genes was provided that can be used in fine-mapping and marker assisted selection to improve conformation traits in pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0289-2) contains supplementary material, which is available to authorized users.

Genetic diversity, extent of linkage disequilibrium and persistence of gametic phase in Canadian pigs

Background

Knowledge on the levels of linkage disequilibrium (LD) across the genome, persistence of gametic phase between breed pairs, genetic diversity and population structure are important parameters for the successful implementation of genomic selection. Therefore, the objectives of this study were to investigate these parameters in order to assess the feasibility of a multi-herd and multi-breed training population for genomic selection in important purebred and crossbred pig populations in Canada. A total of 3,057 animals, representative of the national populations, were genotyped with the Illumina Porcine SNP60 BeadChip (62,163 markers).

Results

The overall LD (r²) between adjacent SNPs was 0.49, 0.38, 0.40 and 0.31 for Duroc, Landrace, Yorkshire and Crossbred (Landrace x Yorkshire) populations, respectively. The highest correlation of phase (r) across breeds was observed between Crossbred animals and either Landrace or Yorkshire breeds, in which r was approximately 0.80 at 1 Mbp of distance. Landrace and Yorkshire breeds presented r ≥ 0.80 in distances up to 0.1 Mbp, while Duroc breed showed r ≥ 0.80 for distances up to 0.03 Mbp with all other populations. The persistence of phase across herds were strong for all breeds, with r ≥ 0.80 up to 1.81 Mbp for Yorkshire, 1.20 Mbp for Duroc, and 0.70 Mbp for Landrace. The first two principal components clearly discriminate all the breeds. Similar levels of genetic diversity were observed among all breed groups. The current effective population size was equal to 75 for Duroc and 92 for both Landrace and Yorkshire.

Conclusions

An overview of population structure, LD decay, demographic history and inbreeding of important pig breeds in Canada was presented. The rate of LD decay for the three Canadian pig breeds indicates that genomic selection can be successfully implemented within breeds with the current 60 K SNP panel. The use of a multi-breed training population involving Landrace and Yorkshire to estimate the genomic breeding values of crossbred animals (Landrace × Yorkshire) should be further evaluated. The lower correlation of phase at short distances between Duroc and the other breeds indicates that a denser panel may be required for the use of a multi-breed training population including Duroc.

Electronic supplementary material

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Sparse single-step method for genomic evaluation in pigs

Background

In many animal breeding programs, with the increasing number of genotyped animals, estimation of genomic breeding values by the single-step method is becoming limited by excessive computing requirements. A recently proposed algorithm for proven and young animals (APY) is an approximation that reduces computing time drastically by dividing genotyped animals into core and non-core animals, with only computations for core animals being time-consuming. We hypothesized that choosing core animals based on representing all generations, minimizing the relatedness within the core group, or maximizing the number of genotyped offspring, would result in greater accuracies of estimated breeding values (EBV).

Methods

We compared eight different core groups for the three pig breeds DanAvl Duroc, DanAvl Landrace and DanAvl Yorkshire. These eight sparse approximations of the single-step method were evaluated based on correlations of EBV for genotyped animals obtained from the sparse methods with those obtained from the usual version of the single-step method. We used a single-trait model with daily gain as trait.

Results

For core groups that distributed animals across generations, correlations for genotyped animals (from 0.977 to 0.989) were higher than for those that did not distribute core animals across generations (from 0.934 to 0.956). For core groups that maximized the number of genotyped offspring, correlations for genotyped animals (from 0.983 to 0.989) were higher than for other core groups (from 0.934 to 0.981). There was no clear association between low relatedness within the core group and accuracy of approximations.

Conclusions

We found that for core groups that represent all generations and that maximize the number of genotyped offspring, accurate approximations of EBV were obtained. However, we did not find a clear association between accuracy and relatedness within the core group. For the APY method, this is the first study that reports systematic criteria for the creation of core groups that result in more accurate EBV than a similar-sized random core group. Random core groups only ensure across-generation representation. Therefore, we recommend choosing a core group that represents all generations and that maximizes the number of genotyped offspring for single-step genomic evaluation using the APY method.

Genomic prediction of crossbred performance based on purebred Landrace and Yorkshire data using a dominance model

Background

In pig breeding, selection is usually carried out in purebred populations, although the final goal is to improve crossbred performance. Genomic selection can be used to select purebred parental lines for crossbred performance. Dominance is the likely genetic basis of heterosis and explicitly including dominance in the genomic selection model may be an advantage when selecting purebreds for crossbred performance. Our objectives were two-fold: (1) to compare the predictive ability of genomic prediction models with additive or additive plus dominance effects, when the validation criterion is crossbred performance; and (2) to compare the use of two pure line reference populations to a single combined reference population.

Methods

We used data on litter size in the first parity from two pure pig lines (Landrace and Yorkshire) and their reciprocal crosses. Training was performed (1) separately on pure Landrace (2085) and Yorkshire (2145) sows and (2) the two combined pure lines (4230), which were genotyped for 38 k single nucleotide polymorphisms (SNPs). Prediction accuracy was measured as the correlation between genomic estimated breeding values (GEBV) of pure line boars and mean corrected crossbred-progeny performance, divided by the average accuracy of mean-progeny performance. We evaluated a model with additive effects only (MA) and a model with both additive and dominance effects (MAD). Two types of GEBV were computed: GEBV for purebred performance (GEBV) based on either the MA or MAD models, and GEBV for crossbred performance (GEBV-C) based on the MAD. GEBV-C were calculated based on SNP allele frequencies of genotyped animals in the opposite line.

Results

Compared to MA, MAD improved prediction accuracy for both lines. For MAD, GEBV-C improved prediction accuracy compared to GEBV. For Landrace (Yorkshire) boars, prediction accuracies were equal to 0.11 (0.32) for GEBV based on MA, and 0.13 (0.34) and 0.14 (0.36) for GEBV and GEBV-C based on MAD, respectively. Combining animals from both lines into a single reference population yielded higher accuracies than training on each pure line separately. In conclusion, the use of a dominance model increased the accuracy of genomic predictions of crossbred performance based on purebred data.

Electronic supplementary material

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Genetic diversity analysis of two commercial breeds of pigs using genomic and pedigree data

Background

Genetic improvement in livestock populations can be achieved without significantly affecting genetic diversity if mating systems and selection decisions take genetic relationships among individuals into consideration. The objective of this study was to examine the genetic diversity of two commercial breeds of pigs. Genotypes from 1168 Landrace (LA) and 1094 Large White (LW) animals from a commercial breeding program in Brazil were obtained using the Illumina PorcineSNP60 Beadchip. Inbreeding estimates based on pedigree (F_x) and genomic information using runs of homozygosity (F_ROH) and the single nucleotide polymorphisms (SNP) by SNP inbreeding coefficient (F_SNP) were obtained. Linkage disequilibrium (LD), correlation of linkage phase (r) and effective population size (N_e) were also estimated.

Results

Estimates of inbreeding obtained with pedigree information were lower than those obtained with genomic data in both breeds. We observed that the extent of LD was slightly larger at shorter distances between SNPs in the LW population than in the LA population, which indicates that the LW population was derived from a smaller N_e. Estimates of N_e based on genomic data were equal to 53 and 40 for the current populations of LA and LW, respectively. The correlation of linkage phase between the two breeds was equal to 0.77 at distances up to 50 kb, which suggests that genome-wide association and selection should be performed within breed. Although selection intensities have been stronger in the LA breed than in the LW breed, levels of genomic and pedigree inbreeding were lower for the LA than for the LW breed.

Conclusions

The use of genomic data to evaluate population diversity in livestock animals can provide new and more precise insights about the effects of intense selection for production traits. Resulting information and knowledge can be used to effectively increase response to selection by appropriately managing the rate of inbreeding, minimizing negative effects of inbreeding depression and therefore maintaining desirable levels of genetic diversity.

Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations

Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs

Background

In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.

Results

We analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.

Conclusions

The GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.

Electronic supplementary material

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Red clover (Trifolium pratense L.) draft genome provides a platform for trait improvement

Red clover (Trifolium pratense L.) is a globally significant forage legume in pastoral livestock farming systems. It is an attractive component of grassland farming, because of its high yield and protein content, nutritional value and ability to fix atmospheric nitrogen. Enhancing its role further in sustainable agriculture requires genetic improvement of persistency, disease resistance, and tolerance to grazing. To help address these challenges, we have assembled a chromosome-scale reference genome for red clover. We observed large blocks of conserved synteny with Medicago truncatula and estimated that the two species diverged ~23 million years ago. Among the 40,868 annotated genes, we identified gene clusters involved in biochemical pathways of importance for forage quality and livestock nutrition. Genotyping by sequencing of a synthetic population of 86 genotypes show that the number of markers required for genomics-based breeding approaches is tractable, making red clover a suitable candidate for association studies and genomic selection.

Imputation of genotypes in Danish purebred and two-way crossbred pigs using low-density panels

Background

Genotype imputation is commonly used as an initial step in genomic selection since the accuracy of genomic selection does not decline if accurately imputed genotypes are used instead of actual genotypes but for a lower cost. Performance of imputation has rarely been investigated in crossbred animals and, in particular, in pigs. The extent and pattern of linkage disequilibrium differ in crossbred versus purebred animals, which may impact the performance of imputation. In this study, first we compared different scenarios of imputation from 5 K to 8 K single nucleotide polymorphisms (SNPs) in genotyped Danish Landrace and Yorkshire and crossbred Landrace-Yorkshire datasets and, second, we compared imputation from 8 K to 60 K SNPs in genotyped purebred and simulated crossbred datasets. All imputations were done using software Beagle version 3.3.2. Then, we investigated the reasons that could explain the differences observed.

Results

Genotype imputation performs as well in crossbred animals as in purebred animals when both parental breeds are included in the reference population. When the size of the reference population is very large, it is not necessary to use a reference population that combines the two breeds to impute the genotypes of purebred animals because a within-breed reference population can provide a very high level of imputation accuracy (correct rate ≥ 0.99, correlation ≥ 0.95). However, to ensure that similar imputation accuracies are obtained for crossbred animals, a reference population that combines both parental purebred animals is required. Imputation accuracies are higher when a larger proportion of haplotypes are shared between the reference population and the validation (imputed) populations.

Conclusions

The results from both real data and pedigree-based simulated data demonstrate that genotype imputation from low-density panels to medium-density panels is highly accurate in both purebred and crossbred pigs. In crossbred pigs, combining the parental purebred animals in the reference population is necessary to obtain high imputation accuracy.

Electronic supplementary material

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Genomic best linear unbiased prediction method reflecting the degree of linkage disequilibrium

The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP-LD). In the three GBLUP-LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP-LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP-LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP-LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP-LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP-LD methods were lower than GBLUP and Bayesian lasso.

Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations

Background

Genomic selection and genomic wide association studies are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor that may be overcome by combining data from multiple breeds or using crossbred information. However, the estimated effect of a marker in one breed or a crossbred can only be useful for the selection of animals in another breed if there is a correspondence of the phase between the marker and the QTL across breeds. Using data of five pure pig (Sus scrofa) lines (SL1, SL2, SL3, DL1, DL2), one F₁ cross (DLF1) and two commercial finishing crosses (TER1 and TER2), the objectives of this study were: (i) to compare the equality of LD decay curves of different pig populations; and (ii) to evaluate the persistence of the LD phase across lines or final crosses.

Results

Almost all of the lines presented different extents of LD, except for the SL2 and DL3, both of which exhibited the same extent of LD. Similar levels of LD over large distances were found in crossbred and pure lines. The crossbred animals (DLF1, TER1 and TER2) presented a high persistence of phase with their parental lines, suggesting that the available porcine single nucleotide polymorphism (SNP) chip should be dense enough to include markers that have the same LD phase with QTL across crossbred and parental pure lines. The persistence of phase across pure lines varied considerably between the different line comparisons; however, correlations were above 0.8 for all line comparisons when marker distances were smaller than 50 kb.

Conclusions

This study showed that crossbred populations could be very useful as a reference for the selection of pure lines by means of the available SNP chip panel. Here, we also pinpoint pure lines that could be combined in a multiline training population. However, if multiline reference populations are used for genomic selection, the required density of SNP panels should be higher compared with a single breed reference population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0126-3) contains supplementary material, which is available to authorized users.

Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake

Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. GWA analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as xin actin-binding repeat-containing protein 2 (XIRP2),tetratricopeptide repeat domain 29 (TTC29),suppressor of glucose, autophagy associated 1 (SOGA1),MAS1,G-protein-coupled receptor (GPCR) kinase 5 (GRK5),prospero-homeobox protein 1 (PROX1),GPCR 155 (GPR155), and FYVE domain containing the 26 (ZFYVE26) were identified as putative candidates for RFI based on their genomic location in the vicinity of these SNPs. Genes located within 50 kbp of SNPs significantly associated with RFI and RFI2 (q-value ≤ 0.2) were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher's exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs.