Outbred Pig Populations Research Articles

Genome-wide association analyses for meat quality traits in Chinese Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) commercial population.

BackgroundUnderstanding the genetic mechanisms that underlie meat quality traits is essential to improve pork quality. To date, most quantitative trait loci (QTL) analyses have been performed on F2 crosses between outbred pig strains and have led to the identification of numerous QTL. However, because linkage disequilibrium is high in such crosses, QTL mapping precision is unsatisfactory and only a few QTL have been found to segregate within outbred strains, which limits their use to improve animal performance. To detect QTL in outbred pig populations of Chinese and Western origins, we performed genome-wide association studies (GWAS) for meat quality traits in Chinese purebred Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) (DLY) commercial population.MethodsThree hundred and thirty six Chinese Erhualian and 610 DLY pigs were genotyped using the Illumina PorcineSNP60K Beadchip and evaluated for 20 meat quality traits. After quality control, 35 985 and 56 216 single nucleotide polymorphisms (SNPs) were available for the Chinese Erhualian and DLY datasets, respectively, and were used to perform two separate GWAS. We also performed a meta-analysis that combined P-values and effects of 29 516 SNPs that were common to Erhualian, DLY, F2 and Sutai pig populations.ResultsWe detected 28 and nine suggestive SNPs that surpassed the significance level for meat quality in Erhualian and DLY pigs, respectively. Among these SNPs, ss131261254 on pig chromosome 4 (SSC4) was the most significant (P = 7.97E-09) and was associated with drip loss in Erhualian pigs. Our results suggested that at least two QTL on SSC12 and on SSC15 may have pleiotropic effects on several related traits. All the QTL that were detected by GWAS were population-specific, including 12 novel regions. However, the meta-analysis revealed seven novel QTL for meat characteristics, which suggests the existence of common underlying variants that may differ in frequency across populations. These QTL regions contain several relevant candidate genes.ConclusionsThese findings provide valuable insights into the molecular basis of convergent evolution of meat quality traits in Chinese and Western breeds that show divergent phenotypes. They may contribute to genetic improvement of purebreds for crossbred performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0120-x) contains supplementary material, which is available to authorized users.

A new genotype imputation method with tolerance to high missing rate and rare variants.

We report a novel algorithm, iBLUP, to impute missing genotypes by simultaneously and comprehensively using identity by descent and linkage disequilibrium information. The simulation studies showed that the algorithm exhibited drastically tolerance to high missing rate, especially for rare variants than other common imputation methods, e.g. BEAGLE and fastPHASE. At a missing rate of 70%, the accuracy of BEAGLE and fastPHASE dropped to 0.82 and 0.74 respectively while iBLUP retained an accuracy of 0.95. For minor allele, the accuracy of BEAGLE and fastPHASE decreased to −0.1 and 0.03, while iBLUP still had an accuracy of 0.61.We implemented the algorithm in a publicly available software package also named iBLUP. The application of iBLUP for processing real sequencing data in an outbred pig population was demonstrated.

Genomic growth curves of an outbred pig population.

In the current post-genomic era, the genetic basis of pig growth can be understood by assessing SNP marker effects and genomic breeding values (GEBV) based on estimates of these growth curve parameters as phenotypes. Although various statistical methods, such as random regression (RR-BLUP) and Bayesian LASSO (BL), have been applied to genomic selection (GS), none of these has yet been used in a growth curve approach. In this work, we compared the accuracies of RR-BLUP and BL using empirical weight-age data from an outbred F2 (Brazilian Piau X commercial) population. The phenotypes were determined by parameter estimates using a nonlinear logistic regression model and the halothane gene was considered as a marker for evaluating the assumptions of the GS methods in relation to the genetic variation explained by each locus. BL yielded more accurate values for all of the phenotypes evaluated and was used to estimate SNP effects and GEBV vectors. The latter allowed the construction of genomic growth curves, which showed substantial genetic discrimination among animals in the final growth phase. The SNP effect estimates allowed identification of the most relevant markers for each phenotype, the positions of which were coincident with reported QTL regions for growth traits.

Three-step Bayesian factor analysis applied to QTL detection in crosses between outbred pig populations

Marker assisted selection (MAS) can be used to improve the efficiency of genetic selection of traits for which phenotypic measurements are expensive or cannot be obtained on selection candidates, such as carcass traits. Marker information required for MAS may be acquired through the identification of QTLs. Generally, univariate models are used for QTL detection, although multiple-trait models (MTM) may enhance QTL detection and breeding value estimation. In MTM, however, the number of parameters can be large and, if traits are highly correlated, such as carcass traits, estimates of (co)variance matrices may be close to singular. Because of this, dimension reduction techniques such as Factor Analysis (FA) may be useful. The aim of our project is to evaluate the use of FA for structuring (co)variance matrices in the context of Bayesian models for QTL detection in crosses between outbred populations. In our method, QTL effects are postulated at the level of common factors (CF) rather than the original traits, using a three-step approach. In a first step, a MTM is fitted to arrive at estimates of systematic effects and prediction of breeding values (procedure A) and only systematic effect (procedure B). These estimates/predictions are then used to generate an adjusted phenotype that is further analyzed with a Bayesian FA model. This step yields estimates of factor scores for each animal and CF. In the last step, the scores relative to each CF are analyzed independently using probabilities for the line of origin combination. To illustrate the methodology, data on 416 F2 pigs (Brazilian Piau X commercial) with ten traits (5 fat-related, 2 loin measurements, and 3 carcass classification systems) were analyzed. For each of the three resulting CFs, an independent QTL scan was performed on chromosome 7 considering three models: I) null (i.e., absence of QTL); II) additive effect QTL, and III) additive and dominance effect QTL. The posterior probability (PP) of each model was calculated from Bayes factor for each considered procedures (A and B). A Three-step Bayesian factor analysis allowed us to calculate the probability of QTLs that simultaneously affect a group of carcass traits for each position of SSC 7. The removal of systematic effects in the first step of the evaluation (procedure B) allowed that the factor analysis, which was performed in the second step, identify three distinct factors that explained 85% of the total traits variation. For the common factor that represented fat-related traits (bacon depth, midline lower backfat thickness, higher backfat thickness on the shoulder; midline backfat thickness after the last rib; midline backfat thickness on the last lumbar vertebrae) the third step of the analysis showed that the highest probability of an additive QTL effect at the 65cM position was 86%.

Molecular characterization of swine leucocyte antigen class II genes in outbred pig populations

The highly polymorphic swine leucocyte antigen (SLA) genes are among the most important determinants of swine immune responses to disease and vaccines. Accurate and effective SLA genotyping methods are required to understand how SLA gene polymorphisms affect immunity, especially in outbred pigs with diverse genetic backgrounds. In this study, we present a simple and rapid molecular-based typing system for characterizing SLA class II alleles of the DRB1, DQB1 and DQA loci. This system utilizes a set of 47 sequence-specific PCR primers developed to differentiate alleles by groups that share similar sequence motifs. We applied this typing method to investigate the SLA class II diversity in four populations of outbred pigs (n = 206) and characterized a total of 19 SLA class II haplotypes, six of which were shared by at least three of the sampled pig populations. We found that Lr-0.1 (DRB1*01XX-DQB1*01XX-DQA*01XX) was the most prevalent haplotype with a combined frequency of 16.0%, followed by Lr-0.2 (DRB1*02XX-DQB1*02XX-DQA*02XX) with 14.6% and Lr-0.15b (DRB1*04XX-DQB1*0202-DQA*02XX) with 14.1%. Over 70% of the pigs (n = 147) had at least one copy of one of these three haplotypes. The PCR-based typing system described in this study demonstrates a reliable and unambiguous detection method for SLA class II alleles. It will be a valuable tool for studying the influence of SLA diversity on various immunological, pathological and physiological traits in outbred pigs.

Detection of multiple quantitative trait loci and their pleiotropic effects in outbred pig populations

BackgroundSimultaneous detection of multiple QTLs (quantitative trait loci) may allow more accurate estimation of genetic effects. We have analyzed outbred commercial pig populations with different single and multiple models to clarify their genetic properties and in addition, we have investigated pleiotropy among growth and obesity traits based on allelic correlation within a gamete.MethodsThree closed populations, (A) 427 individuals from a Yorkshire and Large White synthetic breed, (B) 547 Large White individuals and (C) 531 Large White individuals, were analyzed using a variance component method with one-QTL and two-QTL models. Six markers on chromosome 4 and five to seven markers on chromosome 7 were used.ResultsPopulation A displayed a high test statistic for the fat trait when applying the two-QTL model with two positions on two chromosomes. The estimated heritabilities for polygenic effects and for the first and second QTL were 19%, 17% and 21%, respectively. The high correlation of the estimated allelic effect on the same gamete and QTL test statistics suggested that the two separate QTL which were detected on different chromosomes both have pleiotropic effects on the two fat traits. Analysis of population B using the one-QTL model for three fat traits found a similar peak position on chromosome 7. Allelic effects of three fat traits from the same gamete were highly correlated suggesting the presence of a pleiotropic QTL. In population C, three growth traits also displayed similar peak positions on chromosome 7 and allelic effects from the same gamete were correlated.ConclusionDetection of the second QTL in a model reduced the polygenic heritability and should improve accuracy of estimated heritabilities for both QTLs.

Genes located on a SSC17 meat quality QTL region are associated with growth in outbred pig populations

The objective of this study was to evaluate the effect of markers developed in eight genes, located in a previously detected meat quality QTL region on SSC17, on growth, fat and meat quality traits collected in commercial pig populations of different genetic backgrounds. The genes had been previously mapped to SSC17 as part of a fine-mapping effort. Association analyses were conducted between each marker and the available phenotypic traits. Results showed that three genes (CTSZ, CSTF1 and C20orf43) were significantly associated with the growth traits. In addition, CTSZ also impacted on meat colour, with the less favourable genotype for growth being associated with darker meat. The differences observed between genotypes were substantial and may be of economic importance to pig producers. These markers may be useful for selecting for faster growth or improved meat quality.

Molecular characterization of swine leucocyte antigen class I genes in outbred pig populations

The highly polymorphic swine leucocyte antigen (SLA) genes are one of the most important determinants in swine immune responses to infectious diseases, vaccines, and in transplantation success. Study of SLA influence requires accurate and effective typing methods. We developed a simple and rapid method to type alleles at the three classical SLA class I loci (SLA-1, SLA-3 and SLA-2) using the PCR-sequence-specific primer (PCR-SSP) strategy. This typing system relies on 47 discriminatory PCR primer pairs designed to amplify the SLA class I alleles by groups that have similar sequence motifs. We applied this low-resolution group-specific typing method to characterize the SLA class I alleles present in three outbred pig populations (n = 202). Alleles from 24 class I allele groups corresponding to 56 class I genotypes were detected. We also identified 23 low-resolution SLA class I haplotypes in these pigs and found haplotypes Lr-1.0 (SLA-1*01XX-SLA-3*01XX-SLA-2*01XX) and Lr-4.0 (SLA-1*04XX-SLA-3*04XX-SLA-2*04XX) in all three pig populations with a high prevalence. Over 80% of the pigs examined (n = 162) were found to bear at least one of these haplotypes, resulting in a combined haplotype frequency of nearly 50%. This PCR-SSP-based typing system demonstrates a reliable and unambiguous detection of SLA class I alleles, and can be used to effectively investigate the SLA diversity in outbred pig populations. It will help to identify the role of SLA antigens in disease-resistant pigs and may facilitate the development of effective vaccines.

QTL detection and allelic effects for growth and fat traits in outbred pig populations

QTL detection and allelic effects for growth and fat traits in outbred pig populations

QTL detection and allelic effects for growth and fat traits in outbred pig populations.

Quantitative trait loci (QTL) for growth and fatness traits have previously been identified on chromosomes 4 and 7 in several experimental pig populations. The segregation of these QTL in commercial pigs was studied in a sample of 2713 animals from five different populations. Variance component analysis (VCA) using a marker-based identity by descent (IBD) matrix was applied. The IBD coefficient was estimated with simple deterministic (SMD) and Markov chain Monte Carlo (MCMC) methods. Data for two growth traits, average daily gain on test and whole life daily gain, and back fat thickness were analysed. With both methods, seven out of 26 combinations of population, chromosome and trait, were significant. Additionally, QTL genotypic and allelic effects were estimated when the QTL effect was significant. The range of QTL genotypic effects in a population varied from 4.8% to 10.9% of the phenotypic mean for growth traits and 7.9% to 19.5% for back fat trait. Heritabilities of the QTL genotypic values ranged from 8.6% to 18.2% for growth traits, and 14.5% to 19.2% for back fat. Very similar results were obtained with both SMD and MCMC. However, the MCMC method required a large number of iterations, and hence computation time, especially when the QTL test position was close to the marker.

Simple deterministic identity-by-descent coefficients and estimation of QTL allelic effects in full and half sibs.

Accurate and rapid methods for the detection of quantitative trait loci (QTLs) and evaluation of consequent allelic effects are required to implement marker-assisted selection in outbred populations. In this study, we present a simple deterministic method for estimating identity-by-descent (IBD) coefficients in full- and half-sib families that can be used for the detection of QTLs via a variance-component approach. In a simulated dataset, IBD coefficients among sibs estimated by the simple deterministic and Markov chain Monte Carlo (MCMC) methods with three or four alleles at each marker locus exhibited a correlation of greater than 0.99. This high correlation was also found in QTL analyses of data from an outbred pig population. Variance component analysis used both the simple deterministic and MCMC methods to estimate IBD coefficients. Both procedures detected a QTL at the same position and gave similar test statistics and heritabilities. The MCMC method, however, required much longer computation than the simple method. The conversion of estimated QTL genotypic effects into allelic effects for use in marker-assisted selection is also demonstrated.

Mapping Quantitative Trait Loci for Immune Capacity in the Pig

Immune capacity traits show considerable genetic variation in outbred populations. To identify quantitative trait loci (QTLs) for immune capacity in the pig, various measures of immune function (total and differential leukocyte counts, neutrophil phagocytosis, mitogen-induced proliferation, IL-2 production, and virus induced IFN-alpha production in whole blood cultures, and Ab responses to two Escherichia coli antigens) were determined in 200 F2 animals from a wild pig-Swedish Yorkshire intercross. The pedigree has been typed for 236 genetic markers covering all autosomes, the X chromosome and the X/Y pseudoautosomal region. Through interval mapping using a least-squares method, four QTLs with significant effects were identified; one for total leukocyte counts, one for mitogen-induced proliferation, one for prevaccination levels of Abs to E. coli Ag K88, and one for Ab response to the O149 Ag. In addition, several putative QTLs were indicated. The results from the present study conclusively show that it is possible to identify QTLs for immune capacity traits in outbred pig populations by genome analysis.