Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.

BACKGROUND: Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs. RESULTS: Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat. CONCLUSION: Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Evidence for transcriptome-wide RNA editing among Sus scrofa PRE-1 SINE elements.

BACKGROUND: RNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome. RESULTS: Here, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates. CONCLUSIONS: Thus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs.

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations.

BACKGROUND: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. RESULTS: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest - log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. CONCLUSIONS: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.

Methods of tagSNP selection and other variables affecting imputation accuracy in swine.

BACKGROUND: Genotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation. RESULTS: TagSNP density of at least 1 tagSNP per 340kb (~7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end). CONCLUSION: The new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels.

BACKGROUND: F(2) resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F(2) populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F(2) individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F(2) cross to estimate imputation accuracy under several genotyping scenarios. RESULTS: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F(2), IA reaches 0.99. In order to attain such high imputation accuracy the F(0) and F(1) generations should be genotyped at high density. Alternatively, when only the F(0) is genotyped at HD, while F(1) and F(2) are genotyped with a 9K panel, IA drops to 0.90. CONCLUSIONS: Combining 60K and 9K panels with imputation in F(2) populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Estimation of linkage disequilibrium in four US pig breeds.

BACKGROUND: The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance. RESULTS: Average r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire. CONCLUSIONS: Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

The Social Life of Pigs: Changes in Affiliative and Agonistic Behaviors following Mixing.

This study investigated potentially affiliative behaviors in grow-finish pigs, how these behaviors changed over time and their relationship to agonistic behaviors. A total of 257 Yorkshire barrows were observed for agonistic (reciprocal fights, attacks) and affiliative (nosing, play, non-agonistic contact) behaviors after mixing (at 10 weeks of age), and weeks 3, 6, and 9 after mix. The least square means of affiliative behaviors were compared across time points. Relationships among affiliative and agonistic behaviors were assessed using generalized linear mixed models. Non-agonistic contact with conspecifics increased until week 6 then remained stable between weeks 6 and 9. Nosing was highest at mix, then decreased in the following weeks. Play was lowest at mix and highest at week 3. Affiliative behaviors were negatively related with aggression at mix (p < 0.001). Pigs who engaged in play and nosing behaviors were more likely to be involved in agonistic interactions in the weeks after mixing (p < 0.05), while pigs engaging in non-agonistic contact were less likely to be involved in agonistic interactions (p < 0.001). There appear to be relationships between affiliative and agonistic behaviors in pigs, with contact being the most predictive of less aggression. Future studies could focus on promoting positive non-agonistic contact in unfamiliar pigs as a way to mitigate aggressive interactions.

Integrated Genome-Wide Analysis of MicroRNA Expression Quantitative Trait Loci in Pig Longissimus Dorsi Muscle.

Determining mechanisms regulating complex traits in pigs is essential to improve the production efficiency of this globally important protein source. MicroRNAs (miRNAs) are a class of non-coding RNAs known to post-transcriptionally regulate gene expression affecting numerous phenotypes, including those important to the pig industry. To facilitate a more comprehensive understanding of the regulatory mechanisms controlling growth, carcass composition, and meat quality phenotypes in pigs, we integrated miRNA and gene expression data from longissimus dorsi muscle samples with genotypic and phenotypic data from the same animals. We identified 23 miRNA expression Quantitative Trait Loci (miR-eQTL) at the genome-wide level and examined their potential effects on these important production phenotypes through miRNA target prediction, correlation, and colocalization analyses. One miR-eQTL miRNA, miR-874, has target genes that colocalize with phenotypic QTL for 12 production traits across the genome including backfat thickness, dressing percentage, muscle pH at 24 h post-mortem, and cook yield. The results of our study reveal genomic regions underlying variation in miRNA expression and identify miRNAs and genes for future validation of their regulatory effects on traits of economic importance to the global pig industry.

Time budgets of group-housed pigs in relation to social aggression and production.

Commercial producers house growing pigs by sex and weight to allow for efficient use of resources and provide pigs the welfare benefits of interacting with their conspecifics and more freedom of movement. However, the introduction of unfamiliar pigs can cause increased aggression for 24 to 48 h as pigs establish social relationships. To address this issue, a better understanding of pig behavior is needed. The objectives of this study were to quantify time budgets of pigs following introduction into a new social group and how these changed over time and to investigate how social aggression influences the overall time budgets and production parameters. A total of 257 grow-finish Yorkshire barrows across 20 pens were introduced into new social groups at 10 wk of age (~23 kg) and observed for aggression and time budgets of behavior at four periods: immediately after introduction and 3, 6, and 9 wk later. Pigs were observed for the duration of total aggression and initiated aggression (s) for 9 h after introduction and for 4 h at 3, 6, and 9 wk later. Time budgets were created by scan sampling inactive, movement, ingestion, social, and exploration behaviors every 2 min for 4 h in the afternoon and summarizing the proportion of time each behavior was performed by period. The least square means of each behavior were compared across time points. Pigs spent most of their time inactive. In general, the greatest change in pig behavior was observed between introduction and week 3 (P < 0.003), with gradual changes throughout the study period as pigs became more inactive (week 3 vs. week 6: P = 0.209; week 6 vs. week 9: P = 0.007) and spent less time on other behaviors. Pigs' nonaggressive behavior and production parameters were compared with aggression using generalized linear mixed models. The time pigs spent on nonaggressive behaviors was negatively related to aggression (P < 0.045) with few exceptions. Initiated aggression after introduction was negatively related to loin muscle area (P = 0.003). These results show how finishing pigs spend their time in commercial facilities and indicate that behavior continues to change for up to 9 wk after introduction into a new social group. Efforts to reduce chronic levels of aggression should focus on promoting nonaggressive behaviors, such as exploration and movement, after the initial fighting that occurs immediately after introduction has waned, and should be implemented for up to 9 wk after introduction into new social groups.

Application of alternative models to identify QTL for growth traits in an F2 Duroc x Pietrain pig resource population.

BACKGROUND: A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in experimental populations. The initial genome scan of our Duroc x Pietrain F2 resource population included 510 F2 animals genotyped with 124 microsatellite markers and analyzed using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three least-squares Mendelian models for QTL analysis were applied for the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib model. RESULTS: In total, 26 QTL using the line-cross model, 12 QTL using the half-sib model and 3 additional QTL using the combined line-cross and half-sib model were detected for growth traits with a 5% false discovery rate (FDR) significance level. In the line-cross analysis, highly significant QTL for fat deposition at 10-, 13-, 16-, 19-, and 22-wk of age were detected on SSC6. In the half-sib analysis, a QTL for loin muscle area at 19-wk of age was detected on SSC7 and QTL for 10th-rib backfat at 19- and 22-wk of age were detected on SSC15. CONCLUSIONS: Additional markers and animals contributed to reduce the confidence intervals and increase the test statistics for QTL detection. Different models allowed detection of new QTL which indicated differing frequencies for alternative alleles in parental breeds.

Selective transcriptional profiling and data analysis strategies for expression quantitative trait loci mapping in outbred F2 populations.

Genetic analysis of transcriptional profiling experiments is emerging as a promising approach for unraveling genes and pathways that underlie variation of complex biological traits. However, these genetical genomics approaches are currently limited by the high cost of microarrays. We studied five different strategies to optimally select subsets of individuals for transcriptional profiling, including (1) maximizing genetic dissimilarity between selected individuals, (2) maximizing the number of recombination events in selected individuals, (3) selecting phenotypic extremes within inferred genotypes of a previously identified quantitative trait locus (QTL), (4) purely random selection, and (5) profiling animals with the highest and lowest phenotypic values within each family-gender subclass. A simulation study was conducted on the basis of a linkage map and marker genotypes were derived from data on chromosome 6 for 510 F2 animals from an existing pig resource population and on a simulated biallelic QTL with pleiotropic effects on performance and gene expression traits. Bivariate analyses were conducted for selected subset sample sizes of 80, 160, and 240 individuals under three different correlation scenarios between the two traits. The genetic dissimilarity and phenotypic extremes within genotype methods had the smallest mean square error on QTL effects and maximum sensitivity on QTL detection, thereby outperforming all other selection strategies, particularly at the smallest proportion of samples selected for gene expression profiling (80/510).

Estimation of genome-wide and locus-specific breed composition in pigs.

Advances in pig genomic technologies enable implementation of new methods to estimate breed composition, allowing innovative and efficient ways to evaluate and ensure breed and line background. Existing methods to test for homozygosity at key loci involve test mating the animal in question and observing phenotypic patterns among offspring, requiring extensive resources. In this study, whole-genome pig DNA microarray data from over 8,000 SNP was used to profile the composition of U.S. registered purebred pigs using a refined linear regression method that enhances the interpretation of coefficients. In a simulation analysis, a strong correlation between true and estimated breed composition was observed (R2 = 0.94). Applying these methods to 930 Yorkshire animals registered with the National Swine Registry, 95% were estimated to have a "genome-wide" Yorkshire breed composition of at least 0.825 or 82.5%, with similar performance for evaluating datasets of registered Duroc (n = 88) Landrace (n = 129), and Hampshire (n = 17) breeds. We also developed new methods to evaluate locus-based breed probabilities. Such methods have been applied to multi-locus SNP genotypes flanking the KIT gene known to predominantly control coat color, thereby inferring the probability that an animal has haplotypes in the KIT region that are predominant in white breeds. These methods have been adopted by the National Swine Registry as a means to identify purebred Yorkshire animals.

Profiling and characterization of a longissimus dorsi muscle microRNA dataset from an F2 Duroc × Pietrain pig resource population.

To elucidate the effects of microRNA (miRNA) regulation in skeletal muscle of adult pigs, miRNA expression profiling was performed with RNA extracted from longissimus dorsi (LD) muscle samples from 174 F2 pigs (~ 5.5 months of age) from a Duroc × Pietrain resource population. Total RNA was extracted from LD samples, and libraries were sequenced on an Illumina HiSeq 2500 platform in 1 × 50 bp format. After processing, 232,826,977 total reads were aligned to the Sus scrofa reference genome (v10.2.79), with 74.8% of total reads mapping successfully. The miRDeep2 software package was utilized to quantify annotated Sus scrofa mature miRNAs from miRBase (Release 21) and to predict candidate novel miRNA precursors. Among the retained 295 normalized mature miRNA expression profiles ssc-miR-1, ssc-miR-133a-3p, ssc-miR-378, ssc-miR-206, and ssc-miR-10b were the most abundant, all of which have previously been shown to be expressed in pig skeletal muscle. Additionally, 27 unique candidate novel miRNA precursors were identified exhibiting homologous sequence to annotated human miRNAs. The composition of classes of small RNA present in this dataset was also characterized; while the majority of unique expressed sequence tags were not annotated in any of the queried databases, the most abundantly expressed class of small RNA in this dataset was miRNAs. This data provides a resource to evaluate miRNA regulation of gene expression and effects on complex trait phenotypes in adult pig skeletal muscle. The raw sequencing data were deposited in the Sequence Read Archive, BioProject PRJNA363073.

Association of Insulin-like growth factor binding protein 2 genotypes with growth, carcass and meat quality traits in pigs.

BACKGROUND: This study was conducted to investigate the potential association of variation in the insulin-like growth factor binding protein 2 (IGFBP2) gene with growth, carcass and meat quality traits in pigs. IGFBP2 is a member of the insulin-like growth factor binding protein family that is involved in regulating growth, and it maps to a region of pig chromosome 15 containing significant quantitative trait loci that affect economically important trait phenotypes. RESULTS: An IGFBP2 polymorphism was identified in the Michigan State University (MSU) Duroc × Pietrain F2 resource population (n = 408), and pigs were genotyped by MspI PCR-RFLP. Subsequently, a Duroc pig population from the National Swine Registry, USA, (n = 326) was genotyped using an Illumina Golden Gate assay. The IGFBP2 genotypic frequencies among the MSU resource population pigs were 3.43, 47.06 and 49.51 % for the AA, AB and BB genotypes, respectively. The genotypic frequencies for the Duroc pigs were 9.82, 47.85, and 42.33 % for the AA, AB and BB genotypes, respectively. Genotype effects (P < 0.05) were found in the MSU resource population for backfat thickness at 10(th) rib and last rib as determined by ultrasound at 10, 13, 16 and 19 weeks of age, ADG from 10 to 22 weeks of age, and age to reach 105 kg. A genotype effect (P < 0.05) was also found for off test Longissimus muscle area in the Duroc population. Significant effects of IGFBP2 genotype (P < 0.05) were found for drip loss, 24 h postmortem pH, pH decline from 45 min to 24 h postmortem, subjective color score, CIE L* and b*, Warner-Bratzler shear force, and sensory panel scores for juiciness, tenderness, connective tissue and overall tenderness in MSU resource population pigs. Genotype effects (P < 0.05) were found for 45-min pH, CIE L* and color score in the Duroc population. CONCLUSIONS: Results of this study revealed associations of the IGFBP2 genotypes with growth, carcass and meat quality traits in pigs. The results indicate IGFBP2 as a potential candidate gene for growth rate, backfat thickness, loin muscle area and some pork quality traits.

Genomic Prediction Accounting for Residual Heteroskedasticity.

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit.

Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data.

BACKGROUND: Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e. undirected connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F2 Duroc x Pietrain resource pig population. RESULTS: We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region on SSC6 showed significant associations with three relevant phenotypes, off-midline10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of seven genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the three phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight. CONCLUSIONS: Our findings shed light on the antagonist relationship between carcass fat deposition and lean meat content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes.

Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.

Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65-0.68). Using genotypes imputed from a large reference panel (accuracy: R(2) = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R(2) = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.

Evaluation of QTL for carcass merit and meat quality traits in a US commercial Duroc population.

Putative quantitative trait loci (QTL) regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected from our previous genome scans of a Duroc×Pietrain F(2) resource population for further evaluation in a US commercial Duroc population (n=331). A total of 81 gene-specific single nucleotide polymorphism (SNP) markers were genotyped and 33 markers were segregating. The MDH1 SNP on SSC3 was associated with 45-min and ultimate pH (pHu), and pH decline. PRKAG3 on SSC15 was associated with pHu. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113kg. Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*, respectively. The SSC15 marker SF3B1 was associated with L* and LMA, and the SSC18 marker ARF5 was associated with pHu and color score. These results in a commercial Duroc population showed a general consistency with our previous genome scan.

Identification of Carcass and Meat Quality QTL in an F(2) Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models.

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F(2) animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F(2) animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F(2) animals and 20 markers used in the first scan were genotyped for 444 additional F(2) animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL.

Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

BACKGROUND: Nearly 6,000 QTL have been reported for 588 different traits in pigs, more than in any other livestock species. However, this effort has translated into only a few confirmed causative variants. A powerful strategy for revealing candidate genes involves expression QTL (eQTL) mapping, where the mRNA abundance of a set of transcripts is used as the response variable for a QTL scan. METHODOLOGY/PRINCIPAL FINDINGS: We utilized a whole genome expression microarray and an F(2) pig resource population to conduct a global eQTL analysis in loin muscle tissue, and compared results to previously inferred phenotypic QTL (pQTL) from the same experimental cross. We found 62 unique eQTL (FDR <10%) and identified 3 gene networks enriched with genes subject to genetic control involved in lipid metabolism, DNA replication, and cell cycle regulation. We observed strong evidence of local regulation (40 out of 59 eQTL with known genomic position) and compared these eQTL to pQTL to help identify potential candidate genes. Among the interesting associations, we found aldo-keto reductase 7A2 (AKR7A2) and thioredoxin domain containing 12 (TXNDC12) eQTL that are part of a network associated with lipid metabolism and in turn overlap with pQTL regions for marbling, % intramuscular fat (% fat) and loin muscle area on Sus scrofa (SSC) chromosome 6. Additionally, we report 13 genomic regions with overlapping eQTL and pQTL involving 14 local eQTL. CONCLUSIONS/SIGNIFICANCE: Results of this analysis provide novel candidate genes for important complex pig phenotypes.