Genome-wide identification of quantitative trait loci for pork temperature, pH decline, and glycolytic potential in a large-scale White Duroc x Chinese Erhualian resource population

Genetic architecture for skeletal muscle glycolytic potential in Chinese Erhualian pigs revealed by a genome-wide association study using 1.4M SNP array

Introduction: Muscle glycolytic potential (GP) is a key factor affecting multiple meat quality traits. It is calculated based on the contents of residual glycogen and glucose (RG), glucose-6-phosphate (G6P), and lactate (LAT) contents in muscle. However, the genetic mechanism of glycolytic metabolism in skeletal muscle of pigs remains poorly understood. With a history of more than 400 years and some unique characteristics, the Erhualian pig is called the “giant panda” (very precious) in the world’s pig species by Chinese animal husbandry.

Methods: Here, we performed a genome-wide association study (GWAS) using 1.4M single nucleotide polymorphisms (SNPs) chips for longissimus RG, G6P, LAT, and GP levels in 301 purebred Erhualian pigs.

Results: We found that the average GP value of Erhualian was unusually low (68.09 μmol/g), but the variation was large (10.4–112.7 μmol/g). The SNP-based heritability estimates for the four traits ranged from 0.16–0.32. In total, our GWAS revealed 31 quantitative trait loci (QTLs), including eight for RG, nine for G6P, nine for LAT, five for GP. Of these loci, eight were genome-wide significant (p < 3.8 × 10−7), and six loci were common to two or three traits. Multiple promising candidate genes such as FTO, MINPP1, RIPOR2, SCL8A3, LIFR and SRGAP1 were identified. The genotype combinations of the five GP-associated SNPs also showed significant effect on other meat quality traits.

Discussion: These results not only provide insights into the genetic architecture of GP related traits in Erhualian, but also are useful for pig breeding programs involving this breed.

Epigenome-wide association studies of meat traits in Chinese Yorkshire pigs highlights several DNA methylation loci and genes

In this study, we aimed to identified CpG sites at which DNA methylation levels are associated with meat quality traits in 140 Yorkshire pigs, including pH at 45 min (pH_45min), pH at 24 h (pH_24h), drip loss (DL), meat redness value (a*), yellowness (b*) and lightness (L*). Genome-wide methylation levels were measured in muscular tissue using reduced representation bisulfite sequencing (RRBS). Associations between DNA methylation levels and meat quality traits were examined using linear mixed-effect models that were adjusted for gender, year, month and body weight. A Bonferroni-corrected p-value lower than 7.79 × 10 - 8 was considered statistically significant threshold. Eight CpG sites were associated with DL, including CpG sites annotated to RBM4 gene (cpg301054, cpg301055, cpg301058, cpg301059, cpg301066, cpg301072 and cpg301073) and NCAM1 gene (cpg1802985). Two CpG sites were associated with b*, including RNFT1 and MED13 (cpg2272837) and TRIM37 gene (cpg2270611). Five CpG sites were associated with L*, including GSDMA and LRRC3C gene (cpg2252750) and ENSSSCG00000043539 and IRX1 gene (cpg2820178, cpg2820179, cpg2820181 and cpg2820182). No significant associations were observed with pH_45min, pH_24h or a*. We reported associations of meat quality traits with DNA methylation and identified some candidate genes associated with these traits, such as NCAM1, MED13 and TRIM37 gene. These results provide new insight into the epigenetic molecular mechanisms of meat quality traits in pigs.

An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs

Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig's muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10^-4) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10^-5), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality.

An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F₂ population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.

Heat stress decreases metabolic flexibility in skeletal muscle of growing pigs

Heat-stressed pigs experience metabolic alterations, including altered insulin profiles, reduced lipid mobilization, and compromised intestinal integrity. This is bioenergetically distinct from thermal neutral pigs on a similar nutritional plane. To delineate differences in substrate preferences between direct and indirect (via reduced feed intake) heat stress effects, skeletal muscle fuel metabolism was assessed. Pigs (35.3 ± 0.8 kg) were randomly assigned to three treatments: thermal neutral fed ad libitum (TN; 21°C, n = 8), heat stress fed ad libitum (HS; 35°C, n = 8), and TN, pair-fed/HS intake (PF; n = 8) for 7 days. Body temperature (T_B) and feed intake (FI) were recorded daily. Longissimus dorsi muscle was biopsied for metabolic assays on days -2, 3, and 7 relative to initiation of environmental treatments. Heat stress increased T_B and decreased FI ( P < 0.05). Heat stress inhibited incomplete fatty acid oxidation and glucose oxidation ( P < 0.05). Metabolic flexibility decreased in HS pigs compared with TN and PF controls ( P < 0.05). Both phosphofructokinase and pyruvate dehydrogenase (PDH) activities increased in PF ( P < 0.05); however, TN and HS did not differ. Heat stress inhibited citrate synthase and β-hydroxyacyl-CoA dehydrogenase (β-HAD) activities ( P < 0.05). Heat stress did not alter PDH phosphorylation or carnitine palmitoyltransferase 1 abundance but reduced acetyl-CoA carboxylase 1 (ACC1) protein abundance ( P < 0.05). In conclusion, HS decreased skeletal muscle fatty acid oxidation and metabolic flexibility, likely involving β-HAD and ACC regulation.

Integrated application of transcriptomics and metabolomics provides insights into glycogen content regulation in the Pacific oyster Crassostrea gigas

BACKGROUND

The Pacific oyster Crassostrea gigas is an important marine fishery resource, which contains high levels of glycogen that contributes to the flavor and the quality of the oyster. However, little is known about the molecular and chemical mechanisms underlying glycogen content differences in Pacific oysters. Using a homogeneous cultured Pacific oyster family, we explored these regulatory networks at the level of the metabolome and the transcriptome.

RESULTS

Oysters with the highest and lowest natural glycogen content were selected for differential transcriptome and metabolome analysis. We identified 1888 differentially-expressed genes, seventy-five differentially-abundant metabolites, which are part of twenty-seven signaling pathways that were enriched using an integrated analysis of the interaction between the differentially-expressed genes and the differentially-abundant metabolites. Based on these results, we found that a high expression of carnitine O-palmitoyltransferase 2 (CPT2), indicative of increased fatty acid degradation, is associated with a lower glycogen content. Together, a high level of expression of phosphoenolpyruvate carboxykinase (PEPCK), and high levels of glucogenic amino acids likely underlie the increased glycogen production in high-glycogen oysters. In addition, the higher levels of the glycolytic enzymes hexokinase (HK) and pyruvate kinase (PK), as well as of the TCA cycle enzymes malate dehydrogenase (MDH) and pyruvate carboxylase (PYC), imply that there is a concomitant up-regulation of energy metabolism in high-glycogen oysters. High-glycogen oysters also appeared to have an increased ability to cope with stress, since the levels of the antioxidant glutathione peroxidase enzyme 5 (GPX5) gene were also increased.

CONCLUSION

Our results suggest that amino acids and free fatty acids are closely related to glycogen content in oysters. In addition, oysters with a high glycogen content have a greater energy production capacity and a greater ability to cope with stress. These findings will not only provide insights into the molecular mechanisms underlying oyster quality, but also promote research into the molecular breeding of oysters.

Genome-wide association analysis reveals genetic loci and candidate genes for feeding behavior and eating efficiency in Duroc boars

Efficient use of feed resources is a challenge in the pork industry because the largest variability in expenditure is attributed to the cost of fodder. Efficiency of feeding is directly related to feeding behavior. In order to identify genomic regions controlling feeding behavior and eating efficiency traits, 338 Duroc boars were used in this study. The Illumina Porcine SNP60K BeadChip was used for genotyping. Data pertaining to individual daily feed intake (DFI), total daily time spent in feeder (TPD), number of daily visits to feeder (NVD), average duration of each visit (TPV), mean feed intake per visit (FPV), mean feed intake rate (FR), and feed conversion ratio (FCR) were collected for these pigs. Despite the limited sample size, the genome-wide association study was acceptable to detect candidate regions association with feeding behavior and eating efficiency traits in pigs. We detected three genome-wide (P < 1.40E-06) and 11 suggestive (P < 2.79E-05) single nucleotide polymorphism (SNP)-trait associations. Six SNPs were located in genomic regions where quantitative trait loci (QTLs) have previously been reported for feeding behavior and eating efficiency traits in pigs. Five candidate genes (SERPINA3, MYC, LEF1, PITX2, and MAP3K14) with biochemical and physiological roles that were relevant to feeding behavior and eating efficiency were discovered proximal to significant or suggestive markers. Gene ontology analysis indicated that most of the candidate genes were involved in the development of the hypothalamus (GO:0021854, P < 0.0398). Our results provide new insights into the genetic basis of feeding behavior and eating efficiency in pigs. Furthermore, some significant SNPs identified in this study could be incorporated into artificial selection programs for Duroc-related pigs to select for increased feeding efficiency.

Genetic Factors Affecting Pork Quality: Halothane and Rendement Napole Genes

The most common pork quality problems are pale, soft, and exudative (PSE) and acid pork (AP). PSE is associated with the expression of recessive halothane (Hal) allele Halⁿ. Recessive Hal pigs (Halⁿⁿ) have defective Ca²⁺ release channels (CRC) or Ryanodine Receptors (RYR1) within the sarcoplasmic reticulum that allow uncontrolled release of Ca²⁺ in response to stress. Abnormal lactic acid metabolism caused by stress prior to slaughter leads to the sudden drop in postmortem muscle pH producing the PSE pork. Conversely, AP is caused by the dominant RN^- allele of the Rendement Napole gene. RN^- pigs have high glycolytic potential that causes the lower ultimate pH_u due to excessive lactic acid production postmortem. Poor water holding capacity of muscle cells in PSE and AP causes excessive drip loss leading to low cooking and processing yields. The conventional methods to evaluate Hal and RN genotypes are less effective compared to the more accurate gene marker tests. Selection against the Halⁿ and RN^- alleles by genomic selection can potentially reduce the frequencies of the defective genes with high accuracy in less time. As more quantitative trait loci (QTL) are identified, pig breeders are able to select traits more effectively to increase efficiency of pig production and enhance pork quality.

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

BACKGROUND

Understanding 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.

METHODS

Three 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.

RESULTS

We 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.

CONCLUSIONS

These 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.

Genome-wide association analysis reveals genetic loci and candidate genes for meat quality traits in Chinese Laiwu pigs

Meat quality traits have economically significant impacts on the pig industry, and can be improved using molecular approaches in pig breeding. Since 1994 when the first genome-wide scan for quantitative trait loci (QTLs) in pig was reported, over the past two decades, numerous QTLs have been identified for meat quality traits by family based linkage analyses. However, little is known about the genetic variants for meat quality traits in Chinese purebred or outbred populations. To unveil it, we performed a genome-wide association study for 10 meat quality traits in Chinese purebred Laiwu pigs. In total, 75 significant SNPs (P < 1.01 × 10(-6)) and 33 suggestive SNPs (P < 2.03 × 10(-5)) were identified. On SSC12, a region between 56.22 and 61.49 Mb harbored a cluster of SNPs that were associated with meat color parameters (L*, lightness; a*, redness; b*, yellowness) and moisture content of longissimus muscle (LM) and semimembranosus muscle at the genome-wide significance level. A region on SSC4 also has pleiotropic effects on moisture content and drip loss of LM. In addition, this study revealed at least five novel QTLs and several candidate genes including 4-linked MYH genes (MYH1, MYH2, MYH3, and MYH13), MAL2, LPAR1, and PRKAG3 at four significant loci. Except for the SSC12 QTL, other QTLs are likely tissue-specific. These results provide new insights into the genetic basis of meat quality traits in Chinese Laiwu pigs and some significant SNPs reported here could be incorporated into the selection programs involving this breed.

A splice mutation in the PHKG1 gene causes high glycogen content and low meat quality in pig skeletal muscle

Glycolytic potential (GP) in skeletal muscle is economically important in the pig industry because of its effect on pork processing yield. We have previously mapped a major quantitative trait loci (QTL) for GP on chromosome 3 in a White Duroc × Erhualian F₂ intercross. We herein performed a systems genetic analysis to identify the causal variant underlying the phenotype QTL (pQTL). We first conducted genome-wide association analyses in the F₂ intercross and an F₁₉ Sutai pig population. The QTL was then refined to an 180-kb interval based on the 2-LOD drop method. We then performed expression QTL (eQTL) mapping using muscle transcriptome data from 497 F₂ animals. Within the QTL interval, only one gene (PHKG1) has a cis-eQTL that was colocolizated with pQTL peaked at the same SNP. The PHKG1 gene encodes a catalytic subunit of the phosphorylase kinase (PhK), which functions in the cascade activation of glycogen breakdown. Deep sequencing of PHKG1 revealed a point mutation (C>A) in a splice acceptor site of intron 9, resulting in a 32-bp deletion in the open reading frame and generating a premature stop codon. The aberrant transcript induces nonsense-mediated decay, leading to lower protein level and weaker enzymatic activity in affected animals. The mutation causes an increase of 43% in GP and a decrease of>20% in water-holding capacity of pork. These effects were consistent across the F₂ and Sutai populations, as well as Duroc × (Landrace × Yorkshire) hybrid pigs. The unfavorable allele exists predominantly in Duroc-derived pigs. The findings provide new insights into understanding risk factors affecting glucose metabolism, and would greatly contribute to the genetic improvement of meat quality in Duroc related pigs.

Glycogen storage diseases (GSD) are a group of inherited disorders characterized by storage of excess glycogen, which are mainly caused by the abnormality of a particular enzyme essential for releasing glucose from glycogen. GSD-like conditions have been described in a wide variety of species. Pigs are a valuable model for the study of human GSD. Moreover, pigs affected by GSD usually produce inferior pork with a lower ultimate pH (so-called “acid meat”) and less processing yield due to post-mortem degradation of the excess glycogen. So far, only one causal variant, PRKAG3 R225Q, has been identified for GSD in pigs. Here we reported a loss-of-function mutation in the PHKG1 gene that causes the deficiency of the glycogen breakdown, consequently leading to GSD and acid meat in Duroc-sired pigs. Eliminating the undesirable mutation from the breeding stock by a diagnostic DNA test will greatly reduce the incidence of GSD and significantly improve pork quality and productivity in the pig.

SNPs detection in DHPS-WDR83 overlapping genes mapping on porcine chromosome 2 in a QTL region for meat pH

Background

The pH is an important parameter influencing technological quality of pig meat, a trait affected by environmental and genetic factors. Several quantitative trait loci associated to meat pH are described on PigQTL database but only two genes influencing this parameter have been so far detected: Ryanodine receptor 1 and Protein kinase, AMP-activated, gamma 3 non-catalytic subunit. To search for genes influencing meat pH we analyzed genomic regions with quantitative effect on this trait in order to detect SNPs to use for an association study.

Results

The expressed sequences mapping on porcine chromosomes 1, 2, 3 in regions associated to pork pH were searched in silico to find SNPs. 356 out of 617 detected SNPs were used to genotype Italian Large White pigs and to perform an association analysis with meat pH values recorded in semimembranosus muscle at about 1 hour (pH1) and 24 hours (pHu) post mortem.

The results of the analysis showed that 5 markers mapping on chromosomes 1 or 3 were associated with pH1 and 10 markers mapping on chromosomes 1 or 2 were associated with pHu. After False Discovery Rate correction only one SNP mapping on chromosome 2 was confirmed to be associated to pHu. This polymorphism was located in the 3’UTR of two partly overlapping genes, Deoxyhypusine synthase (DHPS) and WD repeat domain 83 (WDR83). The overlapping of the 3’UTRs allows the co-regulation of mRNAs stability by a cis-natural antisense transcript method of regulation. DHPS catalyzes the first step in hypusine formation, a unique amino acid formed by the posttranslational modification of the protein eukaryotic translation initiation factor 5A in a specific lysine residue. WDR83 has an important role in the modulation of a cascade of genes involved in cellular hypoxia defense by intensifying the glycolytic pathway and, theoretically, the meat pH value.

Conclusions

The involvement of the SNP detected in the DHPS/WDR83 genes on meat pH phenotypic variability and their functional role are suggestive of molecular and biological processes related to glycolysis increase during post-mortem phase. This finding, after validation, can be applied to identify new biomarkers to be used to improve pig meat quality.

Genome-wide association study of meat quality traits in a White Duroc×Erhualian F2 intercross and Chinese Sutai pigs

Thousands of QTLs for meat quality traits have been identified by linkage mapping studies, but most of them lack precise position or replication between populations, which hinder their application in pig breeding programs. To localize QTLs for meat quality traits to precise genomic regions, we performed a genome-wide association (GWA) study using the Illumina PorcineSNP60K Beadchip in two swine populations: 434 Sutai pigs and 933 F2 pigs from a White Duroc×Erhualian intercross. Meat quality traits, including pH, color, drip loss, moisture content, protein content and intramuscular fat content (IMF), marbling and firmness scores in the M. longissimus (LM) and M. semimembranosus (SM) muscles, were recorded on the two populations. In total, 127 chromosome-wide significant SNPs for these traits were identified. Among them, 11 SNPs reached genome-wise significance level, including 1 on SSC3 for pH, 1 on SSC3 and 3 on SSC15 for drip loss, 3 (unmapped) for color a*, and 2 for IMF each on SSC9 and SSCX. Except for 11 unmapped SNPs, 116 significant SNPs fell into 28 genomic regions of approximately 10 Mb or less. Most of these regions corresponded to previously reported QTL regions and spanned smaller intervals than before. The loci on SSC3 and SSC7 appeared to have pleiotropic effects on several related traits. Besides them, a few QTL signals were replicated between the two populations. Further, we identified thirteen new candidate genes for IMF, marbling and firmness, on the basis of their positions, functional annotations and reported expression patterns. The findings will contribute to further identification of the causal mutation underlying these QTLs and future marker-assisted selection in pigs.

Association mapping of quantitative trait loci for carcass and meat quality traits at the central part of chromosome 2 in Italian Large White pigs

Association mapping of the central part of porcine chromosome 2 harboring QTLs for carcass and meat quality traits was performed with 17 gene-tagged SNPs located between 44.0 and 77.5 Mb on a physical map (Sscrofa10.2) in Italian Large White pigs. For the analyzed animals records of estimated breeding values for average daily gain, back fat thickness, lean cuts, ham weight, feed conversion ratio, pH1, pHu, CIE L*, CIE a*, CIE b* and drip loss were available. A significant QTL for fat deposition (adjusted P=0.0081) and pH1 (adjusted P=0.0972) to MYOD1 at position 44.4 Mb and a QTL for growth and meatiness (adjusted P=0.0238-0.0601) to UBL5 at position 68.9 Mb were mapped. These results from association mapping are much more accurate than those from linkage mapping and facilitate further search for position candidate genes and causative mutations needed for application of markers through marker assisted selection.

UBE3B and ZRANB1 polymorphisms and transcript abundance are associated with water holding capacity of porcine M. longissimus dorsi

The degradation of myofibrillar proteins during meat maturation affects the water holding capacity (WHC) of meat. Our study sought to identify polymorphisms in UBE3B and ZRANB1, genes encoding proteins involved in ubiquitination, and to evaluate the relationship between genotype, transcript abundance, and WHC of pork. A single SNP of ZRANB1, c.552A>G (p.Ile153Val), and two silent SNPs of UBE3B, c.1921A>T and c.4292C>T, were associated with muscle pH, conductivity, meat colour, or drip loss in German Landrace (GL, n=266) and Pietrain×(Large White×German Landrace) (PiF1, n=316). Further, carriers of the minor alleles at the SNPs tended to have increased transcript abundance. Consistent with the protein degradation promoting and inhibiting effects of UBE3B and ZRANB1, respectively, and the expected impact on WHC, their expressions were positively and negatively associated with WHC. The results implicate that the SNPs in both genes are in linkage with a causal site that affects transcript abundance and WHC.

Identification of porcine glycogen synthase kinase 3α (GSK-3α) gene and its association with carcass traits

GSK-3 plays an important role on numerous cellular processes involved in the regulation of embryonic development, protein synthesis, glycogen metabolism, inflammatory, mitosis and apoptosis. In this study, we obtained the cDNA and promoter sequences of the porcine GSK-3α gene, analyzed its genomic organization and mapped it to SSC6q12 through comparative mapping method. Moreover, the qRT-PCR analysis revealed that porcine GSK-3α gene was widely expressed in many tissues, and a high expression level was observed in the brain and spleen. In addition, seven single-nucleotide polymorphisms were detected in the promoter region of porcine GSK-3α gene. Association analysis revealed that the GSK-3α Hin1I and MspI polymorphisms both had significant associations (p < 0.05) with loin muscle area, average backfat thickness, thorax-waist fat thickness, and buttock fat thickness. These results provide useful information for further investigation on the function of porcine GSK-3α gene.

Association and expression quantitative trait loci (eQTL) analysis of porcine AMBP, GC and PPP1R3B genes with meat quality traits

The aim of this research was to screen polymorphism and to perform association study of porcine AMBP (alpha-1-microglobulin/bikunin precursor), GC (group-specific component protein) and PPP1R3B (protein phosphatase 1, regulatory (inhibitor) subunit 3B) genes with meat quality traits as well as to unravel the transcriptional regulation of these genes by expression QTL (eQTL) study. For this purpose, Duroc × Pietrain F2 resource population (DuPi; n = 313) and a commercial breed Pietrain (Pi; n = 110) were used for association and only DuPi for expression and eQTL study. A SNP was identified in the genes AMBP (g.22229C>T), GC (g.398C>T) and PPP1R3B (c.479A>G), respectively. In DuPi SNP of AMBP was associated (P < 0.05) with meat colour, pH(1L), pH(24L), pH(24H) and conductivity(24L); SNP of GC showed tendency to association (P < 0.10) with pH24H, conductivity(1L) and thawing loss, and SNP of PPP1R3B was associated (P < 0.05) with meat colour, pH(1L), pH(24L), pH(24H) and shear force. In Pi SNPs of AMBP and GC was associated with pH(24H) and PPP1R3B SNP was associated with pH(24L). The mRNA levels in Longissimus dorsi muscle tissue of these three genes were evaluated by using qRT-PCR to identify association between gene expression and meat quality traits as well as to analyse eQTL. The mRNA expression of PPP1R3B associated with pH(24L) (P < 0.05). Expression of these three genes was higher in animals with low pH of muscle. Linkage analysis using QTL Express revealed ten trans-regulated eQTL on seven porcine autosomes. Suggestive eQTL [P < 0.05, CW (chromosome-wide)] were found for PPP1R3B on SSC3 and 13. These results revealed that genetic variation and gene expression of these genes are associated with the meat quality traits. These three genes could influence meat quality and could be potential positional, physiological and functional candidate gene for meat quality traits in pigs. However, the analysis of eQTL also suggested that we need to consider additional genes encoding for transcription factors (TF), via fine-mapping underlying the eQTL peaks, in order to understand interaction among these genes.

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs

Background

Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations.

Results

Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait.

Conclusions

Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs.

Validation of a paternally imprinted QTL affecting pH24h distinct from PRKAG3 on SSC15

We previously performed a genome scan in a White Duroc × Erhualian F(2) population and identified a QTL on SSC15 with strong effect on pH24h in M. Longissimus dorsi and M. Semimembranous muscle tissue. At that time, the mode of inheritance of this QTL was not clarified, and it was also unclear whether the observed QTL effect was completely or partially caused by mutations in the PRKAG3 gene, which is the only major gene on SSC15 so far known to influence pH24h. In this study, effects of the PRKAG3 gene on meat quality traits were estimated by association analyses. Two substitutions in PRKAG3, p.Ile199Val (p.I199V) and p.Thr30Asn (p.T30N), were found to be segregating in the F(2) population and to significantly affect pH24h and total glycogen in meat, respectively. However, we excluded PRKAG3 as a causative gene for the detected QTL based on the following reasons: (i) the gene was located outside of the QTL confidence interval; (ii) when the PRKAG3 substitution was included as a fixed effect in the QTL model, the F-ratio for the QTL increased rather than decreased; (iii) favourable alleles for pH24h at the QTL and at the PRKAG3 p.I199V locus originated from Erhualian and White Duroc founders, respectively; (iv) more importantly, this QTL showed exclusive maternal expression, differing from the Mendelian expression of PRKAG3. In conclusion, this study is the first to report a maternally-expressed QTL for pH24h on SSC15, which is distinct from PRKAG3.

Genes with expression levels correlating to drip loss prove association of their polymorphism with water holding capacity of pork

Six genes that were known to exhibit expression levels that are correlated to drip loss BVES, SLC3A2, ZDHHC5, CS, COQ9, and EGFR have been for candidate gene analysis. Based on in silico analysis SNPs were detected, confirmed by sequencing, and used for genotyping. The SNPs were genotyped in about 1,800 animals from six pig populations including commercial herds of Pietrain (PI) and German Landrace (DL), different commercial herds of Pietrain×(German Large White×German Landrace) (PIF1(a/b/c)), and one experimental F2-population Duroc×Pietrain (DUPI). Comparative and genetic mapping established the location of BVES on SSC1, of SLC3A2 and ZDHHC5 on SSC2, of CS on SSC5, of COQ9 on SSC6 and of EGFR on SSC9, respectively, coinciding with QTL regions for carcass and meat quality traits. BVES, SLC3A2, and CS revealed association at least with drip loss and with several other measures of water holding capacity (WHC). Moreover, COQ9 and EGFR were associated with several meat quality traits such as meat color and/or thawing loss. This study reveals statistic evidence in addition to the functional relationship of these genes to WHC previously evidenced by expression analysis. This study reveals positional and genetic statistical evidence for a link of genetic variation at these loci or close to them and promotes those six candidate genes as functional and/or positional candidate genes for meat quality traits.

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 F₂ 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 F₂ animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F₂ animals and 20 markers used in the first scan were genotyped for 444 additional F₂ 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.

High throughput analyses of epistasis for swine body dimensions and organ weights

High throughput analyses were performed to detect epistatic QTL in 17 body dimension and organ weight traits from a large F(2) pig population derived from a White Duroc and Erhualian intercross. The analyses used a nested test framework to handle multiple tests and a combined search algorithm to map epistatic QTL with empirical genome-wide thresholds derived via prior permutation. Alternative statistical models (e.g. including vs. excluding carcass weight as a covariate) were tested to develop an in-depth understanding of the role of epistasis in these kinds of traits. Epistasis signals were detected in only two or three traits under each statistical model studied. The interaction component of each pair of epistatic QTL explained a small proportion (0.7 to 2.1%) of the phenotypic variance in general. About half of the detected epistatic QTL pairs involved one of the two major QTL on porcine chromosomes 7 and 4. In those traits, the Erhualian allele consistently increased the phenotypes for the chromosome 7 QTL but decreased them for the chromosome 4 QTL. Models including carcass weight as covariate detected epistasis in body dimension traits whereas those excluding carcass weight found epistasis in organ weight traits. In addition, the epistasis results suggested that a QTL on chromosome 14 could be important for a number of organ weight traits. Using the high-throughput analysis tool to examine different statistical models was essential for the generation of a complete picture of epistasis in a whole category of traits.

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Background

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig.

Methods

Five hundred and eighty five F₂ pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F₂ animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program.

Results

A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance.

Conclusions

Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.

Genome-wide identification of quantitative trait loci for carcass composition and meat quality in a large-scale White Duroc x Chinese Erhualian resource population

Carcass and meat quality traits are economically important in pigs. In this study, 17 carcass composition traits and 23 meat quality traits were recorded in 1028 F(2) animals from a White Duroc x Erhualian resource population. All pigs in this experimental population were genotyped for 194 informative markers covering the entire porcine genome. Seventy-seven genome-wide significant quantitative trait loci (QTL) for carcass traits and 68 for meat quality were mapped to 34 genomic regions. These results not only confirmed many previously reported QTL but also revealed novel regions associated with the measured traits. For carcass traits, the most prominent QTL was identified for carcass length and head weight at 57 cM on SSC7, which explained up to 50% of the phenotypic variance and had a 95% confidence interval of only 3 cM. Moreover, QTL for kidney and spleen weight and lengths of cervical vertebrae were reported for the first time in pigs. For meat quality traits, two significant QTL on SSC5 and X were identified for both intramuscular fat content and marbling score in the longissimus muscle, while three significant QTL on SSC1 and SSC9 were found exclusively for IMF. Both LM and the semimembranous muscle showed common QTL for colour score on SSC4, 5, 7, 8, 13 and X and discordant QTL on other chromosomes. White Duroc alleles at a majority of QTL detected were favourable for carcass composition, while favourable QTL alleles for meat quality originated from both White Duroc and Erhualian.