A quantitative trait locus genome scan for porcine muscle fiber traits reveals overdominance and epistasis

Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig

As a major source of protein in human diets, pig meat plays a crucial role in ensuring global food security. Key determinants of meat production refer to the chemical and physical compositions or characteristics of muscle fibers, such as the number, hypertrophy potential, fiber-type conversion and intramuscular fat deposition. However, the growth and formation of muscle fibers comprises a complex process under spatio-temporal regulation, that is, the intermingled and concomitant proliferation, differentiation, migration and fusion of myoblasts. Recently, with the fast and continuous development of next-generation sequencing technology, the integration of quantitative trait loci mapping with genome-wide association studies (GWAS) has greatly helped animal geneticists to discover and explore thousands of functional or causal genetic elements underlying muscle growth and development. However, owing to the underlying complex molecular mechanisms, challenges to in-depth understanding and utilization remain, and the cost of large-scale sequencing, which requires integrated analyses of high-throughput omics data, is high. In this review, we mainly elaborate on research advances in integrative analyses (e.g. GWAS, omics) for identifying functional genes or genomic elements for longissimus dorsi muscle growth and development for different pig breeds, describing several successful transcriptome analyses and functional genomics cases, in an attempt to provide some perspective on the future functional annotation of genetic elements for muscle growth and development in pigs.

Whole-genome sequence-based association analyses on an eight-breed crossed heterogeneous stock of pigs reveal the genetic basis of skeletal muscle fiber characteristics

Skeletal muscle fiber characteristics (MFCs) have been extensively studied due to their importance to human health and athletic ability, as well as to the quantity and quality of livestock meat production. Hence, we performed a genome-wide association study (GWAS) on nine muscle fiber traits by using whole genome sequence data in an eight-breed crossed heterogeneous stock pig population. This GWAS revealed 67 quantitative trait loci (QTLs) for these traits. The most significant GWAS signal was detected in the region of Sus scrofa chromosome 12 (SSC12) containing the MYH gene family. Notably, we identified a significant SNP rs322008693 (P = 7.52E-09) as the most likely causal mutation for the total number of muscle fibers (TNMF) QTL on SSC1. The results of EMSA and luciferase assays indicated that the rs322008693 SNP resided in a functional element. These findings provide valuable molecular markers for pig meat production selection as well as for deciphering the genetic mechanisms of the muscle fiber physiology.

Copy Number Variations Contribute to Intramuscular Fat Content Differences by Affecting the Expression of PELP1 Alternative Splices in Pigs

Simple Summary

Copy number variation (CNV) is a type of variant that may influence meat quality of, for example intramuscular fat (IMF). In this study, a genome-wide association study (GWAS) was then performed between CNVs and IMF in a pig F2 resource population. A total of 19 CNVRs were found to be significantly associated with IMF. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 (PELP1) gene may affect the expression of PELP1 alternative splices. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.

Abstract

Intramuscular fat (IMF) is a key meat quality trait. Research on the genetic mechanisms of IMF decomposition is valuable for both pork quality improvement and the treatment of obesity and type 2 diabetes. Copy number variations (CNVs) are a type of variant that may influence meat quality. In this study, a total of 1185 CNV regions (CNVRs) including 393 duplicated CNVRs, 432 deleted CNVRs, and 361 CNVRs with both duplicated and deleted status were identified in a pig F2 resource population using next-generation sequencing data. A genome-wide association study (GWAS) was then performed between CNVs and IMF, and a total of 19 CNVRs were found to be significantly associated with IMF. QTL colocation analysis indicated that 3 of the 19 CNVRs overlapped with known QTLs. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 (PELP1) gene may affect the expression of PELP1 alternative splices. Sequence alignment and Alphafold2 structure prediction results indicated that the two alternative splices of PELP1 have a 23 AA sequence variation and a helix-fold structure variation. This region is located in the region of interaction between PELP1 and other proteins which have been reported to be significantly associated with fat deposition or insulin resistance. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. In conclusion, we found some CNVRs, especially CNV150, located in PELP1 that affect IMF. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.

Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep

Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.

Genome-Wide Epistatic Interaction Networks Affecting Feed Efficiency in Duroc and Landrace Pigs

Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc (1,247 SNPs) and Landrace (1,215 SNPs) across the population and for low feed efficient (Duroc-80 SNPs, Landrace-146 SNPs) and high feed efficient group (Duroc-198 SNPs, Landrace-232 SNPs) for FCR and RFI were identified. Gene and pathway analyses identified ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.

Identification of genetic loci associated with growth traits at weaning in yak through a genome-wide association study

A multilocus GWAS was performed to explore the genetic architecture of four growth traits in yak. In total, 354 female yaks for which measurements of body weight (BW), withers height (WH), body length (BL) and chest girth (CG) at weaning were available underwent genotyping with the Illumina BovineHD BeadChip (770K). After quality control, we retained 98 688 SNPs and 354 animals for GWAS analysis. We identified seven, 18, seven and nine SNPs (corresponding to seven, 17, seven and eight candidate genes) associated with BW, WH, BL and CG at weaning respectively. Interestingly, most of these candidate genes were reported to be involved in growth-related processes such as muscle formation, lipid deposition, feed efficiency, carcass composition and development of the central and peripheral nervous system. Our results offer novel insight into the molecular architecture underpinning yak growth traits. Further functional analyses are thus warranted to explore the molecular mechanisms whereby these genes affect these traits of interest.

A whole genome sequence association study of muscle fiber traits in a White Duroc×Erhualian F2 resource population

Objective

Muscle fiber types, numbers and area are crucial aspects associated with meat production and quality. However, there are few studies of pig muscle fibre traits in terms of the detection power, false discovery rate and confidence interval precision of whole-genome quantitative trait loci (QTL). We had previously performed genome scanning for muscle fibre traits using 183 microsatellites and detected 8 significant QTLs in a White Duroc× Erhualian F₂ population. The confidence intervals of these QTLs ranged between 11 and 127 centimorgan (cM), which contained hundreds of genes and hampered the identification of QTLs. A whole-genome sequence imputation of the population was used for fine mapping in this study.

Methods

A whole-genome sequences association study was performed in the F₂ population. Genotyping was performed for 1,020 individuals (19 F₀, 68 F₁, and 933 F₂). The whole-genome variants were imputed and 21,624,800 single nucleotide polymorphisms (SNPs) were identified and examined for associations to 11 longissimus dorsi muscle fiber traits.

Results

A total of 3,201 significant SNPs comprising 7 novel QTLs showing associations with the relative area of fiber type I (I_RA), the fiber number per square centimeter (FN) and the total fiber number (TFN). Moreover, one QTL on pig chromosome 14 was found to affect both FN and TFN. Furthermore, four plausible candidate genes associated with FN (kinase non-catalytic C-lobe domain containing [KNDC1]), TFN (KNDC1), and I_RA (solute carrier family 36 member 4, contactin associated protein like 5, and glutamate metabotropic receptor 8) were identified.

Conclusion

An efficient and powerful imputation-based association approach was utilized to identify genes potentially associated with muscle fiber traits. These identified genes and SNPs could be explored to improve meat production and quality via marker-assisted selection in pigs.

Screening for candidate genes related with histological microstructure, meat quality and carcass characteristic in pig based on RNA-seq data

Objective

The aim of the present study was to identify genetic variants based on RNA-seq data, obtained via transcriptome sequencing of muscle tissue of pigs differing in muscle histological structure, and to verify the variants’ effect on histological microstructure and production traits in a larger pig population.

Methods

RNA-seq data was used to identify the panel of single nucleotide polymorphisms (SNPs) significantly related with percentage and diameter of each fiber type (I, IIA, IIB). Detected polymorphisms were mapped to quantitative trait loci (QTLs) regions. Next, the association study was performed on 944 animals representing five breeds (Landrace, Large White, Pietrain, Duroc, and native Puławska breed) in order to evaluate the relationship of selected SNPs and histological characteristics, meat quality and carcasses traits.

Results

Mapping of detected genetic variants to QTL regions showed that chromosome 14 was the most overrepresented with the identification of four QTLs related to percentage of fiber types I and IIA. The association study performed on a 293 longissimus muscle samples confirmed a significant positive effect of transforming acidic coiled-coil-containing protein 2 (TACC2) polymorphisms on fiber diameter, while SNP within forkhead box O1 (FOXO1) locus was associated with decrease of diameter of fiber types IIA and IIB. Moreover, subsequent general linear model analysis showed significant relationship of FOXO1, delta 4-desaturase, sphingolipid 1 (DEGS1), and troponin T2 (TNNT2) genes with loin ‘eye’ area, FOXO1 with loin weight, as well as FOXO1 and TACC2 with lean meat percentage. Furthermore, the intramuscular fat content was positively associated (p<0.01) with occurrence of polymorphisms within DEGS1, TNNT2 genes and negatively with occurrence of TACC2 polymorphism.

Conclusion

This study’s results indicate that the SNP calling analysis based on RNA-seq data can be used to search candidate genes and establish the genetic basis of phenotypic traits. The presented results can be used for future studies evaluating the use of selected SNPs as genetic markers related to muscle histological profile and production traits in pig breeding.

Fine-mapping of genes determining extrafusal fiber properties in murine soleus muscle

Muscle fiber cross-sectional area (CSA) and proportion of different fiber types are important determinants of muscle function and overall metabolism. Genetic variation plays a substantial role in phenotypic variation of these traits; however, the underlying genes remain poorly understood. This study aimed to map quantitative trait loci (QTL) affecting differences in soleus muscle fiber traits between the LG/J and SM/J mouse strains. Fiber number, CSA, and proportion of oxidative type I fibers were assessed in the soleus of 334 genotyped female and male mice of the F₃₄ generation of advanced intercross lines (AIL) derived from the LG/J and SM/J strains. To increase the QTL detection power, these data were combined with 94 soleus samples from the F₂ intercross of the same strains. Transcriptome of the soleus muscle of LG/J and SM/J females was analyzed by microarray. Genome-wide association analysis mapped four QTL (genome-wide P < 0.05) affecting the properties of muscle fibers to chromosome 2, 3, 4, and 11. A 1.5-LOD QTL support interval ranged between 2.36 and 4.67 Mb. On the basis of the genomic sequence information and functional and transcriptome data, we identified candidate genes for each of these QTL. The combination of analyses in F₂ and F₃₄ AIL populations with transcriptome and genomic sequence data in the parental strains is an effective strategy for refining QTL and nomination of the candidate genes.

Transcriptomic gene profiling of porcine muscle tissue depending on histological properties

In pig, the histological profile of muscle tissue, especially the proportion of individual fiber types, is one of the main factors affecting meat quality properties. In the present research, RNA sequencing (RNA-seq) by using next generation sequencing method was applied to estimate the whole gene expression profile of Longissimus lumborum muscle of pigs (Large White breed) differing in the percentage of two fiber types (slow-twitch (type I) fibers and fast-twitch glycolytic (type IIB) fibers). The RNA-seq approach allowed us to identify 355 differentially expressed genes (DEGs) indicated as significant (false discovery rate-adjusted P < 0.05) using three types of software: DESeq2, edgeR and baySeq. Detected genes and pathways deregulated in muscle depending on tissue microstructure were associated with: metabolic processes - 158 genes; cellular processes - 122; biological regulation - 62; localization - 51; and 35 genes with developmental processes. The DEGs were included in: PI3K-Akt; FoxO and MAPK signaling pathways, regulation of actin cytoskeleton, lysine degradation and insulin signaling pathway as well as mTOR and Hippo signaling pathways. These results highlight the mainly metabolic pathways related to glucose metabolism and contraction processes of muscle cells. Detection of genes involved in variation of fiber-type distribution will be useful in understanding of the genetic factors affecting muscle structure, metabolic process and indirectly, meat quality traits.

Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat

The aim of this work is to better understand the genetic mechanisms determining two complex traits affecting porcine meat quality: intramuscular fat (IMF) content and its fatty acid (FA) composition. With this purpose, expression Genome-Wide Association Study (eGWAS) of 45 lipid-related genes associated with meat quality traits in swine muscle (Longissimus dorsi) of 114 Iberian × Landrace backcross animals was performed. The eGWAS identified 241 SNPs associated with 11 genes: ACSM5, CROT, FABP3, FOS, HIF1AN, IGF2, MGLL, NCOA1, PIK3R1, PLA2G12A and PPARA. Three expression Quantitative Trait Loci (eQTLs) for IGF2, ACSM5 and MGLL were identified, showing cis-acting effects, whereas 16 eQTLs had trans regulatory effects. A polymorphism in the ACSM5 promoter region associated with its expression was identified. In addition, strong candidate genes regulating ACSM5, FOS, PPARA, PIK3R1, PLA2G12A and HIF1AN gene expression were also seen. Notably, the analysis highlighted the NR3C1 transcription factor as a strong candidate gene involved in the regulation of the 45 genes analysed. Finally, the IGF2, MGLL, MC2R, ARHGAP6, and NR3C1 genes were identified as potential regulators co-localizing within QTLs for fatness and growth traits in the IBMAP population. The results obtained increase our knowledge in the functional regulatory mechanisms involved in these complex traits.

Copy number variation-based genome wide association study reveals additional variants contributing to meat quality in Swine

Pork quality is important both to the meat processing industry and consumers' purchasing attitude. Copy number variation (CNV) is a burgeoning kind of variants that may influence meat quality. In this study, a genome-wide association study (GWAS) was performed between CNVs and meat quality traits in swine. After false discovery rate (FDR) correction, a total of 8 CNVs on 6 chromosomes were identified to be significantly associated with at least one meat quality trait. All of the 8 CNVs were verified by next generation sequencing and six of them were verified by qPCR. Only the haplotype block containing CNV12 is adjacent to significant SNPs associated with meat quality, suggesting the effects of those CNVs were not likely captured by tag SNPs. The DNA dosage and EST expression of CNV12, which overlap with an obesity related gene Netrin-1 (Ntn1), were consistent with Ntn1 RNA expression, suggesting the CNV12 might be involved in the expression regulation of Ntn1 and finally influence meat quality. We concluded that CNVs may contribute to the genetic variations of meat quality beyond SNPs, and several candidate CNVs were worth further exploration.

Genome-wide DNA Methylation Profiles of Small Intestine and Liver in Fast-growing and Slow-growing Weaning Piglets

Although growth rate is one of the main economic traits of concern in pig production, there is limited knowledge on its epigenetic regulation, such as DNA methylation. In this study, we conducted methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) to compare genome-wide DNA methylation profile of small intestine and liver tissue between fast- and slow-growing weaning piglets. The genome-wide methylation pattern between the two different growing groups showed similar proportion of CpG (regions of DNA where a cytosine nucleotide occurs next to a guanine nucleotide in the linear sequence) coverage, genomic regions, and gene regions. Differentially methylated regions and genes were also identified for downstream analysis. In canonical pathway analysis using differentially methylated genes, pathways (triacylglycerol pathway, some cell cycle related pathways, and insulin receptor signaling pathway) expected to be related to growth rate were enriched in the two organ tissues. Differentially methylated genes were also organized in gene networks related to the cellular development, growth, and carbohydrate metabolism. Even though further study is required, the result of this study may contribute to the understanding of epigenetic regulation in pig growth.

Parameters affecting genome simulation for evaluating genomic selection method

The present study investigated the parameter settings for obtaining a simulated genome at steady state of allele frequency (mutation-drift equilibrium) and linkage disequilibrium (LD), and evaluated the impact of whether or not the simulated genome reached steady state of allele frequency and LD on the accuracy of genomic estimated breeding values (GEBVs). After 500 to 50,000 historical generations, the base population and subsequent seven generations were generated as recent populations. The allele frequency distribution of the last generations of the historical population and LD in the base population were calculated when varying the values of five parameters: initial minor allele frequency, mutation rate, effective population size, number of markers and chromosome length. The accuracies of GEBVs in the last generation of the recent population were calculated by genomic best linear unbiased prediction. The number of historical generations required to reach mutation-drift equilibrium depended on the initial allele frequency and mutation rate. Regardless of the parameters, LD reached a steady state before allele frequency distribution reached mutation-drift equilibrium. The accuracies of GEBVs largely reflect the extent of linkage disequilibrium with the exception of varying chromosome length, although there were no associations between the accuracies of GEBVs and allele frequency distribution.

Molecular characterization of porcine SARM1 and its role in regulating TLRs signaling during highly pathogenic porcine reproductive and respiratory syndrome virus infection in vivo

Toll-like receptors (TLRs) are important pattern-recognition receptors (PRRs) that trigger innate immune response and mediate acquired immunity. Evidence has shown that SARM1 (sterile-α and TIR motif containing protein 1) is one of five TIR domain-containing adaptor proteins involved in TLRs signaling transduction. In the present study, a full-length cDNA sequence was cloned for the porcine SARM1 gene, which contains nine exons. Using the radiation hybrid mapping approach, we assigned the porcine gene to SSC12 q13. Under the normal condition, porcine SARM1 was highly expressed in brain and spleen. Polyinosinic-polycytidylic acid (poly (I:C)) weakly induced the porcine SARM1 expression in the early stimulation. We found that porcine SARM1 protein is localized in mitochondria and attenuates NF-κB activation induced by stimulation and infection. The quantitative real-time PCR (Q-PCR) analysis showed that the expression of porcine SARM1 significantly decreased in several tissues of Tongcheng pigs infected with highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV). Gene-interaction network analysis for porcine SARM1 in porcine alveolar macrophages (PAMs) showed that down-regulation of SARM1 gene in infected Tongcheng pig may modulate TRIF-depend TLRs signaling and regulate the expression of disease-resistant genes and inflammatory genes. Our findings provide evidence that porcine SARM1 may play an important role in immune regulation with PRRSV infection.

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

A two-step approach to map quantitative trait loci for meat quality in connected porcine F(2) crosses considering main and epistatic effects

The aim of this study was to map QTL for meat quality traits in three connected porcine F(2) crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two-step procedure. In the first step, the QTL were mapped using a multi-QTL multi-allele model that was tailored to analyse multiple connected F(2) crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.

QTL Analysis of Type I and Type IIA Fibers in Soleus Muscle in a Cross between LG/J and SM/J Mouse Strains

Properties of muscle fibers, i.e., their type, number and size, are important determinants of functional characteristics of skeletal muscle, and of the quality of meat in livestock. Genetic factors play an important role in determining variation in fiber properties, however, specific genes remain largely elusive. We examined histological properties of soleus muscle fibers in two strains of mice exhibiting a twofold difference in muscle mass, LG/J and SM/J, and their F2 intercross. The total number of muscle fibers (555 ± 106; mean ± SD) did not differ between the strains or between males and females. A higher percentage of type I fibers was observed in the LG/J compared to the SM/J strain (P < 0.001) in both males (45 ± 3 vs. 37 ± 4%) and females (58 ± 4 vs. 41 ± 3%). Across strains, females had a higher percentage of type I fibers than males (P < 0.001), and the sex effect was greater in the LG/J strain (strain-by-sex interaction, P < 0.001). The cross-sectional area (CSA) did not differ between type I and type IIA fibers, but was greater in the LG/J than the SM/J strain (1365 ± 268 vs. 825 ± 229 μm(2), P < 0.001). Three significant quantitative trait locus (QTL) affecting CSA for type I and type IIA fibers mapped to chromosomes (Chr) 1, 6, and 11 and three suggestive QTL for percentage of type I fibers mapped to Chr 2, 3, and 4. Within each significant QTL, regions of conserved synteny were also implicated in variation of similar traits in an analogous study in pigs. Our results provide the evidence that the intercross between the SM/J and LG/J strains is a promising model to search for genes affecting muscle fiber properties.

Progeny-testing of full-sibs IBD in a SSC2 QTL region highlights epistatic interactions for fatness traits in pigs

Background

Many QTL have been detected in pigs, but very few of them have been fine-mapped up to the causal mutation. On SSC2, the IGF2-intron3-G3072A mutation has been described as the causative polymorphism for a QTL underlying muscle mass and backfat deposition, but further studies have demonstrated that at least one additional QTL should segregate downstream of this mutation. A marker-assisted backcrossing design was set up in order to confirm the segregation of this second locus, reduce its confidence interval and better understand its mode of segregation.

Results

Five recombinant full-sibs, with genotype G/G at the IGF2 mutation, were progeny-tested. Only two of them displayed significant QTL for fatness traits although four inherited the same paternal and maternal chromosomes, thus exhibiting the same haplotypic contrast in the QTL region. The hypothesis of an interaction with another region in the genome was proposed to explain these discrepancies and after a genome scan, four different regions were retained as potential interacting regions with the SSC2 QTL. A candidate interacting region on SSC13 was confirmed by the analysis of an F2 pedigree, and in the backcross pedigree one haplotype in this region was found to mask the SSC2 QTL effect.

Conclusions

Assuming the hypothesis of interactions with other chromosomal regions, the QTL could be unambiguously mapped to a 30 cM region delimited by recombination points. The marker-assisted backcrossing design was successfully used to confirm the segregation of a QTL on SSC2 and, because full-sibs that inherited the same alleles from their two parents were analysed, the detection of epistatic interactions could be performed between alleles and not between breeds as usually done with the traditional Line-Cross model. Additional analyses of other recombinant sires should provide more information to further improve the fine-mapping of this locus, and confirm or deny the interaction identified between chromosomes 2 and 13.

Genetic linkage mapping and analysis of muscle fiber-related QTLs in common carp (Cyprinus carpio L.)

A genetic linkage map of common carp (Cyprinus carpio L.) was constructed using Type I and Type II microsatellite markers and a pseudo-testcross mapping strategy. The microsatellite markers were isolated from microsatellite-enriched genomic libraries and tested for their segregation in a full-sib mapping panel containing 92 individuals. A total of 161 microsatellite loci were mapped into 54 linkage groups. The total lengths of the female, male and consensus maps were 2,000, 946, and 1,852 cM, with an average marker spacing of approximately 13, 7, and 11 cM, respectively. Muscle fiber-related traits, including muscle fiber cross-section area and muscle fiber density, were mapped to the genetic map. Three QTLs for muscle fiber cross-section area and two QTLs for muscle fiber density were identified when considering both significant and suggestive QTL effects. The QTLs with largest effects for muscle fiber cross-section area and muscle fiber density were 21.9% and 18.9%, and they were located in LG3, respectively.

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.

Epistatic effect between ACACA and FABP2 gene on abdominal fat traits in broilers

Epistasis is generally defined as the interaction between two or more genes or their mRNA or protein products to influence a single trait. Experimental evidence suggested that epistasis could be important in the determination of the genetic architecture of complex traits in domestic animals. Acetyl-coenzyme A carboxylase alpha (ACACA) and fatty acid binding protein 2 (FABP2) are both key factors of lipogenesis and transport. They may play a crucial role in the weight variability of abdominal adipose tissue in the growing chicken. In this study, the polymorphisms of c.2292G>A in ACACA and c.-561A>C in FABP2 were detected among individuals from two broiler lines which were divergently selected for abdominal fat content. Epistasis between the two SNPs on abdominal fat weight (AFW) and abdominal fat percentage (AFP) was analyzed. The additive x additive epistatic components between these two SNPs were found significant or suggestively significant on both AFW and AFP in lean lines of the 9th and 10th generation; whereas, it was not significantly associated with either AFW or AFP in fat lines. At the same time, there were not any other significant epistatic components found in both generations or in both lines. Significant epistatic effects between these two SNPs found only in the lean lines could partly be due to the fact that the abdominal fat traits in these two experimental lines have been greatly modified by strong artificial selection. The results suggested that the epistasis mode may be different between the lean and fat chicken lines. Our results could be helpful in further understanding the genetic interaction between candidate genes contributing to phenotypic variation of abdominal fat content in broilers.

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.

Genetic epistasis analysis of 10 peroxisome proliferator-activated receptor γ-correlated genes in broiler lines divergently selected for abdominal fat content

Chicken peroxisome proliferator-activated receptor γ (PPARγ), which is highly expressed in adipose tissues, is a key factor in fat accumulation in the abdominal fat pad. In this study, association and pairwise epistasis analyses were performed for all the polymorphisms detected in PPARγ and for 9 genes from PPARγ-correlated lipid metabolic pathways for abdominal fat weight (AFW) in 10th-generation populations of Northeast Agricultural University broiler lines divergently selected for abdominal fat content. Epistatic networks were then reconstructed with the identified epistatic effects. Single-marker association analyses showed that 5 of the 20 screened polymorphisms were significantly associated with AFW (P < 0.05), and CCAAT/enhancer-binding protein α (C/EBPα) c.552G>A was 1 of the 5 significant loci. Pairwise interaction analyses showed that 15 pairs of polymorphisms reached a significance level of P < 2.64 × 10(-4) (adjusted by Bonferroni correction) in the lean line, 41 pairs reached significance in the fat line, and 7 pairs reached significance in both lines. Interestingly, many other loci interacted with C/EBPα c.552G>A in both lines. In epistatic network analyses, C/EBPα c.552G>A seemed to behave as a hub for the epistatic network in both lines. All these results revealed that the genetic architecture of C/EBPα c.552G>A for AFW seemed to be an apparent individual main-effect QTL but that it could be dissected into a genetic epistatic network. Our results suggest that C/EBPα c.552G>A might be the most important locus contributing to phenotypic variation in AFW among all the polymorphisms detected in this study.

Quantitative trait loci for meat quality traits in pigs considering imprinting and epistatic effects

The aim of the research was to gain a better understanding of the genomic regulation of meat quality by investigating individual and epistatic QTL in a three-generation full-sib population (Pietrain x crossbred dam line). In total, 386 animals were genotyped for 96 markers. Analysed traits included pH, reflectance value, conductivity, and meat colour. Thirteen significant individual QTL were identified. The most significant QTL were detected on SSC1 and SSC9 for pH, on SSC4 for meat colour, and on SSC8 for conductivity, accounting for 3.4% to 4.7% of the phenotypic variance. Nine significant epistatic QTL pairs were detected accounting for between 5.7% and 10.9% of the phenotypic variance. Epistatic QTL pairs showing the largest effects were for reflectance value between two locations of SSC4, and for pH between SSC10 and SSC13, explaining 9.5% and 10.9% of the phenotypic variance, respectively. This study indicates that meat quality traits are influenced by numerous QTL as well as a complex network of interactions.

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.

Epistatic analysis of carcass characteristics in pigs reveals genomic interactions between quantitative trait loci attributable to additive and dominance genetic effects

The present study focused on the identification of epistatic QTL pairs for body composition traits (carcass cut, lean tissue, and fat tissue weights) measured at slaughter weight (140 kg of BW) in a 3-generation full-sib population developed by crossing Pietrain sires with a crossbred dam line. Depending on the trait, phenotypic observations were available for 306 to 315 F(2) animals. For the QTL analysis, 386 animals were genotyped for 88 molecular markers covering chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14. In total, 23 significant epistatic QTL pairs were identified, with the additive x additive genetic interaction being the most prevalent. Epistatic QTL were identified across all chromosomes except for SSC13, and epistatic QTL pairs accounted for between 5.8 and 10.2% of the phenotypic variance. Seven epistatic QTL pairs were between QTL that resided on the same chromosome, and 16 were between QTL that resided on different chromosomes. Sus scrofa chromosome 1, SSC2, SSC4, SSC6, SSC8, and SSC9 harbored the greatest number of epistatic QTL. The epistatic QTL pair with the greatest effect was for the entire loin weight between 2 locations on SSC7, explaining 10.2% of the phenotypic variance. Epistatic associations were identified between regions of the genome that contain the IGF-2 or melanocortin-4 receptor genes, with QTL residing in other genomic locations. Quantitative trait loci in the region of the melanocortin-4 receptor gene and on SSC7 showed significant positive dominance effects for entire belly weight, which were offset by negative dominance x dominance interactions between these QTL. In contrast, the QTL in the region of the IGF-2 gene showed significant negative dominance effects for entire ham weight, which were largely overcompensated for by positive additive x dominance genetic effects with a QTL on SSC9. The study shows that epistasis is of great importance for the genomic regulation of body composition in pigs and contributes substantially to the variation in complex traits.