Implementing meta-analysis from genome-wide association studies for pork quality traits

Function Identification of Bovine ACSF3 Gene and Its Association With Lipid Metabolism Traits in Beef Cattle

Acyl-CoA synthetase family member 3 (ACSF3) carries out the first step of mitochondrial fatty acid synthesis II, which is the linkage of malonate and, to a lesser extent, methylmalonate onto CoA. Malonyl-coenzyme A (malonyl-CoA) is a central metabolite in mammalian fatty acid biochemistry that is generated and utilized in the cytoplasm. In this research, we verified the relationship between expression of the ACSF3 and the production of triglycerides (TGs) at the cellular level by silencing and over-expressing ACSF3. Subsequently, through Sanger sequencing, five polymorphisms were found in the functional domain of the bovine ACSF3, and the relationship between ACSF3 polymorphism and the economic traits and fatty acid composition of Chinese Simmental cattle was analyzed by a means of variance analysis and multiple comparison. The results illustrated that the expression of ACSF3 promoted triglyceride synthesis in bovine mammary epithelial cells and bovine fetal fibroblast cells. Further association analysis also indicated that individuals with the AG genotype (g.14211090 G > A) of ACSF3 were significantly associated with the fatty acid composition of intramuscular fat (higher content of linoleic acid, α-linolenic acid, and arachidonic acid), and that CTCAG haplotype individuals were significantly related to the fatty acid composition of intramuscular fat (higher linoleic acid content). Individuals with the AA genotypes of g.14211055 A > G and g.14211090 G > A were substantially associated with a larger eye muscle area in the Chinese Simmental cattle population. ACSF3 played a pivotal role in the regulation of cellular triacylglycerol and long-chain polyunsaturated fatty acid levels, and polymorphism could serve as a useful molecular marker for future marker-assisted selection in the breeding of intramuscular fat deposition traits in beef cattle.

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.

Genetic parameters and purebred-crossbred genetic correlations for growth, meat quality, and carcass traits in pigs

Growth, meat quality, and carcass traits are of economic importance in swine breeding. Understanding their genetic basis in purebred (PB) and commercial crossbred (CB) pigs is necessary for a successful breeding program because, although the breeding goal is to improve CB performance, phenotype collection and selection are usually carried out in PB populations housed in biosecure nucleus herds. Thus, the selection is indirect, and the accuracy of selection depends on the genetic correlation between PB and CB performance (rpc). The objectives of this study were to 1) estimate genetic parameters for growth, meat quality, and carcass traits in a PB sire line and related commercial CB pigs and 2) estimate the corresponding genetic correlations between purebred and crossbred performance (rpc). Both objectives were investigated by using pedigree information only (PBLUP) and by combining pedigree and genomic information in a single-step genomic BLUP (ssGBLUP) procedure. Growth rate showed moderate estimates of heritability for both PB and CB based on PBLUP, while estimates were higher in CB based on ssGBLUP. Heritability estimates for meat quality traits were diverse and slightly different based on PB and CB data with both methods. Carcass traits had higher heritability estimates based on PB compared with CB data based on PBLUP and slightly higher estimates for CB data based on ssGBLUP. A wide range of estimates of genetic correlations were obtained among traits within the PB and CB data. In the PB population, estimates of heritabilities and genetic correlations were similar based on PBLUP and ssGBLUP for all traits, while based on the CB data, ssGBLUP resulted in different estimates of genetic parameters with lower SEs. With some exceptions, estimates of rpc were moderate to high. The SE on the rpc estimates was generally large when based on PBLUP due to limited sample size, especially for CBs. In contrast, estimates of rpc based on ssGBLUP were not only more precise but also more consistent among pairs of traits, considering their genetic correlations within the PB and CB data. The wide range of estimates of rpc (less than 0.70 for 7 out of 13 traits) indicates that the use of CB phenotypes recorded on commercial farms, along with genomic information, for selection in the PB population has potential to increase the genetic progress of CB performance.

Signatures of positive selection underlying beef production traits in Korean cattle breeds

The difference in the breeding programs and population history may have diversely shaped the genomes of Korean native cattle breeds. In the absence of phenotypic data, comparisons of breeds that have been subjected to different selective pressures can aid to identify genomic regions and genes controlling qualitative and complex traits. In this study to decipher genetic variation and identify evidence of divergent selection, 3 Korean cattle breeds were genotyped using the recently developed high-density GeneSeek Genomic Profiler F250 (GGP-F250) array. The three Korean cattle breeds clustered according to their coat color phenotypes and breeding programs. The Heugu breed reliably showed smaller effective population size at all generations considered. Across the autosomal chromosomes, 113 and 83 annotated genes were identified from Hanwoo-Chikso and Hanwoo-Heugu comparisons, respectively of which 16 genes were shared between the two pairwise comparisons. The most important signals of selection were detected on bovine chromosomes 14 (24.39–25.13 Mb) and 18 (13.34–15.07 Mb), containing genes related to body size, and coat color (XKR4, LYN, PLAG1, SDR16C5, TMEM68, CDH15, MC1R, and GALNS). Some of the candidate genes are also associated with meat quality traits (ACSF3, EIF2B1, BANP, APCDD1, and GALM) and harbor quantitative trait locus (QTL) for beef production traits. Further functional analysis revealed that the candidate genes (DBI, ACSF3, HINT2, GBA2, AGPAT5, SCAP, ELP6, APOB, and RBL1) were involved in gene ontology (GO) terms relevant to meat quality including fatty acid oxidation, biosynthesis, and lipid storage. Candidate genes previously known to affect beef production and quality traits could be used in the beef cattle selection strategies.

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the population-specific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multi-population data sets to undertake joint GWAS.

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.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5386-2) contains supplementary material, which is available to authorized users.

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

Genome-Wide Association of Myoglobin Concentrations in Pork Loins

Lean color is a major focus for identifying pork loins for export markets, and myoglobin is the primary pigment driving pork color. Thus, increasing myoglobin concentration should increase redness of pork products and the number of loins acceptable for exportation. Therefore, understanding genetic variation and parameters affecting myoglobin concentration is critical for improving pork color. The objective of this study was to identify genetic markers associated with myoglobin concentration in pork loin muscle. Ultimate pH and myoglobin concentrations were measured in longissimus thoracis et lumborum samples of pigs (n = 599) from two different commercial finishing swine facilities. A Bayes-C model implemented in GenSel identified regions within 7 chromosomes that explained greater than 63% of the genetic variance in myoglobin concentration. Chromosome 7 had 1 significant region which accounted for 37% of the genetic variance, while chromosome 14 had 4 significant regions accounting for 9.8% of the genetic variance. Candidate genes in the region on chromosome 7 were involved in iron homeostasis, and genes in the significant regions on chromosome 14 were involved in calcium regulation. Genes identified in this study represent potential biomarkers that could be used to select for higher myoglobin concentrations in pork, which may improve lean meat color.

Deep sequencing of a QTL-rich region spanning 128-136Mbp of pig chromosome 15

The present study shows the characterization of the chromosome 15 (SSC15) region that is highly rich in quantitative traits loci (QTLs) associated with pork quality, growth performance, fat and meat carcass contents. The analytic method that was utilized included targeted enrichment DNA sequencing and RNA hybridisation probes. The research included two pig breeds (Puławska and Polish Landrace) that are significantly different in terms of carcass and meat quality features. Filtered sequences were aligned to the Sscrofa10.2 assembly genome with the STAR aligner and GATK HaplotypeCaller was used for identified gene variants in SSC15 region. In Puławska pigs, which were characterized by high meat quality, mutations were predominantly observed in non-coding regions such as introns and intergenic regions. The highest over 50% frequencies of alternate alleles were identified in the introns of TNS1, VIL1 and USP37 genes. In the upstream gene regions of the Polish Landrace pigs, were observed more mutations than in the upstream gene regions of Puławska. The present study showed interesting gene variant panel that could be analyzed in the further association studies in order to understand the impact on important productive pig traits.

Genome-wide association study in an F2 Duroc x Pietrain resource population for economically important meat quality and carcass traits

Meat quality is essential for consumer acceptance, it ultimately impacts pork production profitability and it is subject to genetic control. The objective of this study was to map genomic regions associated with economically important meat quality and carcass traits. We performed a genome-wide association (GWA) analysis to map regions associated with 38 meat quality and carcass traits recorded for 948 F2 pigs from the Michigan State University Duroc × Pietrain resource population. The F0, F1, and 336 F2 pigs were genotyped with the Illumina Porcine SNP60 BeadChip, while the remaining F2 pigs were genotyped with the GeneSeek Genomic Profiler for Porcine Low Desnisty (LD) chip, and imputed with high accuracy ( = 0.97). Altogether the genomic dataset comprised 1,019 animals and 44,911 SNP. A Gaussian linear mixed model was fitted to estimate the breeding values and the variance components. A linear transformation was performed to estimate the marker effects and variances. Type I error rate was controlled at a False Discovery Rate of 5%. Seven putative QTL found in this study were previously reported in other studies. Two novel QTL associated with tenderness (TEN) were located on SSC3 [135.6:137.5Mb; False Discovery rate (FDR) < 0.03] and SSC5 (67.3:69.1Mb; FDR < 0.02). The QTL region identified on SSC15 includes Protein Kinase AMP-activated ɣ 3-subunit gene (), which has been associated with 24-h pH (pH24), drip loss (DL) and cook yield (CY). Also, novel candidate genes were identified for TEN in the region on SSC5 [A Kinase (PRKA) Anchor Protein 3 (], and for tenth rib backfat thickness (BF10) [Carnitine O-Acetyltransferase ()] on SSC1. The association of gene polymorphisms with pork quality traits has been reported for several pig populations. However, there are no SNP for this gene on the chip used, thus we genotyped the animals for 2 non-synonymous variants ( and ). We then performed a GWA conditioning on the genotype of both SNP and was associated with pH24, DL, protein content (PRO) and CY ( < 0.004) and T30N with Juiciness, TEN, shear force, pH24, PRO, and CY < 0.04). Finally, we performed a GWA conditioning on the genotype of the SNP peak detected in this study, and T30N remained associated only with PRO ( < 0.02). Therefore, in this study we identified 2 novel QTL regions, suggest 2 novel candidate genes, and conclude that other SNP in PRKAG3 or nearby gene(s) explain the observed associations on SSC15 in this population.

Whole-genome association analysis of pork meat pH revealed three significant regions and several potential genes in Finnish Yorkshire pigs

Background

One of the most commonly used quality measurements of pork is pH measured 24 h after slaughter. The most probable mode of inheritance for this trait is oligogenic with several known major genes, such as PRKAG3. In this study, we used whole-genome SNP genotypes of over 700 AI boars; after a quality check, 42,385 SNPs remained for association analysis. All the boars were purebred Finnish Yorkshire. To account for relatedness of the animals, a pedigree-based relationship matrix was used in a mixed linear model to test the effect of SNPs on pH measured from loin. A bioinformatics analysis was performed to identify the most promising genes in the significant regions related to meat quality.

Results

Genome-wide association study (GWAS) revealed three significant chromosomal regions: one on chromosome 3 (39.9 Mb–40.1 Mb) and two on chromosome 15 (58.5 Mb–60.5 Mb and 132 Mb–135 Mb including PRKAG3). A conditional analysis with a significant SNP in the PRKAG3 region, MARC0083357, as a covariate in the model retained the significant SNPs on chromosome 3. Even though linkage disequilibrium was relatively high over a long distance between MARC0083357 and other significant SNPs on chromosome 15, some SNPs retained their significance in the conditional analysis, even in the vicinity of PRKAG3. The significant regions harbored several genes, including two genes involved in cyclic AMP (cAMP) signaling: ADCY9 and CREBBP. Based on functional and transcription factor-gene networks, the most promising candidate genes for meat pH are ADCY9, CREBBP, TRAP1, NRG1, PRKAG3, VIL1, TNS1, and IGFBP5, and the key transcription factors related to these genes are HNF4A, PPARG, and Nkx2-5.

Conclusions

Based on SNP association, pathway, and transcription factor analysis, we were able to identify several genes with potential to control muscle cell homeostasis and meat quality. The associated SNPs can be used in selection for better pork. We also showed that post-GWAS analysis reveals important information about the genes’ potential role on meat quality. The gained information can be used in later functional studies.

Joint QTL mapping and gene expression analysis identify positional candidate genes influencing pork quality traits

Meat quality traits have an increasing importance in the pig industry because of their strong impact on consumer acceptance. Herewith, we have combined phenotypic and microarray expression data to map loci with potential effects on five meat quality traits recorded in the longissimus dorsi (LD) and gluteus medius (GM) muscles of 350 Duroc pigs, i.e. pH at 24 hours post-mortem (pH₂₄), electric conductivity (CE) and muscle redness (a*), lightness (L*) and yellowness (b*). We have found significant genome-wide associations for CE of LD on SSC4 (~104 Mb), SSC5 (~15 Mb) and SSC13 (~137 Mb), while several additional regions were significantly associated with meat quality traits at the chromosome-wide level. There was a low positional concordance between the associations found for LD and GM traits, a feature that reflects the existence of differences in the genetic determinism of meat quality phenotypes in these two muscles. The performance of an eQTL search for SNPs mapping to the regions associated with meat quality traits demonstrated that the GM a* SSC3 and pH₂₄ SSC17 QTL display positional concordance with cis-eQTL regulating the expression of several genes with a potential role on muscle metabolism.