Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs

Identification of a novel QTL for lean meat percentage using imputed genotypes

Lean meat percentage is a critical production trait in pig breeding systems with direct implications for the sustainability of the industry. In this study, we conducted a genome-wide association study for lean meat percentage using a cohort of 850 Duroc × (Landrace × Yorkshire) crossbred pigs and we identified QTL on SSC3 and SSC18. Based on the predicted effect of imputed variants and using the PigGTEx database of molecular QTL, we prioritized candidate genes and SNPs located within the QTL regions, which may be involved in the regulation of porcine leanness. Our results indicate that a nonsense mutation in ZC3HAV1L on SSC18 has a direct effect on lean meat percentage.

Smartphone-based digital phenotyping for genome-wide association study of intramuscular fat traits in longissimus dorsi muscle of pigs

Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time-consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image-based IMF traits through embedded deep-learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome-wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user-friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.

Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig

BACKGROUND

As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts.

RESULTS

We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs.

CONCLUSIONS

We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

Genetic dissection of 26 meat cut, meat quality and carcass traits in four pig populations

BACKGROUND

Currently, meat cut traits are integrated in pig breeding objectives to gain extra profit. However, little is known about the heritability of meat cut proportions (MCP) and their correlations with other traits. The aims of this study were to assess the heritability and genetic correlation of MCP with carcass and meat quality traits using single nucleotide polymorphism chips and conduct a genome-wide association study (GWAS) to identify candidate genes for MCP.

RESULTS

Seventeen MCP, 12 carcass, and seven meat quality traits were measured in 2012 pigs from four populations (Landrace; Yorkshire; Landrace and Yorkshire hybrid pigs; Duroc, and Landrace and Yorkshire hybrid pigs). Estimates of the heritability for MCP ranged from 0.10 to 0.55, with most estimates being moderate to high and highly consistent across populations. In the combined population, the heritability estimates for the proportions of scapula bone, loin, back fat, leg bones, and boneless picnic shoulder were 0.44 ± 0.04, 0.36 ± 0.04, 0.44 ± 0.04, 0.38 ± 0.04, and 0.39 ± 0.04, respectively. Proportion of middle cuts was genetically significantly positively correlated with intramuscular fat content and backfat depth. Proportion of ribs was genetically positively correlated with carcass oblique length and straight length (0.35 ± 0.08 to 0.45 ± 0.07) and negatively correlated with backfat depth (- 0.26 ± 0.10 to - 0.45 ± 0.10). However, weak or nonsignificant genetic correlations were observed between most MCP, indicating their independence. Twenty-eight quantitative trait loci (QTL) for MCP were detected by GWAS, and 24 new candidate genes related to MCP were identified, which are involved with growth, height, and skeletal development. Most importantly, we found that the development of the bones in different parts of the body may be regulated by different genes, among which HMGA1 may be the strongest candidate gene affecting forelimb bone development. Moreover, as previously shown, VRTN is a causal gene affecting vertebra number, and BMP2 may be the strongest candidate gene affecting hindlimb bone development.

CONCLUSIONS

Our results indicate that breeding programs for MCP have the potential to enhance carcass composition by increasing the proportion of expensive cuts and decreasing the proportion of inexpensive cuts. Since MCP are post-slaughter traits, the QTL and candidate genes related to these traits can be used for marker-assisted and genomic selection.

Identification of Candidate Genes and Regulatory Competitive Endogenous RNA (ceRNA) Networks Underlying Intramuscular Fat Content in Yorkshire Pigs with Extreme Fat Deposition Phenotypes

Intramuscular fat (IMF) content is vital for pork quality, serving an important role in economic performance in pig industry. Non-coding RNAs, with mRNAs, are involved in IMF deposition; however, their functions and regulatory mechanisms in porcine IMF remain elusive. This study assessed the whole transcriptome expression profiles of the Longissimus dorsi muscle of pigs with high (H) and low (L) IMF content to identify genes implicated in porcine IMF adipogenesis and their regulatory functions. Hundreds of differentially expressed RNAs were found to be involved in fatty acid metabolic processes, lipid metabolism, and fat cell differentiation. Furthermore, combing co-differential expression analyses, we constructed competing endogenous RNAs (ceRNA) regulatory networks, showing crosstalk among 30 lncRNAs and 61 mRNAs through 20 miRNAs, five circRNAs and 11 mRNAs through four miRNAs, and potential IMF deposition-related ceRNA subnetworks. Functional lncRNAs and circRNAs (such as MSTRG.12440.1, ENSSSCT00000066779, novel_circ_011355, novel_circ_011355) were found to act as ceRNAs of important lipid metabolism-related mRNAs (LEP, IP6K1, FFAR4, CEBPA, etc.) by sponging functional miRNAs (such as ssc-miR-196a, ssc-miR-200b, ssc-miR10391, miR486-y). These findings provide potential regulators and molecular regulatory networks that can be utilized for research on IMF traits in pigs, which would aid in marker-assisted selection to improve pork quality.

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.

Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs

Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF% using computer vision technology and meat quality traits.