Combined GWAS and LDLA approaches to improve genome-wide quantitative trait loci detection affecting carcass and meat quality traits in pig

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.

Multi-Omics Reveals the Effect of Crossbreeding on Some Precursors of Flavor and Nutritional Quality of Pork

Over the last several decades, China has continuously introduced Duroc boars and used them as breeding boars. Although this crossbreeding method has increased pork production, it has affected pork quality. Nowadays, one of the primary goals of industrial breeding and production systems is to enhance the quality of meat. This research analyzed the molecular mechanisms that control the quality of pork and may be used as a guide for future efforts to enhance meat quality. The genetic mechanisms of cross-breeding for meat quality improvement were investigated by combining transcriptome and metabolome analysis, using Chinese native Jiaxing black (JXB) pigs and crossbred Duroc × Duroc × Berkshire × JXB (DDBJ) pigs. In the longissimus Dorsi muscle, the content of inosine monophosphate, polyunsaturated fatty acid, and amino acids were considerably higher in JXB pigs in contrast with that of DDBJ pigs, whereas DDBJ pigs have remarkably greater levels of polyunsaturated fatty acids than JXB pigs. Differentially expressed genes (DEGs) and differential metabolites were identified using transcriptomic and metabolomic KEGG enrichment analyses. Differential metabolites mainly include amino acids, fatty acids, and phospholipids. In addition, several DEGs that may explain differences in meat quality between the two pig types were found, including genes associated with the metabolism of lipids (e.g., DGKA, LIPG, and LPINI), fatty acid (e.g., ELOVL5, ELOVL4, and ACAT2), and amino acid (e.g., SLC7A2, SLC7A4). Combined with the DEGS-enriched signaling pathways, the regulatory mechanisms related to amino acids, fatty acids, and phospholipids were mapped. The abundant metabolic pathways and DEGs may provide insight into the specific molecular mechanism that regulates meat quality. Optimizing the composition of fatty acids, phospholipids, amino acids, and other compounds in pork is conducive to improving meat quality. Overall, these findings will provide useful information and further groundwork for enhancing the meat quality that may be achieved via hybrid breeding.

Genome-wide association study reveals a genomic region on SSC7 simultaneously associated with backfat thickness, skin thickness and carcass length in a Large White × Tongcheng advanced generation intercross resource population

In order to identify important genetic markers associated with backfat thickness, skin thickness and carcass length, we first constructed Large White × Tongcheng (Chinese local breed), an advanced generation intercross population, then performed a genome-wide association study (GWAS) to reveal the key genomic region associated with these traits through whole genome sequencing. The GWAS results of backfat thickness, skin thickness and carcass length showed that all the most significant SNPs associated with these three traits were located on SSC7, and that 14.9, 27.0 and 21.1% of phenotypic variances were explained by these three SNPs, respectively. Through linkage disequilibrium analysis, we found that a 66.9 kb (30.23-30.31 Mb) genetic region was overlapped among these three traits, and that NUDT3 and HMGA1 were identified as major candidate genes for backfat thickness and carcass length, and GRM4 as a potential candidate gene for skin thickness. In addition, 13 highly linked SNPs significantly associated with the three traits were also identified in overlapped region, and three completely linked SNPs formed two haplotypes Q and q. The backfat thickness of individuals with the qq genotype was significantly lower than that of individuals with the QQ genotype, but their carcass length and skin thickness were significantly higher than those with the QQ genotype. Our detected candidate genes and SNPs will provide the foundation for genetic improvement of these three traits.

Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R² , concordance rate, and dosage R² were 0.959, 0.952, and 0.933, respectively. The heritability values of a*_45 min , b*_45 min , L*_45 min , C*, and H⁰ were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology

Background

Carcass backfat thickness (BFT), carcass lean percentage (CLP) and carcass fat percentage (CFP) are important to the commercial pig industry. Nevertheless, the genetic architecture of BFT, CLP and CFP is still elusive. Here, we performed a genome-wide association study (GWAS) based on specific-locus amplified fragment sequencing (SLAF-seq) to analyze seven fatness-related traits, including five BFTs, CLP, and CFP on 223 four-way crossbred pigs.

Results

A total of 227, 921 highly consistent single nucleotide polymorphisms (SNPs) evenly distributed throughout the genome were used to perform GWAS. Using the mixed linear model (MLM), a total of 20 SNP loci significantly related to these traits were identified on ten Sus scrofa chromosomes (SSC), of which 10 SNPs were located in previously reported quantitative trait loci (QTL) regions. On SSC7, two SNPs (SSC7:29,503,670 and rs1112937671) for average backfat thickness (ABFT) exceeded 1% and 10% Bonferroni genome-wide significance levels, respectively. These two SNP loci were located within an intron region of the COL21A1 gene, which was a protein-coding gene that played an important role in the porcine backfat deposition by affecting extracellular matrix (ECM) remodeling. In addition, based on the other three significant SNPs on SSC7, five candidate genes, ZNF184, ZNF391, HMGA1, GRM4 and NUDT3 were proposed to influence BFT. On SSC9, two SNPs for backfat thickness at 6–7 ribs (67RBFT) and one SNP for CLP were in the same locus region (19 kb interval). These three SNPs were located in the PGM2L1 gene, which encoded a protein that played an indispensable role in glycogen metabolism, glycolysis and gluconeogenesis as a key enzyme. Finally, one significant SNP on SSC14 for CLP was located within the PLBD2 gene, which participated in the lipid catabolic process.

Conclusions

A total of two regions on SSC7 and SSC9 and eight potential candidate genes were found for fatness-related traits in pigs. The results of this GWAS based on SLAF-seq will greatly advance our understanding of the genetic architecture of BFT, CLP, and CFP traits. These identified SNP loci and candidate genes might serve as a biological basis for improving the important fatness-related traits of pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08827-8.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Transcriptome Analysis of Bovine Rumen Tissue in Three Developmental Stages

Rumen development is a crucial physiological challenge for ruminants. However, the molecular mechanism regulating rumen development has not been clearly elucidated. In this study, we investigated genes involved in rumen development in 13 rumen tissues from three developmental stages (birth, youth, and adult) using RNA sequencing. We identified that 6,048 genes were differentially expressed among three developmental stages. Using weighted correlation network analysis, we found that 12 modules were significantly associated with developmental stages. Functional annotation and protein–protein interaction (PPI) network analysis revealed that CCNB1, CCNB2, IGF1, IGF2, HMGCL, BDH1, ACAT1, HMGCS2, and CREBBP involved in rumen development. Integrated transcriptome with GWAS information of carcass weight (CW), stomach weight (SW), marbling score (MS), backfat thickness (BFT), ribeye area (REA), and lean meat weight (LMW), we found that upregulated DEGs (fold change 0∼1) in birth–youth comparison were significantly enriched with GWAS signals of MS, downregulated DEGs (fold change >3) were significantly enriched with GWAS signals of SW, and fold change 0∼1 up/downregulated DEGs in birth–adult comparison were significantly enriched with GWAS signals of CW, LMW, REA, and BFT. Furthermore, we found that GWAS signals for CW, LMW, and REA were enriched in turquoise module, and GWAS signals for CW was enriched in lightgreen module. Our study provides novel insights into the molecular mechanism underlying rumen development in cattle and highlights an integrative analysis for illustrating the genetic architecture of beef complex traits.

Identification of genetic loci associated with rough dwarf disease resistance in maize by integrating GWAS and linkage mapping

Maize rough dwarf disease (MRDD) is a viral disease that causes substantial yield loss, especially in China's summer planted maize area. Discovery of resistance genes would help in developing high-yielding resistant maize hybrids. Genome-wide association studies (GWASs) have advanced quickly and are now a powerful tool for dissecting complex genetic architectures. In this study, the disease severity index (DSI) of 292 maize inbred lines and an F₆ linkage population were investigated across multiple environments for two years. Using the genotypes obtained from the Maize SNP 50K chip, a GWAS was performed with four analytical models. The results showed that 22 SNPs distributed on chromosomes 1, 3, 4, 6, 7 and 8 were significantly associated with resistance to MRDD (P<0.0001). The SNPs on chromosomes 3, 6 and 8 were consistent with the quantitative trait locus (QTL) regions from linkage mapping in an RIL population. Candidate genes identified by GWAS included an LRR receptor-like serine/threonine-protein kinase (GRMZM2G141288), and a DRE-binding protein (GRMZM2G006745). In addition, we performed an allele variation analysis of the SNP loci selected by GWAS and linkage mapping and found that the main alleles of the two SNP loci PZE_101170408 and PZE_106082685 on chromosome 1 differed in terms of disease-resistant materials and disease-susceptible materials. The identified SNPs and genes provide useful information for MRDD-related gene cloning and insights on the underlying disease resistance mechanisms, and they can be used in marker-assisted breeding to develop MRDD-resistant maize.

Weighted single-step GWAS for body mass index and scans for recent signatures of selection in Yorkshire pigs

Controlling extra fat deposition is economically favorable in modern swine industry. Understanding the genetic architecture of fat deposition traits such as body mass index (BMI) can help in improving genomic selection for such traits. We utilized a weighted single-step genome-wide association study (WssGWAS) to detect genetic regions and candidate genes associated with BMI in a Yorkshire pig population. Three extended haplotype homozygosity (EHH)-related statistics were also incorporated within a de-correlated composite of multiple signals (DCMS) framework to detect recent selection signatures signals. Overall, the full pedigree consisted of 7,016 pigs, of which 5,561 had BMI records and 598 pigs were genotyped with an 80 K single nucleotide polymorphism (SNP) array. Results showed that the most significant windows (top 15) explained 9.35% of BMI genetic variance. Several genes were detected in regions previously associated with pig fat deposition traits and treated as potential candidate genes for BMI in Yorkshire pigs: FTMT, SRFBP1, KHDRBS3, FOXG1, SOD3, LRRC32, TSKU, ACER3, B3GNT6, CCDC201, ADCY1, RAMP3, TBRG4, CCM2. Signature of selection analysis revealed multiple candidate genes previously associated with various economic traits. However, BMI genetic variance explained by regions under selection pressure was minimal (1.31%). In conclusion, candidate genes associated with Yorkshire pigs’ BMI trait were identified using WssGWAS. Gene enrichment analysis indicated that the identified candidate genes were enriched in the insulin secretion pathway. We anticipate that these results further advance our understanding of the genetic architecture of BMI in Yorkshire pigs and provide information for genomic selection for fat deposition in this breed.

Trait-specific Selection Signature Detection Reveals Novel Loci of Meat Quality in Large White Pigs

In past decades, meat quality traits have been shaped by human-driven selection in the process of genetic improvement programs. Exploring the potential genetic basis of artificial selection and mapping functional candidate genes for economic traits are of great significance in genetic improvement of pigs. In this study, we focus on investigating the genetic basis of five meat quality traits, including intramuscular fat content (IMF), drip loss, water binding capacity, pH at 45 min (pH45min), and ultimate pH (pH24h). Through making phenotypic gradient differential population pairs, Wright’s fixation index (F_ST) and the cross-population extended haplotype homozogysity (XPEHH) were applied to detect selection signatures for these five traits. Finally, a total of 427 and 307 trait-specific selection signatures were revealed by F_ST and XPEHH, respectively. Further bioinformatics analysis indicates that some genes, such as USF1, NDUFS2, PIGM, IGSF8, CASQ1, and ACBD6, overlapping with the trait-specific selection signatures are responsible for the phenotypes including fat metabolism and muscle development. Among them, a series of promising trait-specific selection signatures that were detected in the high IMF subpopulation are located in the region of 93544042-95179724bp on SSC4, and the genes harboring in this region are all related to lipids and muscle development. Overall, these candidate genes of meat quality traits identified in this analysis may provide some fundamental information for further exploring the genetic basis of this complex trait.

Investigating the growth performance, meat quality, immune function and proteomic profiles of plasmal exosomes in Lactobacillus plantarum-treated broilers with immunological stress

Exosomes are extracellular membranous nanovesicles that carry functional molecules to mediate cell-to-cell communication. To date, whether probiotics improve the immune function of broilers by plasmal exosome cargo is unclear. In this study, 300 broilers were allocated to three treatments: control diet (CON group), control diet + dexamethasone injection (DEX group), and control diet containing 1 × 10⁸ cfu g^-1 P8 + DEX injection (P8 + DEX group). The growth performance, meat quality and immune function of plasma and jejunal mucosa were detected. Exosomes were isolated from the plasma and characterized. Then, the exosome protein profile was determined by proteomic analysis. Correlation analyses between the exosomal proteins and growth performance, meat quality, immune function were performed. Lastly, the related protein levels were verified by multiple reaction monitoring (MRM). Results showed that P8 treatment increased the growth performance, meat quality and immune function of DEX-induced broilers with immunological stress. Moreover, the average diameters, cup-shaped morphology and expressed exosomal proteins confirmed that the isolated extracellular vesicles were exosomes. A total of 784 proteins were identified in the exosomes; among which, 126 differentially expressed proteins (DEPs) were found between the DEX and CON groups and 102 DEPs were found between the P8 + DEX and DEX groups. Gene ontology analysis indicated that DEPs between the DEX and CON groups are mainly involved in the metabolic process, cellular anatomical entity, cytoplasm, etc. DEPs between the P8 + DEX and DEX groups are mainly involved in the multicellular organismal process, response to stimulus, cytoplasm, etc. Pathway analysis revealed that most of the DEPs between the DEX and CON groups participated in the ECM-receptor interaction, focal adhesion, regulation of actin cytoskeleton, etc. Most of the DEPs between the P8 + DEX and DEX groups participated in the ErbB and PPAR signaling pathways. Moreover, many DEPs were correlated with the altered parameters of growth performance, meat quality and immunity in P8-treated broilers. MRM further revealed that the upregulated FABP6 and EPCAM in the DEX group were decreased by P8 + DEX treatment, and the downregulated C1QTNF3 in the DEX group was increased by P8 + DEX treatment. In conclusion, our findings demonstrated that P8 may promote the immune function, growth performance and meat quality of broilers with immunological stress by regulating the plasma exosomal proteins, especially the proteins of FABP6, EPCAM and C1QTNF3 and the pathway of PPAR (ILK/FABP6).

Genetic Association Analysis for Relative Growths of Body Compositions and Metabolic Traits to Body Weights in Broilers

Simple Summary

The relative growth of body components and metabolic traits relative to body weights are phenotypically characterized using joint allometric scaling models, and random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in broilers. Prior to statistically inferring the QTLs for the allometric scalings, the QTL candidates in RRMs are obtained by rapidly shrinking most of marker genetic effects to zero with the LASSO technique. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is demonstrated by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulate optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant, were detected to control the allometric scalings of fat, shank-w and liver to body weights; while for metabolic traits, total 10 QTLs, were mapped to govern the allometries of T4 and GLC to body weights, among which 6 QTLs were of dominant genetic effect. The detected QTLs or highly linked markers can be used to regulate relative growths for meat quality traits to body weight in marker-assisted breeding of broilers.

Abstract

In animal breeding, body components and metabolic traits always fall behind body weights in genetic improvement, which leads to the decline in standards and qualities of animal products. Phenotypically, the relative growth of multiple body components and metabolic traits relative to body weights are characterized by using joint allometric scaling models, and then random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for relative grwoth allometries of body compositions and metabolic traits in chicken. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is given a demonstration by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulated optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant QTLs, were detected to control the allometric scalings of fat, shank-w, and liver to body weights; while a total of 10 QTLs of which 6 were dominant, were mapped to govern the allometries of T4 and GLC to body weights. We characterized relative growths of body compositions and metabolic traits to body weights in broilers with joint allometric scaling models and detected QTLs for the allometry scalings of the relative growths by using RRMs. The identified QTLs, including their highly linked genetic markers, could be used to order relative growths of the body components or metabolic traits to body weights in marker-assisted breeding programs for improving the standard and quality of broiler meat products.

Whole-genome sequencing association analysis reveals the genetic architecture of meat quality traits in Chinese Qingyu pigs

The Chinese Qingyu pig breed is an invaluable indigenous genetic resource. However, few studies have investigated the genetic architecture of meat quality traits in Qingyu pigs. Here, 30 purebred Qingyu pigs were subjected to whole-genome sequencing. After quality control, 18 436 759 SNPs were retained. Genome-wide association studies (GWAS) were then performed for meat pH and color at three postmortem time points (45 min, 24 h, and 48 h) using single-marker regression analysis. In total, 11 and 69 SNPs were associated with meat pH and color of the longissimus thoracis muscle (LTM), respectively, while 54 and 29 SNPs were associated with meat pH and color of the semimembranosus muscle (SM), respectively. Seven SNPs associated with pork pH were shared by all three postmortem time points. Several candidate genes for meat traits were identified, including four genes (CXXC5, RYR3, BNIP3, and MYCT1) related to skeletal muscle development, regulation of Ca²⁺ release in the muscle, and anaerobic respiration, which are promising candidates for selecting superior meat quality traits in Qingyu pigs. To our knowledge, this is the first study investigating the postmortem genetic architecture of pork pH and color in Qingyu pigs. Our findings further the current understanding of the genetic factors influencing meat quality.

Genome-wide association study of birth weight and pre-weaning body weight of crossbred pigs

In piggery, birth weight and body weight remains most vital economic trait as they directly influence on the production performance of the farm. Implementing the genomic selection would pay way for rapid genetic gain along with increased accuracy than conventional breeding. Prior to genomic selection, genome wide association study (GWAS) has to be conducted in order to find informative SNPs associated with the traits of interest in a given population. Under this study 96 crossbred pigs were genotyped using double digest genotype by sequencing (GBS) technique using Hiseq platform. Raw FASTQ data were processed using dDOCENT Pipeline on Reference based method and variants were called using Free Bayes (version 1.1.0-3). Using Plink (v1.09b), variants having MAF>0.01, HWE<0.001 and genotyping rate >80% were filtered out and 20,467 SNPs were retained after quality control, for ascertaining GWAS in 96 pigs. Before conducting association studies, the data were adjusted for significant nongenetic factors affecting the traits of interest. GWAS was performed using Plink software (v1.9b) identified 9, 11, 12, 23, 28, 24, 30, 33 and 42 SNPs significantly (adjusted P<0.001) associated with birth weight, body weight at weekly interval from 1st week to 8th week, respectively. A large proportion of significant (adjusted P<0.001) SNPs were located on SSC10, SSC6, SSC13, SSC8 and SSC1. One genome wide significant SNP and four genome wide suggestive SNPs were identified. Two common SNPs affecting all body weight at different weeks were located on SSC5:40197442 and SSC13:140562 base pair position. This study helps to identify the genome wide scattered significant SNPs associated with traits of interest which could be used for genomic selection, but further validation studies of these loci in larger population are recommended.