Genome-wide association analysis identifies quantitative trait loci for growth in a Landrace purebred population

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.

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

Changes in the accuracy of the genomic estimates obtained by the ssGBLUP and wssGBLUP methods were evaluated using different reference groups. The weighting procedure’s reasonableness of application Pwas considered to improve the accuracy of genomic predictions for meat, fattening and reproduction traits in pigs. Six reference groups were formed to assess the genomic data quantity impact on the accuracy of predicted values (groups of genotyped animals). The datasets included 62,927 records of meat and fattening productivity (fat thickness over 6–7 ribs (BF1, mm)), muscle depth (MD, mm) and precocity up to 100 kg (age, days) and 16,070 observations of reproductive qualities (the number of all born piglets (TNB) and the number of live-born piglets (NBA), according to the results of the first farrowing). The wssGBLUP method has an advantage over ssGBLUP in terms of estimation reliability. When using a small reference group, the difference in the accuracy of ssGBLUP over BLUP AM is from −1.9 to +7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the wssGBLUP is also maintained for the largest group of genotyped animals: from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. However, for all analyzed traits, the number of markers explaining 5% of genetic variability varied from 71 to 108, and the number of such SNPs varied depending on the size of the reference group (79–88 for BF1, 72–81 for MD, 71–108 for age). The results of the genetic variation distribution have the greatest similarity between groups of about 1000 and about 1500 individuals. Thus, the size of the reference group of more than 1000 individuals gives more stable results for the estimation based on the wssGBLUP method, while using the reference group of 500 individuals can lead to distorted results of GEBV.

Genome-wide association studies for growth-related traits in a crossbreed pig population

Growth-related traits are important economic traits in the pig industry that directly influence pork production efficiency. To detect quantitative trait loci and candidate genes affecting growth traits, genome-wide association studies were performed for backfat thickness (BF) and loin muscle depth (LMD) in 370 Chuying-black pigs using Illumina PorcineSNP50 BeadChip array. We totally identified 14 BF-associated SNPs, which included 11 genome-wide SNPs (P < 1.39E-06) and 3 chromosome-wide suggestive SNPs (P < 2.79E-05) and for LMD, 9 SNPs surpassed the genome-wide significant threshold (P < 1.39E-06). These SNPs explained 30.33 and 27.51% phenotypic variance for BF and LMD respectively. Furthermore, 14 and 9 genes nearest to the significant SNPs were selected to be candidate genes, including MAGED1, GPHN, CCSER1, and GUCY2D for BF and PARM1, COL18A1, HSF5, and SCML2 genes for LMD. One significant SNP, which explained 6.07% of phenotypic variance for BF, mapped to a pleiotropic quantitative trait locus with a 494-kb interval. Together, the SNPs and candidate genes identified in this study will advance our understanding of the complex genetic architecture of BF and LMD traits, and they will also provide important clues for future implementation of a genomic selection program in Chuying-black pigs.

Identification of Whole-Genome Significant Single Nucleotide Polymorphisms in Candidate Genes Associated With Serum Biochemical Traits in Chinese Holstein Cattle

A genome-wide association study (GWAS) was conducted on 23 serum biochemical traits in Chinese Holstein cattle. The experimental population consisted of 399 cattle, each genotyped by a commercial bovine 50K SNP chip, which had 49,663 SNPs. After data cleaning, 41,092 SNPs from 361 Holstein cattle were retained for GWAS. The phenotypes were measured values of serum measurements of these animals that were taken at 11 days after parturition. Two statistical models, a fixed-effect linear regression model (FLM) and a mixed-effect linear model (MLM), were used to estimate the association effects of SNPs. Genome-wide significant and suggestive thresholds were set up to be 1.22E-06 and 2.43E-06, respectively. In the Chinese Holstein population, FLM identified 81 genome-wide significant (0.05/41,092 = 1.22E-06) SNPs associated with 11 serum traits. Among these SNPs, five SNPs (BovineHD0100005950, ARS-BFGL-NGS-115158, BovineHD1500021175, BovineHD0800028900, and BTB-00442438) were also identified by the MLM to have genome-wide suggestive effects on CHE, DBIL, and LDL. Both statistical models pinpointed two SNPs that had significant effects on the Holstein population. The SNP BovineHD0800028900 (located near the gene LOC101903458 on chromosome 8) was identified to be significantly associated with serum high- and low-density lipoprotein (HDL and LDL), whereas BovineHD1500021175 (located in 73.4Mb on chromosome 15) was an SNP significantly associated with total bilirubin and direct bilirubin (TBIL and DBIL). Further analyses are needed to identify the causal mutations affecting serum traits and to investigate the correlation of effects for loci associated with fatty liver disease in dairy cattle.

Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations

Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes (GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.

Genome-wide association study for backfat thickness at 100 kg and loin muscle thickness in domestic pigs based on genotyping by sequencing

Both backfat thickness at 100 kg (B100) and loin muscle thickness (LMT) are economically important traits in pigs. In this study, a total of 1,200 pigs (600 Landrace and 600 Yorkshire pigs) were examined with genotyping by sequencing. A total of 345,570 single nucleotide polymorphisms (SNPs) were obtained from 1,200 pigs. Then, a single marker regression test was used to conduct a genome-wide association study for B100 and LMT. A total of 8 and 90 significant SNPs were detected for LMT and B100, respectively. Interestingly, two shared significant loci [located at Sus scrofa chromosome (SSC) 6: 149876694 and SSC12: 46226580] were detected in two breeds for B100. Furthermore, three potential candidate genes were found for LMT and B100. The positional candidate gene FAM3C (SSC18: 25573656, P = 2.48 × 10^-9), which controls the survival, growth, and differentiation of tissues and cells, was found for LMT in Landrace pigs. At SSC9: 6.78-6.82 Mb in Landrace pigs, the positional candidate gene, INPPL1, which has a negative regulatory effect on diet-induced obesity and is involved in the regulation of insulin function, was found for B100. The candidate gene, RAB35, which regulates the adipocyte glucose transporter SLC2A4/GLUT4, was identified at approximately SSC14: 40.09-40.13 Mb in Yorkshire pigs. The results of this GWAS will greatly advance our understanding of the genetic architecture of the LMT and B100 traits. However, these identified loci and genes need to be further verified in more pig populations, and their functions also need to be validated by more biological experiments in pigs.

A genome scan for selection signatures in Taihu pig breeds using next-generation sequencing

Taihu pig breeds are the most prolific breeds of swine in the world, and they also have superior economic traits, including high resistance to disease, superior meat quality, high resistance to crude feed and a docile temperament. The formation of these phenotypic characteristics is largely a result of long-term artificial or natural selection. Therefore, exploring selection signatures in the genomes of the Taihu pigs will help us to identify porcine genes related to productivity traits, disease and behaviour. In this study, we used both intra-population (Relative Extend Haplotype Homozygosity Test (REHH)) and inter-population (the Cross-Population Extend Haplotype Homozygosity Test (XPEHH); F-STATISTICS, F ST ) methods to detect genomic regions that might be under selection process in Taihu pig breeds. As a result, we found 282 (REHH) and 112 (XPEHH) selection signature candidate regions corresponding to 159.78 Mb (6.15%) and 62.29 Mb (2.40%) genomic regions, respectively. Further investigations of the selection candidate regions revealed that many genes under these genomic regions were related to reproductive traits (such as the TLR9 gene), coat colour (such as the KIT gene) and fat metabolism (such as the CPT1A and MAML3 genes). Furthermore, gene enrichment analyses showed that genes under the selection candidate regions were significantly over-represented in pathways related to diseases, such as autoimmune thyroid and asthma diseases. In conclusion, several candidate genes potentially under positive selection were involved in characteristics of Taihu pig. These results will further allow us to better understand the mechanisms of selection in pig breeding.

A genome-wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle

A genome-wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)-BovineLD V3 SNP chip, which had 26 151 public SNPs in its manifest file. After data cleaning, 20 326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3 years. Two statistical models, a fixed-effect linear regression model and a mixed-effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome-wide significant and suggestive thresholds were set at 2.46E-06 and 4.95E-05 respectively. The two statistical models concurrently identified two genome-wide significant (P < 0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies.

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

Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a meta-analysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining -scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [] and calpastatin []), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit []). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial []) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [] and ferritin, light polypeptide []). Thus, implementation of MA-GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.

Genome Wide Association Analysis Reveals New Production Trait Genes in a Male Duroc Population

In this study, 796 male Duroc pigs were used to identify genomic regions controlling growth traits. Three production traits were studied: food conversion ratio, days to 100 KG, and average daily gain, using a panel of 39,436 single nucleotide polymorphisms. In total, we detected 11 genome-wide and 162 chromosome-wide single nucleotide polymorphism trait associations. The Gene ontology analysis identified 14 candidate genes close to significant single nucleotide polymorphisms, with growth-related functions: six for days to 100 KG (WT1, FBXO3, DOCK7, PPP3CA, AGPAT9, and NKX6-1), seven for food conversion ratio (MAP2, TBX15, IVL, ARL15, CPS1, VWC2L, and VAV3), and one for average daily gain (COL27A1). Gene ontology analysis indicated that most of the candidate genes are involved in muscle, fat, bone or nervous system development, nutrient absorption, and metabolism, which are all either directly or indirectly related to growth traits in pigs. Additionally, we found four haplotype blocks composed of suggestive single nucleotide polymorphisms located in the growth trait-related quantitative trait loci and further narrowed down the ranges, the largest of which decreased by ~60 Mb. Hence, our results could be used to improve pig production traits by increasing the frequency of favorable alleles via artificial selection.