Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

Abstract

Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon (PRKCE) and myosin light chain 2 (MYL2). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improvement (0.006), adding significant iWGS SNPs led to an improvement of reliability by 0.050 when using a one‐component GBLUP, a further increase of 0.033 when using a two‐component GBLUP model. For BayesMix models, compared with using 80 K SNPs alone, adding additional significant iWGS SNPs into one‐ or two‐component BayesMix models led to improvements of reliabilities for IFC by 0.040 and 0.089, respectively. Our results may facilitate further identification of causal genes for IFC and may be beneficial for the improvement of IFC in pig breeding programs.

Construction of Co-expression Network affecting Intramuscular Fat Content and Meat Color Redness Based on Transcriptome Analysis

“…A large number of genes significantly associated with IFC and a* values were identified. However, as these two traits are both of medium to high heritability [5,6], it is hypothesized that large-effect genes regulating them may be existed. Furthermore, due to the significant correlation between these two traits, it is likely that there may also be molecular pathways that regulate them simultaneously.…”

“…Despite DLY pork effectively meeting the 2 quantitative demand, its muscle quality falls short of eliciting satisfaction. Both IFC and a* value are traits with relatively high heritability [5,6] and are the most intuitive indicators of high-quality pork. Consequently, increasing the IFC and a* value through genetic improvement is a major research focal point for pig breeding enterprises.…”

Single-Cell RNA Sequencing Reveals the Cellular Landscape of Longissimus Dorsi in a Newborn Suhuai Pig

“…The Chinese Suhuai pig is a recently developed lean-type breed, comprised of 25% Chinese Huai pig lineage and 75% Large White lineage [20]. However, the cellular composition of LD remains unclear in newborn Suhuai pigs.…”

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

“…Genome‐wide association studies (GWASs) have been performed to identify candidate genes and QTL associated with IMF in different pig breeds. A GWAS of 482 Suhuai pigs found two QTL associated with IMF content, which identified candidate genes PRKCE and MYL2 on SCC3 and SCC14, respectively (Wang et al., 2022). ABLIM3 on SSC2, DPH5 on SSC4 and DOCK10 on SSC15 were found to be related to IMF content in 1518 commercial Duroc × Landrace × Large White crossbreed pigs (Li et al., 2022).…”