Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.

Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process.

The functions of noncoding variants associated with complex traits in livestock remain poorly understood. In this study, we investigated two candidate noncoding variants within the XKR4-CHCHD7 locus identified from our previous analysis. Dual-luciferase reporter assays demonstrated allele-specific regulatory activity of these two regions in bovine muscle satellite cells (BMSCs), 293T cells, and C2C12 cells. Endogenous deletion of the candidate regions using a clustered regularly interspaced short palindromic repeats (CRISPR)-based high-fidelity Cas12Max (hfCas12Max) system revealed that the region containing chr14:22840845 (SNP-0845) exerted broader effects on BMSC function including reduced proliferation and migration, altered cell-cycle progression, and enhanced myogenic differentiation. Expression screening of candidate effector genes further identified PENK and TMEM68 as downstream candidate genes for SNP-0845. Rescue experiments further showed that PENK exerted stronger recovery effects than TMEM68 on the proliferation and migration defects caused by deletion of this region, supporting PENK as a major candidate effector downstream of the SNP-0845. Functional assays showed that PENK knockdown impaired BMSC proliferation and migration while promoting myogenic differentiation, whereas PENK overexpression partially reversed these effects. In vivo Penk knockdown reduced quadriceps femoris weight and altered muscle fiber composition in mice. Collectively, our findings suggest that a noncoding regulatory region modulates BMSC fate and muscle growth-related processes through PENK.