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

Abstract

Summary Intramuscular fat (IMF) content is a critical indicator of pork quality that affects directly the purchasing desire of consumers. However, to measure IMF content is both laborious and costly, preventing our understanding of its genetic determinants and improvement. In the present study, we constructed an accurate and fast image acquisition and analysis system, to extract and calculate the digital IMF content, the proportion of fat areas in the image (PFAI) of the longissimus muscle of 1709 animals from multiple pig populations. PFAI was highly significantly correlated with marbling scores (MS; 0.95, r2 = 0.90), and also with IMF contents chemically defined for 80 samples (0.79, r2 = 0.63; more accurate than direct analysis between IMF contents and MS). The processing time for one image is only 2.31 s. Genome‐wide association analysis on PFAI for all 1709 animals identified 14 suggestive significant SNPs and 1 genome‐wide significant SNP. On MS, we identified nine suggestive significant SNPs, and seven of them were also identified in PFAI. Furthermore, the significance (−log P) values of the seven common SNPs are higher in PFAI than in MS. Novel candidate genes of biological importance for IMF content were also discovered. Our imaging systems developed for prediction of digital IMF content is closer to IMF measured by Soxhlet extraction and slightly more accurate than MS. It can achieve fast and high‐throughput IMF phenotype, which can be used in improvement of pork quality.

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

“…In order to genetically enhance the IMF content in pigs, it is essential to develop precise methodologies for accurately measuring IMF contents. Techniques based on physical signals including infrared spectroscopy, hyperspectral imaging and the CVS have been widely used in the detection of IMF content in different species (Hoving‐Bolink et al., 2005; Qiao et al., 2007; Xie et al., 2021). Among them, the CVS present a cost‐effective and flexible approach for on‐site phenotype measurement of IMF content in pigs (Xie et al., 2021).…”

“…Techniques based on physical signals including infrared spectroscopy, hyperspectral imaging and the CVS have been widely used in the detection of IMF content in different species (Hoving‐Bolink et al., 2005; Qiao et al., 2007; Xie et al., 2021). Among them, the CVS present a cost‐effective and flexible approach for on‐site phenotype measurement of IMF content in pigs (Xie et al., 2021). Therefore, the Pork IMF App developed in this study integrates the CVS into a small mobile device, making IMF trait measurement more convenient and efficient.…”

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

“…Samples of longissimus dorsi muscle at the 3rd/4th last rib were collected [ 73 ], with a portion of the samples stored at −20 °C for IMF determination and the rest frozen in liquid nitrogen until RNA isolation. The IMF was measured as a percentage using the Soxhlet extraction method [ 74 ]. The average IMF values were 7.18% (SD = ±0.013) and 1.64% (SD = ±0.006) in the high and low live backfat thickness groups, respectively.…”

Copy Number Variations Contribute to Intramuscular Fat Content Differences by Affecting the Expression of PELP1 Alternative Splices in Pigs

“…As stated in the PigQTLdb ( , accessed on 23 August 2021) a total of 842 IMF-related QTLs have been reported [ 6 ]. Although thousands of single-nucleotide polymorphisms (SNPs) have been reported to be associated with IMF in genome-wide association studies (GWAS), most of them only explain a small part of the total genetic variance [ 4 , 7 , 8 ].…”