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

文献类型： 外文期刊

第一作者： Shen, Yang

作者： Shen, Yang;Liu, Bang;Zhou, Xiang;Chen, Yuxi;Zhang, Shufeng;Wu, Ze;Lu, Xiaoyu;Liu, Weizhen;Liu, Bang;Zhou, Xiang;Zhou, Xiang;Zhou, Xiang;Zhou, Xiang

作者机构：

关键词： deep learning; GWAS; image analysis; intramuscular fat traits; pig; smartphone application

期刊名称：ANIMAL GENETICS （ 影响因子：2.4； 五年影响因子：2.8 ）

ISSN： 0268-9146

年卷期： 2024 年 55 卷 2 期

页码：

收录情况： SCI

摘要： Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time-consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image-based IMF traits through embedded deep-learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White x Tongcheng pigs. Genome-wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user-friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.

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