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基于三种机器学习算法和加权基因共表达网络分析（WGCNA）鉴定具有不同啄羽倾向的鸡的特征基因

Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA.

作者信息

Wen Jiying, Yang Shenglin, Zhu Jinjin, Liu Ai, Tan Qisong, Rao Yifu

机构信息

Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, Guizhou, China.

出版信息

Front Vet Sci. 2024 Nov 29;11:1508397. doi: 10.3389/fvets.2024.1508397. eCollection 2024.

DOI:10.3389/fvets.2024.1508397
PMID:39679174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11639596/
Abstract

Feather pecking (FP) is a significant welfare concern in poultry, which can result in reduced egg production, deterioration of feather condition, and an increase in mortality rate. This can harm the health of birds and the economic benefits of breeders. FP, as a complex trait, is regulated by multiple factors, and so far, no one has been able to elucidate its exact mechanism. In order to delve deeper into the genetic mechanism of FP, we acquired the expression matrix of dataset GSE36559. We analyzed the gene modules associated with the trait through WGCNA (Weighted correlation network analysis), and then used KEGG and GO to identify the biological pathways enriched by the modules using KEGG and GO. Subsequently, we analyzed the module with the highest correlation (0.99) using three machine learning (ML) algorithms to identify the feature genes that they collectively recognized. In this study, five feature genes, NUFIP2, ST14, OVM, GLULD1, and LOC424943, were identified. Finally, the discriminant value of the feature genes was evaluated by manipulating the receiver operating curve (ROC) in the external dataset GSE10380.

摘要

啄羽行为（FP）是家禽养殖中一个重要的福利问题，它会导致产蛋量下降、羽毛状况变差以及死亡率上升。这会损害禽类健康和养殖者的经济效益。啄羽行为作为一种复杂性状，受多种因素调控，迄今为止，还没有人能够阐明其确切机制。为了更深入地探究啄羽行为的遗传机制，我们获取了数据集GSE36559的表达矩阵。我们通过加权基因共表达网络分析（WGCNA）分析了与该性状相关的基因模块，然后使用京都基因与基因组百科全书（KEGG）和基因本体论（GO）来确定这些模块所富集的生物学通路。随后，我们使用三种机器学习（ML）算法分析了相关性最高（0.99）的模块，以识别它们共同认可的特征基因。在本研究中，确定了五个特征基因，即核仁蛋白相互作用蛋白2（NUFIP2）、信号肽酶复合体亚基14（ST14）、卵巢黏液瘤（OVM）、谷氨酰胺酶1（GLULD1）和LOC424943。最后，通过在外部数据集GSE10380中操作受试者工作特征曲线（ROC）来评估特征基因的判别值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb7b/11639596/25d53b50c553/fvets-11-1508397-g010.jpg
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