Work

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

Year: 2024

Type: article

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.

Source: Frontiers in Veterinary Science

DOI: 10.3389/fvets.2024.1508397

Authors: Jiying Wen, shenglin yang, Jinjin Zhu, Ai Liu, Qisong Tan, Yifu Rao

Institution: Guizhou University

Language: English

Topic: Animal Behavior and Welfare Studies

Subfield: Small Animals

Field: Veterinary

Domain: Health Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,295