Identification of genes affecting growth traits in broiler chickens using machine learning methods

Document Type : Research Paper


1 Department of Animal Science, Faculty of Agriculture,, Tarbiat Modares University, Tehran, Iran.

2 Department of Animal Science, Agricultural Faculty, Tarbiat Modares University, Tehran, Iran

3 Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

4 Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia.


Knowledge about the association between single nucleotide polymorphisms (SNPs) and important economic traits is one of the crucial tools in breeding programs within the poultry industry. Genome-wide studies for discovering SNP variations related to these traits are often conducted using simple linear models. However, due to certain assumptions of these models, some SNP markers may not be identified. This study aimed to evaluate the performance of random forest and gradient boosting methods compared to linear models in identifying SNP markers associated with body weight traits at 6 and 9 weeks of age in F2 broiler chickens resulting from crosses between the commercial Arian line and native Urmia birds. The results showed that the machine learning approaches were able to identify important markers, such as GGaluGA308573, GGaluGA255033, Gga_rs13614212, Gga_rs13743072, GGaluGA258772, Gga_rs14034395, and Gga_rs13858398, associated with body weight traits, which were related to genes MAP2, ACSL1, CAMSAP2, FAM117B, SLC4A4, TIMP4, and LncRNA, respectively. These genes are primarily involved in cellular division, growth control, regulation of cellular skeleton structure and microtubules, and transcription activity, constituting the most important biological processes. The identification of these novel genes using machine learning methods, which were not detected by linear models and previous studies in this population, could provide new insights into genetic control of growth traits in broiler chickens. Moreover, the discovered significant markers can be utilized in genetic improvement programs for birds.


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 07 April 2024
  • Receive Date: 09 September 2023
  • Revise Date: 21 November 2023
  • Accept Date: 24 November 2023