Genome-wide identification of candidate regions associated with growth traits in Lori-Bakhtiari sheep using the Random Forest algorithm

Articles in Press

Document Type : Research Article (Regular Paper)

Authors

1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

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

Abstract

Nowadays, with the development of arrays containing single-nucleotide polymorphism (SNPs) information for most domestic animal species, genome-wide association studies (GWAS) are some important methods for identifying candidate genomic regions associated with production traits. Therefore, the use of modern approaches, including machine learning methods, especially nonlinear algorithms such as Random Forest (RF), are essential, because these methods are able to reveal complex patterns in genomic data that are often hidden from the view of traditional statistical models. Therefore, this study aimed to identify genes and SNPs affecting body weight traits in Lori-Bakhtiari sheep using RF algorithms. A total of 132 samples of Lori-Bakhtiari sheep were genotyped using Illumina Ovine SNP 50 Bead Chip, containing 51,135 SNP markers. Plink (v 9.1) was used for quality control, where a total of 44,796 SNPs and 122 sheep were retained for downstream analyses. The RF algorithm was used to find significant SNPs associated with weaning weight and 180 days of age’ weight, using the RF package in R (v 4.4.0). The top 50-ranked SNPs were detected based on the RF method. The results indicated 59 candidate genes for weaning weight and 58 candidate genes for weight at 180 days of age. Among the identified candidate genes for weaning weight, the genes including ABL1, INPPL1, and EXOC7, and also for the genes revealed for 180 days of age, ROS1, HCK, and ZFAND4 genes have been identified to be hub genes associated with body weight traits in the current study. Network analysis revealed functional connections among these genes, reinforcing their candidacy for body weight regulation. Notably, the most genes and networks identified in the current study are novel and have not been previously identified by GWAS, underscoring the RF algorithm's effectiveness in discovering novel genetic markers. In conclusion, these findings offer new perspectives on the genetic basis of body weight in sheep and propose targets for enhancing genetic selection in meat-producing breeds.

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References

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Journal of Livestock Science and Technologies

Articles in Press, Accepted Manuscript
Available Online from 17 July 2026

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