Genome-wide identification of candidate regions associated with birth weight in Lori-Bakhtiari sheep using Random Forest algorithm

Birth weight is a critical quantitative trait with significant economic implications in livestock production. This study was conducted to identify genetic loci associated with birth weight in a meat-type sheep using a Random Forest (RF) algorithm applied to genomic data. A total of 132 Lori-Bakhtiari sheep were selected based on breeding values (EBVs) for body weight. The animals in the study were genotyped using Illumina Ovine SNP50 Bead Chip. After quality control, a total of 44,796 single-nucleotide polymorphisms (SNPs) and 122 sheep were retained for downstream analyses. R program (version 4.4.0) was used for RF analyses. The top 50 ranked SNPs were detected based on RF method. The results showed that RF is an efficient method for identifying a subset of SNPs with putative functional roles in birth weight. Among 41 candidate genes identified, <i>DOCK4</i>** <b>and</b><b> </b><b><i>LHCGR</i></b> **genes, which were linked with the highest ranked SNPs, are known to influence body weight traits. Gene ontology results revealed additional candidate genes, including members of *SEMA *family (<i>SEMA3D, SEMA3A)</i>, <i>FLT1</i>, <i>MYCBP2</i>, <i>RHOBTB2</i> and <i>PHIP.</i> Gene network analysis showed functional interconnected among these genes, further supporting their potential role as candidate genes influencing birth weight. Notable, most of these genes were not detected in previous genome wide association studies (GWAS), highlighting the RF algorithm’s utility in uncovering novel genetic markers. Overall, our findings provide new insights into the genetic architecture of birth weight in sheep and suggest potential targets for genetic improvement programs in meat-type sheep.