Machine-Learning-Based Genomic Prioritization and Genome-Wide Association Analysis of Growth Traits in Central Anatolian Merino Sheep

Growth traits are major economic drivers in sheep production, yet their genetic architecture is highly polygenic and influenced by multiple genetic and environmental factors. Here, we integrated machine-learning-based genomic prioritization and prediction with conventional mixed-model genome-wide association analysis to investigate growth traits in Central Anatolian Merino sheep. Phenotypic records for birth weight (BW), weaning weight (WW), six-month weight (W6), and average daily gains before (ADWG1) and after weaning (ADWG2) were obtained from 278 lambs genotyped using the Illumina OvineSNP50 BeadChip. Following genotype quality control, 275 animals and 46,902 SNPs were retained for genome-wide analyses. Machine-learning models were evaluated using five-fold outer cross-validation repeated five times. Within each outer training set, SNP preprocessing, feature prioritization, consensus ranking, marker-panel construction, hyperparameter optimization, and model fitting were performed independently. LASSO, Elastic Net, Random Forest, and XGBoost were used for SNP prioritization, and 50-, 100-, and 200-SNP panels were evaluated using Ridge regression, Random Forest, XGBoost, and Support Vector Regression. Conventional mixed-model GWAS identified no SNP reaching genome-wide significance after Bonferroni or FDR correction, although several suggestive and chromosome-wide signals were detected. Exploratory comparison of ML prioritization and GWAS results revealed concordant genomic signals for related traits, particularly ADWG1–WW and ADWG2–W6; these were interpreted as shared genomic signals rather than evidence of pleiotropy. Candidate gene annotation identified biologically relevant positional candidate genes involved in muscle development, skeletal growth, metabolism, and cellular regulation, including PRMT1, TNC, POU1F1, RASD2, ERCC6L2, SSH2, and DYM. Overall, the complementary use of machine-learning-based genomic prioritization and conventional GWAS provided a conservative framework for investigating the genomic architecture of growth traits. The identified genomic signals and positional candidate genes should be considered exploratory and require validation in larger and independent sheep populations before their potential application in genomic selection.