A Meta-Analysis of Genotype Environment Interaction Based on Growth Traits Across Different Species
Livestock studies, cilt.66, sa.1, ss.41-52, 2026 (TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 66 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.46897/livestockstudies.1916843
- Dergi Adı: Livestock studies
- Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.41-52
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
Genotype × environment (G × E) interaction refers to the phenomenon whereby genotypes express different performance relative to each other under varying environmental conditions. In animal breeding, G × E interaction represents one of the key factors influencing the effectiveness of selection. The aim of this study was to evaluate the impact of G × E interaction on growth traits in pigs, sheep, cattle, and chickens using genetic correlation estimates between environments. For this purpose, a systematic review of the literature was conducted, resulting in a dataset comprising 63 studies and 456 genetic correlation estimates. These data were analyzed using a multilevel meta-analysis approach. The overall pooled genetic correlation estimate for the investigated growth traits was 0.60 (95% CI: 0.57-0.63). The analysis revealed a moderate level of heterogeneity among the studies (I2 = 37.03%). The results indicated that the pooled genetic correlations between environments were statistically significant for each species considered (P <0.001). However, differences among species in terms of pooled correlation estimates were not significant (P = 0.918). Similarly, when environmental groups were evaluated (location, time, and local conditions), the genetic correlations between environments were significant within each environmental category (P <0.001), whereas the differences among environmental groups were not significant (P = 0.606). Overall, the findings of this study indicate that the magnitude of G × E interaction for growth traits across the examined livestock species is moderate when assessed through genetic correlation estimates. The results highlight the importance of accounting for G × E interaction when implementing genetic evaluation and breeding programs in multi-environment production systems.