Heterotic Pooling Strategy for Enhanced Hybrid Performance in Bitter Gourd (Momordica charantia L.)

Abstract

Bitter gourd (Momordica charantia L.), a crop of significant nutritional and medicinal renown, is experiencing a surge in global demand, particularly in Asia. To meet this demand amidst the challenge of limited agricultural land, enhancing productivity through innovative breeding is essential. Hybrid breeding, which harnesses the power of heterosis, offers a proven strategy for boosting crop yields. However, the success of any heterosis breeding program, especially in a naturally cross-pollinated and genetically variable crop like bitter gourd, is fundamentally dependent on robust methods for assessing and strategically utilizing the available genetic diversity. This study compared the efficacy of traditional morphological trait-based clustering with a modern breeding value-based method (Heterotic grouping based on Specific and General Combining Ability - HSGCA) for predicting heterosis. Eleven diverse bitter gourd inbred lines were crossed in a half-diallel fashion to generate 55 F1 hybrids. The hybrids and parents were evaluated for key yield-related traits. K-means clustering based on morphological data resulted in five groups but proved unreliable for predicting heterosis, as high heterosis was often observed within clusters. In contrast, HSGCA-based clustering organized the lines into three coherent groups, where crosses between the two main groups (Group 1 and Group 2) consistently exhibited significantly higher mid-parent (20.89%) and better-parent (7.5%) heterosis compared to intra-group crosses. The HSGCA method provides a far more reliable framework for defining heterotic groups in bitter gourd, enabling more efficient and predictive hybrid breeding programs.