A dataset on multi-trait selection approach for the evaluation of F1 tomato hybrids along with their parents under hot and humid conditions in Bangladesh

TL;DR

This study evaluates summer tomato hybrids in Bangladesh using a multi-trait selection method to identify high-performing varieties under hot and humid conditions.

Contribution

The novel contribution is the application of the multi-trait genotype-ideotype distance index (MGIDI) to assess tomato hybrids in a specific climate.

Findings

Hybrids AVTOV1002×C41 and AVTOV1010×C41 showed strong performance in key traits like yield and heat tolerance.

High heritability estimates (0.54–0.99) suggest effective selection is feasible for most traits.

Traits like NFPP, YPP, and Yield showed the highest predicted genetic gains.

Abstract

This dataset aims to evaluate the use of multiple trait-based selection methods with multi-trait genotype-ideotype distance index (MGIDI) models to identify superior summer F1 tomato hybrids suitable for the climatic conditions of countries like Bangladesh. The dataset was generated using 14 cross combinations from a Line × Tester mating design, along with seven parental lines and two tester parents of tomatoes with diverse genetic bases and heat tolerance qualities in a randomized complete block (RCB) design. The likelihood ratio (LR) test indicated highly significant genotype effects for most of the analyzed traits. A heatmap of correlation analyses between 16 traits identified a highly significant positive correlation (r > 0.8) between NFrPC and NFPC and between AFW and FW, preliminarily indicating a clear trace of multicollinearity among these traits. The traits NFPP, YPP, and Yield showed the highest predicted genetic gains, indicating their potential for substantial improvement through selection. Additionally, the heritability estimates ranged from 0.54 to 0.99, highlighting high heritability across the traits, which suggests favourable conditions for effective selection strategies. The strengths and weaknesses of hybrids AVTOV1002×C41 and AVTOV1010×C41 were evaluated based on their contributions to MGIDI across four major factors. These hybrids demonstrated strong performance, particularly excelling in traits associated with FA1, FA2, and FA4. The dataset of MGIDI can be universally applied to rank treatments based on desired values of multiple traits, with its potential for rapid expansion in evaluating various types of plant experiments.

Figures