Computational Models Growth & Development

Evaluating yield components leads to yield improvement

Yield dissection models in tomato identify promising yield components and corresponding QTLs.

Improving crop yield is necessary to feed a growing population. However, breeding crops with improved yield is difficult: yield is not only controlled by genotype, but also by management, environment, and their interactions.

A new study published in in silico Plants by Yutaka Tsutsumi-Morita and colleagues from Wageningen University and Research collaborating with industry approached this dilemma by breaking yield down so they can build them back up.

The team disentangled the complexity of tomato yield by breaking it down, or dissecting it, into physiologically simpler components less controlled by management or environment. To do this, they used two different dissections methods: one based on harvest, and another one based on biomass production (see image).

They then determined the genetic factors, or quantitative trait loci (QTL) that control the components. QTL analyses were performed on a recombinant inbred line (RIL) population, which were combined to produce hybrids that were grown and phenotyped for the traits in the yield dissection models. QTLs for yield and its components were identified using mixed models.

Considering component traits rather than total yield panned out: trade-offs were observed between the component traits in both dissections. For example, some chromosomes contained QTLs that increased the number of fruits but decreased individual fruit fresh weight.

“Despite these trade-offs, most yield QTLs were colocalized (near each other on a chromosome and therefore inherited and possibly expressed together) with component QTLs, offering options for the construction of high yielding genotypes”, according to PhD candidate Tsutsumi-Morita.

Multi-QTL models were used to associate yield and yield component traits with the QTLs that control them. The accuracy of predicting yield from the component QTLs ranged from 0.56 to 0.63, depending on yield dissection method. The model indicated that genetic improvements in yield will be possible by increasing the number of fruits and the total fruit dry weight to offset losses in fruit fresh weight and fruit fresh-dry weight ratio.

Harvest and biomass yield dissection models may serve as useful tools for yield improvement in tomato by either or both of combining individual component QTLs and multi-QTL component predictions.

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