The Modern Plant Breeding Triangle

FromChromatin, Inc.

SHARE

The text explores the integration of new technologies into traditional plant breeding techniques, emphasizing its significance for advancing food systems. Over the past two decades, numerous advancements have emerged, awaiting incorporation into established breeding programs to enhance efficiency and accuracy. The discussion covers genomic selection, which helps predict breeding values by improving cycle time, selection accuracy, and value precision, as demonstrated in crops like maize and wheat. Moreover, there's a focus on high-throughput phenotype techniques that optimize data processing costs while boosting forecast accuracy. These advancements pave the way for constructing statistical models like Bayesian linear mixed models for effective prediction of genetic values. The text also highlights the synergy of linking genomics and phenomics through advanced statistical approaches, promising improved prediction models. The integration of innovative technologies aims to transition toward a data-driven approach, enhancing the accuracy and efficiency of breeding pipelines through adapted genotypes.

Most popular related searches

breeding program

field phenotyping

crop development

selective breeding

Continued gains in genetic research demonstrate the success of existing breeding programs. Nonetheless, a growing number of technologies have been developed over the last two decades. Today, these technologies are awaiting integration into traditional breeding techniques. This integration has benefits for food systems. However, it comes with the difficulty of making changes to existing and operational systems (Collard et al., 2009).

The utilization of these technologies to give cost-effective and time-saving techniques to plant breeding is described below. We also provide an outline of how these strategies are related.

Predicting breeding values is one of the most popular applications of genomics inbreeding. Genomic selection saves cycle time, improves selection accuracy, and enhances breeding value accuracy. The efficiency of GS in maize has been demonstrated in the case of bi-parental populations. (Vivek et al., 2017)

In wheat, results reveal that using genomic predictions early in the breeding cycle resulted in a significant increase in performance in future generations. (Bonnett et al., 2021)

Breeding programs must begin by generating initial training populations. These reflect the genetic variation observed in existing progeny. They also conform to the testing populations closely. These fundamental parents should be genotyped with high-density marker systems. The parent training sets generate a model for current progenies with high accuracy (Zhang et al., 2017).

The basic purpose of a high-throughput phenotype (HTP) is to lower the cost of data per plot. It also boosts forecast accuracy early in the crop-growing season. The cost of processing HTP data is reduced (Mattias et al., 2020).

Field phenotyping must produce massive, interoperable phenomics datasets. This should be utilized to characterize the core parents in various surroundings. The data, pedigree, and genomic information are utilized to construct Bayesian linear mixed models. These are then used to compute BLUPs of the genetic values of the training set’s material. In multi-trait data, any correlations between characteristics should be exploited to boost prediction accuracy.

The use of non-linear kernels has resulted in increased accuracy in the prediction of novel genotypes. This is done under known conditions. However, it’s primarily used in the prediction of untested environments. This method was expanded to account for a variety of environmental structures at various phases of crop development (Costa-Neto et al., 2020b)

The complexity of connecting big data sets from genetics and phenomics necessitates statistical models that can handle many associated factors. Scientists proposed combining genomics and phenomics using Bayesian functional regression models.

These models consider all the available reflectance bands, the major effects of lines and settings, genetic or pedigree information, and interaction effects (Montesinos-López et al. (2017). Researchers found that models that had wavelength-environment interaction variables were the best for predicting performance in different settings.

Multi-trait and multi-environment data use large-scale correlations between distinct qualities to train reliable GS models. Including GS in this data is a viable way to simplify phenotyping efforts in the field.

Researchers’ findings show that the Bayesian model improves prediction accuracy by capturing the trait and year correlations.

There is a compelling need to apply innovative technology to expedite plant breeding advances. Modern techniques can be integrated into traditional phenotypic breeding programs. Thau can also be used to help redesign existing phenotypic breeding pipelines. This will allow for a gradual shift toward a data-driven approach.

When we choose better-adapted genotypes in a cost-effective manner, we have the potential to bring larger advances in the accuracy and efficiency of breeding pipelines. The interconnectedness of phenomics, genomics, multi-trait, and Environics analyses can be improved based on the available resources and the current program structure.