Soybean Variety A1026708 Genomic Selection Breeding

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Solution Overview

Problem

Current soybean breeding methods face challenges in developing stable, high-yielding soybean varieties that combine desirable traits such as disease resistance, drought tolerance, and improved nutritional quality, due to the unpredictability of genetic combinations and the need for extensive research and time in identifying superior genetic lines.

Innovation Solution

The development of the soybean variety A1026708, which is adapted for early growing regions and possesses specific traits like resistance to diseases, insects, and herbicides, achieved through a breeding program involving crossing, selection, and genetic transformation to introduce desired loci, allowing for the production of seeds and plants with consistent agronomic characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional soybean breeding methods are used to develop new varieties with desirable traits, then genetic diversity and adaptability are improved, but the breeding process requires extensive time and resources with unpredictable outcomes

Engineering Contradiction:
ImproveadaptabilityVSAvoidbreeding time
Core Design Contradiction:
Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-establishing a genomic selection index and training a predictive model before the actual breeding process. Historical data from evaluated germplasm is used to train a random forest model that predicts breeding values, allowing breeders to make informed selections early in the process without waiting for extensive phenotypic evaluation.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical breeding methods (manual crossing, phenotypic selection) with a genomic selection system. DNA markers and genomic data are used to predict trait values, substituting the time-consuming mechanical process of evaluating and selecting individual plants with a computational genomic approach that accelerates the breeding cycle.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional breeding programs are implemented to combine desirable traits, then genetic improvement is achieved, but the complexity of the breeding program increases

Engineering Contradiction:
Improvebreeding reliabilityVSAvoidbreeding program complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary genomic selection index as a mediator between the breeding goals and the actual selection process. This index, derived from multiple traits and weighted by their economic importance, simplifies the complex decision-making process by providing a single composite score that ranks germplasm candidates, reducing the complexity of managing multiple selection criteria.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent creates a universal genomic selection model that can evaluate multiple traits (yield, disease resistance, quality characteristics) simultaneously using a single predictive framework. The random forest model serves multiple functions: predicting breeding values, ranking germplasm, and guiding selection decisions, thereby reducing the need for separate evaluation systems for each trait.

Inventive Principle:
Principle #6Universality (Multi-functionality)

3Measurement precision

If extensive germplasm evaluation is conducted to identify superior lines, then selection accuracy is improved, but the research resources and time required increase

Engineering Contradiction:
Improveselection accuracyVSAvoidresearch resources
Core Design Contradiction:
Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent creates a computational copy of the breeding process through a predictive model trained on historical data. Instead of physically evaluating every possible germplasm combination, the model generates virtual predictions of breeding values, allowing researchers to screen large numbers of candidates in silico before physical evaluation, thereby reducing the quantity of resources needed for actual field testing.

Inventive Principle:
Principle #26Copying

Solution Approach 2:

The patent changes the parameters of evaluation from direct phenotypic measurement to genomic prediction. By using DNA markers and genomic data as proxies for trait values, the system achieves selection accuracy without requiring extensive physical evaluation resources. The model predicts breeding values based on genomic parameters, eliminating the need for costly and time-consuming field trials for each candidate.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS8669434B2Soybean variety A1026708
Publication Date: 2014.03.11 MONSANTO TECHNOLOGY LLC

AI summary

The invention relates to the soybean variety designated A1026708. Provided by the invention are the seeds, plants and derivatives of the soybean variety A1026708. Also provided by the invention are tissue cultures of the soybean variety A1026708 and the plants regenerated therefrom. Still further provided by the invention are methods for producing soybean plants by crossing the soybean variety A1026708 with itself or another soybean variety and plants produced by such methods.