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Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

Trihybrid Crosses

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

Hardy-Weinberg Principle

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

Monohybrid Crosses

Monohybrid Crosses

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Dynamic Programming for Resource Allocation in Multi-Allelic Trait Introgression.

Ye Han¹, John N Cameron², Lizhi Wang¹

¹Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States.

Frontiers in Plant Science

|July 5, 2021

Summary

This summary is machine-generated.

Plant breeders can optimize trait introgression using a Markov Decision Process (MDP) model. This dynamic resource allocation strategy improves efficiency by adapting to genetic outcomes, outperforming static methods.

Keywords:

Markov decision processes dynamic programming multi-allelic trait introgression plant breeding resource allocation

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Area of Science:

Plant breeding
Genetics
Agricultural engineering

Background:

Trait introgression is crucial for introducing desirable genetic alleles between plant varieties or species.
Key decisions in trait introgression include parental selection and resource allocation, which are complex and uncertain.
Current methods often rely on static resource allocation strategies.

Purpose of the Study:

To optimize the resource allocation procedure in trait introgression using an engineering approach.
To propose and evaluate a Markov Decision Process (MDP) model for dynamic resource allocation.
To compare the efficiency of the MDP model against static resource allocation strategies.

Main Methods:

Formulating the trait introgression problem as an engineering process.
Developing a Markov Decision Process (MDP) model to guide resource allocation.
Simulating and comparing the MDP model with static resource allocation strategies.

Main Results:

The MDP model demonstrated significantly improved efficiency in trait introgression compared to static strategies.
Trade-offs between budget, deadline, and probability of success were clearly illustrated.
Dynamic resource allocation, guided by the MDP model, adapts to genetic outcomes of previous generations.

Conclusions:

Dynamic resource allocation using MDP models offers a more efficient approach to trait introgression.
The MDP model provides a framework for optimizing resource allocation based on real-time genetic feedback.
This approach enhances the success rate and efficiency of plant breeding programs.