MateR: a novel genomic mating framework.

Area of Science:

• Quantitative genetics
• Plant breeding
• Genomics

Background:

• Genomic mating utilizes genome-wide data for cross design, aiming to maximize genetic gain and manage diversity.
• Current prediction methods for expected genetic gain, like the usefulness criterion, face challenges with incompatible parameterizations, especially concerning dominance effects.
• Existing diversity control metrics often lack a clear connection to additive variation loss and long-term genetic gain.

Purpose of the Study:

• To derive consistent equations for family means and variances under different parameterizations, incorporating dominance effects.
• To develop a diversity metric directly linked to additive variation loss and long-term gain.
• To integrate these advances into the MateR software for practical application in breeding programs.

Main Methods:

• Developed locus-specific equations for family means and variances using genotypic frequencies and linkage disequilibrium covariances.
• Created a diversity metric estimating the proportion of additive standard deviation lost.
• Integrated new equations and the diversity metric into the MateR software for simulation and evaluation.

Main Results:

• New equations accurately predicted family means and variances when true QTL effects were known.
• With estimated marker effects, correlations for family mean prediction were 0.55-0.90, and for within-family standard deviation, around 0.25.
• The diversity metric effectively tracked additive standard deviation loss and genic variance loss under selection.

Conclusions:

• The developed framework unifies the prediction of cross usefulness, including dominance effects, and provides interpretable diversity control tied to long-term gain.
• The MateR software, updated with these advances, is applicable to diploid and autopolyploid populations and various breeding schemes.
• This work offers a robust approach for optimizing breeding programs for both immediate gains and sustained genetic improvement.