Effects of random and non-random errors on phenotypic selection in autotetraploids

Area of Science:

• Quantitative Genetics
• Population Genetics
• Plant Breeding

Background:

• Phenotypic recurrent selection is a key breeding strategy.
• Errors in phenotypic measurements can arise from random environmental variability or non-random systematic biases.
• Understanding these errors is crucial for accurate genetic gain estimation.

Purpose of the Study:

• To theoretically investigate the impact of random and non-random phenotypic errors on population means and selection response.
• To analyze these effects in an autotetraploid population with a diallelic locus.
• To compare selection response with and without phenotypic errors.

Main Methods:

• A theoretical model was developed for an autotetraploid population at random mating equilibrium.
• Random errors were simulated by uniformly distributing a percentage (α) of individuals across genotype classes.
• Non-random errors modeled as systematic shifts (positive or negative) in genotype classification based on inoculation levels.
• Numerical analysis was performed across various allele frequencies (p) and genic action types.

Main Results:

• Random errors consistently reduced selection response, independent of genic action type.
• Random errors caused upward bias in population means at low allele frequencies (p) and downward bias at high p.
• Non-random errors showed varied effects: negative shifts reduced response (especially for additive gene action), while positive shifts enhanced response for dominant and recessive gene actions.
• Non-random errors significantly biased population means, particularly with positive shifts at low p.

Conclusions:

• Phenotypic errors, both random and non-random, can substantially distort selection response and population means.
• Positive non-random shifts can improve selection efficiency, especially for certain gene actions, while negative shifts are detrimental.
• The interaction between selection pressure, gene action, and genotype classification errors may explain suboptimal responses in some breeding programs.