Genetic models to estimate additive and non-additive effects of marker-associated QTL using multiple regression...

Area of Science:

• Quantitative genetics
• Statistical genetics
• Genomics

Background:

• Molecular markers offer potential for selection programs, but quantitative trait loci (QTL) identification remains challenging.
• Accurate detection and estimation of QTL effects are crucial for marker-assisted selection.

Purpose of the Study:

• Develop statistical genetic models for detecting and locating multi-QTL with additive, dominance, and epistatic effects.
• Analyze biases in genetic estimates arising from linked and unlinked QTL.

Main Methods:

• Developed non-linear models for backcross and Fn generations, considering epistasis.
• Transformed non-linear models into approximate multivariate linear models for regression analysis.
• Derived expressions for biases caused by linked and unlinked QTL under different epistasis assumptions.

Main Results:

• Statistical models were developed to detect multi-QTL with additive, dominance, and epistatic effects.
• Generation analysis of marked progenies can increase observation numbers without additional molecular scoring costs.
• Biases in genetic estimates were quantified, with complexity increasing when epistasis was assumed.

Conclusions:

• The developed models provide a framework for identifying complex genetic architectures underlying quantitative traits.
• Understanding QTL biases is essential for accurate genetic parameter estimation in breeding programs.
• Generation analysis offers a cost-effective strategy to enhance QTL mapping studies.