Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian...

Area of Science:

• Quantitative genetics
• Statistical genomics
• Population genetics

Background:

• Understanding the genetic basis of dynamic traits is crucial for breeding and evolutionary studies.
• Existing quantitative trait locus (QTL) mapping methods often assume trait independence over time or require complex covariance structures.

Purpose of the Study:

• To present a novel hierarchical QTL mapping method for dynamic traits using individual functional growth curves.
• To develop a flexible framework that accommodates time-independent QTL and environmental effects.

Main Methods:

• A multiple-QTL model within a multitrait framework is employed, utilizing polynomial growth functions.
• Each individual's unique growth curve is modeled, with QTLs influencing growth curve parameters.
• A modified Bayesian adaptive shrinkage technique is used for trait-associated locus selection.

Main Results:

• The method allows for the estimation of heritabilities and genetic correlations for individual growth curve parameters (latent traits).
• It simplifies modeling by assuming time-independent QTL and environmental effects, avoiding complex residual covariance structures.
• Demonstrated applicability on simulated and real Scots pine (Pinus sylvestris) data for height growth.

Conclusions:

• The proposed hierarchical QTL mapping method offers a flexible and powerful approach for analyzing dynamic traits.
• It provides insights into the genetic architecture of growth patterns and individual variation.
• This method can be extended to various dynamic traits and populations in quantitative genetics research.