Unraveling additive from nonadditive effects using genomic relationship matrices

Area of Science:

• Quantitative genetics
• Plant breeding
• Forest genetics

Background:

• Additive models are common in quantitative genetics but may not fully capture genetic variance.
• Pedigree-based models (P-BLUP) struggle to partition genetic variance with typical family structures.
• Molecular markers enable new approaches for estimating genetic variance and predicting breeding values.

Purpose of the Study:

• To compare pedigree-based and marker-based models for partitioning genetic variance.
• To evaluate additive, dominance, and epistatic effects in tree height in Pinus taeda.
• To assess the impact of different relationship matrices on breeding value prediction.

Main Methods:

• Utilized quantitative genetics principles and genomic best linear unbiased prediction (G-BLUP).
• Employed pedigree-based (P-BLUP) and marker-based (G-BLUP) models.
• Analyzed height data from a Pinus taeda population, incorporating additive, dominance, and epistatic interactions.

Main Results:

• Marker-based realized genomic relationships provided more precise separation of genetic variance components than pedigree information.
• Models incorporating additive and nonadditive effects using marker-based relationships improved breeding value prediction.
• Additive and nonadditive genetic variances for tree height were found to be of similar magnitude in this population.

Conclusions:

• Marker-based relationship matrices enhance the accuracy of genetic variance partitioning and breeding value prediction.
• The similar magnitude of additive and nonadditive genetic variances for tree height offers novel insights into its genetic architecture.
• These findings have implications for optimizing breeding strategies in forest tree species.