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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Statistical modelling of growth using a mixed model with orthogonal polynomials.
1Department of Animal Genetics, Wrocław University of Environmental and Life Sciences, Kożuchowska 7, 51-631, Wrocław, Poland. tomasz.suchocki@up.wroc.pl
Journal of Applied Genetics
|November 27, 2010
Summary
Investigating gene effects over time using a longitudinal approach reveals how single-nucleotide polymorphisms (SNPs) influence traits. Pre-selecting significant SNPs improves the prediction of future breeding values.
Area of Science:
- Quantitative genetics
- Statistical genomics
- Bioinformatics
Background:
- Traditional statistical models often assume time-independent effects of single-nucleotide polymorphisms (SNPs).
- Traits recorded over time present an opportunity to study the dynamic behavior of gene effects.
- Understanding time-dependent genetic effects is crucial for accurate trait prediction and breeding strategies.
Purpose of the Study:
- To model genetic effects as time-dependent for traits recorded repeatedly.
- To investigate the contribution of individual SNPs to trait variance over time.
- To assess the impact of SNP pre-selection on the prediction of future breeding values.
Main Methods:
- Utilized simulated data from the 13th QTL-MAS Workshop.
- Fitted a mixed model incorporating third-order Legendre orthogonal polynomials to account for correlated measurements.
- Employed the expectation-maximisation (EM) algorithm for maximum likelihood estimation.
- Applied likelihood ratio tests with multiple testing correction to identify significant SNPs.
- Calculated the percentage of total variance contributed by significant SNPs.
- Developed a model for predicting future yields using selected SNPs.
Main Results:
- Successfully modeled time-dependent genetic effects for SNPs.
- Identified 179 significant SNPs out of 453, covering 16 quantitative trait loci (QTL).
- Achieved a correlation of 0.84 between predicted and true breeding values using selected SNPs, compared to 0.73 with all SNPs.
- Demonstrated that pre-selection of SNPs significantly enhances prediction accuracy.
Conclusions:
- A longitudinal approach effectively estimates time-varying SNP variance contributions.
- Pre-selection of significant SNPs is critical for optimizing prediction models.
- This methodology advances the understanding of genetic architecture in longitudinal studies.
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