Related Experiment Video
Updated: Apr 20, 2026
07:01
Processing the Loblolly Pine PtGen2 cDNA Microarray
Published on: March 20, 2009
7.9K
Genomic selection accuracies within and between environments and small breeding groups in white spruce
Jean Beaulieu1, Trevor K Doerksen, John MacKay
1Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, 1055 du P,E,P,S, Stn, Sainte-Foy, P,O, Box 10380, Quebec City, QC G1V 4C7, Canada. Jean.Beaulieu@NRCan.gc.ca.
BMC Genomics
|December 3, 2014
Summary
Genomic selection in white spruce shows high prediction accuracy for wood traits within breeding populations, but requires careful consideration of relatedness and environments for growth traits. Building models within populations is recommended for reliable genomic prediction.
Area of Science:
- Forest genetics
- Quantitative genetics
- Tree breeding
Background:
- Genomic selection (GS) offers potential advantages over conventional pedigree-based selection in tree species.
- Investigating GS effectiveness requires understanding marker information versus pedigree data in recently domesticated species.
- White spruce (Picea glauca) serves as a model for evaluating GS in tree breeding.
Purpose of the Study:
- To determine genomic prediction accuracies for growth and wood traits in white spruce.
- To compare GS performance within and between environments and breeding groups (BG).
- To assess the impact of marker density and relatedness on prediction accuracy.
Main Methods:
- Utilized 1748 trees and 6932 single nucleotide polymorphisms (SNPs) for genomic prediction.
- Employed ridge regression (RR) and least absolute shrinkage and selection operator (LASSO) models.
- Conducted cross-validation (CV) within and between environments and BGs, with and without relatedness.
Main Results:
- RR and LASSO models achieved prediction accuracies comparable to pedigree-based models.
- High prediction accuracies (r = 0.71-0.79 for wood, r = 0.52-0.69 for growth) were observed within environments and BGs with strong relatedness.
- Prediction accuracies decreased significantly when predicting into untested environments or between unrelated BGs.
- Marker subsets yielded similar patterns but with reduced accuracy compared to the full marker set.
Conclusions:
- High relatedness within breeding populations is crucial for accurate genomic prediction.
- GS models are recommended for use within the same breeding population, with merged BGs acceptable if effective population size (Ne) < 50.
- A few hundred markers are sufficient for accurate GS, but accuracy may decline over generations.
- Vegetative propagation of superior individuals within large families is a promising short-term GS strategy for multiclonal forestry.
More Related Videos
Related Concept Videos
02:27Background and Environment Affect Phenotype
8.2K
Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
8.2K
01:20Gene-Environment Interactions
1.6K
Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
1.6K
02:16Genetics of Speciation
23.5K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
23.5K
01:21Frequency-dependent Selection
24.6K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.6K
02:54Evolutionary Relationships through Genome Comparisons
7.3K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.3K
02:05Gene Evolution - Fast or Slow?
8.4K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
In contrast, regions which code...
8.4K