Imagine a group of caged animals where everyone has it’s own set of abilities to acquire food. Among them, there is a notably competitive animal named Max. Max is driven by a strong desire to outshine his companions, particularly when it comes to securing a limited food resource within the cage. In this confined space, Max competitive nature tends to create a stressful environment, negatively affecting the access to food of his cage mates. (Example extracted from this blog)
Just like Max, plants also compete. In this case, space, light, water and nutrients are the main limited resources. In a competition environment, breeders cannot be fully assured that a candidate had a poor performance per se, or it had overly competitive neighbours that suppressed its development. This is how competition affects the selection, and that is why it should not be overlooked in the breeding pipeline.
gencomp is an R package designed to aid plant breeders in dealing
with genetic and spatial competition in field trials. The package has
functions to: i) fit (spatial) genetic competition models, ii) define
competition classes, iii) compute the total genotypic value and iv)
calculate the total heritable variation. Specifically for tree breeding,
gencomp can also predict the performance of commercial forest stands
composed of clonal mixtures, based on clones’ competition capacity.
Install gencomp running the following code:
# install.packages("devtools")
devtools::install_github("Kaio-Olimpio/gencomp", build_vignettes = TRUE)IMPORTANT: gencomp fits linear mixed models using the ASReml-R
machinery. Thus, you must have ASReml-R installed prior to installing
gencomp.
Currently, gencomp can be applied in three situations:
- Single-trial tree breeding data
(
vignette(topic = "tree_competition", package = "gencomp")) - Single-trial crop breeding data
(
vignette(topic = "crop_competition", package = "gencomp")) - Multi-ages (repeated measures) tree breeding data
(
vignette(topic = "multi_age", package = "gencomp"))
If you used the package to analyse data for a publication, please cite:
Chaves SFS, Ferreira FM, Ferreira GC, Gezan SA, Dias KOG (2025)
Incorporating spatial and genetic competition into breeding pipelines
with the R package gencomp. Heredity 134, 129-141.
https://doi.org/10.1038/s41437-024-00743-9
Bijma P (2014) The quantitative genetics of indirect genetic effects: a selective review of modelling issues. Heredity 112:61-69. https://doi.org/10.1038/hdy.2013.15
Cappa EP, Cantet RJC (2008) Direct and competition additive effects in tree breeding: bayesian estimation from an individual tree mixed model. Silvae Genet 57:45–56. https://doi.org/10.1515/sg-2008-0008
Costa e Silva J, Kerr RJ (2013) Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials. Tree Genet Genomes 9:1–17. https://doi.org/10.1007/s11295-012-0521-8
Ferreira FM, Chaves SFS, Bhering LL, Alves RS, Takahashi EK, Sousa JE, Resende MDV, Leite FP, Gezan SA, Viana JMS, Fernandes SB, Dias KOG (2023) A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. For Ecol Manag 548:121393. https://doi.org/10.1016/j.foreco.2023.121393
Ferreira FM, Chaves SFS, Santos OP, Nunes ACP, Tambarussi EV, Pereira GS, Santos GA, Bhering LL, Dias KOG (2024) Competition effects can mislead selection in eucalypt breeding trials. For Ecol Manag 561:121892. https://doi.org/10.1016/j.foreco.2024.121892
Muir WM (2005) Incorporation of competitive effects in forest tree or animal breeding programs. Genetics 170:1247–1259. https://doi.org/10.1534/genetics.104.035956
Stringer JK, Cullis BR, Thompson R (2011) Joint modeling of spatial variability and within-row interplot competition to increase the efficiency of plant improvement. J Agric Biol Environ Stat 16:269–281. https://doi.org/10.1007/s13253-010-0051-5
Walsh B, Lynch M (2018) Evolution and selection of quantitative traits, vol. 1. Oxford University Press. (Chapter 22)