- Date de réalisation : 27 Janvier 2020
- Durée du programme : 23 min
- Classification Dewey : SCIENCES nature - Botanique Arbres, Arbustes, Plantes grimpantes
Dans la même collection[COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 1 [COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 2 [COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 3 [COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 4 [COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 5 [COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 6
[COLLOQUE] GENTREE Final Conference 27-31 January 2020 séance 29
GENTREE Final Conference :
Juan Pablo JARAMILLO-CORREA - UNAM Mexico City · Mexico
GENTREE Final Conference 'Genetics to the rescue - managing forests sustainably in a changing environment'
27-31 January 2020, Avignon, France
Juan Pablo JARAMILLO-CORREA - UNAM Mexico City · Mexico : Evaluating the accuracy of genomic prediction for the management and conservation of small secluded natural tree populations mall isolated populations are threatened by the action of inbreeding and genetic drift, which may lead to the accumulation of (partially) deleterious variants and compromise survival.
In forest trees, such effects can be counteracted by large seed output over long periods of time, combined with strong selective pressures against inbreds
-(1). Such a drift-selection balance is expected to maintain overly constant levels of genetic variation
-(2), and even facilitate local adaptation
-(3), particularly when selective regimes are constant over time. However, under strong inbreeding regimes, inbreeding depression posses a serious threat, and assisted migration has been proposed to “genetically rescuing” such populations, at the expense of outbreeding depression risks (i.e. introducing maladapted individuals;
-4). Genomic prediction models (GPM) are built for small breeding populations submitted to strong artificial selection in domesticated taxa.
They aim predicting phenotypic performance from genomic information, for ultimately increasing genetic gain.
When combined with genomic association studies (GWAS), this methodology is expected to enhance genetic improvement and reduce breeding cycle length (5).
Whether this approach can be extended for the management and conservation of natural populations and guide assisted migration programs, is still a pending question.
The rationale is that historically small populations can be viewed as natural ‘mimics’ of breeding populations in domestication programs, such that historical recombination and selective regimes have produced non-random genotype-phenotype associations, which can be integrated into GPMs.
We tested such possibility within natural populations of Sacred fir (Abies religiosa; Pinaceae) in central Mexico, to study growth and physiological traits in a multi-environment test-trial. We genotyped over 200 naturally re-generated and introduced individuals for 2,286 single nucleotide polymorphisms (SNP), derived from genotyping by sequencing.
These markers were used to develop GPMs for each trait.
After testing different training and validation datasets, and determining the models’ predictive ability with randomization and cross-validation techniques, the highest predictabilities (R2; ranging between 0.3 and 0.45) were obtained for growth characters.
They were similar to those previously reported for forest trees undergoing selective breeding (5, 6, 7). The best models were always those built for natural saplings and used to predict the performance of introduced individuals in the same environment.
Integrating microenvironmental soil variability allowed detecting genotype-phenotype interactions, which further increased predictability.
Our results open a promising avenue for implementing GPMs in management and conservation programs of natural populations (see also 8), particularly for non-model species with poorly developed genomic resources. Such models should be particularly useful for predicting the performance on introduced seedlings and guiding assisted migration programs.