ORAL PRESENTATION: Evaluating the accuracy of genomic prediction for the management and conservation of small secluded natural tree populations

Submitted by : Fady Bruno
Abstract type : Oral presentation
Session type : Conference session 4: EVOLUTIONARY MANAGEMENT of FORESTS
Author Speaker : Juan Pablo Jaramillo-Correa

Information about other authors :

Sebastián Arenas1, Verónica Reyes-Pérez1, Andrés J. Cortés2, Alicia Mastretta-Yanes3

1. Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, México

2. Universidad Nacional de Colombia – Sede Medellín, Facultad de Ciencias Agrarias – Departamento de Ciencias Forestales, Medellín, Colombia

3. CONACYT-CONABIO, Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Ciudad de México, México

Abstract :

Small 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.

This study was financially supported by grants from CONACyT (CB-2016-284457 and 278987) and the "Dirección General de Asuntos del Personal Académico" at UNAM (PAPIIT: IN208416).

Bibliografic references :

1. Petit, R.J., & Hampe A. (2006). Some evolutionary consequences of being a tree. Annual Review of Ecology, Evolution, and Systematics, 37, 187-214

2. Ledig, T. F., & Kitzmiller, J. H. (1992). Genetic strategies for reforestation in the face of global climate change. For. Ecol. Manage. 50, 153–169.

3. Le Corre, V., & Kremer, A. (2003). Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection. Genetics 164, 1205–1219.

4. Aitken, S. N., & Whitlock, M. C. (2013). Assisted Gene Flow to Facilitate Local Adaptation to Climate Change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388.

5. Resende, M. D. V., Resende, M. F. R., Sansaloni, C. P., et al. (2012). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194, 116–128.

6. Isik, F., Bartholomé, J., Farjat, A., et al. (2016). Plant science genomic selection in maritime pine. Plant Sci. 242, 108–119.

7. Thistlethwaite, F. R., Ratcliffe, B., Klápště, J., et al. (2017). Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genomics 18, 1–16.

8. Gienapp, P., Calus, M.P.L., Laine, V.N., Visser, M.E. (2019). Genomic selection on breeding time in a wild bird population. Evol. Lett. 3, 142-151.

Keywords : Abies, Mexico, assisted migration, de novo GWAS – GPMs, genotype x environment
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