Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

/pdf/modern-strategies-to-assess-and-breed-forest-tree-adaptation-25vvnt4esu.pdf

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate.

Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.

/pdf/crop-genomic-selection-with-deep-learning-and-environmental-1axpu5lp.pdf

Crop genomic selection with deep learning and environmental data: A survey

This chapter provides an overview of the genomic selection progress in long-lived forest tree species. Factors affecting the prediction accuracy in genomic prediction are assessed with examples from empirical studies. Infrastructure and resources required for the implementation of genomic selection are evaluated. Some general guidelines are provided for the successful application of genomic selection in forest tree breeding programs. 

Genomic Prediction of Complex Traits in Perennial Plants: A Case for Forest Trees.

Influence de l’ecartement et du semencier sur la croissance des plants de Pericopsis elata durant les vingt premiers mois d’un essai de plantation Description du sujet. Pericopsis elata (Fabaceae) est un arbre heliophile des forets tropicales africaines qui produit du bois de grande valeur economique. Ses populations naturelles souffrent de surexploitation et presentent un deficit de regeneration naturelle. Les plantations pourraient augmenter sa production et limiter la pression sur les forets naturelles. Cependant, les connaissances sur l'influence de l'ecartement et du semencier sur la croissance des arbres font encore defaut pour les protocoles de plantation. Objectifs. Cette etude evalue l'influence de la densite de plantation et du semencier sur les croissances en diametre et en hauteur des plants de P. elata, 20 mois apres la plantation. Methode. Les graines ont ete collectees sur 19 semenciers dans un peuplement naturel de 400 ha pres de Kisangani (RDC). En octobre 2017, les plantules ont ete transplantees dans une plantation suivant le dispositif de Nelder en trois repetitions, pour comparer la croissance de 540 plants en plein decouvert suivant 10 densites differentes. Les accroissements en diametre et en hauteur ont ete analyses en fonction de la competition locale et du semencier, a l'aide des modeles additifs generalises. Resultats. Vingt mois apres la plantation, les croissances etaient maximales, en hauteur (165,6 ± 39,2 cm·an-1) a une densite intermediaire de 47 000 tiges·ha-1, en diametre (24,6 ± 8,3 mm·an-1) a une faible densite de 4 200 tiges·ha-1. L’origine genetique des plants a affecte leur croissance, les coefficients de variation entre semenciers ont atteint 7,6 % et 6,4 % pour les accroissements en diametre et en hauteur, respectivement. Le diametre initial a influence positivement la croissance diametrique mais la hauteur initiale n'a pas affecte la croissance en hauteur. Conclusions. Le dispositif de Nelder s'est avere utile pour confirmer la dependance de la croissance des plants vis-a-vis de l’ecartement et mettre en evidence l’influence de facteurs genetiques sur la croissance.

/pdf/influence-of-spacing-and-seed-trees-on-the-growth-of-1ryhufdns0.pdf

Influence of spacing and seed trees on the growth of Pericopsis elata saplings during the first twenty months of a planting trial

Abstract Terpenes are an important group of plant specialized metabolites influencing, amongst other functions, defence mechanisms against pests. We used a genome-wide association study to identify single nucleotide polymorphism (SNP) markers and putative candidate genes for terpene traits. We tested 15,387 informative SNP markers derived from genotyping 416 Eucalyptus grandis individuals for association with 3 terpene traits, 1,8-cineole, γ-terpinene, and p-cymene. A multilocus mixed model analysis identified 21 SNP markers for 1,8-cineole on chromosomes 2, 4, 6, 7, 8, 9, 10, and 11, that individually explained 3.0%–8.4% and jointly 42.7% of the phenotypic variation. Association analysis of γ-terpinene found 32 significant SNP markers on chromosomes 1, 2, 4, 5, 6, 9, and 11, explaining 3.4–15.5% and jointly 54.5% of phenotypic variation. For p-cymene, 28 significant SNP markers were identified on chromosomes 1, 2, 3, 5, 6, 7, 10, and 11, explaining 3.4–16.1% of the phenotypic variation and jointly 46.9%. Our results show that variation underlying the 3 terpene traits is influenced by a few minor loci in combination with a few major effect loci, suggesting an oligogenic nature of the traits.

/pdf/genome-wide-association-study-identifies-snp-markers-and-3mfkvwkp.pdf

Genome-wide association study identifies SNP markers and putative candidate genes for terpene traits important for Leptocybe invasa resistance in Eucalyptus grandis

Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.

https://www.mdpi.com/1999-4907/11/2/239/pdf

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados