Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species.

BACKGROUND: Planting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias. RESULTS: Genotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP). CONCLUSIONS: This study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.

Identification and high-throughput genotyping of single nucleotide polymorphism markers in a non-model conifer (Abies nordmanniana (Steven) Spach).

Single nucleotide polymorphism (SNP) markers are powerful tools for investigating population structures, linkage analysis, and genome-wide association studies, as well as for breeding and population management. The availability of SNP markers has been limited to the most commercially important timber species, primarily due to the cost of genome sequencing required for SNP discovery. In this study, a combination of reference-based and reference-free approaches were used to identify SNPs in Nordmann fir (Abies nordmanniana), a species previously lacking genomic sequence information. Using a combination of a genome assembly of the closely related Silver fir (Abies alba) species and a de novo assembly of low-copy regions of the Nordmann fir genome, we identified a high density of reliable SNPs. Reference-based approaches identified two million SNPs in common between the Silver fir genome and low-copy regions of Nordmann fir. A combination of one reference-free and two reference-based approaches identified 250 shared SNPs. A subset of 200 SNPs were used to genotype 342 individuals and thereby tested and validated in the context of identity analysis and/or clone identification. The tested SNPs successfully identified all ramets per clone and five mislabeled individuals via identity and genomic relatedness analysis. The identified SNPs will be used in ad hoc breeding of Nordmann fir in Denmark.