Search

Additional file 4: Fig. S1. Frequency distributions for the phenotypic traits measured in the QTL and FWK Pinus radiata populations in this study. QTL population: (A) Ring area (mm2); (B) Density (kg/m3) Silviscan; (C) Radial cell diameter (μm); (D) Tangential cell diameter (μm); (E) Fibre coarseness (μm/m); (F) Cell wall thickness (μm); (G) Specific surface area (m2/kg); (H) Microfibril angle (degrees); (I) Modulus of elasticity (GPa); (J) Density prediction for first 5 mm core (maximum moisture content method) (kg/m3); (K) Density prediction for second 5 mm core (maximum moisture content method) (kg/m3); (L) Average of density predictions for two cores above; (M) Diameter at breast height (mm). FWK population: (N) Area weighted percent late wood ages 1–10 (%); (O) Wood density (kg/m3) (maximum moisture content method); (P) Diameter at breast height (mm).

Additional file 1: Table S1. SNP filtering summary for the Pinus radiata QTL and FWK mapping populations.

Additional file 16: Fig. S2. Synteny and collinearity between linkage groups 8 and 10 amongst the Pinus radiata parental linkage maps in this study. Vertical bars represent linkage groups, horizontal lines within bars show the position of markers within each group, lines between groups indicate homologous markers at the contig level. Scale bars shows cM (Kosambi). Fig. S1A shows perfect synteny and high collinearity between these linkage groups in three parental linkage maps. Fig. S2B shows that linkage group 8 in parent 850,096 has markers from both linkage groups 8 and 10 in parents 268,345 and 268,405. However, all mapped markers in parent 850,096 matched only their homologous chromosome in parent 850,055 (Fig. S1C). The red box in fig. S1b highlights a region of ~ 10 cM, on linkage group 8 in parent 850,096, with a mixture of homologs from linkage groups 8 and 10 as well as very tightly clustered markers.

Additional file 3: Table S3. Descriptive statistics for the phenotypic traits measured in the QTL and FWK Pinus radiata populations in this study.

Additional file 2: Table S2. SNP ranking criteria used for the Pinus radiata QTL and FWK mapping populations.

Additional file 18: Fig. S3. Genomic predictive ability for diameter at breast height (DBH) and wood density (WD) as a function of training population size (A) and number of markers (B) used in random cross-validations of 81 Pinus radiata genotypes from the FWK population. Error bars correspond to standard deviations across 100 random cross-validations for each set of parameters. Analyses in (A) were based on all markers (M = 9353). The training population size in (B) was N = 40 (two-fold cross-validation) and subsets of markers were selected at random.

Data for Genetic variation for economically important traits in Cupressus lusitanica in New Zealand&nbsp

Alpine ecosystems are seriously threatened by climate change. One of the key mechanisms by which plants can adapt to changing environmental conditions is through evolutionary change. However, we still know little about the evolutionary potential in wild populations of long-lived alpine plants. Here, we investigated heritabilities of phenological traits, leaf size, and performance traits in natural populations of the long-lived alpine dwarf shrub Salix herbacea using relatedness estimates inferred from SSR (Simple Sequence Repeat) markers. Salix herbacea occurs in early- and late-snowmelt microhabitats (ridges and snowbeds), and we assessed how performance consequences of phenological traits and leaf size differ between these microhabitats in order to infer potential for evolutionary responses. Salix herbacea showed low, but significant, heritabilities of leaf size, clonal and sexual reproduction, and moderate heritabilities of phenological traits. In both microhabitats, we found that larger leaves, longer intervals between snowmelt and leaf expansion, and longer GDD (growing-degree days) until leaf expansion resulted in a stronger increase in the number of stems (clonal reproduction). In snowbeds, clonal reproduction increased with a shorter GDD until flowering, while the opposite was found on ridges. Furthermore, the proportion of flowering stems increased with GDD until flowering in both microhabitats. Our results suggest that the presence of significant heritable variation in morphology and phenology might help S. herbacea to adapt to changing environmental conditions. However, it remains to be seen if the rate of such an evolutionary response can keep pace with the rapid rate of climate change. published

The abstract is available here: https://uscholar.univie.ac.at/o:527618

In plants, genes may sustain extensive pleiotropic functional properties by individually affecting multiple, distinct traits. We discuss results from three genome-wide association studies of approximately 400 natural poplar (Populus trichocarpa) accessions phenotyped for 60 ecological/biomass, wood quality, and rust fungus resistance traits. Single-nucleotide polymorphisms (SNPs) in the poplar ortholog of the class III homeodomain-leucine zipper transcription factor gene REVOLUTA (PtREV) were significantly associated with three specific traits. Based on SNP associations with fungal resistance, leaf drop, and cellulose content, the PtREV gene contains three potential regulatory sites within noncoding regions at the gene's 3' end, where alternative splicing and messenger RNA processing actively occur. The polymorphisms in this region associated with leaf abscission and cellulose content are suggested to represent more recent variants, whereas the SNP associated with leaf rust resistance may be more ancient, consistent with REV's primary role in auxin signaling and its functional evolution in supporting fundamental processes of vascular plant development.

Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions: The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

Background: QTL cloning for the discovery of genes underlying polygenic traits has historically been cumbersome in long-lived perennial plants like Populus. Linkage disequilibrium-based association mapping has been proposed as a cloning tool, and recent advances in high-throughput genotyping and whole-genome resequencing enable marker saturation to levels sufficient for association mapping with no a priori candidate gene selection. Here, multiyear and multienvironment evaluation of cell wall phenotypes was conducted in an interspecific P. trichocarpa x P. deltoides pseudo-backcross mapping pedigree and two partially overlapping populations of unrelated P. trichocarpa genotypes using pyrolysis molecular beam mass spectrometry, saccharification, and/ or traditional wet chemistry. QTL mapping was conducted using a high-density genetic map with 3,568 SNP markers. As a fine-mapping approach, chromosome-wide association mapping targeting a QTL hot-spot on linkage group XIV was performed in the two P. trichocarpa populations. Both populations were genotyped using the 34 K Populus Infinium SNP array and whole-genome resequencing of one of the populations facilitated marker-saturation of candidate intervals for gene identification. Results Five QTLs ranging in size from 0.6 to 1.8 Mb were mapped on linkage group XIV for lignin content, syringyl to guaiacyl (S/G) ratio, 5- and 6-carbon sugars using the mapping pedigree. Six candidate loci exhibiting significant associations with phenotypes were identified within QTL intervals. These associations were reproducible across multiple environments, two independent genotyping platforms, and different plant growth stages. cDNA sequencing for allelic variants of three of the six loci identified polymorphisms leading to variable length poly glutamine (PolyQ) stretch in a transcription factor annotated as an ANGUSTIFOLIA C-terminus Binding Protein (CtBP) and premature stop codons in a KANADI transcription factor as well as a protein kinase. Results from protoplast transient expression assays suggested that each of the polymorphisms conferred allelic differences in the activation of cellulose, hemicelluloses, and lignin pathway marker genes. Conclusion This study illustrates the utility of complementary QTL and association mapping as tools for gene discovery with no a priori candidate gene selection. This proof of concept in a perennial organism opens up opportunities for discovery of novel genetic determinants of economically important but complex traits in plants.

Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

Background: Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. Here, we used: 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full-sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RR-BLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross-sites, multi-site, and multi-site to single site) and time (age-age/ trait-trait). Results: The RR-BLUP and GRR models produced similar predictive accuracies across the studied traits. Within-site GS prediction accuracies with models trained on EBVs were high (RR-BLUP: 0.79–0.91 and GRR: 0.80–0.91), and were generally similar to the multi-site (RR-BLUP: 0.83–0.91, GRR: 0.83–0.91) and multi-site to single-site predictive accuracies (RR-BLUP: 0.79–0.92, GRR: 0.79–0.92). Cross-site predictions were surprisingly high, with predictive accuracies within a similar range (RR-BLUP: 0.79–0.92, GRR: 0.78–0.91). Height at 12 years was deemed the earliest acceptable age at which accurate predictions can be made concerning future height (age-age) and wood density (trait-trait). Using DEBVs reduced the accuracies of all cross-validation procedures dramatically, indicating that the models were tracking pedigree (family means), rather than marker-QTL LD. Conclusions: While GS models’ prediction accuracies were high, the main driving force was the pedigree tracking rather than LD. It is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.