QTL analysis for growth and wood properties across multiple pedigrees and sites in Eucalyptus globulus

Eucalyptus globulus is the most widely planted species for pulpwood production in temperate regions of the world and there are breeding programs in numerous countries. There is interest in molecular approaches to breeding, particularly marker assisted selection of wood properties. QTL analysis has an important role in identifying positional candidate genes responsible for variation in wood properties. This is one approach to targeting genes which may harbour functional allelic variants (SNPs). The objective of this study was to detect and validate QTL across multiple sites and pedigrees, in order to identify genomic regions and genes affecting growth and wood properties with wide applicability in the species. We also aimed to determine the proportion of QTL which were stable in their expression across sites of contrasting productivity. Such information will be important to exploit the full potential of the impending Eucalyptus genome sequences. [Oral Presentation

Determining the Genetic Architecture of Reproductive Stage Drought Tolerance in Wheat Using a Correlated Trait and Correlated Marker Effect Model

Water stress during reproductive growth is a major yield constraint for wheat (Triticum aestivum L). We previously established a controlled environment drought tolerance phenotyping method targeting the young microspore stage of pollen development. This method eliminates stress avoidance based on flowering time. We substituted soil drought treatments by a reproducible osmotic stress treatment using hydroponics and NaCl as osmolyte. Salt exclusion in hexaploid wheat avoids salt toxicity, causing osmotic stress. A Cranbrook x Halberd doubled haploid (DH) population was phenotyped by scoring spike grain numbers of unstressed (SGNCon) and osmotically stressed (SGNTrt) plants. Grain number data were analyzed using a linear mixed model (LMM) that included genetic correlations between the SGNCon and SGNTrt traits. Viewing this as a genetic regression of SGNTrt on SGNCon allowed derivation of a stress tolerance trait (SGNTol). Importantly, and by definition of the trait, the genetic effects for SGNTol are statistically independent of those for SGNCon. Thus they represent non-pleiotropic effects associated with the stress treatment that are independent of the control treatment. QTL mapping was conducted using a whole genome approach in which the LMM included all traits and all markers simultaneously. The marker effects within chromosomes were assumed to follow a spatial correlation model. This resulted in smooth marker profiles that could be used to identify positions of putative QTL. The most influential QTL were located on chromosome 5A for SGNTol (126cM; contributed by Halberd), 5A for SGNCon (141cM; Cranbrook) and 2A for SGNTrt (116cM; Cranbrook). Sensitive and tolerant population tail lines all showed matching soil drought tolerance phenotypes, confirming that osmotic stress is a valid surrogate screening method

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyze the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP – ssGBLUP and genomic BLUP – GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes

Distribution of Kraft Pulp Yield for samples collected from the Meunna and Florentine trials.

<p>Distribution of Kraft Pulp Yield for samples collected from the Meunna and Florentine trials.</p

Genes showing signatures of positive selection and differential expression between low and high KPY samples.

<p>Genes showing signatures of positive selection and differential expression between low and high KPY samples.</p

Distribution of SNPs from different regions of the <i>E. nitens</i> transcriptome.

<p>All SNPs – All SNPs identified that are common in both Florentine and Meunna; DAE SNPs – SNPs that showed differential allelic expression (Bonferonni P<0.0001) in both trials; DAE+DGE SNPs – SNPs with differential allelic expression present in genes with differential gene expression (FDR<0.05).</p

Differential allelic expression between low and high KPY samples in two populations.

<p>Freq – Frequency of Allele-B; DGE - Differential Gene Expression; *Genes also showing DGE.</p

Top 25 up-regulated transcripts in low KPY samples.

<p><i>E. grandis</i> gene names are used when the predicted genes are mapped to E. grandis gene coordinates otherwise the predicted gene names are used with a prefix “Transcript”.</p

Heatmap of 500 most differentially expressed genes between low and high KPY samples in the Meunna and Florentine trials.

<p>Heatmap of 500 most differentially expressed genes between low and high KPY samples in the Meunna and Florentine trials.</p

Gene categories enriched among genes that had both DGE and DAE.

<p>FDR - Fisher’s exact <i>p</i> value corrected for multiple comparisons.</p