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.

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.

Comparison of Genomic Selection Models to Predict Flowering Time and Spike Grain Number in Two Hexaploid Wheat Doubled Haploid Populations.

Genomic selection (GS) is becoming an important selection tool in crop breeding. In this study, we compared the ability of different GS models to predict time to young microspore (TYM), a flowering time-related trait, spike grain number under control conditions (SGNC) and spike grain number under osmotic stress conditions (SGNO) in two wheat biparental doubled haploid populations with unrelated parents. Prediction accuracies were compared using BayesB, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO / BL), ridge regression best linear unbiased prediction (RR-BLUP), partial least square regression (PLS), and sparse partial least square regression (SPLS) models. Prediction accuracy was tested with 10-fold cross-validation within a population and with independent validation in which marker effects from one population were used to predict traits in the other population. High prediction accuracies were obtained for TYM (0.51-0.84), whereas moderate to low accuracies were observed for SGNC (0.10-0.42) and SGNO (0.27-0.46) using cross-validation. Prediction accuracies based on independent validation are generally lower than those based on cross-validation. BayesB and SPLS outperformed all other models in predicting TYM with both cross-validation and independent validation. Although the accuracies of all models are similar in predicting SGNC and SGNO with cross-validation, BayesB and SPLS had the highest accuracy in predicting SGNC with independent validation. In independent validation, accuracies of all the models increased by using only the QTL-linked markers. Results from this study indicate that BayesB and SPLS capture the linkage disequilibrium between markers and traits effectively leading to higher accuracies. Excluding markers from QTL studies reduces prediction accuracies.

RNA-Seq using two populations reveals genes and alleles controlling wood traits and growth in Eucalyptus nitens.

Eucalyptus nitens is a perennial forest tree species grown mainly for kraft pulp production in many parts of the world. Kraft pulp yield (KPY) is a key determinant of plantation profitability and increasing the KPY of trees grown in plantations is a major breeding objective. To speed up the breeding process, molecular markers that can predict KPY are desirable. To achieve this goal, we carried out RNA-Seq studies on trees at extremes of KPY in two different trials to identify genes and alleles whose expression correlated with KPY. KPY is positively correlated with growth measured as diameter at breast height (DBH) in both trials. In total, six RNA bulks from two treatments were sequenced on an Illumina HiSeq platform. At 5% false discovery rate level, 3953 transcripts showed differential expression in the same direction in both trials; 2551 (65%) were down-regulated and 1402 (35%) were up-regulated in low KPY samples. The genes up-regulated in low KPY trees were largely involved in biotic and abiotic stress response reflecting the low growth among low KPY trees. Genes down-regulated in low KPY trees mainly belonged to gene categories involved in wood formation and growth. Differential allelic expression was observed in 2103 SNPs (in 1068 genes) and of these 640 SNPs (30%) occurred in 313 unique genes that were also differentially expressed. These SNPs may represent the cis-acting regulatory variants that influence total gene expression. In addition we also identified 196 genes which had Ka/Ks ratios greater than 1.5, suggesting that these genes are under positive selection. Candidate genes and alleles identified in this study will provide a valuable resource for future association studies aimed at identifying molecular markers for KPY and growth.

Stability of quantitative trait loci for growth and wood properties across multiple pedigrees and environments in Eucalyptus globulus.

· Eucalypts are one of the most planted tree genera worldwide, and there is increasing interest in marker-assisted selection for tree improvement. Implementation of marker-assisted selection requires a knowledge of the stability of quantitative trait loci (QTLs). This study aims to investigate the stability of QTLs for wood properties and growth across contrasting sites and multiple pedigrees of Eucalyptus globulus. · Saturated linkage maps were constructed using 663 genotypes from four separate families, grown at three widely separated sites, and were employed to construct a consensus map. This map was used for QTL analysis of growth, wood density and wood chemical traits, including pulp yield. · Ninety-eight QTLs were identified across families and sites: 87 for wood properties and 11 for growth. These QTLs mapped to 38 discrete regions, some of which co-located with candidate genes. Although 16% of QTLs were verified across different families, 24% of wood property QTLs and 38% of growth QTLs exhibited significant genotype-by-environment interaction. · This study provides the most detailed assessment of the effect of environment and pedigree on QTL detection in the genus. Despite markedly different environments and pedigrees, many QTLs were stable, providing promising targets for the application of marker-assisted selection.