Gary F. Peter

Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.)

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments

• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.

Quantitative genetic analysis of biomass and wood chemistry of Populus under different nitrogen levels.

The genetic control of carbon allocation and partitioning in woody perennial plants is poorly understood despite its importance for carbon sequestration, biofuels and other wood-based industries. It is also unclear how environmental cues, such as nitrogen availability, impact the genes that regulate growth, biomass allocation and wood composition in trees. We phenotyped 396 clonally replicated genotypes of an interspecific pseudo-backcross pedigree of Populus for wood composition and biomass traits in above- and below-ground organs. The loci that regulate growth, carbon allocation and partitioning under two nitrogen conditions were identified, defining the contribution of environmental cues to their genetic control. Sixty-three quantitative trait loci were identified for the 20 traits analyzed. The majority of quantitative trait loci are specific to one of the two nitrogen treatments, demonstrating significant nitrogen-dependent genetic control. A highly significant genetic correlation was observed between plant growth and lignin/cellulose composition, and quantitative trait loci co-localization identified the genomic position of potential pleiotropic regulators. Pleiotropic loci linking higher growth rates to wood with less lignin are excellent targets to engineer tree germplasm improved for pulp, paper and cellulosic ethanol production. The causative genes are being identified with a genetical genomics approach.

High-Throughput Screening of Plant Cell-Wall Composition Using Pyrolysis Molecular Beam Mass Spectroscopy

We describe a high-throughput method for estimating cell-wall chemistry traits using analytical pyrolysis. The instrument used to perform the high-throughput cell-wall chemistry analysis consists of a commercially available pyrolysis unit and autosampler coupled to a custom-built molecular beam mass spectrometer. The system is capable of analyzing approximately 42 biomass samples per hour. Lignin content and syringyl to guaiacol (S/G) ratios can be estimated directly from the spectra and differences in cell wall chemistry in large groups of samples can easily be identified using multivariate statistical data analysis methods. The utility of the system is demonstrated on a set of 800 greenhouse-grown poplar trees grown under two contrasting nitrogen treatments. High-throughput analytical pyrolysis was able to determine that the lignin content varied between 13 and 28% and the S/G ratio ranged from 0.5 to 1.5. There was more cell-wall chemistry variation in the plants grown under high nitrogen conditions than trees grown under nitrogen-deficiency conditions. Analytical pyrolysis allows the user to rapidly screen large numbers of samples at low cost, using very little sample material while producing reliable and reproducible results.

Unraveling additive from nonadditive effects using genomic relationship matrices.

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

Comparison of standard exponential and linear techniques to amplify small cDNA samples for microarrays

BackgroundThe need to perform microarray experiments with small amounts of tissue has led to the development of several protocols for amplifying the target transcripts. The use of different amplification protocols could affect the comparability of microarray experiments.ResultsHere we compare expression data from Pinus taeda cDNA microarrays using transcripts amplified either exponentially by PCR or linearly by T7 transcription. The amplified transcripts vary significantly in estimated length, GC content and expression depending on amplification technique. Amplification by T7 RNA polymerase gives transcripts with a greater range of lengths, greater estimated mean length, and greater variation of expression levels, but lower average GC content, than those from PCR amplification. For genes with significantly higher expression after T7 transcription than after PCR, the transcripts were 27% longer and had about 2 percentage units lower GC content. The correlation of expression intensities between technical repeats was high for both methods (R2 = 0.98) whereas the correlation of expression intensities using the different methods was considerably lower (R2 = 0.52). Correlation of expression intensities between amplified and unamplified transcripts were intermediate (R2 = 0.68–0.77).ConclusionAmplification with T7 transcription better reflects the variation of the unamplified transcriptome than PCR based methods owing to the better representation of long transcripts. If transcripts of particular interest are known to have high GC content and are of limited length, however, PCR-based methods may be preferable.

Genetically engineered trees for plantation forests: key considerations for environmental risk assessment

Forests are vital to the worlds ecological, social, cultural and economic well-being yet sustainable provision of goods and services from forests is increasingly challenged by pressures such as growing demand for wood and other forest products, land conversion and degradation, and climate change. Intensively managed, highly productive forestry incorporating the most advanced methods for tree breeding, including the application of genetic engineering (GE), has tremendous potential for producing more wood on less land. However, the deployment of GE trees in plantation forests is a controversial topic and concerns have been particularly expressed about potential harms to the environment. This paper, prepared by an international group of experts in silviculture, forest tree breeding, forest biotechnology and environmental risk assessment (ERA) that met in April 2012, examines how the ERA paradigm used for GE crop plants may be applied to GE trees for use in plantation forests. It emphasizes the importance of differentiating between ERA for confined field trials of GE trees, and ERA for unconfined or commercial-scale releases. In the case of the latter, particular attention is paid to characteristics of forest trees that distinguish them from shorter-lived plant species, the temporal and spatial scale of forests, and the biodiversity of the plantation forest as a receiving environment.

Estimation of Microfibril Angle and Stiffness by near infrared Spectroscopy using sample sets having Limited wood Density Variation

Near infrared (NIR) spectroscopy offers a rapid method for the estimation of microfibril angle (MFA) and SilviScan-estimated wood stiffness (EL(SS)). The success of these NIR calibrations may be related to airdry density, because density varies in wood simultaneously with MFA and stiffness. The importance of density variation was investigated by developing calibrations for MFA and EL(SS) using Pinus radiata D. Don (radiata pine) and Pinus taeda L. (loblolly pine) sample sets where the density range was small and the relationships between density and MFA and density and EL(SS) were poor. Excellent calibrations for MFA and EL(SS) were obtained, particularly when sets had densities greater than 500 kg/m3, can provide strong relationships for MFA and stiffness even when density variation is limited. Examination of loading plots from the MFA and EL(SS) calibrations indicates that variation in wood components such as cellulose, lignin and possibly hemicellulose is important.

Breeding for improved growth and juvenile corewood stiffness in slash pine

The importance of integrating measures of juvenile corewood mechanical properties, modulus of elasticity in particular, with growth and disease resistance in tree improvement programs has increased. We investigated the utility of in-tree velocity stiffness measurements to estimate the genetic control of corewood stiffness and to select for trees with superior growth and stiffness in a progeny trial of 139 families of slash pine, Pinus elliottii Engelm. grown on six sites. Narrow-sense heritability estimates across all six sites for in-tree acoustic velocity stiffness at 8 years (0.42) were higher than observed for height (0.36) and diameter at breast height (DBH) (0.28) at 5 years. The overall type B genetic correlation across sites for velocity stiffness was 0.68, comparable to those found for DBH and volume growth, indicating that family rankings were moderately repeatable across all sites for these traits. No significant genetic correlations were observed between velocity stiffness, DBH, and volume gro...

Estimation of the physical wood properties of Pinus taeda L. radial strips using least squares support vector machines

Near infrared (NIR) spectroscopy offers a rapid method for estimating many important wood properties, including air-dry density, microfibril angle (MFA) and SilviScan estimated stiffness (EL(SS)). Wood property calibrations may be improved by using non-linear calibration methods. In this study, we compare calibrations developed using partial least squares (PLS) regression and least-squares support vector machine (LS-SVM) regression, a relatively new technique for modelling multivariate, non-linear systems. LS-SVM regression provided the strongest calibration statistics for all wood properties. For an equivalent number of latent variables, the predictive performance of the MFA LS-SVM calibrations were superior to those of the corresponding PLS calibration, while predictive results for air-dry density and EL(SS) were similar for both calibration methods.