Matias Kirst

High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome.

BackgroundBenefits from high-throughput sequencing using 454 pyrosequencing technology may be most apparent for species with high societal or economic value but few genomic resources. Rapid means of gene sequence and SNP discovery using this novel sequencing technology provide a set of baseline tools for genome-level research. However, it is questionable how effective the sequencing of large numbers of short reads for species with essentially no prior gene sequence information will support contig assemblies and sequence annotation.ResultsWith the purpose of generating the first broad survey of gene sequences in Eucalyptus grandis, the most widely planted hardwood tree species, we used 454 technology to sequence and assemble 148 Mbp of expressed sequences (EST). EST sequences were generated from a normalized cDNA pool comprised of multiple tissues and genotypes, promoting discovery of homologues to almost half of Arabidopsis genes, and a comprehensive survey of allelic variation in the transcriptome. By aligning the sequencing reads from multiple genotypes we detected 23,742 SNPs, 83% of which were validated in a sample. Genome-wide nucleotide diversity was estimated for 2,392 contigs using a modified theta (θ) parameter, adapted for measuring genetic diversity from polymorphisms detected by randomly sequencing a multi-genotype cDNA pool. Diversity estimates in non-synonymous nucleotides were on average 4x smaller than in synonymous, suggesting purifying selection. Non-synonymous to synonymous substitutions (Ka/Ks) among 2,001 contigs averaged 0.30 and was skewed to the right, further supporting that most genes are under purifying selection. Comparison of these estimates among contigs identified major functional classes of genes under purifying and diversifying selection in agreement with previous researches.ConclusionIn providing an abundance of foundational transcript sequences where limited prior genomic information existed, this work created part of the foundation for the annotation of the E. grandis genome that is being sequenced by the US Department of Energy. In addition we demonstrated that SNPs sampled in large-scale with 454 pyrosequencing can be used to detect evolutionary signatures among genes, providing one of the first genome-wide assessments of nucleotide diversity and Ka/Ks for a non-model plant species.

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.

Coordinated genetic regulation of growth and lignin revealed by quantitative trait locus analysis of cDNA microarray data in an interspecific backcross of eucalyptus.

Phenotypic, genotypic, and transcript level (microarray) data from an interspecific backcross population of Eucalyptus grandis and Eucalyptus globulus were integrated to dissect the genetic and metabolic network underlying growth variation. Transcript abundance, measured for 2,608 genes in the differentiating xylem of a 91 (E. grandis × E. globulus) × E. grandis backcross progeny was correlated with diameter variation, revealing coordinated down-regulation of genes encoding enzymes of the lignin biosynthesis and associated methylation pathways in fast growing individuals. Lignin analysis of wood samples confirmed the content and quality predicted by the transcript levels measured on the microarrays. Quantitative trait locus (QTL) analysis of transcript levels of lignin-related genes showed that their mRNA abundance is regulated by two genetic loci, demonstrating coordinated genetic control over lignin biosynthesis. These two loci colocalize with QTLs for growth, suggesting that the same genomic regions are regulating growth, and lignin content and composition in the progeny. Genetic mapping of the lignin genes revealed that most of the key biosynthetic genes do not colocalize with growth and transcript level QTLs, with the exception of the locus encoding the enzyme S-adenosylmethionine synthase. This study illustrates the power of integrating quantitative analysis of gene expression data and genetic map information to discover genetic and metabolic networks regulating complex biological traits.

Eucalyptus applied genomics: from gene sequences to breeding tools

Eucalyptus is the most widely planted hardwood crop in the tropical and subtropical world because of its superior growth, broad adaptability and multipurpose wood properties. Plantation forestry of Eucalyptus supplies high-quality woody biomass for several industrial applications while reducing the pressure on tropical forests and associated biodiversity. This review links current eucalypt breeding practices with existing and emerging genomic tools. A brief discussion provides a background to modern eucalypt breeding together with some current applications of molecular markers in support of operational breeding. Quantitative trait locus (QTL) mapping and genetical genomics are reviewed and an in-depth perspective is provided on the power of association genetics to dissect quantitative variation in this highly diverse organism. Finally, some challenges and opportunities to integrate genomic information into directional selective breeding are discussed in light of the upcoming draft of the Eucalyptus grandis genome. Given the extraordinary genetic variation that exists in the genus Eucalyptus, the ingenuity of most breeders, and the powerful genomic tools that have become available, the prospects of applied genomics in Eucalyptus forest production are encouraging.

Apparent homology of expressed genes from wood-forming tissues of loblolly pine (Pinus taeda L.) with Arabidopsis thaliana

Pinus taeda L. (loblolly pine) and Arabidopsis thaliana differ greatly in form, ecological niche, evolutionary history, and genome size. Arabidopsis is a small, herbaceous, annual dicotyledon, whereas pines are large, long-lived, coniferous forest trees. Such diverse plants might be expected to differ in a large number of functional genes. We have obtained and analyzed 59,797 expressed sequence tags (ESTs) from wood-forming tissues of loblolly pine and compared them to the gene sequences inferred from the complete sequence of the Arabidopsis genome. Approximately 50% of pine ESTs have no apparent homologs in Arabidopsis or any other angiosperm in public databases. When evaluated by using contigs containing long, high-quality sequences, we find a higher level of apparent homology between the inferred genes of these two species. For those contigs 1,100 bp or longer, ≈90% have an apparent Arabidopsis homolog (E value < 10-10). Pines and Arabidopsis last shared a common ancestor ≈300 million years ago. Few genes would be expected to retain high sequence similarity for this time if they did not have essential functions. These observations suggest substantial conservation of gene sequence in seed plants.

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.

Lignin and Biomass: A Negative Correlation for Wood Formation and Lignin Content in Trees

Studies in populations of forest tree hybrids have shown a negative correlation of biomass growth (usually measured as wood volume) and lignin content ([Kirst et al., 2004][1]; [Novaes et al., 2009][2]). The control of growth and lignin appears to be highly regulated, implying that selection for

Aluminum tolerance in maize is associated with higher MATE1 gene copy number

Genome structure variation, including copy number variation and presence/absence variation, comprises a large extent of maize genetic diversity; however, its effect on phenotypes remains largely unexplored. Here, we describe how copy number variation underlies a rare allele that contributes to maize aluminum (Al) tolerance. Al toxicity is the primary limitation for crop production on acid soils, which make up 50% of the world’s potentially arable lands. In a recombinant inbred line mapping population, copy number variation of the Al tolerance gene multidrug and toxic compound extrusion 1 (MATE1) is the basis for the quantitative trait locus of largest effect on phenotypic variation. This expansion in MATE1 copy number is associated with higher MATE1 expression, which in turn results in superior Al tolerance. The three MATE1 copies are identical and are part of a tandem triplication. Only three maize inbred lines carrying the three-copy allele were identified from maize and teosinte diversity panels, indicating that copy number variation for MATE1 is a rare, and quite likely recent, event. These maize lines with higher MATE1 copy number are also Al-tolerant, have high MATE1 expression, and originate from regions of highly acidic soils. Our findings show a role for copy number variation in the adaptation of maize to acidic soils in the tropics and suggest that genome structural changes may be a rapid evolutionary response to new environments.

Transcriptional profiling of aluminum toxicity and tolerance responses in maize roots.

Aluminum (Al) toxicity is a major factor limiting crop yields on acid soils. There is considerable genotypic variation for Al tolerance in most common plant species. In maize (Zea mays), Al tolerance is a complex phenomenon involving multiple genes and physiological mechanisms yet uncharacterized. To begin elucidating the molecular basis of maize Al toxicity and tolerance, a detailed temporal analysis of root gene expression under Al stress was performed using microarrays with Al-tolerant and Al-sensitive genotypes. Al altered the expression of significantly more genes in the Al-sensitive genotype, presumably as a result of more severe Al toxicity. Nevertheless, several Al-regulated genes exhibited higher expression in the Al-tolerant genotype. Cell wall-related genes, as well as low phosphate-responsive genes, were found to be regulated by Al. In addition, the expression patterns of genes related to Al-activated citrate release indicated that in maize this mechanism is probably regulated by the expression level and/or function of the citrate transporter. This study is the first comprehensive survey of global transcriptional regulation under Al stress. The results described here will help to further our understanding of how mechanisms of Al toxicity and tolerance in maize are regulated at the transcriptional level.

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.