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Establishing links between phenotypes and molecular variants is of central importance to accelerate genetic improvement of economically important plant species. Our work represents the first genome-wide association study to the inherently complex and currently poorly understood genetic architecture of industrially relevant wood traits. Here, we employed an Illumina Infinium 34K single nucleotide polymorphism (SNP) genotyping array that generated 29,233 high-quality SNPs in c. 3500 broad-based candidate genes within a population of 334 unrelated Populus trichocarpa individuals to establish genome-wide associations. The analysis revealed 141 significant SNPs (α ≤ 0.05) associated with 16 wood chemistry/ultrastructure traits, individually explaining 3-7% of the phenotypic variance. A large set of associations (41% of all hits) occurred in candidate genes preselected for their suggested a priori involvement with secondary growth. For example, an allelic variant in the FRA8 ortholog explained 21% of the total genetic variance in fiber length, when the trait's heritability estimate was considered. The remaining associations identified SNPs in genes not previously implicated in wood or secondary wall formation. Our findings provide unique insights into wood trait architecture and support efforts for population improvement based on desirable allelic variants., (© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.)

High-throughput approaches have been widely applied to elucidate the genetic underpinnings of industrially important wood properties. Wood traits are polygenic in nature, but gene hierarchies can be assessed to identify the most important gene variants controlling specific traits within complex networks defining the overall wood phenotype. We tested a large set of genetic, genomic, and phenotypic information in an integrative approach to predict wood properties in Populus trichocarpa. Nine-yr-old natural P. trichocarpa trees including accessions with high contrasts in six traits related to wood chemistry and ultrastructure were profiled for gene expression on 49k Nimblegen (Roche NimbleGen Inc., Madison, WI, USA) array elements and for 28,831 polymorphic single nucleotide polymorphisms (SNPs). Pre-selected transcripts and SNPs with high statistical dependence on phenotypic traits were used in Bayesian network learning procedures with a stepwise K2 algorithm to infer phenotype-centric networks. Transcripts were pre-selected at a much lower logarithm of Bayes factor (logBF) threshold than SNPs and were not accommodated in the networks. Using persistent variables, we constructed cross-validated networks for variability in wood attributes, which contained four to six variables with 94-100% predictive accuracy. Accommodated gene variants revealed the hierarchy in the genetic architecture that underpins substantial phenotypic variability, and represent new tools to support the maximization of response to selection., (© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.)