Genome-Wide Association Study of Wood Anatomical and Morphological Traits in Populus trichocarpa

Abstract

We thank the multitude of researchers from the Bioenergy Science Center and the DOE Joint Genome Institute who provided invaluable logistical support for this work. In particular, we would like to thank Kat Haiby, Brian Stanton, Rich Shuren, Carlos Gantz, and Austin Himes of Greenwood Resources for their work in establishing and maintaining the plantation, for facilitating our work at the site, and for the many insights that they have provided about Populus biology and silviculture. We would also like to thank Crissa Doeppke and Robert Sykes for their help with py-MBMS analysis.

Genetic markers and tree properties predicting wood biorefining potential in aspen (Populus tremula) bioenergy feedstock

“…While in theory GWAS is a promising strategy to identify single nucleotide polymorphisms associated with traits of interest (Tuskan et al, 2019), GWAS approaches have often found only few genetic variants associated with wood properties. Previous attempts to optimize GWAS in tree species have consisted in data-mining approaches such as reducing the genomic space under investigation to genomic areas previously associated with wood (Porth et al, 2013), or combining traits into multi-trait phenotypes that may reveal associations with pleiotropic genes (Chhetri et al, 2019, Chhetri et al, 2020. In this study, we employ another approach: decomposing complex traits into better defined traits that are more likely to relate to specific loci within the genome.…”

“…Xie et al (2018) re-evaluated previous associations in Populus trichocarpa (Porth et al, 2013, Muchero et al, 2015 by focusing on a chromosome known to harbour quantitative trait loci (QTL) for lignin composition, resulting in the identification and characterization of a new transcriptional regulator of lignin biosynthesis. Using multivariate GWAS, whereby traits can be aggregated into multi-traits for GWAS (Porter andO'Reilly, 2017, Chhetri et al, 2019), 19 SNPs related to 13 genes were identified in association to wood anatomical properties of a Populus trichocarpa natural population (Chhetri et al, 2020).…”

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

“…Moreover, to understand the genetic mechanisms underlying wood anatomical and morphological traits in Populus trichocarpa Torr. & A.Gray ex Hook., Chhetri et al (2020) [ 228 ] performed a functional enrichment analysis on coexpression nearest neighbors for gene models by the wood anatomical and morphological trait GWAS analyses. The results evidenced that the genes affecting cell wall composition and transport related genes were enriched in wood anatomy and stomatal density trait networks.…”

“…High-throughput genotyping technology and phenotyping platforms have enabled large-scale marker-trait association analysis, such as GWAS, to precisely dissect the genetic architecture of plant traits [222]. In trees, several studies have reported many putative genomic regions associated with variation of related-traits to tree phenology [223,224], wood properties [118,120,165,[225][226][227][228][229], growth (i.e., wood volume, tree height and diameter; [108,111,117,120,226,[230][231][232], resistance to pests and diseases [233][234][235][236], among others. For example, McKown et al (2018) [223] implemented a GWAS analysis with the motivation to understand the molecular mechanisms of the variation in bud-break of flowers in Populus trichocarpa Torr.…”

The Phylogenetic Roots of Addiction: Compulsive Drug Seeking, Natural and Drug-Sensitive Reward, and the Acquisition of Learned Habits

“…In addition, multiplex networks can be constructed from the vast array of omics data publically available for Drosophila. These networks can be used with lines of evidence (LOE) algorithms in order to filter GWAS/GWES results to remove false positives and capture false negatives [Weighill et al, 2018[Weighill et al, , 2019Furches et al, 2019;Chhetri et al, 2020]. The use of algorithms that can reveal interactions amongst genes and across defined phenotypes collectively offer strong predictors of addiction outcomes.…”