Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce (original) (raw)
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Genetics, 2011
Marker-assisted selection holds promise for highly influencing tree breeding, especially for wood traits, by considerably reducing breeding cycles and increasing selection accuracy. In this study, we used a candidate gene approach to test for associations between 944 single-nucleotide polymorphism markers from 549 candidate genes and 25 wood quality traits in white spruce. A mixed-linear model approach, including a weak but nonsignificant population structure, was implemented for each marker–trait combination. Relatedness among individuals was controlled using a kinship matrix estimated either from the known half-sib structure or from the markers. Both additive and dominance effect models were tested. Between 8 and 21 single-nucleotide polymorphisms (SNPs) were found to be significantly associated (P ≤ 0.01) with each of earlywood, latewood, or total wood traits. After controlling for multiple testing (Q ≤ 0.10), 13 SNPs were still significant across as many genes belonging to diffe...
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Association mapping identified novel candidate loci affecting wood formation in Norway spruce
The project aims at providing fundamental information towards the building of an efficient and highly informative breeders toolkit for Norway spruce (Picea abies). To increase the number of known single nucleotide polymorphisms (SNPs) and functional markers available for research and tree breeding, we used the exome capture method. The exons were captured in 521 mother trees using in house designed probes. 80 000 hybridization probes were designed from the Norway spruce genome 1.0. SNP detection was performed using GATK haplotype caller using scaffolds from the 80 000 probe design after initially mapping to the whole Norway spruce genome v1.0. A total of 545 546 high quality SNPs were acquired. After filtering the data using MAF 0.05 and Missingness 20% 160 366 SNPs remained. The SNPs were then used for association mapping studies of wood chemistry traits, which also include growth, density separated into latewood and earlywood. Estimated breeding values from 5618 trees of the 521 open-pollinated families in two 21-year-old Norway spruce progeny trials in southern Sweden were used as the phenotype data for all the 21 traits studied. The growth trajectories of the phenotype data were determined as non-linear. A simple quadratic spline model was then applied in order to define the latent traits. Association mapping was performed using a multilocus LASSO penalized regression method and a stability selection method in order to calculate the stability selection probability of each SNP being selected to the model. With the first five principle components calculated based on the genotype data being included as covariates into the model to take the population structure into account. Their stability probabilities were also calculated. The threshold for declaring a SNP to be significant or suggestive was 0.6976 and 0.5395, by maximum allowing 1 and 5 false positive selections, respectively. Association studies has already yielded SNPs significantly associated with density and growth, our focus now turning to using this method to perform association analysis phenology datasets. The mining of scaffolds with significant SNPs has already started in order to try and identify candidate genes for the traits studied.
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New Phytologist, 2013
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.
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Association mapping identified novel candidate loci affecting wood formation in Norway 1 spruce 2
The dissection of the genetic background underlying wood formation in Norway spruce (Picea abies (L.) Karst.) is of large importance economically. Our study presents the first genome wide level approach at revealing the genetic basis underlying these vital wood traits in Norway spruce. 80000 probes designed from the Norway spruce genome v1.0 were employed in sequence capture resulting in 178101 high quality SNPs. Breeding values were estimated (EBVs) from 5618 trees of 521 open-pollinated families in two 21-year-old progeny trials in southern Sweden for 21 traits. With three novel traits, number of cells per ring (NC), wood percentage (WP) and trees with positive correlation between density and growth, that is correlated with more biomass, termed Mass Index (MI) being introduced. A quadratic spline model was applied in order to extract latent traits from these EBVs. Association mapping was performed using a multilocus LASSO penalized regression and a stability selection method for SNP inclusion to the model. Our QTL model resulted in 51 significant associations with 39 candidate genes that are involved in wood formation. The results advance our understanding of the genetics influencing wood traits, identify novel candidate genes for further functional studies and support current Norway spruce breeding efforts.
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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.
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▲ Through the use of genome-wide association (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our genome-wide association mapping study presents the first such an effort in Norway spruce (Picea abies (L). Karst.) for the traits related to wood tracheid characteristics. ▲ The study employed an exome capture genotyping approach that generated 178 101 high quality Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. ▲ We applied a LASSO based association mapping method using a functional multi-locus mapping approach that utilizes latent traits, with a stability selection probability method (Frequency) as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). Latent traits were derived from estimated breeding values (EBVs) by the application of a quadratic spline model. ▲ The findings presented from the GWAS analysis have provided 30 loci that are potentially controlling tracheids dimensions, their cell wall thickness and microfibril
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The New phytologist, 2017
Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments ...
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