Genetic control of tracheid properties in Norway spruce wood. Baison, J., Zhou, L., Forsberg, N., Mörling, T., Grahn, T., Olsson, L., Karlsson, B., Wu, H. X., Mellerowicz, E. J., Lundqvist, S., & García-Gil, M. R. Scientific Reports, 10(1):18089, December, 2020.
Genetic control of tracheid properties in Norway spruce wood [link]Paper  doi  abstract   bibtex   1 download  
Abstract Through the use of genome-wide association studies (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our GWAS study presents the first such 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 Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. We applied a least absolute shrinkage and selection operator (LASSO) based association mapping method using a functional multi-locus mapping approach, with a stability selection probability method as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). The analysis has provided 30 significant associations, the majority of which show specific expression in wood-forming tissues or high ubiquitous expression, potentially controlling tracheids dimensions, their cell wall thickness and microfibril angle. Among the most promising candidates based on our results and prior information for other species are: Picea abies BIG GRAIN 2 ( PabBG2) with a predicted function in auxin transport and sensitivity, and MA_373300g0010 encoding a protein similar to wall-associated receptor kinases, which were both associated with cell wall thickness. The results demonstrate feasibility of GWAS to identify novel candidate genes controlling industrially-relevant tracheid traits in Norway spruce.
@article{baison_genetic_2020,
	title = {Genetic control of tracheid properties in {Norway} spruce wood},
	volume = {10},
	issn = {2045-2322},
	url = {http://www.nature.com/articles/s41598-020-72586-3},
	doi = {10.1038/s41598-020-72586-3},
	abstract = {Abstract
            
              Through the use of genome-wide association studies (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our GWAS study presents the first such 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 Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. We applied a least absolute shrinkage and selection operator (LASSO) based association mapping method using a functional multi-locus mapping approach, with a stability selection probability method as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). The analysis has provided 30 significant associations, the majority of which show specific expression in wood-forming tissues or high ubiquitous expression, potentially controlling tracheids dimensions, their cell wall thickness and microfibril angle. Among the most promising candidates based on our results and prior information for other species are:
              Picea abies BIG GRAIN 2
              (
              PabBG2)
              with a predicted function in auxin transport and sensitivity, and
              MA\_373300g0010
              encoding a protein similar to wall-associated receptor kinases, which were both associated with cell wall thickness. The results demonstrate feasibility of GWAS to identify novel candidate genes controlling industrially-relevant tracheid traits in Norway spruce.},
	language = {en},
	number = {1},
	urldate = {2021-06-07},
	journal = {Scientific Reports},
	author = {Baison, J. and Zhou, Linghua and Forsberg, Nils and Mörling, Tommy and Grahn, Thomas and Olsson, Lars and Karlsson, Bo and Wu, Harry X. and Mellerowicz, Ewa J. and Lundqvist, Sven-Olof and García-Gil, María Rosario},
	month = dec,
	year = {2020},
	pages = {18089},
}

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