Literature DB >> 21385726

Association genetics of wood physical traits in the conifer white spruce and relationships with gene expression.

Jean Beaulieu1, Trevor Doerksen, Brian Boyle, Sébastien Clément, Marie Deslauriers, Stéphanie Beauseigle, Sylvie Blais, Pier-Luc Poulin, Patrick Lenz, Sébastien Caron, Philippe Rigault, Paul Bicho, Jean Bousquet, John Mackay.   

Abstract

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 different families, each accounting for between 3 and 5% of the phenotypic variance in 10 wood characters. Transcript accumulation was determined for genes containing SNPs associated with these traits. Significantly different transcript levels (P ≤ 0.05) were found among the SNP genotypes of a 1-aminocyclopropane-1-carboxylate oxidase, a β-tonoplast intrinsic protein, and a long-chain acyl-CoA synthetase 9. These results should contribute toward the development of efficient marker-assisted selection in an economically important tree species.

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Year:  2011        PMID: 21385726      PMCID: PMC3120141          DOI: 10.1534/genetics.110.125781

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  91 in total

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  58 in total

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