Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping. Issue 1 (January 2014)

• Record Type: Journal Article
• Authors: Soto‐Cerda, Braulio J.; Duguid, Scott; Booker, Helen; Rowland, Gordon; Diederichsen, Axel; Cloutier, Sylvie
• Is Part Of: Journal of integrative plant biology. Volume 56:Issue 1(2014)
• Page Start: 75
• Page End: 87
• Publication Date: 2014-01
• Subjects: Plants -- Periodicals; Plants -- China -- Periodicals; Electronic journals; 580.5
• DOI: 10.1111/jipb.12118
• Languages: English
• ISSNs: 1672-9072

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="jipb12118-sec-0001" sec-type="section"> <p>The extreme climate of the Canadian Prairies poses a major challenge to improve yield. Although it is possible to breed for yield per se, focusing on yield‐related traits could be advantageous because of their simpler genetic architecture. The Canadian flax core collection of 390 accessions was genotyped with 464 simple sequence repeat markers, and phenotypic data for nine agronomic traits including yield, bolls per area, 1, 000 seed weight, seeds per boll, start of flowering, end of flowering, plant height, plant branching, and lodging collected from up to eight environments was used for association mapping. Based on a mixed model (principal component analysis (PCA) + kinship matrix (<italic>K</italic>)), 12 significant marker‐trait associations for six agronomic traits were identified. Most of the associations were stable across environments as revealed by multivariate analyses. Statistical simulation for five markers associated with 1000 seed weight indicated that the favorable alleles have additive effects. None of the modern cultivars carried the five favorable alleles and the maximum number of four observed in any accessions was mostly in breeding lines. Our results confirmed the complex genetic architecture of yield‐related traits and the inherent difficulties associated with their identification while illustrating the potential for improvement through marker‐assisted selection.</p> </sec> </abstract>