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Identification of loci governing eight agronomic traits using a GBS ‐ GWAS approach and validation by QTL mapping in soya bean
Author(s) -
Sonah Humira,
O'Donoughue Louise,
Cober Elroy,
Rajcan Istvan,
Belzile François
Publication year - 2015
Publication title -
plant biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.525
H-Index - 115
eISSN - 1467-7652
pISSN - 1467-7644
DOI - 10.1111/pbi.12249
Subject(s) - quantitative trait locus , biology , genome wide association study , locus (genetics) , phaseolus , genetics , genetic association , association mapping , microbiology and biotechnology , single nucleotide polymorphism , gene , genotype , agronomy
Summary Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study ( GWAS ) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing ( GBS ) approach was used to provide dense genome‐wide marker coverage (>47 000 SNP s) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNP s. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus ( QTL ) for these traits. In some cases, the reported QTL s were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.

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