Background: Grain weight/size influences not only grain yield (GY) but also nutritional and appearance quality and consumer preference in Tartary buckwheat. The identification of quantitative trait loci (QTLs)/genes for grain weight/size is an important objective of Tartary buckwheat genetic research and breeding programs.
Results: Herein, we mapped the QTLs for GY, 1000-grain weight (TGW), grain length (GL), grain width (GW) and grain length-width ratio (L/W) in four environments using 221 recombinant inbred lines (XJ-RILs) derived from a cross of 'Xiaomiqiao × Jinqiaomai 2'. In total, 32 QTLs, including 7 for GY, 5 for TGW, 6 for GL, 11 for GW and 3 for L/W, were detected and distributed in 24 genomic regions. Two QTL clusters, qClu-1-3 and qClu-1-5, located on chromosome Ft1, were revealed to harbour 7 stable major QTLs for GY (qGY1.2), TGW (qTGW1.2), GL (qGL1.1 and qGL1.4), GW (qGW1.7 and qGW1.10) and L/W (qL/W1.2) repeatedly detected in three and above environments. A total of 59 homologues of 27 known plant grain weight/size genes were found within the physical intervals of qClu-1-3 and qClu-1-5. Six homologues, FtBRI1, FtAGB1, FtTGW6, FtMADS1, FtMKK4 and FtANT, were identified with both non-synonymous SNP/InDel variations and significantly differential expression levels between the two parents, which may play important roles in Tatary buckwheat grain weight/size control and were chosen as core candidate genes for further investigation.
Conclusions: Two stable major QTL clusters related to grain weight/size and six potential key candidate genes were identified by homology comparison, SNP/InDel variations and qRT‒qPCR analysis between the two parents. Our research provides valuable information for improving grain weight/size and yield in Tartary buckwheat breeding.
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http://dx.doi.org/10.1186/s12870-022-04004-x | DOI Listing |
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Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, USA.
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Accurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction network was used instead of CSPDarkNet53 as the backbone network of YOLOv4.
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