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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Genetics Research » Research » Publications at this Location » Publication #324024
Title: Meta-analysis to refine map position and reduce confidence intervals for delayed canopy wilting QTLs in soybean

Author
item SADAL, HWANG - University Of Arkansas
item KING, CHARLES - University Of Arkansas
item CHEN, PENGYIN - University Of Arkansas
item Ray, Jeffery - Jeff
item Cregan, Perry
item Carter Jr, Thomas
item LI, ZENGLU - University Of Georgia
item Abdel-Haleem, Hussein
item MATSON, KEVIN - Monsanto Corporation
item SCHAPAUGH JR, WILLIAM - Kansas State University
item PURCELL, LARRY - University Of Arkansas

Submitted to: Molecular Breeding
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/20/2016
Publication Date: 6/27/2016
Citation: Sadal, H., King, C., Chen, P., Ray, J.D., Cregan, P.B., Carter Jr, T.E., Li, Z., Abdel-Haleem, H.A., Matson, K.W., Schapaugh Jr, W., Purcell, L.C. 2016. Meta-analysis to refine map position and reduce confidence intervals for delayed canopy wilting QTLs in soybean. Molecular Breeding. 36:91-105.

Interpretive Summary: Drought is the number one limitation to soybean yield, globally. The most obvious solution to the problem, irrigation, is usually cost prohibitive. Thus, genetic improvement of the crop is a more cost effective means to address the problem. In this study we refine the genomic location of 8 drought tolerance genes using a meta-analysis approach, that we believe is novel in drought research. This new result is so much more precise than those from earlier approaches, that it enables breeders to use a short-cut ‘marker assisted selection’ breeding method to speed up the development of drought tolerant cultivars. We believe that this information will be of great interest to readers to the general agricultural and breeding communities.

Technical Abstract: Slow canopy wilting in soybean has been identified as a potentially beneficial trait for ameliorating drought effects on yield. Previous research identified QTLs for slow wilting from two different bi-parental populations and this information was combined with data from three other populations to identify ten QTL clusters for slow wilting on Gm02, Gm05, Gm08, Gm11, Gm14, and Gm19. A QTL cluster on Gm08 was eliminated from meta-QTL analysis because QTLs at this locus were not from independent populations; likewise, a QTL cluster on Gm14 was eliminated because results of a simulation study indicated that these QTLs could be false positives. QTLs in the remaining QTL clusters were compiled on the soybean consensus map for meta-QTL analysis. Eight meta-QTLs were identified from these eight QTL clusters. Five model selection criteria were used to determine the most appropriate number of meta-QTLs at these eight chromosomal regions. For a QTL cluster on Gm02, both one and two meta-QTL models were identified as appropriate meta-QTL models, whereas for the remaining seven QTL clusters the single meta-QTL model was most appropriate. Meta analysis decreased the confidence intervals from an average of 21.4 cM for the eight QTL clusters to 5.16 cM for the meta-QTLs. Averaged R2 values of meta-QTLs in eight QTL clusters ranged from 0.10 to 0.24. Meta-QTLs on Gm11 and Gm19 had the highest R2 values (0.22 and 0.24, respectively) as major QTLs.