Identification of Major QTLs Associated With First Pod Height and Candidate Gene Mining in Soybean

The plant hormone auxin promotes cell expansion. Forty years ago, the acid growth theory was proposed, whereby auxin promotes proton efflux to acidify the apoplast and facilitate the uptake of solutes and water to drive plant cell expansion. However, the underlying molecular and genetic bases of this process remain unclear. We have previously shown that the SAUR19-24 subfamily of auxin-induced SMALL AUXIN UP-RNA (SAUR) genes promotes cell expansion. Here, we demonstrate that SAUR proteins provide a mechanistic link between auxin and plasma membrane H(+)-ATPases (PM H(+)-ATPases) in Arabidopsis thaliana. Plants overexpressing stabilized SAUR19 fusion proteins exhibit increased PM H(+)-ATPase activity, and the increased growth phenotypes conferred by SAUR19 overexpression are dependent upon normal PM H(+)-ATPase function. We find that SAUR19 stimulates PM H(+)-ATPase activity by promoting phosphorylation of the C-terminal autoinhibitory domain. Additionally, we identify a regulatory mechanism by which SAUR19 modulates PM H(+)-ATPase phosphorylation status. SAUR19 as well as additional SAUR proteins interact with the PP2C-D subfamily of type 2C protein phosphatases. We demonstrate that these phosphatases are inhibited upon SAUR binding, act antagonistically to SAURs in vivo, can physically interact with PM H(+)-ATPases, and negatively regulate PM H(+)-ATPase activity. Our findings provide a molecular framework for elucidating auxin-mediated control of plant cell expansion. © 2014 American Society of Plant Biologists. All rights reserved.

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from approximately 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed approximately 10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0. 3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).