Submitted to: Molecular Breeding
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/4/1997
Publication Date: N/A
Citation: N/A Interpretive Summary: Plant death due to iron deficiency is a serious problem in soybean, especially on soils with high pH. Leaf yellowing and subsequent poor plant health due to iron deficiency causes millions of dollars in lost revenue to soybean growers every year. Breeders have been attempting to breed more iron efficient soybeans for years with some success. A better understanding of the genetics controlling this trait would help us to develop more efficient beans. In this study the authors discovered that one gene can make a very large contribution to the trait, but that the gene has different effects in different cultivars. They also 'tagged' the gene with markers that can help breeders select better cultivars. This work is a breakthrough in understanding the genetic control of this complex physiological trait and will help breeders in designing strategies for developing iron efficient cultivars.
Technical Abstract: Chlorosis symptoms for one hundred and twenty F2:4 lines from a Pride B216 x A15 cross, and 92 F2:4 lines in a Anoka x A7 population were evaluated at the V4 stage (third trifoliolate leaf fully developed) in a field of calcareous soil in 1993, and at V2 (first trifoliolate leaf fully developed) and V4 stages in 1994. Eighty-nine RFLP and ten SSR markers in the Pride B216 x A15 population, and 82 RFLP, 14 SSR, and I (hilum color) markers in the Anoka x A7 population were used to construct linkage maps and to locate quantitative trait loci (QTL) affecting iron deficiency chlorosis. QTL controlling visual scores and/or chlorophyll concentrations were detected on linkage groups B2, G, H, I, L, and N of the Pride B216 x A15 population. No QTL with large effects were detected suggesting a typical polygene mechanism in this population. In contrast, in the Anoka x A7 population, one QTL contributed an average of 72.7% of the visual score variation and 68.8% of the chlorophyll concentration variation and was mapped on linkage group N. Another QTL for visual score variation, and one for chlorophyll concentration variation were detected on linkage groups A1 and I, respectively. We reclassified the quantitative data into qualitative data fitting a one major gene model according to the means of the QTL genotypic classes. The putative major gene was mapped in the same interval of linkage group N using both visual scores and chlorophyll concentrations, thus verifying that one major gene is involved in segregation for iron chlorosis deficiency in the Anoka x A7 population.