The application of gene transfer technology to plant breeding suggests opportunities for exciting improvements, but the approach in general is limited to the transfer of single genes with obvious phenotypes. Presently, one can consider improving a cultivar through the incorporation of herbicide resistance and possibly some disease resistance genes, but this technology does not address those traits of greatest concern to breeders such as yield, standability, maturity, i.e. any trait exhibiting quantitative expression and inheritance. This limitation stems principally from our lack of understanding as to exactly how single genes influence complex traits as well as to a lack of methods to identify and clone such genes.
D. Robertson previously suggested (MNL 58:10-11, 1984 and 59:8, 1985) that different alleles at a single locus could result in a broad range of phenotypes. In other words, the level of expression or type of gene product produced from an individual locus can determine whether the isolate is recognized as a simply inherited, extreme phenotype mutant or as a variant for a trait with multigenic expression and inheritance. Therefore the same gene with different mutations could be identified as a "qualitative" locus but in other cases as a "quantitative" locus. He then hypothesized that the cloning of QTLs (quantitative trait loci) could be facilitated by recognizing this relationship and targeting those loci with similar but more extreme phenotypes. For example, cloning a gene corresponding to an extreme dwarf or defective kernel phenotype would then permit the isolation of other alleles at the same locus from lines with more moderate and useful alterations in plant height or seed yield characteristics. A difficulty with this approach is that although it does allow one to clone a QTL for a trait of interest, it does not predict whether that gene contributes any measurable effect to the trait in the lines of interest, i.e., is it a major gene?
We have recently explored a new method for identifying major QTLs, as well as testing whether QTLs can be associated with loci corresponding to extreme phenotype mutants in maize. Such a methodology would then facilitate cloning major QTLs through conventional transposon tagging. RFLP analysis (Nienhuis et al., Crop Science, in press) can be used to pinpoint major QTLs when examining quantitative traits, much as isozyme analysis has been previously used. The advantage here is that with our current RFLP map of maize (greater than 250 marker loci located on all ten chromosomes), we can systematically analyze individuals from segregating populations for their expression of the trait of interest as well as check all chromosomal segments for their genetic contribution to the trait. With these data, one can pinpoint major QTLs with respect to the locations of our RFLP marker loci.
In a preliminary experiment supportive both of Robertson's hypothesis and this strategy, we have examined several quantitative traits by both isozyme and RFLP analyses in collaboration with C. Stuber and M. Edwards at N.C. State. In an examination of a segregating F2 population derived from the cross Tx303 x Co159, several major QTLs for plant height were identified. In the attached figure, we show the results with several markers on chromosome 9 and their genetic contribution to overall plant height (denoted in circles above the marker loci as the percent variance accounted for by them). The advantage of multiple markers is obvious here as one can scan up and down the chromosome to find the marker which accounts for the most variance for plant height and presumably is located closest to the actual QTL, in this case the isozyme, Acp1 and RFLP #222. We would therefore pinpoint this major QTL, which accounts for at least 27% of the variance in plant height, as being located near the centromere and of interest, also very close to the known GA dwarf locus, d3. If one could confirm that this proximity was not just coincidence, then this relationship could be exploited to clone the d3 allele through transposon tagging and subsequently obtain related alleles from other lines which might function as major genes for plant height in lines with less extreme variance. Interestingly, this type of analysis allows one to deduce that the QTL for shortened plant height near d3 is recessive in gene action as also is d3.
This type of analysis can identify the genomic location of those genes accounting for the most variance for a particular trait, which we feel are most likely to be "rate-limiting" at least in the particular lines examined. Consequently changes in gene expression at that locus are most likely to result in changes in the overall phenotype and we believe these genes would therefore be the best candidate genes to clone, alter, and transfer to effect plant improvement. Hence one might be able to significantly affect quantitative traits through the introduction of single genes. An attractive precedent here is the result of Palmiter et al. (Science 222:809-814, 1983), who were able to obtain a much greater than naturally observed variation in body size, an obvious quantitative trait, after introduction of a single altered growth hormone gene into mice.
We cannot overemphasize the importance of the conventional maize map with all of its mapped morphological markers in the successful application of this approach. As we improve the correlation of our RFLP linkage map with the conventional map, major QTLs can first be identified as tightly linked to RFLP markers and then subsequently to extreme mutant phenotype loci located on the conventional map. Obviously for the purposes of this approach, one should not discontinue efforts to map morphological markers and we should in the future stress the further correlation of the conventional and RFLP linkage maps.
Tim Helentjaris and Donna Shattuck-Eidens
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