DNA marker maps based on single populations are the basis for gene, loci and genomic analyses. Individual maps can be integrated to produce composite maps with higher marker densities if shared marker orders are consistent. However, estimates of marker order in composite maps must include sets of markers that were not polymorphic in multiple populations. Often some of the pooled markers were not codominant, or were not correctly scored. The soybean composite map was composed of data from five separate populations based on northern US germplasm but does not yet include 'Essex' by 'Forrest' recombinant inbred line (RIL) population (E x F) or any southern US soybean cultivars. The objectives were, to update the E x F map with codominant markers, to compare marker orders among this map, the Forrest physical map and the composite soybean map and to compare QTL identified by composite interval maps to the earlier interval maps. Two hundred and thirty seven markers were used to construct the core of the E x F map. The majority of marker orders were consistent between the maps. However, 19 putative marker inversions were detected on 12 of 20 linkage groups (LG). Eleven marker distance compressions were also found. The number of inverted markers ranged from 1 to 2 per LG. Thus, marker order inversions may be common in southern compared to northern US germplasm. A total of 61 QTL among 37 measures of six traits were detected by composite interval maps, interval maps and single point analysis. Seventeen of the QTL found in composite intervals had previously been detected among the 29 QTL found in simple interval maps. The genomic locations of the known QTL were more closely delimited. A genome sequencing project to compare Southern and Northern US soybean cultivars would catalog and delimit inverted regions and the associated QTL. Gene introgression in cultivar development programs would be accelerated.

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ments over several years which is expensive, time consuming, and labor intensive (Maughan et al., 1996). Molecular makers linked to quantitative trait loci (QTL) can assistComponents of yield are often identifiable which aid soybean [Glycine max (L.) Merr.] breeders to combine traits of low heritability, such as yield, with disease resistance. The objective of this the selection of yield (Fehr, 1987;Specht et al., 1999). study was to identify markers linked to yield QTL in two recombinantIn soybeans, the basis of yield improvement is unclear, inbred line (RIL) populations ['Essex' ϫ 'Forrest' (EϫF; n ϭ 100) but maturity and growth habit have major effects (Manand 'Flyer' ϫ 'Hartwig' (FϫH; n ϭ 94)] that also segregate for soybean sur et al., 1996;Orf et al., 1999;Specht et al., 1999). cyst nematode (SCN) resistance genes (rhg1 and Rhg4 ). Each popula-Resistance to disease is usually a strong component of tion was yield tested in four environments between 1996 and 1999. yield in disease infested environments (Njiti et al., 1998). The resistant parents produced lower yields. Heritability of yield Disease resistance in cultivars (particularly SCN resisacross four environments was 47% for EϫF and 57% for FϫH. Yield tance) has consistently been associated with a 1-2% was normally distributed in both populations. High yielding, SCN decrease in yield when disease was absent (Concibido resistant transgressive segregants were not observed. In the EϫF RIL et al., 1997). In addition, many SCN resistant cultivars population, 134 microsatellite markers were compared against yield by ANOVA and MAPMAKER QTL. Regions associated with yield appear to display poor combining ability during interwere identified by SATT294 on linkage group (LG.) C1 (P ϭ 0.006, crossing (Concibido et al., 1997). Sudden death syn-R 2 ϭ 10%), SATT440 on LG. I (P ϭ 0.007, R 2 ϭ 10%), and SATT337 drome (SDS) resistance has also been associated with on LG. K (P ϭ 0.004, R 2 ϭ 10%). Essex provided the beneficial allele low yield potential (Rupe et al., 1993). at SATT337. Mean yields among FϫH RILs were compared against Genetic maps have been useful for soybean genome 33 microsatellite markers from LG. K. In addition 136 markers from analysis. Maps have allowed the identification of many randomly selected LGs were compared with extreme phenotypes by economically important soybean genes conditioning bulk segregant analysis. Two regions on LG. K (20 cM apart) associquantitative trait loci (QTL), including those for disease ated with yield were identified by SATT326 (P ϭ 0.0004, R 2 ϭ 15%)

Soybean [ Glycine max (L.) Merr.] sudden death syndrome (SDS) caused by Fusarium solani f. sp. glycines results in severe yield losses. Resistant cultivars offer the most-effective protection against yield losses but resistant cultivars such as 'Forrest' and 'Pyramid' vary in the nature of their response to SDS. Loci underlying SDS resistance in 'Essex' x Forrest are well defined. Our objectives were to identify and characterize loci and alleles that underlie field resistance to SDS in Pyramidx'Douglas'. SDS disease incidence and disease severity were determined in replicated field trials in six environments over 4 years. One hundred and twelve polymorphic DNA markers were compared with SDS disease response among 90 recombinant inbred lines from the cross PyramidxDouglas. Two quantitative trait loci (QTLs) for resistance to SDS derived their beneficial alleles from Pyramid, identified on linkage group G by BARC-Satt163 (261-bp allele, P=0.0005, R(2)=16.0%) and linkage group N by BARC-Satt080 (230-bp allele, P=0.0009, R(2)=15.6%). Beneficial alleles of both QTLs were previously identified in Forrest. A QTL for re- sistance to SDS on linkage group C2 identified by BARC-Satt307 (292-bp allele, P=0.0008, R(2)=13.6%) derived the beneficial allele from Douglas. A beneficial allele of this QTL was previously identified in Essex. Recombinant inbred lines that carry the beneficial alleles for all three QTLs for resistance to SDS were significantly ( P

Soybean seeds contain large amounts of isoflavones or phytoestrogens such as genistein, daidzein, and glycitein that display biological effects when ingested by humans and animals. In seeds, the total amount, and amount of each type, of isoflavone varies by 5 fold between cultivars and locations. Isoflavone content and quality are one key to the biological effects of soy foods, dietary supplements, and nutraceuticals. Previously we had identified 6 loci (QTL) controlling isoflavone content using 150 DNA markers. This study aimed to identify and delimit loci underlying heritable variation in isoflavone content with additional DNA markers. We used a recombinant inbred line (RIL) population (n=100) derived from the cross of “Essex” by “Forrest,” two cultivars that contrast for isoflavone content. Seed isoflavone content of each RIL was determined by HPLC and compared against 240 polymorphic microsatellite markers by one-way analysis of variance. Two QTL that underlie seed isoflavone content were newly discovered. The additional markers confirmed and refined the positions of the six QTL already reported. The first new region anchored by the marker BARC_Satt063 was significantly associated with genistein (P=0.009, R2=29.5%) and daidzein (P=0.007 , R2=17.0%). The region is located on linkage group B2 and derived the beneficial allele from Essex. The second new region defined by the marker BARC_Satt129 was significantly associated with total glycitein (P=0.0005 , R2=32.0%). The region is located on linkage group D1a+Q and also derived the beneficial allele from Essex. Jointly the eight loci can explain the heritable variation in isoflavone content. The loci may be used to stabilize seed isoflavone content by selection and to isolate the underlying genes.