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a Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, St. Paul, MN 55108 USA
b Dep. of Biology, Univ. of Utah, Salt Lake City, UT 84112 USA
c Facultad de Agronomia, Universidad Catholica de Valpariso, Casilla 4-D, Quillota, Chile
d USDA-ARS Soybean and Alfalfa Research Lab., Beltsville, MD 20705 USA
orfxx001{at}maroon.tc.umn.edu
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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LOD 3 on 17 of the 20 linkage groups and tended to be clustered on three. QTLs with major effects (R2 > 10%) were identified for all traits, and for many, these explained more than half of the heritable variation. Comparison of QTLs between the three RI populations established that for the majority of the traits, only two alleles could be identified. In only a few instances could a third allele be detected. Many of the significant QTLs identified in one population were confirmed in another. However, an almost equal number were found in only one population, suggesting that a dependence on the genetic background for expression (epistasis) was common.
Abbreviations: cM, centimorgan • QTLs, quantitative trait loci • RI, recombinant inbred • RFLP, restriction fragment length polymorphism • SSR, simple sequence repeat
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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, it has become possible to analyze in detail the genetic basis for complex, polygenic traits (Dudley, 1993). Because quantitative traits are strongly influenced by environmental factors, deducing their genetic basis usually requires compairing mean trait values in different environments. For this purpose, recombinant inbred (RI) populations are particularly useful because segregants are homozygous and their genotypes can be reproduced by different research groups for repeated experiments in a variety of environments (Mather and Jinks, 1977). Availability of multiple RI populations makes it possible to extend such analyses to identify different segregating loci, and to compare the effects of these loci in populations with different genetic backgrounds.
We have studied the effects of genetic background in three RI populations of soybean. In a previous publication, we presented a genetic analysis of agronomic traits using a RI population of segregants derived from a cross between the soybean cultivars Minsoy and Noir 1 (Mansur et al., 1996). Because this RI population was large (240 segregants), we also were able to demonstrate the existence of epistatic effects within that population (Lark et al., 1995; Chase et al., 1997).
We now have analyzed two related large RI populations derived from crosses between Minsoy and `Archer' and between Noir 1 and Archer. Whereas Minsoy and Noir 1 were plant introductions, Archer is an elite cultivar, the product of an extensive series of crosses (Cianzio et al., 1991). It presents a genetic background in which we have the opportunity to evaluate the distortion of phenotypes that result from the introduction of QTL alleles from genotypes not used in the northern U.S. breeding program. The objective of this study was to compare the three RI populations derived from Archer, Minsoy, and Noir 1 for linkage of QTLs and molecular markers. From such a comparison we can (i) analyze the role of genetic background in the expression of quantitative trait phenotypes, (ii) identify new QTLs in segregants from different genetic backgrounds, and (iii) confirm many of the previously identified QTLs by identifying them in other genetic backgrounds.
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Materials and methods |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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Traits were measured in each of four environments with three replications in each location for the MN population and two replications each for the MA and NA populations. The MA and NA populations shared locations, whereas the MN data had been collected in earlier experiments (Mansur and Orf, 1995). Some of the trait measurements were restricted to two or three environments (e.g., leaf length, leaf width, or leaf area), and traits affected by maturity measured in one environment, Minnesota 1995, were limited by an early frost. The data presented are averages of field data across environments and have not focused on the effects of individual environments. The parental cultivars were included in all experiments. Evaluation of the MN population has already been described (Mansur et al., 1996). Parents and RI lines from the MA and NA populations were evaluated at Rosemount, MN, (45°N 93°W) in 1995, Waseca, MN, (44°N 93°W) in 1996 and 1997 and in 1995-96 at Los Andes, Chile, (34°S 70°W). A randomized complete block design with two replications in each of the four environments was used. The Chile site was irrigated. Two plots of each parent were included in each block.
The following traits were evaluated: flowering date as days from planting to flowering (R1); maturity as days from planting to maturity (R8); plant height in cm (HT); lodging scored from 1 = erect to 5 = prostrate (LDG); seed yield as kg/ha (YD); seed weight as mg/seed (SW); seed protein (PRO) and oil content (OIL) on a 130 g kg-1 moisture basis as g/kg; and leaf length (LL) as well as leaf width (LW) in cm (measured on only 10 plants per replication). The leaf measurements were made on the center leaflet of a fully expanded trifoliate leaf four nodes from the top of the plant. A detailed description of each of these traits is presented in Mansur et al. (1993a). In addition to these primary traits, several derived traits were analyzed: reproductive period (RP = R8 - R1); leaf area (AR = LLxLW); seed number as yield divided by seed weight (YD/SW); lodging per unit height (HDL), defined as its reciprocal, height divided by lodging (the ability of tall plants to remain upright); and yield per unit of height (YD/HT, for which high values are obtained from short plants with high yields). All derived traits were calculated using the primary trait data.
Creation of the Composite Genetic Map
Markers and their assays, as well as genotyping methods, have been reported previously by Mansur et al. (1996) and Cregan et al. (1999). Methods of mapping with Mapmaker (Lincoln and Lander, 1993) as well as mapping and mapmaker parameters not described below are described in these publications.
In order to assign each marker to a genome position that would be consistent in all three crosses, a composite genetic map was prepared (Fig. 1) . Data from all three populations were combined into a single Mapmaker file containing 713 individuals, and mapping was carried out using all available markers. The marker order established in the MN population was used as a starting point. MA or NA markers not segregating in the MN cross were inserted one by one with the `try' command, and the resulting map was rippled with a window size of three.
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All of these markers can be located on the published MN map (Cregan et al., 1999) or at the website www.larklab.4biz.net (verified 13 May 1999).
Statistical Methods and QTL Mapping
We used the "pearsn" routine from numerical recipes in C (Press et al., 1996) to calculate the correlation coefficients between traits. We estimated confidence intervals for the correlation coefficients by bootstrapping the populations (Press et al., 1996). For detecting QTLs, we used the sample interval mapping feature of the computer package PLABQTL (Utz and Melchinger, 1996). This program employs a multiple regression approach to interval mapping with marker order and distances determined by Mapmaker (Lincoln and Lander, 1993). Permutation tests established empirical LOD thresholds (Churchill and Doerge, 1994). The PLABQTL program carried out a simultaneous fit of all QTL detected above a threshold of 2.5 (Table 1)
. We used analysis of variance to partition the total variance in each population into genetic, environmental and genotype x environmental components (SAS, 1988).
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Results |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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The means of the RI populations for the most part fell between the means of their parents. Exceptions involved traits related to flowering and maturity (R1, R8, and RP), each of which were outside of the parental means in two of the three RI populations. Most importantly, as in the original MN population, the MA and NA populations showed transgressive segregation for most of the traits, the range of trait values for the RI lines exceeding the parental values, often by large amounts. This can only occur if the genotypes that underlie the quantitative trait values are quite different in the three parents.
Correlation coefficients between traits were calculated in each of the three RI populations and compared. Most of the values were either expected (such as positive correlations between height and lodging or between maturity and height) or uninformative because correlations, though significant, were low (r < 0.5). However, an unusual relationship was observed in the NA population between yield and other traits. Whereas the correlations found in the MA and MN populations are similar to values found for a variety of other cultivars, the segregants in the NA population (o) displayed no correlation between yield (YD) and maturity (R8) or reproductive period (RP) (Fig. 2). Similar differences between populations were observed for correlation with height (HT) and lodging (LDG) as well as for the various leaf parameters (length [LL], width [LW], and area [AR]). Finally, yield in the NA population showed a significant positive correlation with seed weight (SW), rather than the negative correlation observed in most cultivars and exhibited here by the segregants in the MN and MA populations. All of these correlations in the NA population suggest that one or more unusual QTLs for yield are segregating.
Identification of QTLs in the Three Populations
Figure 3
compares genome scans from the MN, MA, or NA populations in which the maximum likelihood score of finding a QTL for yield and four other traits is determined by interval mapping across the genome. The QTLs that we have identified are ones that have maintained their significance across environments (since trait values were averaged over environments). In each population, QTLs were determined for yield (YD), maturity (R8), reproductive period (RP), yield per cm of height (YD/HT), and height (HT). We examined these scans to determine if there were yield QTLs which were not attributable to maturity (R8) or reproductive period (RP).
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We have compared the QTL parameters of the three RI populations. Table 2 describes in detail QTLs for the 15 different traits. All QTLs with LOD scores >3.0 are presented. Whereas QTLs were identified on about half of the 20 linkage groups in any individual RI population, when taken together they were distributed over 17 of the 20 linkage groups. For 12 of the linkage groups the number of QTLs found using the three RI populations was modest, ranging from one on LG U26 (YD/HT segregating in NA) to six in LG U1 (representing six different traits, one segregating in MN and five in NA). Five linkage groups contained a majority of the identified QTLs. These were linkage groups U9, U11, U13, U14, and U22, in which 23, 26, 10, 45, and 10 QTLs were segregating respectively. The number of traits segregating varied from a low of seven in LGs U13 and U22 to as many as 13 or 14 in LGs U9, U11, or U14. In linkage groups U9, U13, U14, and U22, QTLs were found in all three RI populations. However, in LG U11, QTLs were only identified in the MA and MN populations. In many cases, QTLs found in one population also could be identified in the same location in another population (Table 4) . Finally, the three linkage groups in which no QTLs could be found (LGs U17, U21 and U24) are each as large (120–145 cM), and contain as many markers, as linkage groups which have many QTLs (such as LG U14). Therefore, the absence of QTLs cannot simply be attributed to a lack of opportunity for establishing linkage to a marker locus.
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In two populations, MN and MA, QTLs for yield did not explain much of the heritable variation (Table 1 and Fig. 3) and in the MN population the major QTL which affected yield was tightly linked to a very important maturity QTL, suggesting pleiotropy (Table 2 and Fig. 3). In contrast, QTLs accounted for more of the yield variation in the NA population and neither of the major QTLs affecting this trait were associated with maturity.
Segregation of QTLs in Different RI Populations
In Fig. 3, QTLs for maturity that occurred in LG U14 were found to segregate in all three populations, suggesting the presence of three different alleles. In contrast, maturity QTLs in LG U11 segregated only in two of the populations, consistent with two alleles. The QTL for YD/HT in LG U11 segregated only in the MA population. This suggested to us that the failure to segregate in the other two populations might be determined by additional genomic information involving epistatic effects. We therefore examined the three populations for segregation of QTLs linked to particular marker loci. In each case, a QTL was chosen if it was highly significant (LOD >4) in one of the three populations. The other two populations then were examined for the presence of the same QTL at a LOD of two or higher. In this manner, it was possible to form an estimate of the occurrence of two or three alleles as well as of the frequency of cases in which segregation occurred in only one population (Table 4).
Segregation of a particular QTL in all three of the RI populations constitutes evidence for three alleles of a QTL. There were only 12 cases in which segregation was observed in all three populations (Table 4A); 10 were found in LG U14 and two in LG U9. All involved height (HT), date of flowering (R1) or maturity (R8), and in each linkage group QTLs for the different traits were closely clustered. Thus there might be only two examples of loci with three alleles, both involving maturity QTLs with pleiotropic effects.
In contrast, there were many examples of segregation in two populations (two alleles, Table 4B) or of segregation in only one population (Table 4C). Examples of segregation in two populations involved QTLs for all of the traits, located on seven linkage groups, whose spacing indicated at least 12 clusters of QTLs. Examples of segregation in only one population involved QTLs for all but one of the traits, located on nine different linkage groups, whose spacing indicated at least 19 clusters. These data strongly indicate that for most traits, only two QTL alleles were segregating and that for many QTLs effects of genetic background limited segregation of phenotypic variation to one population, indicating epistasis.
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Discussion |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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Previous analysis of the MN population (Mansur et al., 1996) had led to three important conclusions: (i) the genomes of Minsoy and Noir 1 were quite different, leading to pronounced transgressive segregation of trait values in the progeny; (ii) for many traits, major QTLs (R2 > 10%) were observed; and (iii) agronomic QTLs were clustered on three linkage groups, U9, U11, and U14. A primary objective of this research was to determine whether these conclusions remained valid as more molecular markers became available and if so, whether the conclusions were restricted to the MN population or could be extended to other genomes.
Segregation of traits was transgressive in both the MA and NA populations as had been observed in the MN population, but was more pronounced for some traits than for others (Table 3). We have concluded from this that the QTL genotypes that control these traits are different in all three parents, and in particular, that Archer is genotypically distinct from both Minsoy and Noir 1. Consistent with this, we have identified 50 new QTLs (LOD > 3) in the MA and NA populations that did not segregate in the MN population.
A similar number of major agronomic QTLs (22–25 QTLs, R2 > 10%) were identified in each of the three RI populations (Tables 1 and 2). In this respect, the Minsoy, Archer, and Noir 1 genomes resembled each other. However, some traits seemed to be better represented by large QTLs than others. For example, height, lodging, flowering date, and maturity, as well as protein and oil all were represented by large QTLs, whereas seed weight, seed number, and leaf related traits were represented by QTLs that explained less variation.
As the number of available markers has increased, we have found that almost every linkage group (17 of 20) had one or more agronomic QTLs. However, clustering of QTLs on linkage groups U9, U11, and U14 continued to be observed (Table 2). The clustering on LG U14 is clearly common to all three parents since 12 or more QTLs were identified on this linkage group in each RI population. In contrast, the QTLs on LG U11 appear to be the result of particular Minsoy alleles, since no QTLs were identified on LG U11 in the NA population despite the fact that 12 were identified in the MA and MN populations (Table 2).
The almost equal frequency of QTLs identified in the three RI populations (Table 2) is misleading, since the maps of the three populations are not equally covered by markers. The map in Fig. 1 comprises about 2585 cM of linkage distributed between the 20 linkage groups of soybean and includes all available markers that are 5 cM or more from any other marker. Nevertheless, gaps of 20 to 40 cM exist in which QTLs cannot be identified. Moreover, the three populations differ in the numbers of these gaps found in their respective genetic maps. Gaps of 20 cM or more account for 22% of the MN linkage map, but similar gaps are much more frequent in the NA (36%) or MA (43%) maps. Thus, the 44 identified QTLs in the MA population is almost certainly a low estimate, as are the 37 identified in the NA population. As these gaps in the linkage map are filled we can expect to identify many more QTLs from the existing trait data.
In general, large QTLs tended to explain much of the heritable variation for highly heritable traits such as height, flowering date, or maturity (Table 1). An exception was seed weight, for which a large number of small QTLs (Table 2) explained as much as 50% of the heritable variation in the MN or NA populations. For yet other traits such as yield, oil or protein, and leaf related traits QTLs often failed to account for much of the heritable variation. This varied from trait to trait and from one population to another. This may have resulted from regions in the genetic maps in which the absence of marker loci prevented identification of QTLs.
A major portion of the heritable variation for yield remains to be explained by individual QTLs in all three populations (Table 1). This is particularly apparent in the MA population (Fig. 3) and underlines the pressing need for more genetic markers in this population. These would allow identification of yield genes with small effects or would identify genes that lie within gaps in the genetic maps (Fig. 1).
The correlation between yield and other traits (such as height, seed weight, R1 or R8) differs between the NA population and the MN or MA populations and most other cultivars show correlations similar to those of the MA and MN populations. Moreover, the high correlation between yield and yield per unit of height in the NA population is an indication that in this population increases in yield might not be dependent on the overall size of the plant. These correlations suggested that the NA population might reveal new genetic information on yield. This was borne out by the QTL data in Fig. 3. The three most significant NA yield QTLs were on linkage groups in which there were no QTLs for height or maturity, whereas the major yield loci in the MN population corresponded to loci for maturity, height, or both. Moreover, two of the major NA loci for yield per unit height (YD/HT) corresponded only to loci for yield, in contrast to the MN population in which the major YD/HT loci corresponded to either height or maturity. Finally, unlike the MN yield loci, the NA yield loci were not linked to loci for reproductive period.
We have compared RI populations to search for QTLs with three rather than two alleles and for QTLs which may be affected by the genotype in which they reside (epistasis). We observed compelling evidence that very few of the QTLs we observed (perhaps only two) have three alleles (Table 4A). All of the examples found could be explained by two maturity QTLs, on LGs U9 and U16, with pleiotropic effects. Indeed, the loci on LG U9 may not be tri-allelic because the NA LOD scores are borderline. There were 34 examples of QTLs in which a significant QTL in one population was confirmed in another (Table 4B). These included loci for all 15 traits located on 6 linkage groups. However, 30 QTLs with LOD > 4 could not be confirmed in another RI population (that is, no second QTL of LOD > 2 could be found). These included QTLs for 14 traits located on 11 linkage groups. (Seventeen of these had a significance greater than LOD = 5.) If other genotypic effects were not present, we would expect each QTL to be observed in at least two of the three RI populations. Therefore, it would appear that a large proportion of the QTLs identified are subject to epistatic effects. This conclusion must be tempered by the fact that only two of the three populations were grown in common environments. However, it should be noted that despite the lack of common environments, 27 QTLs found in the MN population could be confirmed in either the MA or the NA population. It seems likely that by averaging over environments we have avoided specific environmental effects.SAS Institute 1988
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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Received for publication July 15, 1998.
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TOPNOTESABSTRACTINTRODUCTIONMaterials and methodsResultsDiscussionREFERENCES |
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= NA, 
= MA). The first number to the right of the marker names indicates the map distance (in centimorgans) to the next marker, and the second number is the genome position of the marker. Linkage groups are labeled with both the Utah names and [in parentheses, e.g., (J)] the Iowa names (see 
