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Field and nutrient solution tests measure similar mechanisms controlling iron deficiency chlorosis in soybean.

Iron deficiency chlorosis is a common problem in soybean grown on calcareous soil in the midwestern USA (Fehr, 1982). Some soybean varieties are genetically more efficient in iron utilization than others (Cianzio et al., 1979). A single major gene with modifiers (Cianzio and Fehr, 1980) and a polygene mechanism (Cianzio and Fehr, 1982) governing iron deficiency chlorosis in soybean have been proposed.

Field evaluations for iron efficiency in soybean have been limited by the severity of chlorosis because of environmental variability (Jessen et al., 1986). Alternative evaluation methods such as growing plants in pots (Fairbanks et al., 1987), growing plants in growth chamber (Byron and Lambert, 1983), detopping seedlings (Jessen et al., 1986; Piper et al., 1986), and using tissue culture techniques (Stephens et al., 1990) have been studied. All of these methods suffer from their own unique limitations.

Nutrient solution has also been used to evaluate IDC in soybean (Coulombe et al., 1984). The advantage to this system is that (i) it can be done during winter, (ii) each cycle of evaluation can be completed in only about 1 mo., (iii) a higher severity of chlorosis can be induced, and (iv) the problems of the heterogeneity of calcareous soils and varying environmental conditions can be avoided. High rank correlation between field and nutrient solution evaluations was obtained with eight soybean cultivars with a wide range of chlorosis ratings (Jessen, 1988a). Dragonuk et al. (1989b) reported similar genetic gain for IDC with both nutrient solution and field tests in a soybean recurrent selection program but suggested that nutrient solution and field evaluations may identify somewhat different mechanisms of response to iron stress. If this is true then intensive selection in nutrient solution may not always result in predictable genetic improvement under field conditions.

Molecular markers associated with iron efficiency based on field tests on calcareous soil have been previously identified (Diers et al., 1992; Lin et al., 1997). One major gene and several QTL controlling IDC in field tests were mapped and two separate genetic mechanisms controlling the expression of iron deficiency chlorosis in soybeans were confirmed (Lin et al., 1997). If nutrient solution and field tests measure similar genetic mechanisms, then QTL identified in nutrient solution tests should be similar to those identified in field tests.

The objectives of this study were to determine if iron deficiency QTL identified in nutrient solution were similar to QTL identified in field tests.

MATERIALS AND METHODS

Plant Materials

One hundred twenty [F.sub.2:4] lines from Pride B216 X A15 and 92 [F.sub.2:4] lines from Anoka X A7 were used in this study. These lines were evaluated previously in the field on calcareous soil in 1993 and 1994 (Lin et al., 1997).

Nutrient Solution Evaluations

Four periods of nutrient solution evaluations were conducted in greenhouse facilities at Land O'Lakes Inc.,(1) Webster City, IA, during 1994. Each population was evaluated in two different greenhouse environments or periods. In each period, populations were evaluated in a randomized complete block design with two replications. Eight seeds of each [F.sub.2:4] line and 72 seeds of each parent were germinated in plastic trays containing moistened peat moss. After 7 to 8 d, uniform seedlings of each line and parent were transplanted to plastic buckets containing 10 L of nutrient solution. Each bucket was covered with a black Plexiglas plate containing 12 holes, and three plants were grown in each hole.

From each line and parent, two plants were evaluated per replication. In each bucket, there were 36 tagged plants including two plants from each of 15 lines assigned at random to each bucket, the two parents, and one check cultivar. Preparation of nutrient solution medium, greenhouse growth conditions, and plant management were done following the methodology described by Dragonuk et al. (1989a).

Visual scores and chlorophyll concentrations were evaluated at the V2 growth stage (Fehr and Caviness, 1977). Because the chlorosis symptoms were less severe at V2, the visual scores (evaluated in both periods) and chlorophyll concentrations (evaluated in Period 2 only) also were determined at the V4 stage in the Anoka X A7 population. The visual scores were evaluated on the basis of yellowing of the young leaves; 1 = no yellowing, 2 = slight yellowing, 3 = moderate yellowing, 4 = intense yellowing, and 5 = severe yellowing with some necrosis (Cianzio et al., 1979). After rating, one trifoliolate leaf was sampled at random from both plants of a line or parent to determine chlorophyll concentration. A leaf disc (8.8-mm diam) was obtained from the middle leaflet of each trifoliolate leaf. Chlorophyll from leaf samples was extracted in 80% (v/v) acetone solution and absorbance values were measured at two wavelengths (663 and 645 nm) with a spectrophotometer. The chlorophyll concentration ([micro]g/[cm.sup.2]) was estimated according to Arnon (1949).

Phenotypic data evaluated at V2 in the two periods were combined for the analysis of variance. The effects of replication, lines, and periods were considered random factors. Broad sense heritability ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) was estimated on the basis of the expected mean squares from the combined analysis of variance (Fehr, 1987) as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] = genetic variance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] = genetic X environment interaction variance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] = experimental error variance, r number of replications, and e = number of environments. The missing data were estimated as described in Gomez and Gomez (1984).

Test of the Relationship Between the Chlorosis Symptoms and Iron

Three plants of each parent (Pride B216, Anoka, A7, and A15) were grown in two replications at each of two nutrient solutions. One nutrient solution contained a limiting amount of iron [2 [micro]M [Fe([NO.sub.3]).sub.2]] (Dragonuk et al., 1989a). The other nutrient solution [20 [micro]M of [Fe([NO.sub.3]).sub.2]] was considered a high iron availability solution. The response of plants in these two nutrient solutions was evaluated as described at the V2 stage of development and was used to test the relationship between the chlorosis of plants in nutrient solution and the availability of iron.

Construction of Linkage Maps and QTL Mapping

The number and types of markers evaluated, marker distribution, and mapping criteria were exactly as described in Lin et al. (1997). The MAPMAKER/QTL1.1 computer program (Lincoln et al., 1992) was used to determine the location of QTL and to estimate the phenotypic contribution ([R.sup.2]) of each detected QTL. To detect QTL with minor effects, a LOD value of 2.0 was used as the threshold for detecting QTL locations. The position of the LOD peak in each significant interval suggested the location of QTL. The contribution of all QTL also was estimated from multiple loci analysis. One-way analysis of variance (ANOVA) done with the SAS PROC GLM computer command was applied to detect QTL on linkage groups with only one informative marker and to confirm the presence of each QTL (SAS Institute, 1990).

RESULTS

Test of the Relationship between Iron Availability and Chlorosis in Nutrient Solution

Efficient and inefficient parents had significantly different visual scores and chlorophyll concentrations in the high iron availability [20 [micro]M [Fe([NO.sub.3]).sub.2]] nutrient solution compared with the low iron availability [2 [micro]M [Fe([NO.sub.3]).sub.2]] nutrient solution. No chlorosis symptoms were observed in either of the efficient parents in the high iron nutrient solution. Chlorophyll concentrations of efficient parents and inefficient parents in the high iron availability nutrient solution were at least twice as much as those from plants grown in the low iron availability nutrient solution (Table 1). These observations strongly indicated that the chlorosis symptoms evaluated in nutrient solution were in fact due to iron deficiency.
Table 1. Means and standard errors of visual scores and chlorophyll
concentrations at the V2 stage of four parents in 2[micro]M
[Fe([NO.sub.3]).sub.2] and 20 [micro]M [Fe([NO.sub.3]).sub.2]
nutrient solutions.

                  Visual score rating([dagger])
Cultivars/lines   2 [micro]M Fe        20 [micro]M Fe
Pride B216        3.75 [+ or -] 0.25   2.50 [+ or -] 0.50
Anoka             3.25 [+ or -] 0.20   1.80 [+ or -] 0.18
A15               1.25 [+ or -] 0.20   1.00 [+ or -] 0.00
A7                2.25 [+ or -] 0.20   1.00 [+ or -] 0.00

                  Chlorophyll concentration
                  ([micro]/[cm.sup.2])
Cultivars/lines   2 [micro]M Fe        20 [micro]M Fe
Pride B216        0.95 [+ or -] 0.59    3.25 [+ or -] 1.18
Anoka             1.19 [+ or -] 0.05    4.96 [+ or -] 1.31
A15               4.27 [+ or -] 0.35   11.46 [+ or -] 1.15
A7                3.43 [+ or -] 0.89    8.59 [+ or -] 0.64


([dagger]) Rating: 1 = no yellowing, to 5 = severe yellowing with some necrosis.

Genetic Variation

Significant differences were observed between the two periods of nutrient solution tests, and among F2A lines of each population for visual scores and chlorophyll concentrations (data not shown). The interaction between lines and periods for visual scores and chlorophyll concentrations at the V2 growth stage of both populations also was significant. Severe chlorosis was observed at the V2 stage in the Period 1 tests in both populations. The chlorosis symptoms were less severe in the Period 2 tests of both populations and was probably due to low greenhouse temperature during this periods (data not shown).

Generally, less phenotypic variation was observed in the nutrient solution tests than in the field tests (Table 2). Therefore the estimated broad sense heritabilities from nutrient solution tests were less than the field test. In nutrient solution tests, the estimated heritabilities on an entry mean basis were 25.5% for visual scores and 37.4% for chlorophyll concentrations in the Pride B216 X A15 population. The heritabilities were 34.5% and 49.2% for visual scores and chlorophyll concentrations in the Anoka X A7 population, respectively. In the previous field tests, the estimated broad sense heritability of the visual scores was 82.4% and the heritability of the chlorophyll concentration was 64.5% for the Pride B216 X A15 population. For the Anoka X A7 population the heritability of visual scores was 73.7% and for chlorophyll concentrations was 59.9% (Lin et al., 1997).
Table 2. Means and ranges of visual chlorosis score and
chlorophyll concentration distributions for the Pride B216 x A15
and Anoka x A7 populations in field (Lin et W., 1997) and
nutrient solution tests.

                                       Chlorophyll
                    Visual score       concentration
                    rating([dagger])   ([micro]g/[cm.sup.2])
                    Mean   Range       Mean   Range

                    Pride B216 X A15 population

Field               2.34   1.00-4.50   6.2    1.24-11.25
Nutrient solution   3.04   1.50-4.25   2.81   1.03-5.27

                    Anoka X A7 population

Field               2.34   1.00-3.83   6.80   2.55-11.90
Nutrient solution   2.56   1.00-4.75   4.73   1.42-9.78


([dagger]) Rating: 1 = no yellowing, to 5 = severe yellowing with some necrosis.

Linkage Maps

Because SSR (simple sequence repeat) and RFLP (restriction fragment length polymorphism) markers were specifically selected to provide good genome coverage, the markers covered most regions of the USDA-ARS/ISU soybean genetic map (Shoemaker and Olson, 1993). A detailed description of the maps for both populations has been presented previously (Lin et al., 1997).

Mapping of QTL in the Pride B216 X A15 Population

In nutrient solution, measures of chlorophyll concentration detected more QTL than visual score evaluation (Table 3), suggesting that in this test chlorophyll concentration may be more effective in detecting variation in chlorosis than visual score evaluation. QTL for chlorophyll concentration on Linkage Groups B1, B2, G, L, and N were detected (Fig. 1). Most QTL identified in the field test also were identified in the nutrient solution test (Lin et al., 1997). However, a QTL previously detected on Linkage Group H in field studies (Lin et al., 1997) was not detected in this study with combined data although it was detected by analyses of single period data (data not shown). The QTL detected on Linkage Group L in nutrient solution was detected only in 1 yr in field tests (Lin et al., 1997). Also, a newly identified QTL was mapped on Linkage Group B1. In all cases the iron efficient alleles were contributed from the efficient parent, A15. This result was also in agreement with the field test (Lin et al., 1997).

[Figure 1 ILLUSTRATION OMITTED]
Table 3. Intervals significantly associated with variations
for visual scores and chlorophyll concentrations for
combined periods at the V2 growth stage in the
Pride B216 x A15 population.

                        Visual score
                Linkage   [R.sup.2]    LOD    Parental
Interval         group       (%)      value   contrib.

Satt12-T.16        G        11.3       2.7      A15
Mng456-K41         N        21.6       3.7      A15
Multiple loci               28.0

Interval        Interval
Satt12-T.16     A118-A702
Mng456-K41      A519-Satt63
Multiple loci   Satt12-T36
                A132-A461
                Mmg456-K418
                Multiple loci

                        Chlorophyll concentration
                Linkage   [R.sup.2]    LOD    Parental
Interval         group       (%)      value   contrib.

Satt12-T.16       B1        19.6       2.3      A15
Mng456-K41        B2        12.1       3.0      A15
Multiple loci     G          9.6       2.6      A15
                  L         16.3       2.7      A15
                  N         21.7       3.8      A15
                            50.9


Mapping of QTL in the Anoka X A7 Population

A major gene affecting visual scores and chlorophyll concentrations was mapped on Linkage Group N in the Anoka X A7 population in the previous field test (Lin et al., 1997). Results from the nutrient solution test revealed that this gene also was detected by visual score evaluation at the V2 stage in nutrient solution, but it contributed less to the phenotypic variation than was observed in the field tests (Table 4). However, when visual scores were determined at V4, the major gene on Linkage Group N contributed 82% (LOD = 13.2) to visual score variation (Table 4).

Two QTL for visual scores and/or chlorophyll concentration on Linkage Groups Al and I detected in the field test (Lin et al., 1997) also were detected in the nutrient solution test for visual scores at stage V4 (Table 4 and Fig. 2). The iron efficient alleles of the QTL on Linkage Groups A1 and I were contributed by the inefficient parent Anoka.

[Figure 2 ILLUSTRATION OMITTED]

DISCUSSION

The nutrient solution developed to identify iron efficient genotypes was based on the field conditions of calcareous soil; i.e., high bicarbonate ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) concentration and low iron availability (Coulombe et al., 1984). Because the severity of chlorosis is mainly induced by increasing the concentration of bicarbonate (Jessen et al., 1986; Dragonuk et al., 1989a) and because many nutritional deficiencies, such as nitrogen, manganese, phosphorus, boron, calcium, and iron, produce chlorosis symptoms on leaves (Taiz and Zeiger, 1991), we were concerned that the variation in chlorosis in nutrient solution was truly related to iron deficiency. The demonstration of an essential element deficiency involves growing plants in nutrient solutions containing all essential elements except the element in question (Arnon and Stout, 1939). Our results showed that increasing the concentration of available iron could reduce or relieve the symptoms of chlorosis, and thus demonstrated that the chlorosis of plants grown in nutrient solution was caused by iron deficiency.

This study has demonstrated that nutrient solution and field tests identify similar QTL controlling IDC in soybean. Since the nutrient solution system identifies mechanisms of iron deficiency chlorosis of soybean similar to those detected in the field test and the evaluation using nutrient solution technique can be done during winter and is completed in a short period of time (about 1 mo.), use of field and nutrient solution tests sequentially may increase the efficiency of breeding for iron efficiency.

Generally, variation in IDC scores in nutrient solution was less than in the field test. This result was in agreement with a previous study (Dragonuk et al., 1989b). Additionally, Diers and Fehr (1989) reported that single plant evaluations had lower heritabilities than those of line evaluations in field tests and nutrient solution tests. Because of limitations in cost and space for growing plants in the greenhouse, only two plants were grown to represent an [F.sub.2:4] line. This number might be insufficient to estimate the performance of each [F.sub.2:4] line. As a result, the estimated broad sense heritabilities on an entry mean basis for visual scores and for chlorophyll concentrations were less than in the field test.

In practical plant breeding programs, breeders are careful about selecting a few of the most iron efficient plants. Diers and Fehr (1989) reported that the actual genetic gains for plants identified in nutrient solution were significantly higher than the gains from selection in the field when the upper 20% of progenies were selected. Because severity of chlorosis can be adjusted in the nutrient solution test, a gridding procedure that selects for one or a few of the best genotypes in each bucket (Gardner, 1961) might be suitable to control environmental effects and increase efficiency in the selection of the most iron efficient individuals. To obtain high indirect selection efficiency and to minimize the cost of marker tests, applying marker-assisted selection in single plant selection of early generation populations, in which the linkage disequilibrum between markers and QTL can be maintained for the trait with environmentally unstable expression is necessary. Recently, a very effective method has been suggested to increase the frequencies of both major and minor genes simultaneously in a recurrent selection program using linked markers to select for favorable alleles, or discarding all individuals with more than one major favorable allele and then selecting the most efficient of the remaining individuals (Cox, 1995).

Diers and Fehr (1989) reported no detectable difference between field and nutrient solution tests and saw no significant difference between the tests for heritabilities. Other researchers (Dragonuk et al., 1989b) suggested that nutrient solution and field tests might be partially selecting for different genetic mechanisms of responses to iron stress. The newly identified QTL on Linkage Group B1 in the Pride B216 X A15 population does not allow us to reject the possibility that slightly different factors may affect the expressing of IDC in field and nutrient conditions. However, in our work we show that similar QTL for iron deficiency chlorosis were expressed in both field and nutrient solution tests, and that most QTL detected in the field tests were also detected in the nutrient solution tests for both populations. We concluded therefore that nutrient solution and field tests resolved similar mechanisms controlling iron deficiency chlorosis in soybean.

Table 4. Intervals significantly associated with variations for visual scores and chlorophyll concentrations for combined periods at V2 and V4 growth stages in the Anoka x A7 population.
                               Visual score

                Linkage    [R.sup.2]    LOD      Parental
Interval         group       (%)        value    contrib.

V2 stage
BLT15-Sat33       N           34.7       6.2      A7
V4 stage
K258-A256         A1          28.4       4.0      Anoka
A515-K644         I           66.3       3.9      Anoka
BLT15-Sat33       N           82.0      13.2      A7
Multiple loci                 85.3

                        Chlorophyll concentration

                Linkage    [R.sup.2]    LOD      Parental
Interval         group       (%)        value    contrib.

BLT15-Sat33       N           19.2      2.8        A7

V2 stage
BLT15-Sat33
V4 stage
K258-A256
A515-K644
BLT15-Sat33
Multiple loci


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Shun-Fu Lin, James S. Baumer, Drew Ivers, Silvia Rodriguez de Cianzio, and Randy C. Shoemaker(*)

S.-F. Lin and S.R. de Cianzio, Dep of Agronomy, Iowa State Univ, Ames, IA 50011; J.S. Baumer and D. Ivers, Land O'Lakes, Inc., Webster, IA 50595; R.C. Shoemaker, USDA-ARS, Corn Insect and Crop Genetics Unit, Dep. of Agronomy, Iowa State Univ. Contribution of the North Central Region, USDA-ARS, projects No. 3236 and 3107 of the Iowa Agriculture and Home Economic Exp. Stn, Ames, IA 50011. Journal paper No. J-17337. Received 30 April 1997. (*)Corresponding author (rcsshoe@iastate.edu).

Published in Crop Sci. 38:254-259 (1998).

(1) Names are necessary to report factually on the available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by the USDA implies no approval of the product to the exclusion of others that may also be available.
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Date:Jan 1, 1998
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