# Soybean Genomics: Developments through the Use  of Cultivar “Forrest”

Authors: David A. Lightfoot
Journal: International Journal of Plant Genomics
 (2008)

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## Abstract

Legume crops are particularly important due to their ability to
support symbiotic nitrogen fixation, a key to sustainable crop
production and reduced carbon emissions. Soybean (Glycine max) has
a special position as a major source of increased protein and oil
production in the common grass-legume rotation. The cultivar
“Forrest” has saved US growers billions of dollars in crop
losses due to resistances programmed into the genome. Moreover,
since Forrest grows well in the north-south transition zone,
breeders have used this cultivar as a bridge between the southern
and northern US gene pools. Investment in Forrest genomics
resulted in the development of the following research tools: (i) a
genetic map, (ii) three RIL populations (96>n>975), (iii)
~200 NILs, (iv) 115 220 BACs and BIBACs, (v) a physical map,
(vi) 4 different minimum tiling path (MTP) sets, (vii) 25 123 BAC
end sequences (BESs) that encompass 18.5 Mbp spaced out from the
MTPs, and 2 000 microsatellite markers within them (viii) a map of
2408 regions each found at a single position in the genome and
2104 regions found in 2 or 4 similar copies at different genomic
locations (each of >150 kbp), (ix) a map of homoeologous
regions among both sets of regions, (x) a set of transcript
abundance measurements that address biotic stress resistance, (xi)
methods for transformation, (xii) methods for RNAi, (xiii) a
TILLING resource for directed mutant isolation, and (xiv) analyses
of conserved synteny with other sequenced genomes. The SoyGD
portal at sprovides access to the data. To date these resources assisted in
the genomic analysis of soybean nodulation and disease resistance.
This review summarizes the resources and their uses.


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## Body

## 1. Introduction

The soybean cultivar “Forrest,” a product of a USDA breeding program, represents a determinate,
Southern germplasm [1]. It was the first cultivar to possess soybean cyst
nematode (SCN) resistance associated with high yield, and is believed to have
played a key role in saving billions of US dollars during 1970s and 1980s that
would have otherwise been lost, either due to SCN or due to the poor agronomic
performance of earlier SCN resistant cultivars (see [2] and references
therein). Forrest was an important parent of modern cultivars, “Hartwig,” “Ina”
and many others that have an improved SCN resistance gene from PI437654 introgressed
into their genome [3–5]. Forrest was
also central to an understanding of the genetics of resistance to sudden death
syndrome, an important new disease of soybean [6–9].Forrest is also one of the two cultivars (the other being “Williams 82”), providing the majority of genomic tools for soybean,
available in USA (Figure1) [10, 11]. These two cultivars provide models for soybean genomics
research in the same way as are the cultivars Col
and Ler in Arabidopsis
thaliana or Mo17 and B73 in Zea mays. However, since the genomics of “Williams 82” was recently
reviewed [11], its inclusion in this article would be repetitive. The other
cultivars, which represent the worldwide germplasm variation for soybean
genomics, include the following: (i) “Noir 1,” a Korean plant introduction (PI)
[12], (ii) “Misuzudaizu,” a Japanese cultivar [13], and (iii) “Suinong14,” a Chinese cultivar [14]. The soybean community is committed to advance the genomics of all these
cultivars, which have been used in the past as resources for genomics research.
However, the intent of this review is to present an overview of the genomic
resources derived from Forrest; these genomics resources enable a wide range of
analyses that address several fundamental questions, like the following: (i)
what is the source of genetic variation in soybean improvement? [15]; (ii) what
is the role of variation in regions of genome duplication in paleopolyploid
species? [16]; (iii) how does the nodulation of legumes work? [17]; (iv) why
are protein and oil contents of seed inversely related? [18, 19]; (v) why are
seed yield and disease resistance so hard to combine? [4, 5, 15, 20]; (vi) why
is seed isoflavone content limited below 6 mg/kg? [18, 21–24]; (vii) how
does partial resistance to disease work [6–9, 18]? It is
believed that the development and use of genomics tools derived from Forrest
will help soybean researchers to provide answers to these questions.Figure 1
Soybean genomic resources and products schematic for Forrest (A) compared to
the SoyGD representation (B). Panel A. Germplasm that are exemplars of soybean
genetic diversity are shown. Selected germplasm encompass in mapped QTL a wide
variety of traits placed on the composite genetic map. BAC libraries exist for
many of the germplasm sources. Forrest BACs (shown in black) form the basis of
an MICF physical map with 6-fold coverage. A region of conserved duplication
(12-fold coverage) is shown on the right of the figure. In this region,
fingerprinted clones from two homoeologous linkage groups coalesce. Genetic
markers identified in, or derived from, BAC end sequences (BESs)  will
separate some of the duplicated conserved regions. Genetic markers anchored
from map to BAC are of little use in conserved duplicated regions. BACs from
diverse germplasm are shown as blue bars. There are 3 levels of DNA sequence
envisioned. At level 1, BESs provide a sequence every 10–15 kbp with which
to identify gene rich regions for later complete sequence determination (level
2). Arrayed BAC end sequences will be used to identify conserved syntenic
regions in the genomes of model plant species. This information will also
separate some of the duplicated conserved regions in soybean. Panel B. Shown
are the chromosome (cursor), DNA markers (top row of features, red); QTL in the
region (second row, blue); coalesced clones (purple) comprising the anchored
contigs (third row, green); BAC end sequences (fourth row black); BESs
encoding gene fragments (fifth row, puce); EST hybridizations to MTP2BH (sixth
row gold); MTP4BH clones (seventh row, dark blue); BESs-derived SSR (eight row, green).


## 2. Genetic Variation between Forrest  and Other Cultivars

An important question that received the attention of soybean researchers in the
past is how much sequence variation one can expect between Forrest and other
cultivars, if many are to be sequenced. This variation is extensive (about 1 bp
difference per 100–300 bp), when judged by using the criteria like the
following: (i) the coefficient of parentage [25], (ii) the number of shared
RFLP bands [26], (iii) polymorphism among microsatellite markers [27], and (iv)
DNA sequence comparisons (Figure 2). In soybean, the degree of linkage disequilibria among loci is high,
extending over distances that range from 50 kbp to 150 kbp [28]. Few meioses
have occurred within these regions to reshuffle the gene or DNA sequences, because
soybean is largely an inbreeding crop. In recent times, only seven or eight
crosses have been made, starting from the time when the PIs were collected to the
development of most modern US cultivars (Figure 3). Therefore, in
different parts of the genome, LD encompasses large segments and sets of genes.Comparison of MegaBlast analysis of an unduplicated region and a twice duplicated region as inferred by the
fingerprint physical map (a). Analysis of the BESs from H53F21 in quadruplicated contig 9077. These BESs contained a
very common repeat with 400 copies per haploid genome. Sequence analysis supported the inference of four copies of the region per haploid genome made from BAC fingerprint data (a). MegaBlast of H53F21 (Build4MTP8A23, gi89261445) against 7.3 million
reads with repeated masking gave 7 identical matches among 24 homoeologous sequences. Cluster 1 was composed of traces
ending in…822,…160,…569,…607,…662,…749, and …105 that shared A at position 172 (circled). Homoeolog specific
variations (polymorphisms) were evident among the 4 clusters inferred. Cluster 2 was composed of clones ending
in 749, 850, and 601 that shared C at position 172. Cluster 3 was composed of clones ending in 100, 117, and 535 that shared G at
position 172. Cluster 4 also had G at that position. TreeCluster analysis showed the most similar homoeologs clustered
into 4 separate sets as expected for regions duplicated twice (circled) (b). Analysis of the BESs from B47P08 in contig
321 from an unduplicated region. Sequence analysis supported the inferrence of an  unduplicated region made from fingerprints at 90% sequence identity (c). The sequences found among BACs resequenced from contig 9077 showing a set of SNHs (HSVs)
separated two groups of the four inferred to be present: the A cluster and
the G cluster (adapted from [29]).
(a)(b)(c)Genetic systems used with Forrest
germplasm and the inbred soybean crop (a). The ancestry
of Forrest and Hartwig showing the known cultivars that were crossed and the
relationship between Flyer and Williams 82 (b). A diagram showing how NILs
derived from RILs fix most loci but allow the continued segregation of
heterozygous regions in inbred crops like soybean. The effect is to Mendelize a
few of the loci contributing to QT while causing the majority to be fixed. A dark pod parent was crossed with a light colored pod parent;
the F1 heterozygous type (shown as purple pods) was selfed; and F2 progeny was advanced to the F5 by selfing. A
heterozygous plant at any time or heterogeneous RIL atF5:7 or later
identified is shown as purple pods. Single plants are extracted and seed increased. NILs that
result may fix the heterogeneous region to the parent 1 allele, the parent 2
allele, or are still heterogeneous. Occasionally heterozygous plants are found
within some heterogeneous NILs even at the F5:15 and the progeny of
such plants can be used to find new recombination events. Shown are the results
with Satt309 and NIL11 plant 3 and eighteen of the progeny collected from it
(adapted from [40]).
(a)(b)


### 2.1. The Essex× Forrest Population

A soybean recombinant inbred line (RIL) mapping
population (Reg. no. MP-2, NSL 431663 MAP) involving Forrest was recently
developed from the cross “Essex MAP” (PI 636326 MAP)× “Forrest MAP” (PI 636325 MAP) [10]. This RIL
population was used for constructing a genetic map [9, 24, 30] that has been
used extensively for an analysis of marker-trait associations [7–9, 24, 30–38]. The genetic
marker data encompass thousands of polymorphic markers and tens of thousands of
sequence-tagged site (STS) that were collected at SIUC by Dr. Lightfoot’s group
(Table 1) [10]. The genetic maps of E × F94 will continue to be enriched [27, 39]. The
registration of this population [10] has allowed public access to the
population and data generated from it worldwide.Table 1
Description of 20 linkage groups mapped in the Essex× Forrest mapping population. The map distances and markers distribution for
the linkage groups were generated from analysis of the 100 F5-derived
progeny from E × F.
MapNo. of markersLinkage groupNIL(a) populationsDistance (cM)TotalSSRRFLPRAPDBESs(b)EST(b)BESs(c) SSRA1673.814437458134A28259.022108475707B14164.016112323475B2553.41271415636C14150.113100313609C28213.2301947565144D1a + Q9140.0171403625303D1b+W887.41481512413D27245.419150412204E697.49603362115F4219.929165836902G12242.537191261126335H898.3961242794I9116.916110519263J740.7731357732K9150.918130559014L8103.8129039132M6105.2106138794N3145.121921015603O2116.413100356690Total1002823.43372064190772015279Unlinked(2007)000001052948510(a)NIL populations segregate for 2 or more regions on different chromosomes.(b)ESTs and BESs may appear at 2 or more locations on the linkage map if they appear in
homoeologous regions of different linkage groups.(c)BESs-SSR placedon the genetic map, many more are placed in SoyGD by
inference from marker anchored contigs.A key feature of the above mapping population is that Essex (registered in 1973 [10]) was derived from the
same southern US germplasm pool to which Forrest (registered in 1972 [1]) belongs. Consequently
the RILs share identity across about 25% of their genomes, the portion that was
monomorphic in both of the parents (Figure 3) [25, 26]. Further, the two
cultivars were selected under similar conditions and, therefore, appear rather
similar in most environments [6–10, 15–20, 30–38]. However,
detailed records of maturity dates are important, since even a single day
variation in maturity may influence the results of QTL analysis for many other
traits [10, 41]. Since morphological and developmental traits differ very
little in the population, the RILs have been used extensively to map those genes
which control biochemical and physiological traits (Table 2). For example, the
parents of the mapping population differ by resistance traits, which exhibit
both qualitative and quantitative inheritance (Table 3).Table 2
Ranges and means of selected mean traits
measured across multiple locations and years using the RIL population and the
“Essex” and “Forrest” parents. For traits 1–35 see [24];
traits 36–79 were from [39, 42] and or unpublished.
No. of trait and symbolUnitRIL populationAverageRange1. SDS disease incidenceScore48.54.4–942. SDS disease severityScore1.51.1–2.33. SDS disease indexScore9.31.1–23.94. Soybean cyst nematode IP(%)530–1005. Yield during SDSKg·ha-13.32.9–3.766. Seed daidzein contentμg·g-11314874.5–21817. Seed genistein contentμg·g-1996.8695.5–13298. Seed glycitein contentμg·g-1206.1116–30915. Total seed isoflavone contentμg·g-12516.81774.2–375921. Resistance to manganese toxicityScale 0–52.021.1–4.532. Seed yieldKg ha-13.442.64–4.1333. Leaf trigonelline content (irrigated)μg g-198.8559.87–126.9634. Leaf trigonelline content (rain-fed)μg·g-1417.94245.95–618.1835. Flower color (white: purple)color43:47na38. Mean SDS DX in ArgentinaScale 1–101.60.1–3.143. Tolerance to aluminum toxicity(%)14−20–3747. Seed protein content(%)39.537.5–41.551. Seed oil content(%)18.918.0–20.155. Resistance to insect herbivory (IP)(%)22.313.0–32.560. Seedling root growthmm8.36–11Table 3
Disease resistance that segregates among the RIL and NIL population.
Disease resistance inCausal agentA. ForrestSoybean cyst nematodeHeterodera glycines HG type 0; races 3Root-knot nematodeMeloidogyne incognitaBacterial pustuleXanthomonas
glycinesWildfirePseudomonas
syringae subsp. tabaciTarget spotAlternaria spPartialPhytophthora root rotPhytophthora
sojaeSDS root rotFusarium
virguliformeSDS leaf symptomsToxinB. EssexBacterial pustuleXanthomonas
glycinesDowny mildewPeronospora
manshuricaFrogeye leaf spotCercospora
sojinaPurple seed stain diseaseCercospora
kikuchiiPartialPhytophthora root rotPhytophthora
sojaeSDS leaf symptomsToxinC. HartwigSoybean cyst nematodeAll HG
Types from 1.2.3.4.5.6.7.Root-knot nematodeMeloidogyne
incognitaReniform nematodeRotenlenchulus
reniformisBacterial pustuleXanthomonas
axonopodis pv. glycinesWildfirePseudomonas syringae pv. tabaciTarget spotCorynespora cassiicol aPartialPhytophthora root
rotPhytophthora sojaeSDS root rotFusarium virguliformeSDS leaf symptomsToxinD. FlyerPowdery mildewcaused
byMicrosphaera diffusaPurple seed strain diseaseCercospora
kikuchiiPod and stem blightDiaporthe
phaselorumMultiracePhytophthora root rotPhytophthora
sojaeSDS leaf symptomsToxinA major limitation in using E× F population in genomics research is the small
population size (n = 100) that could preclude fine mapping [10]. To overcome this
problem, populations of near isogeneic lines (NILs; n = 40; Figure 3) were
developed from each RIL [10, 37, 38, 43]. The NIL populations are listed in
Table 1. The residual heterozygosis present in the F5 seed was largely fixed
and captured in these NILs. The heterogeneity across the RILs has been measured
to be 8%, which is more than the 6.25% expected among F5 lines [7, 24]. That increased
heterogeneity appears to be caused by selection, since rare heterozygous plants
still exist in some RILs and NILs [37, 38, 40].  Each locus that segregates in the RIL
population is expected to segregate in about eight NIL populations. Therefore,
each region in the genome will be segregating in about 420 lines (100 + 8 × 40), quite
sufficient to create fine maps of 0.25 cM resolution (Table 4). A 0.25 cM
interval represents 25–100 kbp on the
physical map [16], sufficient for candidate gene identification [37, 38].Table 4
Saturation mapping with markers on chromosome 18 in the 2–4 Mbp encompassingRhg1, Rfs1,
and Rfs2 (SDS) loci with leaf and root phenotype classes shown.
Geno typeSatt214Sat1TMD1Satt309Sat185CGG5OI03CTA13Bng122LeafRoot1EFEEFEFEFSR2EEEEEEEEFRS3EEEHEEEEFRS4EEFFEEEEERS5EFFFFEEEERS6FFFFEFFFFRR7FFFFEEEFFRS8FFFFFFFFERRConsequent to the development of the
NILs, the E× F population
was used to study the genetics of a large number of quantitative traits (QTs),
leading to the identification of quantitative trait loci (QTL; Table 2) underlying
more than seventy different traits [24, 39, 40, 42, 44–46]. Biochemical
and physiological traits included resistance to soybean sudden death syndrome
(SDS) [caused by Fusarium virguliforme] in the US and Argentina, resistance
to soybean cyst nematode (SCN; Heterodera glycine Ichinohe), seed yield,
seed quality traits, agronomic traits, water use efficiency, manganese toxicity,
aluminum toxicity, partial resistance to Phytophthora
sojae, and insect herbivory. However, new opportunities abound because dozens of traits for resistance to
pests and pathogens segregate in the population but were not yet mapped [10].
Further, the concentrations of many secondary metabolites among lines vary
widely during development and among different organs [47]. Pesticide uptake,
metabolism and degradation rates also vary among lines (unpublished).
Preliminary studies have shown the link between the genome, proteome, and
metabolome (the interactome), which can be further explored in these
segregating populations [48]. Therefore, E × F will eventually be used to map thousands of
QTL for hundreds of QT.Importantly, the NILs that have been developed from each RIL for fine mapping also allow
confirmation of QTL detected in the RIL population. For instance,cqSDS001 was assigned to a QTL confirmed by NILs derived from Ripley [49], but earlier
detected through RILs derived from Flyer [50] and “Pyramid” [6, 33]. The QTL
have also been renamed under the new rules for QTL adopted by the Soybean
Genetic Committee in 2006 [51], as a result of which cqRfs1, cqRfs2, and cqRfs4 were renamed as cqSDS003,
cqSDS002, and cqSDS004, respectively.The molecular linkage map, the RILs, and the NILs were used during the positional cloning
ofnts1, GmNARK [50], Rpg1 [17, 35], Rhg1, [38] Rhg4, [52],
and Rfs2 [37]. Many opportunities for further gene
isolations exist. Tables 2 and 3 list some of the known phenotypes that differ
between the parents and segregate among the lines and that are candidates for
gene isolation. The RIL and NIL populations provide sets of recombination
events that can be used to identify the positions of genes underlying QT [10].
Since all the lines self-fertilize, the populations can be used to provide an
immortal resource, if seed germination ability can be regenerated every five
years. This type of resource is particularly important for soybean because the
draft genome sequence will be released in April 2008 (unpublished). Combining
knowledge of locus positions with a comprehensive knowledge of gene content
will lead to the rapid isolation of many new and economically important genes
[16].Selected lines from the E× F population that contrast for mapped QTL were
also used for a variety of studies including the following: (i) to validate
assays of pathogenicity [32, 53–55], (ii) to examine the effects of resistance
genes on gene expression [34, 56, 57], (iii) to analyze components of drought
tolerance [24, 31, 36, 42, 46, 58], (iv) to validate methods of marker assisted
selection [6, 31, 59–62], and (v) to provide for germplasm releases (Figure 4)
and cultivars [6, 63]. New cultivars and new methods for selection of improved
soybean genotypes are among the most important spin-offs from the genomics
research involving Forrest soybean. Among the selected lines, E × F78
later became LS-G96 [63] and then “Gateway 512” (Gateway Seeds, Nashville, Ill, USA).
This line together with the line E × F55 was used as parents that combined moderate
resistance (carrying resistance alleles at six loci) to SDS with high yield.
The RIL E × F23 was
released as SD-X for very high resistance to SDS [34] and good yield potential
under license from Access Plant Technologies (Plymouth, Ind, USA), because it contained
beneficial alleles at all eight known resistance loci. In contrast, E × F85 is
susceptible to SDS as it contained no beneficial alleles at the known
resistance loci. It makes a great entry for sentinel plots. For animal feed and
human food, E × F52 has been used as a parent to provide very
high phytoestrogen contents to progeny (unpublished), since it contained
beneficial alleles at all the known loci underlying phytoestrogen content. Low
phytoestrogen contents are also required for estrogen sensitive consumers; E × F89 and
E × F92 were
used as parents to provide parents for low phytoestrogen in the progeny
(unpublished).An example of the use
of Forrest genomics resources for soybean germplasm improvement (a). Summary
of the map locations of the known loci for resistance to SDS. A black rectangle
indicates that the allele is segregating in that population. Nonsegregating
alleles may be either fixed to the resistance or susceptibility forms (b). An example of quantitative variation
for disease resistance identified in lines derived from Forrest. The resistant
line RIL23, left of the line, has beneficial alleles for six QTL for resistance
toFusarium virguliforme. The leaf
scorch associated with the fungal infection is evident in the neighboring RIL80
to the right of the white line.
(a)(b)


### 2.2. Related Populations Flyer by Hartwig (F× H) and Resnik by Hartwig (R × H)

The F× H and R × H
populations are integrated with E × F96
[10], since Forrest was the recurrent parent used to develop Hartwig (Figure 3)
[62] and Essex shares many alleles with the
Flyer and Resnik [15, 27]. Flyer and Resnik were sister lines derived from a
cross between a Williams 82 sister line and a commercial cultivar [64]. The F × H
has 92 RILs and R × H
has 952 RILs that have been used to confirm QTL detected in E × F96 and for fine mapping of these QTL [4, 5, 15, 50, 52]. Flyer and Resnik each
contains many genes conferring resistance against P. sojae. Both
these populations can be used to map genes underlying additional biochemical,
physiological, and some agronomic traits that include the following: (i)
resistance against Phytophthora root
rot, soybean sudden death syndrome (SDS) caused by F. virguliforme and soybean
cyst nematode (SCN), Heterodera glycine Ichinohe, (ii) seed yield [15, 50, 52], and (iii) seed quality traits. These RILs were also used to develop
SSR markers that
anchor contigs and sequence scaffolds (http://soybeangenome.siu.edu/) to
the physical map [27].


## 2.1. The Essex× Forrest Population

A soybean recombinant inbred line (RIL) mapping
population (Reg. no. MP-2, NSL 431663 MAP) involving Forrest was recently
developed from the cross “Essex MAP” (PI 636326 MAP)× “Forrest MAP” (PI 636325 MAP) [10]. This RIL
population was used for constructing a genetic map [9, 24, 30] that has been
used extensively for an analysis of marker-trait associations [7–9, 24, 30–38]. The genetic
marker data encompass thousands of polymorphic markers and tens of thousands of
sequence-tagged site (STS) that were collected at SIUC by Dr. Lightfoot’s group
(Table 1) [10]. The genetic maps of E × F94 will continue to be enriched [27, 39]. The
registration of this population [10] has allowed public access to the
population and data generated from it worldwide.Table 1
Description of 20 linkage groups mapped in the Essex× Forrest mapping population. The map distances and markers distribution for
the linkage groups were generated from analysis of the 100 F5-derived
progeny from E × F.
MapNo. of markersLinkage groupNIL(a) populationsDistance (cM)TotalSSRRFLPRAPDBESs(b)EST(b)BESs(c) SSRA1673.814437458134A28259.022108475707B14164.016112323475B2553.41271415636C14150.113100313609C28213.2301947565144D1a + Q9140.0171403625303D1b+W887.41481512413D27245.419150412204E697.49603362115F4219.929165836902G12242.537191261126335H898.3961242794I9116.916110519263J740.7731357732K9150.918130559014L8103.8129039132M6105.2106138794N3145.121921015603O2116.413100356690Total1002823.43372064190772015279Unlinked(2007)000001052948510(a)NIL populations segregate for 2 or more regions on different chromosomes.(b)ESTs and BESs may appear at 2 or more locations on the linkage map if they appear in
homoeologous regions of different linkage groups.(c)BESs-SSR placedon the genetic map, many more are placed in SoyGD by
inference from marker anchored contigs.A key feature of the above mapping population is that Essex (registered in 1973 [10]) was derived from the
same southern US germplasm pool to which Forrest (registered in 1972 [1]) belongs. Consequently
the RILs share identity across about 25% of their genomes, the portion that was
monomorphic in both of the parents (Figure 3) [25, 26]. Further, the two
cultivars were selected under similar conditions and, therefore, appear rather
similar in most environments [6–10, 15–20, 30–38]. However,
detailed records of maturity dates are important, since even a single day
variation in maturity may influence the results of QTL analysis for many other
traits [10, 41]. Since morphological and developmental traits differ very
little in the population, the RILs have been used extensively to map those genes
which control biochemical and physiological traits (Table 2). For example, the
parents of the mapping population differ by resistance traits, which exhibit
both qualitative and quantitative inheritance (Table 3).Table 2
Ranges and means of selected mean traits
measured across multiple locations and years using the RIL population and the
“Essex” and “Forrest” parents. For traits 1–35 see [24];
traits 36–79 were from [39, 42] and or unpublished.
No. of trait and symbolUnitRIL populationAverageRange1. SDS disease incidenceScore48.54.4–942. SDS disease severityScore1.51.1–2.33. SDS disease indexScore9.31.1–23.94. Soybean cyst nematode IP(%)530–1005. Yield during SDSKg·ha-13.32.9–3.766. Seed daidzein contentμg·g-11314874.5–21817. Seed genistein contentμg·g-1996.8695.5–13298. Seed glycitein contentμg·g-1206.1116–30915. Total seed isoflavone contentμg·g-12516.81774.2–375921. Resistance to manganese toxicityScale 0–52.021.1–4.532. Seed yieldKg ha-13.442.64–4.1333. Leaf trigonelline content (irrigated)μg g-198.8559.87–126.9634. Leaf trigonelline content (rain-fed)μg·g-1417.94245.95–618.1835. Flower color (white: purple)color43:47na38. Mean SDS DX in ArgentinaScale 1–101.60.1–3.143. Tolerance to aluminum toxicity(%)14−20–3747. Seed protein content(%)39.537.5–41.551. Seed oil content(%)18.918.0–20.155. Resistance to insect herbivory (IP)(%)22.313.0–32.560. Seedling root growthmm8.36–11Table 3
Disease resistance that segregates among the RIL and NIL population.
Disease resistance inCausal agentA. ForrestSoybean cyst nematodeHeterodera glycines HG type 0; races 3Root-knot nematodeMeloidogyne incognitaBacterial pustuleXanthomonas
glycinesWildfirePseudomonas
syringae subsp. tabaciTarget spotAlternaria spPartialPhytophthora root rotPhytophthora
sojaeSDS root rotFusarium
virguliformeSDS leaf symptomsToxinB. EssexBacterial pustuleXanthomonas
glycinesDowny mildewPeronospora
manshuricaFrogeye leaf spotCercospora
sojinaPurple seed stain diseaseCercospora
kikuchiiPartialPhytophthora root rotPhytophthora
sojaeSDS leaf symptomsToxinC. HartwigSoybean cyst nematodeAll HG
Types from 1.2.3.4.5.6.7.Root-knot nematodeMeloidogyne
incognitaReniform nematodeRotenlenchulus
reniformisBacterial pustuleXanthomonas
axonopodis pv. glycinesWildfirePseudomonas syringae pv. tabaciTarget spotCorynespora cassiicol aPartialPhytophthora root
rotPhytophthora sojaeSDS root rotFusarium virguliformeSDS leaf symptomsToxinD. FlyerPowdery mildewcaused
byMicrosphaera diffusaPurple seed strain diseaseCercospora
kikuchiiPod and stem blightDiaporthe
phaselorumMultiracePhytophthora root rotPhytophthora
sojaeSDS leaf symptomsToxinA major limitation in using E× F population in genomics research is the small
population size (n = 100) that could preclude fine mapping [10]. To overcome this
problem, populations of near isogeneic lines (NILs; n = 40; Figure 3) were
developed from each RIL [10, 37, 38, 43]. The NIL populations are listed in
Table 1. The residual heterozygosis present in the F5 seed was largely fixed
and captured in these NILs. The heterogeneity across the RILs has been measured
to be 8%, which is more than the 6.25% expected among F5 lines [7, 24]. That increased
heterogeneity appears to be caused by selection, since rare heterozygous plants
still exist in some RILs and NILs [37, 38, 40].  Each locus that segregates in the RIL
population is expected to segregate in about eight NIL populations. Therefore,
each region in the genome will be segregating in about 420 lines (100 + 8 × 40), quite
sufficient to create fine maps of 0.25 cM resolution (Table 4). A 0.25 cM
interval represents 25–100 kbp on the
physical map [16], sufficient for candidate gene identification [37, 38].Table 4
Saturation mapping with markers on chromosome 18 in the 2–4 Mbp encompassingRhg1, Rfs1,
and Rfs2 (SDS) loci with leaf and root phenotype classes shown.
Geno typeSatt214Sat1TMD1Satt309Sat185CGG5OI03CTA13Bng122LeafRoot1EFEEFEFEFSR2EEEEEEEEFRS3EEEHEEEEFRS4EEFFEEEEERS5EFFFFEEEERS6FFFFEFFFFRR7FFFFEEEFFRS8FFFFFFFFERRConsequent to the development of the
NILs, the E× F population
was used to study the genetics of a large number of quantitative traits (QTs),
leading to the identification of quantitative trait loci (QTL; Table 2) underlying
more than seventy different traits [24, 39, 40, 42, 44–46]. Biochemical
and physiological traits included resistance to soybean sudden death syndrome
(SDS) [caused by Fusarium virguliforme] in the US and Argentina, resistance
to soybean cyst nematode (SCN; Heterodera glycine Ichinohe), seed yield,
seed quality traits, agronomic traits, water use efficiency, manganese toxicity,
aluminum toxicity, partial resistance to Phytophthora
sojae, and insect herbivory. However, new opportunities abound because dozens of traits for resistance to
pests and pathogens segregate in the population but were not yet mapped [10].
Further, the concentrations of many secondary metabolites among lines vary
widely during development and among different organs [47]. Pesticide uptake,
metabolism and degradation rates also vary among lines (unpublished).
Preliminary studies have shown the link between the genome, proteome, and
metabolome (the interactome), which can be further explored in these
segregating populations [48]. Therefore, E × F will eventually be used to map thousands of
QTL for hundreds of QT.Importantly, the NILs that have been developed from each RIL for fine mapping also allow
confirmation of QTL detected in the RIL population. For instance,cqSDS001 was assigned to a QTL confirmed by NILs derived from Ripley [49], but earlier
detected through RILs derived from Flyer [50] and “Pyramid” [6, 33]. The QTL
have also been renamed under the new rules for QTL adopted by the Soybean
Genetic Committee in 2006 [51], as a result of which cqRfs1, cqRfs2, and cqRfs4 were renamed as cqSDS003,
cqSDS002, and cqSDS004, respectively.The molecular linkage map, the RILs, and the NILs were used during the positional cloning
ofnts1, GmNARK [50], Rpg1 [17, 35], Rhg1, [38] Rhg4, [52],
and Rfs2 [37]. Many opportunities for further gene
isolations exist. Tables 2 and 3 list some of the known phenotypes that differ
between the parents and segregate among the lines and that are candidates for
gene isolation. The RIL and NIL populations provide sets of recombination
events that can be used to identify the positions of genes underlying QT [10].
Since all the lines self-fertilize, the populations can be used to provide an
immortal resource, if seed germination ability can be regenerated every five
years. This type of resource is particularly important for soybean because the
draft genome sequence will be released in April 2008 (unpublished). Combining
knowledge of locus positions with a comprehensive knowledge of gene content
will lead to the rapid isolation of many new and economically important genes
[16].Selected lines from the E× F population that contrast for mapped QTL were
also used for a variety of studies including the following: (i) to validate
assays of pathogenicity [32, 53–55], (ii) to examine the effects of resistance
genes on gene expression [34, 56, 57], (iii) to analyze components of drought
tolerance [24, 31, 36, 42, 46, 58], (iv) to validate methods of marker assisted
selection [6, 31, 59–62], and (v) to provide for germplasm releases (Figure 4)
and cultivars [6, 63]. New cultivars and new methods for selection of improved
soybean genotypes are among the most important spin-offs from the genomics
research involving Forrest soybean. Among the selected lines, E × F78
later became LS-G96 [63] and then “Gateway 512” (Gateway Seeds, Nashville, Ill, USA).
This line together with the line E × F55 was used as parents that combined moderate
resistance (carrying resistance alleles at six loci) to SDS with high yield.
The RIL E × F23 was
released as SD-X for very high resistance to SDS [34] and good yield potential
under license from Access Plant Technologies (Plymouth, Ind, USA), because it contained
beneficial alleles at all eight known resistance loci. In contrast, E × F85 is
susceptible to SDS as it contained no beneficial alleles at the known
resistance loci. It makes a great entry for sentinel plots. For animal feed and
human food, E × F52 has been used as a parent to provide very
high phytoestrogen contents to progeny (unpublished), since it contained
beneficial alleles at all the known loci underlying phytoestrogen content. Low
phytoestrogen contents are also required for estrogen sensitive consumers; E × F89 and
E × F92 were
used as parents to provide parents for low phytoestrogen in the progeny
(unpublished).An example of the use
of Forrest genomics resources for soybean germplasm improvement (a). Summary
of the map locations of the known loci for resistance to SDS. A black rectangle
indicates that the allele is segregating in that population. Nonsegregating
alleles may be either fixed to the resistance or susceptibility forms (b). An example of quantitative variation
for disease resistance identified in lines derived from Forrest. The resistant
line RIL23, left of the line, has beneficial alleles for six QTL for resistance
toFusarium virguliforme. The leaf
scorch associated with the fungal infection is evident in the neighboring RIL80
to the right of the white line.
(a)(b)


## 2.2. Related Populations Flyer by Hartwig (F× H) and Resnik by Hartwig (R × H)

The F× H and R × H
populations are integrated with E × F96
[10], since Forrest was the recurrent parent used to develop Hartwig (Figure 3)
[62] and Essex shares many alleles with the
Flyer and Resnik [15, 27]. Flyer and Resnik were sister lines derived from a
cross between a Williams 82 sister line and a commercial cultivar [64]. The F × H
has 92 RILs and R × H
has 952 RILs that have been used to confirm QTL detected in E × F96 and for fine mapping of these QTL [4, 5, 15, 50, 52]. Flyer and Resnik each
contains many genes conferring resistance against P. sojae. Both
these populations can be used to map genes underlying additional biochemical,
physiological, and some agronomic traits that include the following: (i)
resistance against Phytophthora root
rot, soybean sudden death syndrome (SDS) caused by F. virguliforme and soybean
cyst nematode (SCN), Heterodera glycine Ichinohe, (ii) seed yield [15, 50, 52], and (iii) seed quality traits. These RILs were also used to develop
SSR markers that
anchor contigs and sequence scaffolds (http://soybeangenome.siu.edu/) to
the physical map [27].


## 3. Phenotypic Variation between Forrest  and Other Cultivars

One major limitation using the resources based on Forrest was the low amount of
genetic variation detected in the populations based on this cultivar [65]. The
implication was that the alleles detected in E × F would not be weaker variants of the
major gene effects found in weedy plant introductions (PIs). It was hypothesized
that, instead, the loci detected in the E × F population and in the material
derived from this population perhaps represented other gene systems of lower
hierarchical position and therefore lower value. Consideration of a few
examples of the locations of QTL underlying phenotypic variation between Forrest and other cultivars has been
informative regarding this issue. The results to date all infer that the
alleles underlying QTs in Forrest are variations in the same genes as the PI
alleles, if weaker in effects on QTs.


### 3.1. The Genetics of Phytoestrogen Content

The phytoestrogen content of soybeans seed mainly consists of daidzein (60%) and
genistein (∼30%) with small proportion of glycitein (∼10%) [66]. Analysis of
germplasm and elite cultivars (18, 21–24, 67–69) indicated that
phytoestrogen concentrations in some elite cultivars (∼2 mg/kg) were higher
than those in many of the ancestors of cultivated soybean (∼1 mg/kg).
Phytoestrogen content and profile varied with environment (year and location
effect) and genotype. However, the final seed content was largely controlled by
the genotype (40–60% of the variation) and is controlled by a set of about 6–12 loci [18, 24, 67]. If the content of each phytoestrogen component was controlled
independently, improvements in content by genetic selection should be possible.
For instance, raising glycitein content to the same amount as that of daidzein
could double the total phytoestrogen content. However, because heritability of
phytoestrogen content is moderate at about 40–60%, direct
selection (without DNA markers) has not been very effective. Through marker-assisted selection (MAS),
the phytoestrogen amounts were raised to 3.6 mg/kg, well above the amounts
found in elite cultivars or weedy PIs. Here, the variation programmed by the
alleles segregating in E × F population
was greater than that among the entire germplasm collection.Recently, crosses have been made betweenlines from southern Illinois and Canada
having the highest phytoestrogen contents [23] and, separately, the lines
having the lowest phytoestrogen content [67]. MAS exercised in the segregating
populations (at F4 in 2007) should lead to improvement in phytoestrogen
content. Opportunities for collaborative studies exist with sets of RILs in
maturity groups that are not adapted to be grown in southern Illinois or Canada.


### 3.2. The genetics of Seed Yield, Protein and Oil Content

The overall average increase of 1-2% per year in soybean yield witnessed during
1960–1999 was only half the yield advances achieved in corn and other out crossing crops, where genetic
diversity was not limiting [68]. As one would expect, there are hundreds of
loci controlling yield in soybean [69]. In view of this, half of the yield loci
detected in E × F
population were those which were earlier detected in other crosses [24]. These
loci could each boost seed yield by 0.2 Mg/Ha.
In contrast, substantial gains (0.9–1.1 Mg/Ha) can be
made in soybean yield by identifying unique alleles in weedy PIs and
introgressions into elite cultivars [70]. The nature of the genes altering seed
yield will be an interesting product from fine map analysis and positional
cloning.The major components ofsoybean seed yield include the following: (i)
protein (∼40%), (ii) oil (∼20%), (iii) structural carbohydrates (∼6%), (iv) water
(∼13%), (v) soluble carbohydrates (∼14%), and (vi) other metabolites (∼7%) [71].
Metabolic changes during development driven by gene expression underlie the
seed composition and yield [72]. Seed yield and composition are under polygenic
control with different genes active at different stages of seed development.
Seed traits are also associated with significant genotype × environment (G × E) interactions as observed in E × F population (see [15, 18, 19]). Again,
the G × E interactions significantly reduce the
effectiveness of visual selection based on the phenotype alone.At harvest, seed protein content is inversely
related to seed oil content and seed yield in E× F population [18, 19] as also in other
germplasm (see [68]). While some loci are implicated in all the three traits, there
are others which influence only one or two of the three traits. Several QTL
underlying soybean yield, protein, and oil content have been mapped in both the
E × F and the F × H RIL populations [5, 18]. They do correspond
with loci detected in crosses between high protein weedy types and low protein
adapted cultivars. Three QTL on linkage groups A1, A2 and linkage group E have
been fine-mapped and localized within 0.25 cM using substitution mapping to identify the
underlying genes. Isolation of these genes will partly explain the molecular
basis of the genetic control of yield and its component traits. However, a
danger here is that because different genes are active at different
stages of seed development, one would generally map only a composite trait,
based on a mean of the action of several loci. Isolation of genes by position
would not be successful in this circumstance.


### 3.3. The Genetics ofPhytophthora Root Rot Resistance

The annual soybean yield loss suffered from the root and stem rot disease
caused by the oomycete pathogen,Phytophthora
sojae is valued at about $273 million in the US
[73]. Monogenic resistance due
to a series of Rps genes has been
providing a reasonable protection to the soybean crop against the pathogen over
the last four decades [74]. Several mapped Rps-genes
are known to occur in Flyer and Resnik [50, 64]. Partial, rate-reducing resistance to many
races of P. sojae is found also in Forrest, Essex, and Hartwig. The loci providing this partial resistance
were not mapped by 2007.


### 3.4. The Genetics of SCN Resistance

Soybean cyst nematodes (Heteroderaglycines I.)
are the most damaging pests of soybean worldwide [73]. Development of resistant
cultivars is the only viable control measure [75]. Resistance genes have been
found to be located on 17 of the 20 chromosomes by 2007. A combination of recessive
genes is necessary to provide resistance against SCN populations because many
are known to be capable of overcoming all known single resistance genes. SCN
populations can be classified into 16 broad races or up to 1024 biotypes (HG
Types) [76] based on the host responses of 8 weedy indicator lines. SCN
resistance in many other adapted and weedy cultivars [9, 31] shared the same
loci underlying bigeneic inheritance in E × F [20]. The E × F population was used to isolate candidate
genes for those two loci (rhg1 and Rhg4 ; Table 4) that control resistance against
SCN race 3 (HG Type 0). Alleles of the candidate genes were identified in many
PIs through association studies [38, 77]. Paralogs of both these genes were found at new locations
in BAC libraries and whole genome shotgun (WGS) sequences [78, 79]. They appear
to be part of multigene families showing homoeology and intragenomic conserved
synteny.Three cultivars including Peking, PI437654, and Hartwig encoded 2–4 additional
genes that provide additional resistances to SCN [52, 80, 81]. Peking has alleles for resistance to races 1 and 5 that were
not transferred to Forrest [20]. Hartwig and PI437654 have complete resistance against
all races of SCN except race 0, HG Type 1.2.3.4.5.6.7.8. The location of SCN
resistance loci in F × H and R × H agreed
with those found in crosses between PIs and adapted germplasm [81, 82].
Therefore, the resistance to SCN traits that are introgressed from PIs to
Forrest-based germplasm is useful and the underlying genes can be isolated from
Forrest.


### 3.5. The Genetics of SDS Resistance

Soybean sudden death syndrome caused byFusarium virguliforme (e.g., solani f. sp. glycines) is
among the most damaging syndrome of diseases affecting soybean in the US and worldwide [73]. The syndrome is composed of a root rot disease and a leaf scorch
disease [53]. Development of resistant cultivars is the only viable control measure.
Twelve resistance loci have now been found on 8 chromosomes (Figure 4), eight
segregate in E × F [24, 44] and two additional loci segregate
in F × H [5, 50]. A combination of loci is needed to
provide resistance to both root rot (2 or more loci) and leaf scorch (all loci).
Loci for resistance to SDS were named Rfs to Rfs11 [39]. Using
NILs (Table 4), a set of candidate genes for the Rfs2 locus were
identified [37]. Among these genes, a receptor like kinase [38] and a laccase
[83] are being tested for their ability to provide resistance following
transformation and mutation (unpublished). However, the presence of a pair of
syntenic genes on linkage group O with similar DNA sequences (84%) and encoding
nearly identical amino acid sequences (98%) complicates the analysis following
reverse genetics approach.One of the two loci underlying
root rot resistance is encoded in the DNA sequence around marker OI03514 that
lies between AFLP derived SCARs, CGG5, and CTA13 on linkage group G [37]. However,
the root rot resistance locus (Table 4) lay in a region not well represented
among BAC libraries [84, 85], so that the gene isolation was delayed until the
local genome sequence could be assembled. Transcript analysis showed that the
fungus attempts to prevent gene transcription in the target roots [34, 55, 56].
Resistant cultivars prevent the poisoning of transcription by inducing stress
and defense genes that produce fungicidal metabolites within 2 days of contact
with the fungus. However, the induced genes do not appear to map to the loci
that control the SDS resistance response [57]. Instead, genes of a higher
hierarchical position in the interactome were found in this interval (unpublished).
One of these genes is expected to underlie root resistance to SDS.For the fungus,F. virguliforme causing SDS, no races are known so far in the
US [86]. When lines from E × F have been used to look at variations in pathogenicity
between strains, no convincing evidence for a host differential response was
observed (unpublished). However, different Fusarium species that are capable of
causing SDS are found in South America [86]. E × F was
planted in Argentina
since 2004, and it was shown that the SDS pathogen(s) invoked responses that
mapped to different resistance loci [39]. Therefore, the fungus does have the
potential to form races that vary in their pathogenicity. Hence, soybean
breeders should be cautious in using the available resistance genes and should
realize that stacking of all the twelve genes for full resistance would not be
wise because it would select for mutants in the pathogen populations that could
lead to the development of races.In conclusion, a variety of approaches
including QTL analysis, fine map development for  some loci,
and analysis of isolated genes have revealed that the
alleles detected in E× F are variants of the same major genes found
in weedy plant introductions (PIs) [5, 24, 41, 53]. Only few loci detected in
the E × F population and in the other materials
derived from this cross seem to represent other gene systems at a lower
hierarchical position [57]. Identification of the lower tier of genetic control
may require intercrosses among NILs or assays that relate to development, time,
position, or cell type.


## 3.1. The Genetics of Phytoestrogen Content

The phytoestrogen content of soybeans seed mainly consists of daidzein (60%) and
genistein (∼30%) with small proportion of glycitein (∼10%) [66]. Analysis of
germplasm and elite cultivars (18, 21–24, 67–69) indicated that
phytoestrogen concentrations in some elite cultivars (∼2 mg/kg) were higher
than those in many of the ancestors of cultivated soybean (∼1 mg/kg).
Phytoestrogen content and profile varied with environment (year and location
effect) and genotype. However, the final seed content was largely controlled by
the genotype (40–60% of the variation) and is controlled by a set of about 6–12 loci [18, 24, 67]. If the content of each phytoestrogen component was controlled
independently, improvements in content by genetic selection should be possible.
For instance, raising glycitein content to the same amount as that of daidzein
could double the total phytoestrogen content. However, because heritability of
phytoestrogen content is moderate at about 40–60%, direct
selection (without DNA markers) has not been very effective. Through marker-assisted selection (MAS),
the phytoestrogen amounts were raised to 3.6 mg/kg, well above the amounts
found in elite cultivars or weedy PIs. Here, the variation programmed by the
alleles segregating in E × F population
was greater than that among the entire germplasm collection.Recently, crosses have been made betweenlines from southern Illinois and Canada
having the highest phytoestrogen contents [23] and, separately, the lines
having the lowest phytoestrogen content [67]. MAS exercised in the segregating
populations (at F4 in 2007) should lead to improvement in phytoestrogen
content. Opportunities for collaborative studies exist with sets of RILs in
maturity groups that are not adapted to be grown in southern Illinois or Canada.


## 3.2. The genetics of Seed Yield, Protein and Oil Content

The overall average increase of 1-2% per year in soybean yield witnessed during
1960–1999 was only half the yield advances achieved in corn and other out crossing crops, where genetic
diversity was not limiting [68]. As one would expect, there are hundreds of
loci controlling yield in soybean [69]. In view of this, half of the yield loci
detected in E × F
population were those which were earlier detected in other crosses [24]. These
loci could each boost seed yield by 0.2 Mg/Ha.
In contrast, substantial gains (0.9–1.1 Mg/Ha) can be
made in soybean yield by identifying unique alleles in weedy PIs and
introgressions into elite cultivars [70]. The nature of the genes altering seed
yield will be an interesting product from fine map analysis and positional
cloning.The major components ofsoybean seed yield include the following: (i)
protein (∼40%), (ii) oil (∼20%), (iii) structural carbohydrates (∼6%), (iv) water
(∼13%), (v) soluble carbohydrates (∼14%), and (vi) other metabolites (∼7%) [71].
Metabolic changes during development driven by gene expression underlie the
seed composition and yield [72]. Seed yield and composition are under polygenic
control with different genes active at different stages of seed development.
Seed traits are also associated with significant genotype × environment (G × E) interactions as observed in E × F population (see [15, 18, 19]). Again,
the G × E interactions significantly reduce the
effectiveness of visual selection based on the phenotype alone.At harvest, seed protein content is inversely
related to seed oil content and seed yield in E× F population [18, 19] as also in other
germplasm (see [68]). While some loci are implicated in all the three traits, there
are others which influence only one or two of the three traits. Several QTL
underlying soybean yield, protein, and oil content have been mapped in both the
E × F and the F × H RIL populations [5, 18]. They do correspond
with loci detected in crosses between high protein weedy types and low protein
adapted cultivars. Three QTL on linkage groups A1, A2 and linkage group E have
been fine-mapped and localized within 0.25 cM using substitution mapping to identify the
underlying genes. Isolation of these genes will partly explain the molecular
basis of the genetic control of yield and its component traits. However, a
danger here is that because different genes are active at different
stages of seed development, one would generally map only a composite trait,
based on a mean of the action of several loci. Isolation of genes by position
would not be successful in this circumstance.


## 3.3. The Genetics ofPhytophthora Root Rot Resistance

The annual soybean yield loss suffered from the root and stem rot disease
caused by the oomycete pathogen,Phytophthora
sojae is valued at about $273 million in the US
[73]. Monogenic resistance due
to a series of Rps genes has been
providing a reasonable protection to the soybean crop against the pathogen over
the last four decades [74]. Several mapped Rps-genes
are known to occur in Flyer and Resnik [50, 64]. Partial, rate-reducing resistance to many
races of P. sojae is found also in Forrest, Essex, and Hartwig. The loci providing this partial resistance
were not mapped by 2007.


## 3.4. The Genetics of SCN Resistance

Soybean cyst nematodes (Heteroderaglycines I.)
are the most damaging pests of soybean worldwide [73]. Development of resistant
cultivars is the only viable control measure [75]. Resistance genes have been
found to be located on 17 of the 20 chromosomes by 2007. A combination of recessive
genes is necessary to provide resistance against SCN populations because many
are known to be capable of overcoming all known single resistance genes. SCN
populations can be classified into 16 broad races or up to 1024 biotypes (HG
Types) [76] based on the host responses of 8 weedy indicator lines. SCN
resistance in many other adapted and weedy cultivars [9, 31] shared the same
loci underlying bigeneic inheritance in E × F [20]. The E × F population was used to isolate candidate
genes for those two loci (rhg1 and Rhg4 ; Table 4) that control resistance against
SCN race 3 (HG Type 0). Alleles of the candidate genes were identified in many
PIs through association studies [38, 77]. Paralogs of both these genes were found at new locations
in BAC libraries and whole genome shotgun (WGS) sequences [78, 79]. They appear
to be part of multigene families showing homoeology and intragenomic conserved
synteny.Three cultivars including Peking, PI437654, and Hartwig encoded 2–4 additional
genes that provide additional resistances to SCN [52, 80, 81]. Peking has alleles for resistance to races 1 and 5 that were
not transferred to Forrest [20]. Hartwig and PI437654 have complete resistance against
all races of SCN except race 0, HG Type 1.2.3.4.5.6.7.8. The location of SCN
resistance loci in F × H and R × H agreed
with those found in crosses between PIs and adapted germplasm [81, 82].
Therefore, the resistance to SCN traits that are introgressed from PIs to
Forrest-based germplasm is useful and the underlying genes can be isolated from
Forrest.


## 3.5. The Genetics of SDS Resistance

Soybean sudden death syndrome caused byFusarium virguliforme (e.g., solani f. sp. glycines) is
among the most damaging syndrome of diseases affecting soybean in the US and worldwide [73]. The syndrome is composed of a root rot disease and a leaf scorch
disease [53]. Development of resistant cultivars is the only viable control measure.
Twelve resistance loci have now been found on 8 chromosomes (Figure 4), eight
segregate in E × F [24, 44] and two additional loci segregate
in F × H [5, 50]. A combination of loci is needed to
provide resistance to both root rot (2 or more loci) and leaf scorch (all loci).
Loci for resistance to SDS were named Rfs to Rfs11 [39]. Using
NILs (Table 4), a set of candidate genes for the Rfs2 locus were
identified [37]. Among these genes, a receptor like kinase [38] and a laccase
[83] are being tested for their ability to provide resistance following
transformation and mutation (unpublished). However, the presence of a pair of
syntenic genes on linkage group O with similar DNA sequences (84%) and encoding
nearly identical amino acid sequences (98%) complicates the analysis following
reverse genetics approach.One of the two loci underlying
root rot resistance is encoded in the DNA sequence around marker OI03514 that
lies between AFLP derived SCARs, CGG5, and CTA13 on linkage group G [37]. However,
the root rot resistance locus (Table 4) lay in a region not well represented
among BAC libraries [84, 85], so that the gene isolation was delayed until the
local genome sequence could be assembled. Transcript analysis showed that the
fungus attempts to prevent gene transcription in the target roots [34, 55, 56].
Resistant cultivars prevent the poisoning of transcription by inducing stress
and defense genes that produce fungicidal metabolites within 2 days of contact
with the fungus. However, the induced genes do not appear to map to the loci
that control the SDS resistance response [57]. Instead, genes of a higher
hierarchical position in the interactome were found in this interval (unpublished).
One of these genes is expected to underlie root resistance to SDS.For the fungus,F. virguliforme causing SDS, no races are known so far in the
US [86]. When lines from E × F have been used to look at variations in pathogenicity
between strains, no convincing evidence for a host differential response was
observed (unpublished). However, different Fusarium species that are capable of
causing SDS are found in South America [86]. E × F was
planted in Argentina
since 2004, and it was shown that the SDS pathogen(s) invoked responses that
mapped to different resistance loci [39]. Therefore, the fungus does have the
potential to form races that vary in their pathogenicity. Hence, soybean
breeders should be cautious in using the available resistance genes and should
realize that stacking of all the twelve genes for full resistance would not be
wise because it would select for mutants in the pathogen populations that could
lead to the development of races.In conclusion, a variety of approaches
including QTL analysis, fine map development for  some loci,
and analysis of isolated genes have revealed that the
alleles detected in E× F are variants of the same major genes found
in weedy plant introductions (PIs) [5, 24, 41, 53]. Only few loci detected in
the E × F population and in the other materials
derived from this cross seem to represent other gene systems at a lower
hierarchical position [57]. Identification of the lower tier of genetic control
may require intercrosses among NILs or assays that relate to development, time,
position, or cell type.


## 4. Structural Genomics Resources

Soybean (Glycine max L. Merr.) has a genome size of 1115 Mbp/1C [87].
The soybean genome is the product of a diploid ancestor (n = 11), that underwent aneuploid loss (n = 10), allo- and
autopolyploidization events separated by millions of years (n = 40) with reversion
to a lower ploidy after one of those two events (n = 20) [88]. Evidence that two genome duplications
occurred, 40–50 MYA and 8–10 MYA, was
supported by RFLP analysis suggesting 4–8 homoeologous
loci for most probes [89] and discontinuous variation among paralagous EST
sequences [90–92]. Even PCR-based markers that can amplify single loci from
genomic DNA amplify multiple amplicons from BAC pool DNA (Figure 2). The duplicated regions have been segmented and reshuffled
after the  polyploidization events  [16, 93–95].Recently, a systematic measurement of DNA sequence divergence between homoeologous
regions was made possible by comparing Forrest BAC end sequences with 7 million
reads from the WGS sequences of Williams 82 [29, 93]. MegaBlast searches distinguished
some regions, resolving up to 10% nonidentity between homoeologs over a 60 bp
window (Figure 2). This implied that significant sequence divergence has
occurred at about half the loci tested, as predicted from the gene-family size
distribution observed in the physical map [57] (Figure 5). Conversely, highly
conserved regions (>90% identity) exceeding about 150 kbp (the size of a
large insert clone) have been inferred in certain regions [29]. Within these
regions, 2 or 4 homoeologs can be distinguished by single nucleotide variants
that correspond to the duplicated regions of a paleopolyploid genome or
recently polyploid genome. These variants have been described as single
nucleotide polymorphisms among homoeologs (SNHs) [93] though they are commonly
called homoeologous sequence variants (HSVs) (see, e.g., [91]).Figure 5
Quality estimate for the physical maps build 4 showing measurements of BAC clones per unique band. Three
sets of distributions were inferred, representing the diverged DNA and the
conserved DNA following the two genome duplications (shown as white
lines). The 2208 single copy contigs (labeled 1–3500 after merges and
splits) encompassed diverged DNA and are each inferred to contain clones from a
single region. Contigs in the 8000
series are inferred to contain clones from two homoeologous regions. Contigs in the 9000 series are inferred to
contain clones from four homoeologous regions. Clearly, some contigs in each
set will be missplaced, hybrid contigs will occur, and ranges will overlap.Overlain on the segmented regions found
in 2 or 4 copies, the soybean genome is a composite of dispersed and contiguous
euchromatic regions [88]. The short arms of four chromosomes are entirely
heterochromatic, but in the remaining 16 chromosomes with potentially gene rich
euchromatic arms, the heterochromatin is restricted to pericentromeric regions.
Euchromatin represents 64% of the soybean genome, with a range of 40–85% on an
individual chromosome. Due to these features and the following other reasons,
analysis of soybean genome has been a challenge: (i) large genome size, (ii) serial
duplication of regions, (iii) small proportion of unique DNA, and (iv) highly
conserved repeated DNA. One reasonable prediction would be that many of the
duplicated regions would be silenced in
heterochromatin. However, a comparison of the genetic map and physical map [93–95] has shown
that duplicated segments are neither clustered nor restricted to
heterochromatic arms. Further, the gene-rich islands are not separate from the
duplicated regions. Therefore, new models to explain gene regulation that
include duplicated conditions must be developed. Lessons learned from this
exercise will help in the analysis of some legume and many dicotyledonous crop
genomes, where genome duplication is believed to have often accompanied
speciation. Breeders, who develop new cultivars through selection from the
available variation within a cultivar, will also utilize this information and
will develop new selection methods through an understanding of the effects and
benefits of partial, segmented, genome duplication.


### 4.1. BAC Libraries and Physical Maps

Construction of fingerprint-based physical maps in soybean
relied on the availability of deep-coverage high-quality
large insert genomic libraries, and a number of such public sector
large insert libraries are available in four different
plasmid vectors, providing >45-fold genome coverage. BAC libraries are available not only for Forrest and PI437654, but
also for someG. soja PIs and the wild
relatives of G. max [84, 85, 96, 97]. Among these libraries, there are three
“Forrest” BAC libraries [84, 85], available in two different plasmid vectors
with different oris and different
selectable markers (Table 5). Despite the availability of these rich BAC
resources, there are still a few regions of the genome that are not well
represented across the above set of BAC libraries. New libraries without
involving restriction digestion may help solve this problem (unpublished).Table 5
Progress in the soybean physical map builds 2 to 5.
Automated Build 2 Sept. 2001Manual Build 3 Oct 2002Manual Build 4 Oct 2003 TotalJudged by BACs/unique band to be (pploid) [unique]Manual Build 5 Jan 2008BAC clones in FPC database81,02483,02678,00178,001BACs used in contig assembly75,56878,00172,94272,837Number of singletons5,884495427,181217,942Marker anchored singletons0012063Clone in contigs (fold genome)69,68473,06945,13558,765Fold genome in contigs8.79.15.662Number of contigs5,4882,9072,854(646)[2208]521Anchoring Markers0385404(280)[124]1,523Anchored Contigs0781742(181)[223]455Contlgs contain: >25 clone220921477(268)[209]33510–25 clones3,0389201,458(433)[1025]1103–9 clones1,845850820(0)[820]432 clones38521699(0)[99]33Unique bands in the
contigs396,843345,457#258,240(64,560)257,356Length of the contigs (Mb)1,667*1,451*1.037(0.258) [0.769]1.034*Based on 4.00 kbp per unique hand. # Based on 4.05 kbp per unique band, for 2,854 contigs containing ∼68 unique bands in 15 clones, 264 duplicated region contigs
containing ∼68 unique bands in 30 clones I5,840 unique bands and 406 highly repeated region contigs containing ∼68
unique bands is 60 clones, 48,720 unique bands.A double-digest-based physical map
for the soybean genome is now nearing completion. For this purpose, soybean BACs
from five libraries belonging to three cultivars were fingerprinted and
assembled [98] using a moderate information content fingerprint method (MICF)
and FPC. The available BACs presently include 1182 Faribault BACs (∼130 kbp, EcoRI inserts, 0.125x), 860 Williams
82 BACs (∼130 kbp, HindIII
inserts, 0.1x) and 78 001 Forrest BACs that were selected from the three
libraries (125–157 kbp EcoRI, HindIII, and BamHI
inserts, 9x). Cultivar sequence variation did not appear to cause incorrect
binning of BACs by FPC. However, the first release (build 3) [98] had many
problems (Table 6), since many individual contigs appeared to contain
noncontiguous genomic regions, and in some cases, different contigs contained
the same region of the genome. Also, the available set of contigs encompassed a
space that was 300 Mbp more than the size of the soybean genome. Clone
contamination caused many of these problems, so that new methods to identify
and eliminate contaminated clones were developed [99].Table 6
Summary of sequence coverage of the three minimum tile paths (MTPs) used for BAC end sequencing
made from three BAC libraries. To calculate the percentage of the soybean
genome covered by the clones (clone coverage) in our EcoRI-(MTP4E) and BamHI
or HindIII insert libraries (MTP2BH and MTP4BH), the genome size of soybean was
assumed to be 1130 Mb. The BAC libraries were each constructed from DNA derived
from twenty five seedlings of an inbred cultivar Forrest.
MTP4EMTP4BHMTP2
BHTotalsVectorpBeloBAC11pCLD04541naInsertion siteEco RIBamHI or HindIIInaDuplicates/region112–41–4naNumber of clones38404608576806417 088Mean insert size (kbp)175 ± 7173 ± 7173 ± 7140 ± 5685Clone coverage0.70.80.21.43.1BESs good reads3 324677292413 47325 123BESs coverage (Mbp)2.95.00.79.918.5Subsequently, the publicly
available soybean BAC fingerprint database was used to create build 4 [16] with
the following specific aims: (i) to increase the number of genetic markers in
the map, (ii) to reduce the frequency of clone contamination, (iii) to rebuild
the physical map at high stringency, (iv) to examine clone density per contig,
and (v) to examine the effectiveness of the generic genome browser in
representing duplicated homoeologous regions (Table 6). Clones suspected of
contamination were listed, fingerprints
were examined, and contaminated clones removed from the FPC database.
Many (7134 about 10%) well-to-well contaminated clones were removed from the
fingerprint database. The edited database produced 2854 contigs and encompassed
1050 Mbp. In addition, homoeologous regions that might cause separate contigs
to coalesce were detected in several ways. First, contigs with high clone
density (23%) were inferred to represent
two copy (240) or four copy (406)
conserved genomic regions per haploid genome (Table 6). If the polyploid
regions could all be split using HSVs (Figure 1) [29], there would be 1624
regions with two copies and 480 regions with four copies in the soybean genome.
A second proof of this genome structure was that pairs of separate contigs that
contained the same marker anchors (69%) were inferred to represent homoeologous
but diverged genomic regions (Figure 6) [16]. A third proof came from EST
hybridizations to BAC libraries where gene families with 1, 2, 4, and 8 members
were more common than those with 3 or 5 members [57]. Finally, similarity
search within the whole genome sequence at 90% similarity showed that the
sequences that map to the contigs with
duplicated regions do have homoeologs in the sequence, whereas sequences
from single copy regions do not (Figure 2) [29, 93].Description of chromosome 18 resources at SoyGD (a). The
current GMOD representation of 50 Mbp of the 51.5 Mbp chromosome 18 (linkage
group G) in SoyGD (a). shows the build 3 version of the chromosome
(cursor), anchored contigs (top row, blue), DNA markers (second row of features,
red), QTL in the region (third row, burgundy), MTP2 clones (B, H, and E fourth
row, dark blue). Not shown here were BAC clones, ESTs, BAC end sequences, and
gene models (b) shows the build 4 representation of 10 Mbp of the 51.5 Mbp
chromosome 18 in SoyGD. Shown are the chromosome (cursor), DNA markers (top row
of features, red); QTL in the region (second row, blue); coalesced clones
(purple) comprising the anchored contigs (third row, green); BAC end sequences
(fourth row black); BESs encoding gene fragments (fifth row, puce);
EST hybridizations to MTP2BH (sixth row gold); MTP4BH clones (seventh row, dark
blue); BESs derived SSR (eighth row, green); EST
hybridizations inferred on build 4 from clones also in MTP2BH (ninth row,
blue); WGS trace file matches from MegaBlast (tenth and last row, light blue).
It is recommended for readers to visit updated site
http://bioinformatics.siu.edu/ to see a full detailed color version and a build
5 view. The gaps between contigs will be filled in build 5 by contig merges
suggested by BESs-SSRs and contig end overlap data.
(a)(b)To deal with duplicated regions, SoyGD
was adapted to distinguish homoeologous regions by showing each contig at all
potential anchor points, spread laterally, rather than as overlapping [16].
Therefore, it should be realized that the genes in such regions have duplicates
in other regions of the genome (Figure 6). This information will prove useful
in future for gene isolation by positional cloning following
a reverse genetics approach, where aneupleurotic pathways regularly cause
wide-spread failures [100–102] due to
inability to predict phenotypes reliably.In build 5, DNA sequence scaffolds (unpublished) have been used to cluster groups of
neighboring contigs. This, however, does not solve the problems faced due to
genome duplication. In many cases, (60–80%),
homoeologous variants may help separation of coalesced regions [29], but this
would require BESs
for every fingerprinted BAC clone. In a minority of regions (20–40%), sequences
longer than BESs
may be needed to correctly separate BAC clones into contigs.


### 4.2. Minimum Tile Paths

The creation of minimally redundant tile
paths (MTP) from contiguous sets of overlapping clones (contigs) in physical
maps is a critical step for structural and functional genomics [95]. The first
minimum tiling path (MTP) developed (from builds 2 and 3) contained 2 fold
redundancy of the haploid genome (2,100 Mbp). MTP2 was 14 208
clones (mean insert size 140 kbp) that were picked from the 5597 contigs of
build 2. MTP2 was constructed from three BAC libraries (BamHI (B), HindIII (H)
and EcoRI (E) inserts), encompassing the contigs of build 3 that were derived
from build 2 by a series of contig merges, but does not distinguish regions by degree
of duplication, so that many regions are redundant. The MTP2 is used in two
parts, MTP2BH and MTP2E (Table 6) because they are largely redundant and
overlap each other. Also, the vectors differ in the antibiotic resistance
conferred. Consequently, only the MTP2BH was used for development of EST map
[57].The third and fourth MTPs, called MTP4BH
and MTP4E (Table6), were each based on build 4 [95]. Each was selected as a
single path through each of the 2854 contigs. MTP4BH had 4608 clones with a mean
size 173 kbp in the large (27.6 kbp) T-DNA vector pCLD04541, which is suitable
for plant transformation and functional genomics. Plates 1–8 contained
clones from the contigs belonging to the single copy regions of the genome.
Plates 9 and 10 were picked from the duplicated and quadruplicated regions
without redundancy, so that an individual clone represented either 2 or 4
regions per haploid genome. Plates 11 and 12 contained the marker anchored
clones also used in MTP2BH. Plate 13 of MTP4BH was developed from just 6
contigs from regions with four copies by redundant picking. This set of clones
should resolve into 48 regions, if methods to separate them can be developed as
the genome sequencing is completed [93]. This set of 13 plates was used for
HICF fingerprinting by the same methods that were used for Williams 82 [11] and
PI437654 BACS [79, 96]. The BACs used for HICF will form a bridge to other
physical maps and a resource to test the ability of HICF to correctly separate
duplicated regions, particularly in the contigs in plate 13.MTP4E was designed to be 4608 BAC clones with large inserts
(mean 175 kbp) in the small (7.5 kbp) pECBAC1 vector [57, 85]. However, only
3840 clones were picked to date. Sequencing efficiency was low on this MTP and
reracking will be needed [103]. The vector is suitable for DNA sequencing and
these clones will be used for sequencing across gaps in the WGS sequence.MTP4BH and MTP4E clones each encompassed about 800 Mbp before
duplicate regions were considered. The single copy regions represented 700 Mbp [57].
In addition there were 50 Mbp from the duplicate and 50 Mbp from the
quadruplicate regions in the MTP. Because those regions were duplicate and
quadruplicate they encompass another 300 Mbp in total. MTP2BH, MTP4E, and
MTP4BH were each used for BAC-end sequencing and microsatellite integration
into the physical map [27, 39]. MTP2BH was used for EST integration to the
physical map [16, 57]. MTP4BH was used for high information content
fingerprinting for integration with the Williams 82 physical map [11, 104]. In conclusion, it appears like each MTP and the
derived BESs will
be useful to deconvolute and finish the whole genome shotgun sequence of
soybean while the whole genome sequence will help complete the physical map. A
complete MTP5BH would be a useful tool for functional genomics because clones
from these libraries were constructed in a T-DNA vector and are ready for plant
transformation. About four thousand transgenic lines made from BACs would be
enough to transfer every soybean sequence to another plant.


### 4.3. BAC End Sequences (BESs)

BAC end sequences (BESs) anchored to a robust physical map constitute an important
tool for genome analysis, and have been developed from BACs belonging to three
available MTPs including MTP2BH, MTP4BH, and MTPE4 [95, 103]. Therefore, three
sets of BESs were
available, of which the first set consisted of 13 474 good BESs derived from 8064 clones of MTP2BH(Table 5). Enquiries to GenBank nr and pat databases identified 7260
potentially geneic homologs, and an analysis of the locations of inferred genes
suggested presence of gene-rich islands on each chromosome [37]. In
addition, 42 BESs showed
homology (extending over a length of 80–341 bp at e−30 to e−300)
with DNA markers (10 RFLPs, 20 microsatellites) that were already genetically
mapped [95]. This amounts to homology with about 2% of the markers, whose
sequences are available in GenBank. Available BESs also carried as many as 1053 new SSR markers
[27, 37] that are described further in the next section.The second set of BESs consisted of 7700 good BESs reads from clones of MTP4BH (Table5) of which 4147 had homologs in the GenBank nr and pat databases [57]. The
clones in plates 11 and 12 were resequenced and so have 2 records for each BAC
end in GenBank. Resequenced clones help determine the sequence error rate and
greatly facilitate SNP detection [18, 19]. Twenty additional genetic anchors
were detected in this second set of BESs (6 RFLPs, 14 microsatellites), which represented about 1%
of the soybean markers with sequences in GenBank. This second set of BESs carried 625 SSR
markers [27, 37] that are described further in the next section. The third set
of BESs from MTP4E have recently been released and are only partly
analyzed (Table 6).The above builds of physical map representing recently duplicated regions of the genome
can be further improved with existing databases and tools. In particular, this
can be achieved by increasing the number of reliable genetic anchors derived
from BESs [27, 37]
and separating BACs from homoeologous regions with diagnostic SNPs (Figure 2)
before contigs were formed [93].


### 4.4. Genetic Map and SSR Markers Derived from BESs

The molecular genetic map for soybean
genome can be improved further through several approaches including (i)
addition of BESs
markers on the available genetic map [27, 37], (ii) bioinformatics analysis of
contig data [16] and (iii) through the use of novel approaches to error detection
[99]. The composite genetic map of soybean at SoyGD (in 2007) contained 3073 DNA
markers [16, 27], which included 1019 class I SSRs, each with >10 di- or trinucleotide repeat motifs (BARC-SSR
markers; Song et al., 2004), and a few class II SSRs with <10 di- to pentanucleotide repeats that were
mostly SIUC-SSR markers. Forrest BESs
helped in increasing the number of class I and II SSR markers for the soybean
genome, and allowed integration of BAC clones into the soybean physical map.SSRs were mined separately from the two sets of BESs described above. As mentioned above, the
first set of 10 Mbp of BAC end sequences (BESs) derived
from 13 474 reads of
7050 clones constituting MTP2BH, had 1053 SSRs (333 class I + 720 from class II),
and the second set of 5.7 Mbp BESs derived from 7700
reads from 5184 clones constituting MTP4BH, had 620 SSRs (150 class I + 480 class II). Potential
markers are shown on the MTP_SSR track at SoyGD (Figure6). About 530 primer
pairs were designed for both the sets of SSRs. These primers were 20–24 mers long with
a Tm of 55 + 1°C, and provided amplicons that were 100–500 bp long. As
many as 123 of these primers belonging to duplicated regions gave multiple
amplified products, and therefore should be avoided.Different possible motifs were not randomly distributed among the above SSRs, with AT
rich motifs being more frequent [27]. Compound SSRs having tetranucleotide
repeats clustered with di- and trinucleotide motifs were also found. About 75%
of class I and 60% of class II SSR markers were polymorphic among the parents
of four recombinant inbred line (RIL) populations. Most of the BESs-SSRs were located on the soybean genetic
map in regions with few BARC-SSR markers [27, 39]. Therefore, BESs-SSRs represent a useful tool for the
improvement of the genetic map of soybean.


### 4.5. SNP Markers Derived from BESs to WGS

The soybean genome has been shown to be
composed of∼8000 short interspersed
regions of one, two, or four copies per haploid genome, as shown by RFLP
analysis, SSR anchors to BACs and by BAC fingerprints [16]. Recently, the
genome has been sequenced by WGS sequencing of 4 kbp inserts in pUC18 [105].
When the extent and homogeneity of duplications within contigs was examined
using BAC end sequences (BESs) derived from
minimum tile paths (MTP2BH and MTP4BH; Figure 2) [29], a strong correlation was
found between the fold of duplication inferred from fingerprinting and that
inferred from WGS matches. Duplicated regions were identified by BAC
fingerprint contig analysis using a criterion of less than 10% mismatch across
a trace with a window size of 60 bp.
Previously, simulations had predicted that fingerprints of clones from
different regions would coalesce, if sequence variation was less than 2%. Hopefully,
the HSVs among contigs from duplicated regions can be used to separate clone
sets from different regions. Ironically, improvements for contig building
methods will result from the whole genome sequence! However, many duplicated
regions with less than 1% sequence divergence were found [29, 93]. The
implication for bioinformatics and functional annotation of the soybean genome
(and other paleopolyploid or polyploid genomes) is that reverse genetics with
many genes will be nearly impossible without tools to simultaneously repress or
mutate several gene family members.


## 4.1. BAC Libraries and Physical Maps

Construction of fingerprint-based physical maps in soybean
relied on the availability of deep-coverage high-quality
large insert genomic libraries, and a number of such public sector
large insert libraries are available in four different
plasmid vectors, providing >45-fold genome coverage. BAC libraries are available not only for Forrest and PI437654, but
also for someG. soja PIs and the wild
relatives of G. max [84, 85, 96, 97]. Among these libraries, there are three
“Forrest” BAC libraries [84, 85], available in two different plasmid vectors
with different oris and different
selectable markers (Table 5). Despite the availability of these rich BAC
resources, there are still a few regions of the genome that are not well
represented across the above set of BAC libraries. New libraries without
involving restriction digestion may help solve this problem (unpublished).Table 5
Progress in the soybean physical map builds 2 to 5.
Automated Build 2 Sept. 2001Manual Build 3 Oct 2002Manual Build 4 Oct 2003 TotalJudged by BACs/unique band to be (pploid) [unique]Manual Build 5 Jan 2008BAC clones in FPC database81,02483,02678,00178,001BACs used in contig assembly75,56878,00172,94272,837Number of singletons5,884495427,181217,942Marker anchored singletons0012063Clone in contigs (fold genome)69,68473,06945,13558,765Fold genome in contigs8.79.15.662Number of contigs5,4882,9072,854(646)[2208]521Anchoring Markers0385404(280)[124]1,523Anchored Contigs0781742(181)[223]455Contlgs contain: >25 clone220921477(268)[209]33510–25 clones3,0389201,458(433)[1025]1103–9 clones1,845850820(0)[820]432 clones38521699(0)[99]33Unique bands in the
contigs396,843345,457#258,240(64,560)257,356Length of the contigs (Mb)1,667*1,451*1.037(0.258) [0.769]1.034*Based on 4.00 kbp per unique hand. # Based on 4.05 kbp per unique band, for 2,854 contigs containing ∼68 unique bands in 15 clones, 264 duplicated region contigs
containing ∼68 unique bands in 30 clones I5,840 unique bands and 406 highly repeated region contigs containing ∼68
unique bands is 60 clones, 48,720 unique bands.A double-digest-based physical map
for the soybean genome is now nearing completion. For this purpose, soybean BACs
from five libraries belonging to three cultivars were fingerprinted and
assembled [98] using a moderate information content fingerprint method (MICF)
and FPC. The available BACs presently include 1182 Faribault BACs (∼130 kbp, EcoRI inserts, 0.125x), 860 Williams
82 BACs (∼130 kbp, HindIII
inserts, 0.1x) and 78 001 Forrest BACs that were selected from the three
libraries (125–157 kbp EcoRI, HindIII, and BamHI
inserts, 9x). Cultivar sequence variation did not appear to cause incorrect
binning of BACs by FPC. However, the first release (build 3) [98] had many
problems (Table 6), since many individual contigs appeared to contain
noncontiguous genomic regions, and in some cases, different contigs contained
the same region of the genome. Also, the available set of contigs encompassed a
space that was 300 Mbp more than the size of the soybean genome. Clone
contamination caused many of these problems, so that new methods to identify
and eliminate contaminated clones were developed [99].Table 6
Summary of sequence coverage of the three minimum tile paths (MTPs) used for BAC end sequencing
made from three BAC libraries. To calculate the percentage of the soybean
genome covered by the clones (clone coverage) in our EcoRI-(MTP4E) and BamHI
or HindIII insert libraries (MTP2BH and MTP4BH), the genome size of soybean was
assumed to be 1130 Mb. The BAC libraries were each constructed from DNA derived
from twenty five seedlings of an inbred cultivar Forrest.
MTP4EMTP4BHMTP2
BHTotalsVectorpBeloBAC11pCLD04541naInsertion siteEco RIBamHI or HindIIInaDuplicates/region112–41–4naNumber of clones38404608576806417 088Mean insert size (kbp)175 ± 7173 ± 7173 ± 7140 ± 5685Clone coverage0.70.80.21.43.1BESs good reads3 324677292413 47325 123BESs coverage (Mbp)2.95.00.79.918.5Subsequently, the publicly
available soybean BAC fingerprint database was used to create build 4 [16] with
the following specific aims: (i) to increase the number of genetic markers in
the map, (ii) to reduce the frequency of clone contamination, (iii) to rebuild
the physical map at high stringency, (iv) to examine clone density per contig,
and (v) to examine the effectiveness of the generic genome browser in
representing duplicated homoeologous regions (Table 6). Clones suspected of
contamination were listed, fingerprints
were examined, and contaminated clones removed from the FPC database.
Many (7134 about 10%) well-to-well contaminated clones were removed from the
fingerprint database. The edited database produced 2854 contigs and encompassed
1050 Mbp. In addition, homoeologous regions that might cause separate contigs
to coalesce were detected in several ways. First, contigs with high clone
density (23%) were inferred to represent
two copy (240) or four copy (406)
conserved genomic regions per haploid genome (Table 6). If the polyploid
regions could all be split using HSVs (Figure 1) [29], there would be 1624
regions with two copies and 480 regions with four copies in the soybean genome.
A second proof of this genome structure was that pairs of separate contigs that
contained the same marker anchors (69%) were inferred to represent homoeologous
but diverged genomic regions (Figure 6) [16]. A third proof came from EST
hybridizations to BAC libraries where gene families with 1, 2, 4, and 8 members
were more common than those with 3 or 5 members [57]. Finally, similarity
search within the whole genome sequence at 90% similarity showed that the
sequences that map to the contigs with
duplicated regions do have homoeologs in the sequence, whereas sequences
from single copy regions do not (Figure 2) [29, 93].Description of chromosome 18 resources at SoyGD (a). The
current GMOD representation of 50 Mbp of the 51.5 Mbp chromosome 18 (linkage
group G) in SoyGD (a). shows the build 3 version of the chromosome
(cursor), anchored contigs (top row, blue), DNA markers (second row of features,
red), QTL in the region (third row, burgundy), MTP2 clones (B, H, and E fourth
row, dark blue). Not shown here were BAC clones, ESTs, BAC end sequences, and
gene models (b) shows the build 4 representation of 10 Mbp of the 51.5 Mbp
chromosome 18 in SoyGD. Shown are the chromosome (cursor), DNA markers (top row
of features, red); QTL in the region (second row, blue); coalesced clones
(purple) comprising the anchored contigs (third row, green); BAC end sequences
(fourth row black); BESs encoding gene fragments (fifth row, puce);
EST hybridizations to MTP2BH (sixth row gold); MTP4BH clones (seventh row, dark
blue); BESs derived SSR (eighth row, green); EST
hybridizations inferred on build 4 from clones also in MTP2BH (ninth row,
blue); WGS trace file matches from MegaBlast (tenth and last row, light blue).
It is recommended for readers to visit updated site
http://bioinformatics.siu.edu/ to see a full detailed color version and a build
5 view. The gaps between contigs will be filled in build 5 by contig merges
suggested by BESs-SSRs and contig end overlap data.
(a)(b)To deal with duplicated regions, SoyGD
was adapted to distinguish homoeologous regions by showing each contig at all
potential anchor points, spread laterally, rather than as overlapping [16].
Therefore, it should be realized that the genes in such regions have duplicates
in other regions of the genome (Figure 6). This information will prove useful
in future for gene isolation by positional cloning following
a reverse genetics approach, where aneupleurotic pathways regularly cause
wide-spread failures [100–102] due to
inability to predict phenotypes reliably.In build 5, DNA sequence scaffolds (unpublished) have been used to cluster groups of
neighboring contigs. This, however, does not solve the problems faced due to
genome duplication. In many cases, (60–80%),
homoeologous variants may help separation of coalesced regions [29], but this
would require BESs
for every fingerprinted BAC clone. In a minority of regions (20–40%), sequences
longer than BESs
may be needed to correctly separate BAC clones into contigs.


## 4.2. Minimum Tile Paths

The creation of minimally redundant tile
paths (MTP) from contiguous sets of overlapping clones (contigs) in physical
maps is a critical step for structural and functional genomics [95]. The first
minimum tiling path (MTP) developed (from builds 2 and 3) contained 2 fold
redundancy of the haploid genome (2,100 Mbp). MTP2 was 14 208
clones (mean insert size 140 kbp) that were picked from the 5597 contigs of
build 2. MTP2 was constructed from three BAC libraries (BamHI (B), HindIII (H)
and EcoRI (E) inserts), encompassing the contigs of build 3 that were derived
from build 2 by a series of contig merges, but does not distinguish regions by degree
of duplication, so that many regions are redundant. The MTP2 is used in two
parts, MTP2BH and MTP2E (Table 6) because they are largely redundant and
overlap each other. Also, the vectors differ in the antibiotic resistance
conferred. Consequently, only the MTP2BH was used for development of EST map
[57].The third and fourth MTPs, called MTP4BH
and MTP4E (Table6), were each based on build 4 [95]. Each was selected as a
single path through each of the 2854 contigs. MTP4BH had 4608 clones with a mean
size 173 kbp in the large (27.6 kbp) T-DNA vector pCLD04541, which is suitable
for plant transformation and functional genomics. Plates 1–8 contained
clones from the contigs belonging to the single copy regions of the genome.
Plates 9 and 10 were picked from the duplicated and quadruplicated regions
without redundancy, so that an individual clone represented either 2 or 4
regions per haploid genome. Plates 11 and 12 contained the marker anchored
clones also used in MTP2BH. Plate 13 of MTP4BH was developed from just 6
contigs from regions with four copies by redundant picking. This set of clones
should resolve into 48 regions, if methods to separate them can be developed as
the genome sequencing is completed [93]. This set of 13 plates was used for
HICF fingerprinting by the same methods that were used for Williams 82 [11] and
PI437654 BACS [79, 96]. The BACs used for HICF will form a bridge to other
physical maps and a resource to test the ability of HICF to correctly separate
duplicated regions, particularly in the contigs in plate 13.MTP4E was designed to be 4608 BAC clones with large inserts
(mean 175 kbp) in the small (7.5 kbp) pECBAC1 vector [57, 85]. However, only
3840 clones were picked to date. Sequencing efficiency was low on this MTP and
reracking will be needed [103]. The vector is suitable for DNA sequencing and
these clones will be used for sequencing across gaps in the WGS sequence.MTP4BH and MTP4E clones each encompassed about 800 Mbp before
duplicate regions were considered. The single copy regions represented 700 Mbp [57].
In addition there were 50 Mbp from the duplicate and 50 Mbp from the
quadruplicate regions in the MTP. Because those regions were duplicate and
quadruplicate they encompass another 300 Mbp in total. MTP2BH, MTP4E, and
MTP4BH were each used for BAC-end sequencing and microsatellite integration
into the physical map [27, 39]. MTP2BH was used for EST integration to the
physical map [16, 57]. MTP4BH was used for high information content
fingerprinting for integration with the Williams 82 physical map [11, 104]. In conclusion, it appears like each MTP and the
derived BESs will
be useful to deconvolute and finish the whole genome shotgun sequence of
soybean while the whole genome sequence will help complete the physical map. A
complete MTP5BH would be a useful tool for functional genomics because clones
from these libraries were constructed in a T-DNA vector and are ready for plant
transformation. About four thousand transgenic lines made from BACs would be
enough to transfer every soybean sequence to another plant.


## 4.3. BAC End Sequences (BESs)

BAC end sequences (BESs) anchored to a robust physical map constitute an important
tool for genome analysis, and have been developed from BACs belonging to three
available MTPs including MTP2BH, MTP4BH, and MTPE4 [95, 103]. Therefore, three
sets of BESs were
available, of which the first set consisted of 13 474 good BESs derived from 8064 clones of MTP2BH(Table 5). Enquiries to GenBank nr and pat databases identified 7260
potentially geneic homologs, and an analysis of the locations of inferred genes
suggested presence of gene-rich islands on each chromosome [37]. In
addition, 42 BESs showed
homology (extending over a length of 80–341 bp at e−30 to e−300)
with DNA markers (10 RFLPs, 20 microsatellites) that were already genetically
mapped [95]. This amounts to homology with about 2% of the markers, whose
sequences are available in GenBank. Available BESs also carried as many as 1053 new SSR markers
[27, 37] that are described further in the next section.The second set of BESs consisted of 7700 good BESs reads from clones of MTP4BH (Table5) of which 4147 had homologs in the GenBank nr and pat databases [57]. The
clones in plates 11 and 12 were resequenced and so have 2 records for each BAC
end in GenBank. Resequenced clones help determine the sequence error rate and
greatly facilitate SNP detection [18, 19]. Twenty additional genetic anchors
were detected in this second set of BESs (6 RFLPs, 14 microsatellites), which represented about 1%
of the soybean markers with sequences in GenBank. This second set of BESs carried 625 SSR
markers [27, 37] that are described further in the next section. The third set
of BESs from MTP4E have recently been released and are only partly
analyzed (Table 6).The above builds of physical map representing recently duplicated regions of the genome
can be further improved with existing databases and tools. In particular, this
can be achieved by increasing the number of reliable genetic anchors derived
from BESs [27, 37]
and separating BACs from homoeologous regions with diagnostic SNPs (Figure 2)
before contigs were formed [93].


## 4.4. Genetic Map and SSR Markers Derived from BESs

The molecular genetic map for soybean
genome can be improved further through several approaches including (i)
addition of BESs
markers on the available genetic map [27, 37], (ii) bioinformatics analysis of
contig data [16] and (iii) through the use of novel approaches to error detection
[99]. The composite genetic map of soybean at SoyGD (in 2007) contained 3073 DNA
markers [16, 27], which included 1019 class I SSRs, each with >10 di- or trinucleotide repeat motifs (BARC-SSR
markers; Song et al., 2004), and a few class II SSRs with <10 di- to pentanucleotide repeats that were
mostly SIUC-SSR markers. Forrest BESs
helped in increasing the number of class I and II SSR markers for the soybean
genome, and allowed integration of BAC clones into the soybean physical map.SSRs were mined separately from the two sets of BESs described above. As mentioned above, the
first set of 10 Mbp of BAC end sequences (BESs) derived
from 13 474 reads of
7050 clones constituting MTP2BH, had 1053 SSRs (333 class I + 720 from class II),
and the second set of 5.7 Mbp BESs derived from 7700
reads from 5184 clones constituting MTP4BH, had 620 SSRs (150 class I + 480 class II). Potential
markers are shown on the MTP_SSR track at SoyGD (Figure6). About 530 primer
pairs were designed for both the sets of SSRs. These primers were 20–24 mers long with
a Tm of 55 + 1°C, and provided amplicons that were 100–500 bp long. As
many as 123 of these primers belonging to duplicated regions gave multiple
amplified products, and therefore should be avoided.Different possible motifs were not randomly distributed among the above SSRs, with AT
rich motifs being more frequent [27]. Compound SSRs having tetranucleotide
repeats clustered with di- and trinucleotide motifs were also found. About 75%
of class I and 60% of class II SSR markers were polymorphic among the parents
of four recombinant inbred line (RIL) populations. Most of the BESs-SSRs were located on the soybean genetic
map in regions with few BARC-SSR markers [27, 39]. Therefore, BESs-SSRs represent a useful tool for the
improvement of the genetic map of soybean.


## 4.5. SNP Markers Derived from BESs to WGS

The soybean genome has been shown to be
composed of∼8000 short interspersed
regions of one, two, or four copies per haploid genome, as shown by RFLP
analysis, SSR anchors to BACs and by BAC fingerprints [16]. Recently, the
genome has been sequenced by WGS sequencing of 4 kbp inserts in pUC18 [105].
When the extent and homogeneity of duplications within contigs was examined
using BAC end sequences (BESs) derived from
minimum tile paths (MTP2BH and MTP4BH; Figure 2) [29], a strong correlation was
found between the fold of duplication inferred from fingerprinting and that
inferred from WGS matches. Duplicated regions were identified by BAC
fingerprint contig analysis using a criterion of less than 10% mismatch across
a trace with a window size of 60 bp.
Previously, simulations had predicted that fingerprints of clones from
different regions would coalesce, if sequence variation was less than 2%. Hopefully,
the HSVs among contigs from duplicated regions can be used to separate clone
sets from different regions. Ironically, improvements for contig building
methods will result from the whole genome sequence! However, many duplicated
regions with less than 1% sequence divergence were found [29, 93]. The
implication for bioinformatics and functional annotation of the soybean genome
(and other paleopolyploid or polyploid genomes) is that reverse genetics with
many genes will be nearly impossible without tools to simultaneously repress or
mutate several gene family members.


## 5. Functional Genomics Tools

Unequivocal identification and map-based cloning of genes underlying quantitative traits
have been a challenge for soybean genomics research. Gene redundancy, gene
action, and low transformation efficiencies seriously hampered positional
cloning [16]. Therefore, a variety of approaches need to be used for soybean
functional genomics research. Two major areas of soybean genomics research
include (i) annotation of genomic sequences (genes with unknown functions) and
(ii) analysis of genome sequences of “Forrest” for synteny with the genomes of
other dicotyledonous genera and with those of other soybean cultivars.


### 5.1. Annotation of Genome Sequences

The three methods that proved
useful for annotation of the genome sequences of Forrest and related germplasm
include (i) mutant complementation using transformation, (ii) gene silencing
through RNAi, and (iii) targeted mutations. Each will be briefly discussed.(i)Mutant complementation using transformation. A popular approach for the study of gene function is mutant
complementation, which involves transformation of mutants with the wild
alleles. Therefore, development of transformation protocols is an essential
component of functional genomics research. In soybean, A. tumefaciens and A.
rhizogenes-mediated transformation of cultured cells with Forrest BAC
clones has been successfully achieved using previously described protocols
involving the T-DNA vector pCLD04541 [84]. In this protocol, npt
II gene is used as a plant selectable marker, and kanamycin as used as a
selective agent [106–109]. Screenable markers are available in some BAC
clones (Table 7). Whole BAC transformation is important because fine maps
locating loci at genetic distance of 0.25 cM that is equivalent to 50–150 kbp were
earlier prepared using RILs and NILs. The clones selected for
transformation are listed in Table 7, and should provide for complementation of
easily scoreable phenotypes in mutants. For instance, dominant mutant
phenotypes of traits like pubescence, color, and disease resistances should be
evident in the very first products of transformation. BAC transformation with sets
of overlapping clones will be the best approach in situations where an
individual locus represents a cluster of genes [37, 38].Table 7
Some of the BACs, mutant and nonmutant soybean lines to be transformed for complementation.
BIBAC clone namesPhenotypesInsert size kbpDominant?Gm-SIU1-B100B10Rhg4 bigenic resistance to SCN(a)140YesGm-SIU1-B73P06rhg1 bigenic resistance to SCN and Rfs2 for SDS(a)79Co-Gm-SIU2-H050N07Rpg1-b resistance to bacterial pustule(b)110YesGm-SIU1-B54E07T tawny pubescence; flavonoid-3-monoxygenase(c)82YesGm-SIU2-H04P03W1 White flower and black hila color(d)153NoGm-SIU2-H82CO8Rfs1 root
resistance to SDS130YesGm-SIU1-TBDRps4 resistance to Phytophthora root rot120Yes(a)Rhg4 and rhg1 each
encodes transmembrane receptor-like kinase. Resistant and susceptible alleles differ by 3–6 amino acid changes and
23 base changes. There are mutant lines derived from Forrest.(b)Rpg1-b encodes a nucleotide binding leucine rich
repeat protein.(c)T encodes flanonoid-3 monoxygenase (EC1.13.14.21). The recessive genes
differ from the dominant by deletion of a single C nucleotide. There are mutant
lines.(d)W encodes an unknown enzyme, probably a glycosidase.(ii)Gene silencing using RNAi. The composite plant system for
RNAi has been tested in NILs derived from Forrest, and has been validated by
Dr. C. G. Taylor at the Danforth Center (St.
Louis, Mo, USA) [110] through expression of
gene-specific dsRNA constructs. Using this system, shoots from stable
transgenic soybean plants showing constitutive expression of uidA (GUS) were transformed with dsRNA
constructs (Figure 7) that were designed using a modified pKannibal vector [111],
with the 35S promoter replaced by the figwort mosaic virus (FMV) promoter. The 600 bp homologous sequences of the GUS or
green fluorescent protein (GFP) gene were introduced in an antisense and sense
orientation separated by the pKannibal intron (spacer) sequence. These
constructs were designed to produce transcripts with a stem loop secondary
structure that would be recognized by the plant cell machinery and activate
RNAi. The dsRNA constructs placed in a
binary vector, introduced into A.
rhizogenes, were used for
composite plant production [112]. GUS-specific RNAi construct silenced, while non-GUS
RNAi (GFP) construct failed to silence GUS expression in hairy roots produced
on shoots of transgenic soybean plants. These results show that the hairy roots can be used to produce dsRNAs.
Further, the RNAi machinery in soybean hairy roots is fully functional in a
sequence-specific manner. Thus, RNAi technology
will allow the rapid analysis of sets of candidate genes for alleles underlying
variation [38].Figure 7
Evidence for RNAi silencing of GUS gene in 35S::GUS soybean plants. Panel A. GUS
expression in composite plant roots expressing and RNAi from the gene encoding GFP. Panel B. GUS expression in composite plant
roots expressing RNAi from the gene encoding GUS. Panel C. The transformation cassette used (thanks to Dr. C. G. Taylor, Danforth Center, unpublished data).(iii)Study of gene function through TILLING. Two
soybean mutagenized M2 libraries are already available for TILLING [113], from
which ∼3000 of the 6000 available M2 lines were phenotyped visually. A soybean mutant database has been developed to
track and sort these mutants (http://www.soybeantilling.org/).
While developing a database that would allow search for “TILLED” genes a search
engine was developed, so that the database can be searched for both phenotype
and gene. The mutations occurred at a rate of ∼1 mutation/170 kbp, so that a screening
of 6150 M2 families may provide a series of up to 40 to 60 alleles within each
1.5 kbp fragment of a target gene. This approach led to the identification of a putative mutant for a
soybean leucine rich repeat receptor like kinase gene Gm-Clavata1A (AF197946; Figure 8). In future, TILLING and crosses among TILLED mutants
[100–102] will allow the testing of candidate genes and will provide new
genetic variation that may lead to germplasm enhancement.Figure 8
Soybean Tilling gel image ofGm_clavata1 pool ps33 screening,
representing 768 individuals, 8 individuals per pool (LI-COR 700 channel
mutations are marked in red boxes; blue boxes represent lane numbers) from http://www.soybeantilling.org/ (thanks to Dr. K. Meksem and Dr. B. Liu SIUC, unpublished data).


### 5.2. Analyses of Conserved Synteny

Forrest genome sequences have also been used for a study of their synteny with genomes of other
dicotyledonous genera/species and also with the genomes of other soybean
cultivars. For this purpose, cross-species transferable genetic markers are available
in the data-based legumeDB1 [114], and can be used to compare the linear order
of markers/genes, which are either species specific or conserved across genera
[115–124]. For instance,
genes for resistance to pathogens will often appear as new genes or gene
clusters inserted in regions, which otherwise exhibit conserved synteny across
genera [35, 115, 122]. Synteny extends beyond genes into repeat DNA, as
exemplified by the distributions of 15 bp sequences that provide
sequence-specific genome fingerprints [94]. Interestingly the fingerprints do
not show the same patterns of relatedness between species found in gene
sequence. Therefore, genome fingerprinting will help identify good candidates
for cross species markers in repeat DNA such as microsatellite markers.Conserved synteny has also been observed among the genomes involved in the constitution
of the allo- and autotetraploids hypothesized for soybean. It has been shown
that about 25–30% of the genome
has extensive conservation of gene order in otherwise shuffled blocks of 150–300 kbp [16].
Consequently, blocks of 3–10 genes are
repeated at 2 or 4 locations per haploid genome [38, 79]. There are also
genomic regions, where synteny among genomes of different cultivars has been
shown to break down. Several interesting features including the following have
been observed in these nonsyntenic regions: (i) in some cases, a loss of
conserved synteny between cultivars is associated with a gene introgressed from
a Plant Introduction [38]. (ii) In another case, a moderately repeated sequence
common in one cultivar is absent in another cultivar [29]. (iii) In still
another case, a sequence inserted in one cultivar appears to alter the
expression of a neighboring gene (unpublished). It is thus apparent that genome
analysis involving study of an association of these nonsyntenic sequence tracts
in otherwise syntenic regions, with phenotypes will be an active area of research,
when genome sequences from a number of soybean cultivars are available.


## 5.1. Annotation of Genome Sequences

The three methods that proved
useful for annotation of the genome sequences of Forrest and related germplasm
include (i) mutant complementation using transformation, (ii) gene silencing
through RNAi, and (iii) targeted mutations. Each will be briefly discussed.(i)Mutant complementation using transformation. A popular approach for the study of gene function is mutant
complementation, which involves transformation of mutants with the wild
alleles. Therefore, development of transformation protocols is an essential
component of functional genomics research. In soybean, A. tumefaciens and A.
rhizogenes-mediated transformation of cultured cells with Forrest BAC
clones has been successfully achieved using previously described protocols
involving the T-DNA vector pCLD04541 [84]. In this protocol, npt
II gene is used as a plant selectable marker, and kanamycin as used as a
selective agent [106–109]. Screenable markers are available in some BAC
clones (Table 7). Whole BAC transformation is important because fine maps
locating loci at genetic distance of 0.25 cM that is equivalent to 50–150 kbp were
earlier prepared using RILs and NILs. The clones selected for
transformation are listed in Table 7, and should provide for complementation of
easily scoreable phenotypes in mutants. For instance, dominant mutant
phenotypes of traits like pubescence, color, and disease resistances should be
evident in the very first products of transformation. BAC transformation with sets
of overlapping clones will be the best approach in situations where an
individual locus represents a cluster of genes [37, 38].Table 7
Some of the BACs, mutant and nonmutant soybean lines to be transformed for complementation.
BIBAC clone namesPhenotypesInsert size kbpDominant?Gm-SIU1-B100B10Rhg4 bigenic resistance to SCN(a)140YesGm-SIU1-B73P06rhg1 bigenic resistance to SCN and Rfs2 for SDS(a)79Co-Gm-SIU2-H050N07Rpg1-b resistance to bacterial pustule(b)110YesGm-SIU1-B54E07T tawny pubescence; flavonoid-3-monoxygenase(c)82YesGm-SIU2-H04P03W1 White flower and black hila color(d)153NoGm-SIU2-H82CO8Rfs1 root
resistance to SDS130YesGm-SIU1-TBDRps4 resistance to Phytophthora root rot120Yes(a)Rhg4 and rhg1 each
encodes transmembrane receptor-like kinase. Resistant and susceptible alleles differ by 3–6 amino acid changes and
23 base changes. There are mutant lines derived from Forrest.(b)Rpg1-b encodes a nucleotide binding leucine rich
repeat protein.(c)T encodes flanonoid-3 monoxygenase (EC1.13.14.21). The recessive genes
differ from the dominant by deletion of a single C nucleotide. There are mutant
lines.(d)W encodes an unknown enzyme, probably a glycosidase.(ii)Gene silencing using RNAi. The composite plant system for
RNAi has been tested in NILs derived from Forrest, and has been validated by
Dr. C. G. Taylor at the Danforth Center (St.
Louis, Mo, USA) [110] through expression of
gene-specific dsRNA constructs. Using this system, shoots from stable
transgenic soybean plants showing constitutive expression of uidA (GUS) were transformed with dsRNA
constructs (Figure 7) that were designed using a modified pKannibal vector [111],
with the 35S promoter replaced by the figwort mosaic virus (FMV) promoter. The 600 bp homologous sequences of the GUS or
green fluorescent protein (GFP) gene were introduced in an antisense and sense
orientation separated by the pKannibal intron (spacer) sequence. These
constructs were designed to produce transcripts with a stem loop secondary
structure that would be recognized by the plant cell machinery and activate
RNAi. The dsRNA constructs placed in a
binary vector, introduced into A.
rhizogenes, were used for
composite plant production [112]. GUS-specific RNAi construct silenced, while non-GUS
RNAi (GFP) construct failed to silence GUS expression in hairy roots produced
on shoots of transgenic soybean plants. These results show that the hairy roots can be used to produce dsRNAs.
Further, the RNAi machinery in soybean hairy roots is fully functional in a
sequence-specific manner. Thus, RNAi technology
will allow the rapid analysis of sets of candidate genes for alleles underlying
variation [38].Figure 7
Evidence for RNAi silencing of GUS gene in 35S::GUS soybean plants. Panel A. GUS
expression in composite plant roots expressing and RNAi from the gene encoding GFP. Panel B. GUS expression in composite plant
roots expressing RNAi from the gene encoding GUS. Panel C. The transformation cassette used (thanks to Dr. C. G. Taylor, Danforth Center, unpublished data).(iii)Study of gene function through TILLING. Two
soybean mutagenized M2 libraries are already available for TILLING [113], from
which ∼3000 of the 6000 available M2 lines were phenotyped visually. A soybean mutant database has been developed to
track and sort these mutants (http://www.soybeantilling.org/).
While developing a database that would allow search for “TILLED” genes a search
engine was developed, so that the database can be searched for both phenotype
and gene. The mutations occurred at a rate of ∼1 mutation/170 kbp, so that a screening
of 6150 M2 families may provide a series of up to 40 to 60 alleles within each
1.5 kbp fragment of a target gene. This approach led to the identification of a putative mutant for a
soybean leucine rich repeat receptor like kinase gene Gm-Clavata1A (AF197946; Figure 8). In future, TILLING and crosses among TILLED mutants
[100–102] will allow the testing of candidate genes and will provide new
genetic variation that may lead to germplasm enhancement.Figure 8
Soybean Tilling gel image ofGm_clavata1 pool ps33 screening,
representing 768 individuals, 8 individuals per pool (LI-COR 700 channel
mutations are marked in red boxes; blue boxes represent lane numbers) from http://www.soybeantilling.org/ (thanks to Dr. K. Meksem and Dr. B. Liu SIUC, unpublished data).


## 5.2. Analyses of Conserved Synteny

Forrest genome sequences have also been used for a study of their synteny with genomes of other
dicotyledonous genera/species and also with the genomes of other soybean
cultivars. For this purpose, cross-species transferable genetic markers are available
in the data-based legumeDB1 [114], and can be used to compare the linear order
of markers/genes, which are either species specific or conserved across genera
[115–124]. For instance,
genes for resistance to pathogens will often appear as new genes or gene
clusters inserted in regions, which otherwise exhibit conserved synteny across
genera [35, 115, 122]. Synteny extends beyond genes into repeat DNA, as
exemplified by the distributions of 15 bp sequences that provide
sequence-specific genome fingerprints [94]. Interestingly the fingerprints do
not show the same patterns of relatedness between species found in gene
sequence. Therefore, genome fingerprinting will help identify good candidates
for cross species markers in repeat DNA such as microsatellite markers.Conserved synteny has also been observed among the genomes involved in the constitution
of the allo- and autotetraploids hypothesized for soybean. It has been shown
that about 25–30% of the genome
has extensive conservation of gene order in otherwise shuffled blocks of 150–300 kbp [16].
Consequently, blocks of 3–10 genes are
repeated at 2 or 4 locations per haploid genome [38, 79]. There are also
genomic regions, where synteny among genomes of different cultivars has been
shown to break down. Several interesting features including the following have
been observed in these nonsyntenic regions: (i) in some cases, a loss of
conserved synteny between cultivars is associated with a gene introgressed from
a Plant Introduction [38]. (ii) In another case, a moderately repeated sequence
common in one cultivar is absent in another cultivar [29]. (iii) In still
another case, a sequence inserted in one cultivar appears to alter the
expression of a neighboring gene (unpublished). It is thus apparent that genome
analysis involving study of an association of these nonsyntenic sequence tracts
in otherwise syntenic regions, with phenotypes will be an active area of research,
when genome sequences from a number of soybean cultivars are available.


## 6. Conclusions

The soybean genomics resources developed
through the use of cultivar Forrest have been used and will continue to be used
in future leading to significant advances in soybean genomics knowledge base. The
soybean genome shows evidence of a paleopolyploid origin with regions,
encompassing gene-rich islands that were highly conserved following duplication
[16]. In fact, it was estimated
that 25–30% of the genome was highly conserved after both duplications. Implications of this feature are
profound. First, a map of homoeology and an associated map of duplicated regions had to be developed. Second, an estimate of sequence
conservation among the duplicated regions was necessary. Third, the implications for
functional genomics were considered. Given that all soybean genes have been
duplicated twice during recent evolution, and that most plant genomes encode
functionally redundant pathways, it is not surprising that TILLING,
RNAi-mediated silencing and overexpression of several genes often did not lead
to phenotypic changes [101, 102, 110, 113]. In future, the E × F
population will continue to be used for (i) an analysis of functions of a
number of gene families, (ii) patenting of inventions based on useful genes [6, 77, 124–126], (iii)
manipulation of soybean seed composition including protein, oil [19] and bioactive factors [127–129], and (iv) an
analysis of the protein interactome [130]. In summary, the newly released E × F population and the other associated genomic resources developed through the use of cultivar “Forrest” will provide
tremendous opportunities for further research in the field of genomics research.



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*Source: 793158-2008-04-23.xml*