1
1
Evaluation of Chinese Soybean Germplasm to Improve Soybean Production
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and Utilization
3
Yao Guo, Mervyn G. Marasinghe and Reid G. Palmer*
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Yao Guo, Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A.
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Mervyn G. Marasinghe, Department of Statistics, Iowa State University, Ames, Iowa 50011,
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U.S.A.
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Reid G. Palmer, USDA ARS CGR and Departments of Agronomy and Zoology/Genetics, Iowa
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State University, Ames, Iowa, 50011, U.S.A.
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Received _____________________. *Corresponding author (rpalmer@iastate.edu).
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This is a joint contribution of the Iowa Agriculture and Home Economics Experiment Station,
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Ames, Iowa, Project No. 3769, and from the USDA-Agricultural Research Service, Corn Insects
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and Crop Genetics Research Unit, and supported by the Hatch Act and the State of Iowa. The
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mention of a trademark or proprietary product does not constitute a guarantee or warranty of the
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product by Iowa State University or the USDA, and the use of the name by Iowa State University
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of the USDA implies no approval of the product to the exclusion of others that may also by
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suitable.
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Abbreviations: PIs, plant introductions; RFLP, restriction fragment length polymorphism; AFLP,
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amplified fragment length polymorphisms; RAPD, random amplified polymorphic DNA; SSR,
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simple sequence repeats; NAC, North American cultivars; CCG, central Chinese germplasm;
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SCG, southern Chinese germplasm; UPGMA, un-weighted pair-group method using arithmetic
2
1
average; GD, genetic distance; PCA, principal coordinate analyses; MDS, multidimensional
2
scaling.
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1
Abstract
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3
Increased genetic homogeneity among modern soybean cultivars has brought more
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attention to the potential use of plant introductions (PIs). North American soybean germplasm
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derives from a limited number of ancestors. Some PIs carry traits that may be useful to add to the
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gene pool of cultivated soybeans. So knowledge of genetic diversity patterns in plant PIs is
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needed to diversify the North American soybean gene pool efficiently. North American cultivars
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and two subsets of cultivars collected from the central provinces of China and the southern
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provinces of China were used in this study. Genomic DNAs from these accessions were
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evaluated by restriction fragment length polymorphisms (RFLP) with 33 selected probes. The
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objectives of this research were to measure the variation among North American cultivars and
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these two subsets of Chinese cultivars and to evaluate the relationships between geographical
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origin and genetic diversity. Based on this study, there were clear genotypic distinctions among
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collections from southern China, central China, and the North American cultivars. The southern
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Chinese germplasm and the central Chinese germplasm were found to have many alleles not
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observed in North American cultivars. Cluster analysis and principal coordinate analysis of the
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genetic distance matrix revealed a clear distinction between the cultivars from southern China and
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those from central China and North America, but only a weak separation between the collections
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from central China and North America. This pattern suggested that the southern Chinese
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cultivars might possess useful genetic diversity that could be exploited by soybean breeders to
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transfer new genes into North American soybean cultivars.
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Introduction
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The objective of plant breeding programs is to develop improved cultivars by using elite
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germplasm as parents. Efforts to diversify elite cultivars could increase the rate of quality
5
improvement. Hybridization of cultivars with genetically diverse lines and introgression into
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cultivars are two ways to introduce high seed yield and other agronomic traits such as resistance
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to disease, insect pests, and tolerance to poor environmental conditions. In contrast, limited or
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reduced genetic diversity is a potential threat to breeders.
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The soybean [G. max (L.) Merr.] germplasm in North America has a narrow genetic base.
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The history of soybean cultivar development is relatively short. It was estimated that 80% of the
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germplasm present in modern cultivars could be traced to just 12 ancestral lines, which were
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introduced into the USA in the early 1900s (Lorenzen et al., 1995). The extensive use of a small
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number of ancestors resulted in a limited genetic diversity in modern soybean cultivars. To
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reduce the risks associated with a narrowed genetic base, new alleles are desired for incorporation
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into modern cultivars. Plant introductions (PIs) and selection are potentially useful in alleviating
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this problem.
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Although often not carrying the most desired traits found in modern cultivars, PIs can
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provide otherwise useful traits. PIs may possess resistance to various diseases or insects but may
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have undesirable agronomic traits such as shattering pods or weak stems. Although PIs generally
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have poor agronomic qualities, some have been shown to contain genes that improve yield
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(Thompson et al., 1998). Recent reviews have enumerated the use of soybean genetic resources
5
1
for agronomic improvement (Hymowitz and Bernard, 1991; Hymowitz et al., 1998; Palmer et al.,
2
1995; Singh and Hymowitz, 1998).
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China is a historical center of genetic diversity for soybean, so it has been a very
4
important resource for soybean germplasm for North American cultivars (Hymowitz, 2003). In
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contrast to this trend in the USA, on average, current Chinese cultivars share less than 2% of their
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genes in common, on the basis of the coefficient of parentage analysis (Cui et al., 2000). This is
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attributed to a continual infusion of new germplasm into applied programs and a strong tendency
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to avoid the mating of close relatives (Cui et al., 2000). So knowledge of genetic diversity
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patterns in Chinese soybean germplasm potentially represents a rich source of elite germplasms
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and PIs for breeding stock. Since the geographical regions of central and southern China are very
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different, we may expect the genetic diversity patterns to be different.
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There are many ways to measure genetic diversity. Traditionally, the assessment of
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genetic variation has been based on geographic origin and morphological characteristics.
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Unfortunately, these variations may not be the best representatives of genetic diversity in the
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entire genome because of the limitation of small numbers of phenotypic markers and the
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interference of various human and environmental factors (Brown-Guedira, et al., 2000; Negash,
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et al., 2002). With the development of molecular technology, molecular genetic markers have
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become valuable tools to determine genetic diversity, since they are usually unaffected by the
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environment and can often be generated in large numbers (Vosman et al., 1999). The more
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common ones include AFLP (Amplified Fragment Length Polymorphisms), RAPD (Random
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Amplified Polymorphic DNA), RFLP (Restriction Fragment Length Polymorphisms), and SSR
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1
(Simple Sequence Repeats). They can be informative, but each has significant benefits and
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drawbacks.
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There are several benefits to using RFLP analysis to evaluate germplasm. RFLPs are
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usually co-dominant markers and the relatively low copy number usually makes them easy to
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‘score’. The greatest drawbacks are that performing RFLP analysis is time consuming and
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relatively costly. Selecting the RFLP probes that are known to detect diversity in soybeans could
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reduce the cost involved. For this study, the use of RFLP probes allows comparisons to be made
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to other soybean collections where RFLP data have already been collected. RFLPs are also highly
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repeatable. AFLPs generally have a high copy number. This makes them more difficult to
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‘score’.
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always believed to be highly reproducible, there is also evidence suggesting otherwise
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(Thompson and Nelson, 1998). SSRs have also increased in popularity, but do not code for
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specific regions of the genome. (Powell et al., 1996)
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RAPDs are known to be easy to perform and cost efficient. Although they are not
In our study, RFLPs have been employed to characterize 302 soybean germplasms from
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North American and central and southern provinces of China. Our objectives were (1) to
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measure the genetic diversity present in North American cultivars (NAC), central provinces of
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China germplasm (CCG) and southern provinces of China germplasm (SCG); (2) to compare the
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genetic relationships among them; (3) to screen the relationship between geographical origin and
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genetic diversity; and (4) to determine if PIs selected from the CCG and SCG could be useful to
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breeding programs in North America for selection of diverse parents.
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Materials and Methods
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Germplasm
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The southern Chinese soybean germplasm collection contained 131 plant introductions
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(PIs). These were collected from the seven provinces Anhui (15), Guangdong (8), Hubei (31),
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Jiangsu (30), Shanghai (6), Sichuan (19), and Zhejiang (22). The soybean germplasm collected
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from the eight central provinces of China Anhui (7), Gansu (26), Hebei (3), Henan(11),
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Jinagsu(10), Shaanxi(19), Shangdong(3), and Shanxi(27), consisted of 106 Pis (see Fig.1). Note
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that, geographically, the division of provinces into southern and central regions is based on the
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Yangtze River.
Both subsets were compared to the 65 lines of the North American cultivars. These
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represent about 80% of the genetic diversity observed in the cultivars currently growing in North
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America (Gizlice et al., 1994).
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As a control, G. max breeding line A81-356022 and the wild annual G. soja (Sieb. E Z
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vec.) plant introduction PI 468916 were used. The controls were used to create the
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USDA/ARS/Iowa State University public molecular soybean linkage map. All alleles scored
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were compared to the controls.
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Probes
The term “probe” refers to the recombinant DNA clone that detects complementary
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restriction fragments. A single probe may detect multiple fragments at different loci (Keim et
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al., 1992). The use of RFLP probes that are able to efficiently detect diversity in a given set of
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1
germplasm is an important part of analyzing new germplasm. The probes included in this study
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were previously determined to detect relatively high levels of genetic diversity in soybean
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cultivars from North America and their ancestors (Lorenzen et al., 1995).
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DNA Preparations and RFLP Hybridization
Selected seed were planted in the greenhouse and grown to the second trifoliolate stage.
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The leaves were collected and freeze-dried. DNA was extracted from the leaves using the
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chloroform extraction method (Sambrook et al., 1989). DNA then was digested using restriction
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enzymes: DraI, EcoRI, EcoRV, HindIII, and TaqI. Electrophoresis, Southern transfers, and
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hybridizations were performed as in Sambrook et al. (1989). Thirty-three DNA probe/enzyme
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combinations were used to detect diversity in soybean cultivars (Lorenzen et al., 1995). For each
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probe used, at least one polymorphism was mapped to the USDA-ARS public soybean map (G.
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max A81-356022 X G. soja PI 468916), the aforementioned control.
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Method of Evaluating RFLP data
Each probe was used to detect at least one mapped polymorphism on the USDA-ARS
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public soybean G. max by G. soja genetic map (Shoemaker and Specht, 1995). Loci were
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distributed throughout the soybean linkage map with at least one locus on each linkage group
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except the Q linkage group (see appendix A). Data were scored according to an allele-locus
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model suggested by Bruebaker and Wendel (1994). Soybean has primarily dominant or co-
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dominant markers (Keim et al., 1989). The low copy number of RFLP fragments seen, as often-
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observed in highly homozygous species facilitates the use of an allele-locus model. Most bands
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1
were monomorphic (only one or two different bands were observed per locus). Any bands at a
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given locus that were too difficult to score were recorded as a no score. Loci that were difficult
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to score or had a high frequency of missing data points were discarded completely.
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Gene Diversity
Variation in populations can be characterized by heterozygosity or gene diversity, the
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latter being more appropriate for inbred populations. Since for inbred populations, there are very
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few heterozygotes but there may be several different homozygous types (Weir, 1996). The
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number of alleles per locus was calculated by dividing the observed number of alleles for a group
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of accessions by the number of loci surveyed. The frequency of a particular allele in a population
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is called the gene or allele frequency. The proportion of polymorphic loci (P) was calculated by
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dividing the number of polymorphic loci (those for which the frequency of the most abundant
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allele was <0.950) by the number of loci surveyed. Considering only codominant alleles in a
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diploid population, Nei’s (1987) diversity was computed for each RFLP locus as follows:
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m ^
^

hl  1   xil2
i 1
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where m is the number of alleles and x̂il ’s represent frequency of allele Ai at locus l within the
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total population, which is calculated as
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xˆ i  Xˆ ii   Xˆ ij / 2
i j
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where X^ ii is the frequency of Ai Ai and X^ ij is the frequency of Ai A j in the sample.
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In a selfing population, ĥl is given by the following:
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m
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hˆl  n(1   xˆ il2 ) /( n  1)
i 1
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where n is the number of individuals sampled. The mean gene diversity that occurred with a
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^
population was estimated by averaging hl estimates from this population over all loci r:
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r
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Hˆ   hˆl / r
l 1
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11
^
The sampling variance of H may be obtained by
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V ( Hˆ )  V (hˆl ) / r
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where V (hˆl ) is the variance of ĥl and is given by
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r
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V (hˆl )   (hˆl  Hˆ ) 2 /( r  1)
l 1
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1
To calculate this value, the fragments observed that were synonymous to either of the
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USDA-ARS-Iowa State public molecular genetic mapping: G. max X G. soja (Shoemaker and
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Specht, 1995; Cregan et al., 1999), were combined to represent a single allele at a locus. For
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example: for accession ‘i’ with a frequency of ‘a’ alleles like G. max (A81-356022), a frequency
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of ‘b’ alleles like G. soja (PI 468916), a frequency of ‘c’ alleles like the first band unlike either of
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the mapping parents, and a frequency of ‘d’ alleles different from either of the controls or other
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“unique” alleles, the formula for gene diversity would be represented as follows.
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hˆl  1   (a 2  b2  c 2  d 2 )
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10
^
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Let Vl (h ) denote the variance for a particular locus. This is the variance generated at the
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time of gene frequency survey for a particular locus. Therefore, if one is interested in testing the
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difference in single-locus heterozygosity between two populations, this variance should be used
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(Nei, 1987). This value is given by
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^ ^
Vl (h ) 
2
^
{2(2n  2)[  xi3  ( x^ i2 )2 ]  ( x^ i2 )2 }
2n(2n  1)
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Genetic Distances
Genetic distances are designed to express the genetic differences between two populations
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as a single number (Nei, 1987). If there are no differences, the distance could be set to zero,
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whereas if the populations have no alleles in common at any locus the distance may be set equal
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1
to its maximum value, say 1 (Weir, 1996). Genetic distances were estimated through RFLP data
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by the method of Nei (1987). A “Genetic identity” I is defined between the two populations as
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I 
Y
X
 xˆ
xˆ
ilX ilY
l
i
 xˆ  xˆ
2
ilX
l
i
2
ilY
l
i
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6
where x̂ ilX and x̂ ilY are the frequencies of allele i at locus l within population X and Y,
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respectively. Nei’s standard genetic distance D is defined as
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D = - ln (I)
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The genetic distance matrix can be obtained by using the POPGENE software version
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1.31 (Yeh et al., 1999), Nei’s genetic identity is above the diagonal and genetic distance is below
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the diagonal of the genetic distance matrix output by POPGENE.
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Cluster Analysis and Multidimensional Scaling
Relationships among varieties based on RFLP information were investigated using several
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statistical programs (SAS Inc., 1990). Dendrograms were constructed from clustering and tree
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analyses using UPGMA (un-weighted pair-group method using arithmetic averages) on the
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distance matrices based on RFLP data. Furthermore, associations between individuals were
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graphically depicted through principal coordinate analyses (PCA), which were constructed by
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1
using the multidimensional scaling (MDS) procedure (Johnson and Wichern, 1998) in SAS. The
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ABSOLUTE option was used in order to maintain the distance scale between 0 and 1, thus
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making interpretation and graphing easier (Thompson et al, 1998).
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Multidimensional scaling (MDS) is a procedure that starts with the ‘distances’ between a
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set of points (or individuals or objects) and finds a configuration of the points, preferably in a
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small number of dimensions, usually 2 or 3. Thus, in an attempt to represent the genetic
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distances among populations in a suitable lower dimensional configuration that is reasonably
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close to the original configuration, the MDS procedure was used on the genetic distance matrix.
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The closeness is measured by “stress”, which measures the extent to which the new configuration
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in the lower dimension deviates from the original. For a specified dimension, the MDS procedure
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finds the configuration that minimizes “stress”. It is standard practice to obtain the minimum
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stress for configurations of several dimensions and construct a plot called the “scree plot”, a plot
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of stress against the dimension. Usually the value of the dimension at which the stress begins to
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level-off is chosen as the “best” lower dimension. Stress is usually represented as a percentage
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and a value between 0% and 5% is considered acceptable.
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Results and Discussion
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Identical thirty-three probes were used for the samples from the three regions used in this
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study. Within region diversity was compared by estimating the average diversity across all loci
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for each region (Table 2). The average diversity over all PIs was 0.47. The average diversity for
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CCG was 0.41, higher than that for SCG (0.34) and NAC (0.37). The CCG was the most diverse
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while the SCG was the least diverse. The lowest average diversity of SCG may be explained by
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the narrow maturity range of the lines, limited geographic sampling, or nonrandom sampling.
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The range of gene diversity was 0.03 to 0.74 for SCG versus 0.08 to 0.56 for CCG, and 0.04 to
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0.60 for NAC (Fig. 2).
We may use V (Hˆ ) to test the difference (0.41-0.34= 0.075) in average gene diversity Ĥ
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between CCG and SCG populations. The standard error of this difference is s d = 0.03656.
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Thus, t = 0.075/0.03656 = 1.94 (p-value>0.05), indicating that the difference is not statistically
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significant. Similarly, the differences between CCG and NAC, NAC and SCG were not
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significantly different. If all the PIs from Central China and North America, Central China and
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Southern China, North America and Southern China were combined, then these combined
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samples appeared to indicate that the combined populations have higher average diversities than a
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single population. For example, the combination of CCG and SCG had average diversity of 0.46
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(Table 2), larger than the simple average of CCG and SCG ((0.41+0.34)/2=0.375). These results
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indicate that more diversity resides among CCG, NAC, and SCG populations than within them,
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and that these populations are diverse from each other. Since the standard error for each locus in
22
a single population was small, this conclusion seems to be reasonable.
15
1
The CCG and SCG population were divided into subgroups according to provinces and a
2
gene diversity analysis carried out. The highest diversity, 0.40, for CCG focused on PIs from
3
Gansu province, which represents a diversity 0.41, almost that of the entire CCG population
4
(Table 3). And the second largest contribution was the Henan region, followed by Jiangsu,
5
Shanxi and Shaanxi. Also all five regions had the highest proportion of polymorphic loci (0.94),
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except Henan (0.88). The highest diversity, 0.33, in SCG population was Sichuan province,
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similar to the 0.34 of SCG population. Hubei and Jiangsu were second (0.31), and third (0.28),
8
respectively. Jiangsu had 0.94 polymorphic loci, and Sichuan and Hubei each had 0.91. The
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measure of genetic variation had the same trend with the proportion of polymorphic loci. And it
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is not surprising that Gansu and Shichuan represent great diversity for each CCG and SCG.
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According to geographic conditions, Gansu province covers a narrow elongated area, more than
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half of it is a mountain region and most farms are separate from each other. Sichuan, one of
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Gansu’s neighbors, has a very complicated geographic shape.
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Of the 33 probes screened, eight had two alleles, twenty-one had three alleles, three had
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four alleles, and one had six alleles (Table 4). A total of 97 alleles were scored and 71% were
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present in all three populations. Only 27 alleles had a frequency of 0.10 or less across all
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populations and most of these were infrequent in each population (Table 4). For loci with more
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than two alleles, the additional allele(s) had frequencies of 0.10 or less across all lines except
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four. Fourteen of 27 infrequent alleles were present in CCG and 3 were present in NAC, while
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16 were present in SCG. Thus, NAC lacked the allelic diversity found in CCG and SCG. CCG
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and SCG have similar allelic diversities.
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1
The PIs from CCG, NAC, and SCG possessed 85, 74, and 87 of the 97 alleles
2
respectively. For the 12 null alleles in CCG, five were present in NAC and nine were present in
3
SCG. Twenty-three alleles were null in NAC, sixteen were present in CCG and in SCG,
4
separately. SCG had ten null alleles: seven of them were present in CCG while three were
5
present in NAC (Table 5). This structure indicates that the PIs that come from CCG and SCG are
6
good sources to diversify the NAC gene pool.
7
Differentiation between populations was investigated through genetic distances (GD).
8
Estimates of genetic distances among CCG, NAC, and SCG populations as measured by RFLPs
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are given in Table 6. As expected, the average GD between the CCG and SCG, SCG and NAC
10
were higher, 0.33 and 0.29 respectively, while the average GD between NAC and CCG was the
11
least, 0.13.
12
The associations among individuals sampled from populations were investigated by MDS
13
based genetic distance matrix (data not shown). The scree plot (Fig. 3) shows that the
14
configuration obtained with two dimensions is sufficient since the drop in stress from two to three
15
dimensions is only around 0.05%. Thus a plot of the first two MDS dimensions was created to
16
graphically represent the relationships among the 302 best-characterized sets of individuals. This
17
plot yielded a good separation between the collections of SCG and CCG, the collections of SCG
18
and NAC, but a weak separation between CCG and NAC. Most of the NACs scattered in CCGs,
19
but some of them were outliers from the CCGs (Fig. 5), also five varieties of NAC were disjunct
20
from the others and grouped together. These five cultivars were Kingwa (IV), Williams (III),
21
S100 (V) and two Tokyo’s (VII). One cultivar of SCG was at the left top of this graph and far
22
away from SCG group. It was PI 588040 and needs further investigation.
17
1
Cluster analyses can help to confirm the above conclusions. UPGMA cluster analyses
2
based on the GD matrix were accomplished. A dendrogram (data not shown) based on the GD
3
matrix of 302 accessions using the 33-probe set showed that accessions of the CCG and NAC
4
groups fell into three relatively distinct clusters according to the region of the origin. Accessions
5
from southern China formed a large cluster. Several accessions of North America grouped
6
together to form a small cluster. Accessions from central China and North America grouped
7
together and then sub-clustered to two. Some of NAC mingled with CCG and some NAC
8
grouped alone (Figure not shown). A dendrogram based on a matrix of the mean GD between
9
pairs of the 17 subgroups divided by provinces of China and NAC cultivars (Fig. 5) showed that
10
11
CCG and NAC groups were distinct from the SCG group.
All of the analyses corresponded to the geographic origin classification. The soybeans
12
with identical pedigrees should have very similar genotypes (Keim et al. 1992). The cluster and
13
PCA patterns generally reflected the geographical origin of the major ancestors. The independent
14
clusters of SCG and NAC indicated that these germplasms used for cultivar development in
15
North America might be distinct from those of the southern provinces of China. From Table 7,
16
all of the North American ancestral lines came from China, especially the northwest region,
17
except for four lines that probably had ancestors from China, Japan, and Korea; eight ancestral
18
lines from China and Korea; two from China and Korea; two from Japan and Korea; one from
19
Korea. The geographical similarity between northern region and central area of China may
20
account for the tendency of grouping together. The large distance and different origin between
21
SCG and other germplasms suggested that soybean breeders can maximize genetic diversity in
22
existing populations by crossing between CCG and SCG, SCG and NAC.
18
1
A further investigation about the genetic structure focused on maturity groups. The
2
dendrogram derived from UPGMA cluster analysis based on the GD matrix for maturity groups
3
within provinces is presented in Fig. 6. The maturity groups clustered based on the provinces and
4
regions rather than the maturity groups. It seems that the separation noticed based upon the
5
provinces signifies the importance of their geographic boundaries. This in itself would make an
6
interesting study. UPGMA cluster analysis based on the GD matrix only for maturity groups of
7
all the 302 accessions showed that the maturity groups 0, I, II, III, IV, V and 00 distributed in one
8
cluster and VI, VII, VIII presented in another cluster (Fig. 7). The small number of sampled
9
accessions may explain the large distance between maturity groups 0 and 00.
10
In conclusion, the accessions from Southern China and Central China were found to have
11
many alleles not observed in North American cultivars. The MDS and cluster analysis revealed
12
that the accessions from southern China were strongly distinct from the North American
13
cultivars; some accessions of the North America cultivars were distributed within the accessions
14
from central Chinese cultivars. Clustering was distinguishable based upon province of origin.
15
Considering the generations of independent breeding done in the United States and the
16
reported levels of homogeneity of the North American cultivars (Gizlice et al. 1993, 1994), it is
17
expected that other collections would be different. Our results indicated that the Chinese
18
germplasm did display greater genetic diversity than the North American cultivars. The
19
accessions from Southern China are clearly genetically distinct from the North American
20
cultivars; they also appear to be much different than the germplasm collection from the central
21
provinces of China. These factors suggest that the Southern Chinese collection would potentially
22
be a good source for new allelic variation for modern cultivars.
23
19
References
1
2
3
Brown-Guedira, G.L., J.A. Thompson, R.L. Nelson, and M.L. Warburton. 2000. Evaluation of
4
genetic diversity of soybean introductions and North American ancestors using RAPD
5
and SSR markers. Crop Sci. 40:815-823.
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Bruebaker, C.L., and J.F. Wendel, 1994. Reevaluation of the origin of domesticated cotton
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(Gossypium hirsutum: Malvaceae) using nuclear restriction fragment length
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Cregan, P.B., T. Jarvick, A.L. Bush, R.C. Shoemaker, K.G. Lark, A.L. Kahler, N. Kaya, T.T.
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Van Toai, D.G. Lohnes, J. Chung, and J. E. Specht. 1999. An integrated genetic linkage
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map of the soybean. Crop Sci. 39:1464-1490.
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23
LIST OF FIGURES
Fig. 1. Chinese Map. Light orange area is the selected central province; light green area is the
selected southern province.
Fig. 2. Side-by-Side boxplots among six populations. CCG = Central Chinese Germplasm. NAC
= North American Cultivars. SCG = Southern Chinese Germplasm. C+N = Combined CCG and
NAC. C+S = Combined CCG and SCG. N+S = Combined NAC and SCG.
Fig. 3. Scree plot of stress against dimensions of configurations attempted.
Fig.4. Principal Coordinate Analysis. Plot in two dimensions based on the distance matrix among
302 soybean lines obtained from multidimensional scaling analysis. C=CCG (central Chinese
germplasm), N=NAC (North American cultivars), and S=SCG (southern Chinese germplasm).
There is a SCG outlier (PI 588040).
Fig. 5. Dendrogram resulting from cluster analysis for the RFLP-based mean genetic distances
among 15 provinces and North American cultivars (NAC).
Fig. 6. Dendrogram resulting from cluster analysis for the RFLP-based mean genetic distances
among maturity group within provinces.
24
Fig. 7. Dendrogram resulting from cluster analysis for the RFLP-based mean genetic distances
among maturity groups of all cultivars.
MG†: maturity group.
N‡: number of accessions.
25
26
70
80
90
100
110
120
130
50
50
Heilongjiang
Jilin
Xingjiang
40
BeijingLiaoning
30
Xizang
20
90
Nei Mongol
Yellow River
Hebei
Ningxia
Shanxi
Qinghai
Shandong
Gansu
Jiangsu
Shaanxi Henan
Shanghai
Sichuan
Anhui
Hubei
Zhejiang
Jiangxi
Yangtze River Hunan
Fujian
Guizhou
Yunnan
Guangdong
Guangxi
100
110
120
40
30
20
27
28
29
1.50
1.25
1.00
0.75
0.50
0.25
0.00
30
3.0
2.5
2.0
1.5
1.0
0.5
0.0
31
1.50
1.25
1.00
0.75
0.50
0.25
0.00
MG†
I
0
III
II
N‡
5
4
64
48
50
V
IV
6
17
00
17
VI
VII
21
VIII
71
32
Table 1. Major Chinese soybean ancestral lines selected for diversity analysis.
PI Number
Ancestral Line
Region
Province
PI548497A
PI548493
PI561354
PI458506
PI578503
PI602502
PI602497
PI464916
PI458510
PI602498
PI458505
PI464917
PI297505
Jin yuan
Huang bao zhu
Zi hua No. 4
Feng di huang
Tie jia si li huang
Ziong yue xiao huang dou
Ke shan si li jia
Ji ti No. 2
Ji ti No. 1
Xiao jin huang
Da bai mei
Ji ti No. 3
Ji ti No. 5
NE†
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
Liaoning
Jilin
Heilongjiang
Jilin
Jilin
Liaoning
Heilongjiang
Liaoning
Liaoning
Jilin
Liaoning
Jilin
Heilongjiang
PI430595
58-161
HHH‡
Jiangsu
PI602501
PI468408A
PI578498B
PI567604A
Tong shan tian e dan
Qi huang No. 1
Ju xuan 23
Xin huang dou
HHH
HHH
HHH
HHH
Jiangsu
Shandong
Shandong
Shandong
PI602499
PI602991
PI602993
PI602992
PI578491A
PI578495
Tie jiao huang
Shandong si jiao qi
Pi xian ruan tiao zhi
Qin yang shui bai dou
Hua xian da lu dou
Jin dou No. 4
HHH
HHH
HHH
HHH
HHH
HHH
Shandong
Shandong
Jiangsu
Henan
Henan
Shanxi
PI578488B
PI464932
PI436562
PI578499A
PI602994
PI32454
PI430620
PI578504
Feng xian sui dao huang
Nan nong 493-1
Ai jiao zao
Shanghai liu yue bai
Pu dong da huang dou
Tai xing hei dou
Hou zi mao
Xiang dou No. 3
South
South
South
South
South
South
South
South
Shanghai
Jiangsu
Hubei
Shanghai
Shanghai
Jiangsu
Hubei
Hunan
† Northeast region in China
‡ Huang Huai Hai region in east central China
33
Table 2. Diversity estimates. Overall = all the populations. CCG = central Chinese germplasms.
NAC = North American cultivars. SCG = southern Chinese germplasm. C+N = Combined CCG
and NAC. C+S = Combined CCG and SCG. N+S = Combined NAC and SCG. P̂ = Proportion
of polymorphism loci.
Locus/probe
Overall
A023
A063-2
A085
A086
A095
A186
A333
A374
A381
A401
A461
A481
A505
A520
A567
A586
A668
A681
A691
A702
A708
A806
A816
A847
B039
B122
B164
B166
K002
K003
K007
K069-1
K070
0.50
0.59
0.51
0.44
0.41
0.33
0.51
0.49
0.50
0.50
0.37
0.40
0.49
0.14
0.49
0.50
0.43
0.66
0.52
0.56
0.43
0.56
0.52
0.38
0.42
0.39
0.50
0.65
0.37
0.50
0.35
0.49
0.56
(Hˆ )
P̂
‡Mean
0.47
 0.02
1.00
CCG
NAC
0.08  0.03† 0.47 
0.52  0.04 0.34 
0.56  0.02 0.49 
0.50  0.01 0.38 
0.48  0.02 0.40 
0.31  0.05 0.31 
0.51  0.02 0.04 
0.53  0.02 0.09 
0.29  0.05 0.36 
0.50  0.00 0.47 
0.45  0.03 0.47 
0.50  0.02 0.09 
0.49  0.01 0.47 
0.14  0.04 0.15 
0.47  0.02 0.30 
0.42  0.03 0.49 
0.42  0.04 0.34 
0.50  0.04 0.45 
0.48  0.02 0.54 
0.50  0.02 0.50 
0.49  0.04 0.53 
0.32  0.05 0.32 
0.42  0.04 0.24 
0.42  0.04 0.49 
0.50  0.01 0.42 
0.26  0.05 0.49 
0.17  0.05 0.48 
0.44  0.06 0.38 
0.39  0.04 0.46 
0.49  0.00 0.39 
0.33  0.05 0.60 
0.48  0.02 0.26 
0.33  0.05 0.12 
0.41
 0.02
1.00
0.37
0.03
0.06
0.01
0.05
0.05
0.06
0.03
0.05
0.05
0.03
0.03
0.05
0.03
0.05
0.06
0.02
0.06
0.05
0.04
0.06
0.04
0.06
0.06
0.02
0.04
0.01
0.02
0.07
0.03
0.00
0.03
0.06
0.05
 0.03
1.00
SCG
0.03 
0.38 
0.44 
0.37 
0.32 
0.35 
0.33 
0.48 
0.40 
0.41 
0.18 
0.41 
0.38 
0.13 
0.14 
0.53 
0.07 
0.53 
0.52 
0.49 
0.31 
0.22 
0.63 
0.14 
0.20 
0.38 
0.13 
0.74 
0.11 
0.46 
0.12 
0.49 
0.40 
0.34
0.02
0.04
0.03
0.04
0.04
0.00
0.04
0.02
0.03
0.03
0.04
0.04
0.04
0.04
0.04
0.02
0.03
0.03
0.02
0.05
0.01
0.04
0.02
0.04
0.04
0.04
0.04
0.01
0.04
0.00
0.04
0.01
0.05
 0.03
1.00
C+N
C+S
N+S
0.29
0.64
0.55
0.48
0.45
0.31
0.42
0.49
0.32
0.49
0.46
0.40
0.48
0.14
0.42
0.46
0.51
0.49
0.50
0.54
0.51
0.33
0.36
0.49
0.49
0.39
0.35
0.42
0.48
0.49
0.48
0.49
0.26
0.49
0.63
0.50
0.45
0.41
0.33
0.47
0.52
0.50
0.48
0.33
0.46
0.47
0.13
0.43
0.50
0.44
0.66
0.52
0.58
0.40
0.57
0.57
0.25
0.42
0.33
0.49
0.69
0.25
0.48
0.22
0.50
0.61
0.33
0.37
0.48
0.37
0.35
0.33
0.49
0.40
0.49
0.48
0.31
0.31
0.47
0.14
0.44
0.52
0.17
0.66
0.54
0.50
0.38
0.46
0.56
0.37
0.29
0.43
0.37
0.70
0.36
0.50
0.36
0.44
0.56
0.44
 0.02
1.00
0.46
 0.02
1.00
0.42
 0.02
1.00
34
1
^
^ 
†: The standard error of h at each locus was obtained by [Vl ( h )] 2 .

‡: The standard error of obtained from the total variance V (H ).
35
Table 3. Estimates of diversities for 33 polymorphic loci for eight subgroups of central Chinese
germplasm (CCG) and seven subgroups of southern Chinese germplasm (SCG).
P̂ = proportion of polymorphic loci.
CCG
Locus
SCG
Anhui Gansu Hebei Henan Jiangsu Shaanxi Shandong Shanxi Anhui Guangdong Hubei Jiangsu Shanghai Sichuan Zhejiang
A023
0.00
0.00
0.00
0.22
0.00
0.12
0.44
0.07
0.00
0.00
0.00
0.00
0.00
0.10
0.00
A063
0.65
0.67
0.00
0.65
0.47
0.20
0.50
0.32
0.50
0.41
0.35
0.39
0.44
0.27
0.31
A085
0.00
0.48
0.67
0.44
0.35
0.43
0.44
0.63
0.32
0.24
0.48
0.37
0.49
0.48
0.24
A086
0.00
0.50
0.44
0.49
0.48
0.28
0.44
0.49
0.15
0.49
0.06
0.50
0.50
0.47
0.17
A095
0.24
0.49
0.00
0.50
0.18
0.47
0.00
0.48
0.49
0.00
0.22
0.19
0.00
0.49
0.24
A186
0.41
0.36
0.00
0.42
0.32
0.34
0.44
0.08
0.15
0.24
0.27
0.34
0.44
0.40
0.44
A333
0.24
0.39
0.44
0.41
0.22
0.55
0.44
0.49
0.15
0.24
0.36
0.21
0.28
0.50
0.24
A374
0.41
0.54
0.44
0.56
0.34
0.49
0.44
0.42
0.28
0.00
0.50
0.46
0.49
0.10
0.48
A381
0.41
0.00
0.00
0.46
0.48
0.10
0.44
0.04
0.41
0.13
0.31
0.27
0.15
0.44
0.31
A401
0.24
0.46
0.44
0.35
0.49
0.49
0.44
0.40
0.22
0.22
0.50
0.39
0.00
0.49
0.09
A461
0.41
0.34
0.44
0.43
0.44
0.20
0.44
0.47
0.28
0.00
0.22
0.10
0.00
0.10
0.24
A481
0.13
0.52
0.44
0.40
0.26
0.50
0.44
0.49
0.31
0.24
0.53
0.13
0.32
0.35
0.33
A505
0.00
0.41
0.44
0.49
0.49
0.39
0.00
0.47
0.46
0.44
0.42
0.12
0.28
0.48
0.31
A520
0.00
0.16
0.00
0.18
0.35
0.23
0.00
0.00
0.00
0.24
0.12
0.07
0.00
0.31
0.00
A567
0.24
0.50
0.44
0.38
0.20
0.44
0.00
0.49
0.15
0.00
0.12
0.07
0.28
0.30
0.00
A586
0.24
0.42
0.50
0.48
0.44
0.38
0.44
0.35
0.44
0.45
0.51
0.42
0.61
0.48
0.50
A668
0.49
0.45
0.00
0.22
0.38
0.29
0.44
0.45
0.00
0.00
0.03
0.07
0.00
0.19
0.05
A681
0.24
0.57
0.44
0.37
0.50
0.21
0.00
0.40
0.29
0.64
0.49
0.53
0.40
0.44
0.50
A691
0.41
0.39
0.44
0.49
0.24
0.47
0.44
0.49
0.22
0.28
0.51
0.35
0.28
0.47
0.45
A702
0.24
0.29
0.00
0.62
0.59
0.37
0.00
0.51
0.15
0.41
0.46
0.50
0.50
0.39
0.47
A708
0.00
0.34
0.00
0.46
0.32
0.00
0.00
0.50
0.00
0.00
0.06
0.50
0.00
0.00
0.47
A806
0.00
0.48
0.44
0.00
0.18
0.50
0.00
0.22
0.32
0.22
0.23
0.11
0.28
0.40
0.00
A816
0.00
0.56
0.50
0.32
0.48
0.00
0.00
0.16
0.54
0.24
0.41
0.30
0.50
0.62
0.31
A847
0.00
0.44
0.44
0.00
0.00
0.10
0.00
0.38
0.00
0.24
0.06
0.00
0.00
0.43
0.09
B039
0.00
0.49
0.44
0.42
0.48
0.48
0.00
0.50
0.31
0.24
0.17
0.29
0.00
0.00
0.17
B122
0.24
0.44
0.44
0.00
0.35
0.22
0.00
0.08
0.15
0.00
0.23
0.41
0.28
0.43
0.24
B164
0.50
0.16
0.00
0.00
0.18
0.18
0.00
0.00
0.32
0.41
0.00
0.14
0.28
0.10
0.00
B166
0.41
0.45
0.00
0.66
0.65
0.48
0.44
0.11
0.75
0.41
0.69
0.68
0.60
0.57
0.73
K002
0.00
0.30
0.44
0.48
0.44
0.19
0.00
0.36
0.15
0.38
0.00
0.12
0.28
0.10
0.00
K003
0.41
0.50
0.44
0.42
0.32
0.23
0.44
0.38
0.30
0.50
0.50
0.50
0.32
0.20
0.48
K007
0.00
0.30
0.44
0.42
0.44
0.23
0.00
0.38
0.08
0.22
0.27
0.07
0.00
0.00
0.00
K069
0.44
0.47
0.44
0.38
0.50
0.50
0.00
0.44
0.48
0.00
0.47
0.49
0.44
0.40
0.43
K070
0.00
0.47
0.00
0.40
0.18
0.43
0.00
0.14
0.63
0.00
0.52
0.22
0.00
0.28
0.35
Mean
0.22
0.40
0.29
0.38
0.36
0.32
0.22
0.34
0.27
0.23
0.31
0.28
0.26
0.33
0.26
Std.err 0.03
0.03
0.04
0.03
0.03
0.03
0.04
0.03
0.03
0.03
0.04
0.03
0.04
0.03
0.03
0.61
0.94
0.64
0.88
0.94
0.94
0.48
0.94
0.85
0.70
0.91
0.94
0.67
0.91
0.79

P
36
Table 4. Allele frequencies for each probe-restriction enzyme combination across all 302 lines
and for central Chinese germplasm (CCG), the North American cultivars (NAC), and southern
Chinese germplasm (SCG).
Probe
A023
Enzyme
EcoRI
A063
EcoRI
A085
EcoRI
A086
HindIII
A095
TaqI
A186
DraI
A333
EcoRI
A374
HindIII
A381
DraI
A401
TaqI
A461
TaqI
A481
EcoRV
A505
EcoRI
A520
TaqI
A567
HindIII
A586
TaqI
A668
EcoRI
Contined next page
Allele
A
B
C
A
B
C
A
B
C
A
B
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
A
B
A
B
C
A
B
A
B
C
A
B
A
B
C
A
B
Overall
0.46
0.54
0.00
0.56
0.22
0.22
0.39
0.57
0.03
0.68
0.32
0.72
0.28
0.00
0.80
0.19
0.01
0.47
0.52
0.01
0.61
0.37
0.02
0.56
0.43
0.00
0.44
0.56
0.00
0.76
0.24
0.74
0.23
0.03
0.56
0.44
0.93
0.07
0.00
0.42
0.58
0.59
0.39
0.02
0.70
0.30
Gene Pool
CCG
0.96
0.03
0.01
0.18
0.18
0.64
0.39
0.53
0.08
0.54
0.46
0.61
0.39
0.00
0.81
0.17
0.02
0.56
0.42
0.02
0.41
0.55
0.04
0.82
0.16
0.01
0.52
0.48
0.00
0.65
0.35
0.60
0.39
0.02
0.43
0.57
0.93
0.07
0.00
0.63
0.37
0.70
0.30
0.00
0.28
0.71
NAC
0.62
0.38
0.00
0.78
0.22
0.00
0.56
0.44
0.00
0.74
0.26
0.73
0.26
0.01
0.81
0.19
0.00
0.98
0.02
0.00
0.95
0.05
0.00
0.77
0.23
0.00
0.62
0.38
0.00
0.63
0.37
0.95
0.05
0.00
0.38
0.62
0.92
0.08
0.00
0.82
0.18
0.58
0.41
0.00
0.78
0.22
SCG
0.02
0.98
0.00
0.75
0.25
0.00
0.31
0.68
0.01
0.76
0.24
0.80
0.20
0.00
0.78
0.21
0.77
0.20
0.80
0.01
0.62
0.38
0.01
0.27
0.73
0.00
0.28
0.71
0.01
0.90
0.10
0.74
0.20
0.06
0.75
0.25
0.93
0.06
0.01
0.08
0.92
0.52
0.44
0.04
0.96
0.04
37
Table 4. Continued.
A681
HindIII
A691
TaqI
A702
HindIII
A708
DraI
A806
DraI
A816
EcoRV
A847
HindIII
B039
EcoRI
B122
EcoRI
B164
DraI
B166
EcoRI
K002
HindIII
K003
EcoRV
K007
HindIII
K069
EcoRI
K070
TaqI
C
A
B
C
A
B
C
A
B
C
D
A
B
C
D
A
B
C
A
B
C
A
B
C
A
B
C
A
B
A
B
C
A
B
C
D
E
F
A
B
A
B
A
B
C
D
A
B
A
B
C
0.00
0.28
0.40
0.31
0.52
0.45
0.03
0.52
0.41
0.07
0.00
0.70
0.29
0.01
0.00
0.50
0.44
0.06
0.63
0.26
0.11
0.75
0.25
0.00
0.70
0.29
0.01
0.26
0.74
0.45
0.54
0.00
0.53
0.22
0.12
0.04
0.07
0.02
0.25
0.75
0.47
0.53
0.18
0.79
0.03
0.00
0.58
0.42
0.57
0.12
0.31
0.01
0.20
0.66
0.13
0.61
0.39
0.00
0.67
0.12
0.20
0.01
0.56
0.44
0.00
0.00
0.81
0.01
0.18
0.72
0.24
0.04
0.70
0.30
0.00
0.46
0.54
0.00
0.15
0.85
0.91
0.08
0.01
0.73
0.08
0.09
0.03
0.01
0.06
0.27
0.74
0.44
0.56
0.21
0.79
0.00
0.00
0.39
0.61
0.79
0.21
0.00
0.00
0.30
0.68
0.02
0.58
0.34
0.08
0.52
0.48
0.00
0.00
0.60
0.34
0.04
0.02
0.80
0.20
0.00
0.86
0.14
0.00
0.43
0.57
0.00
0.70
0.30
0.00
0.45
0.55
0.60
0.40
0.00
0.77
0.12
0.08
0.03
0.00
0.00
0.63
0.37
0.73
0.27
0.41
0.46
0.13
0.00
0.84
0.16
0.94
0.06
0.00
0.00
0.34
0.06
0.60
0.43
0.54
0.03
0.40
0.59
0.01
0.00
0.81
0.19
0.00
0.00
0.12
0.88
0.01
0.46
0.34
0.20
0.92
0.07
0.01
0.89
0.09
0.02
0.25
0.74
0.07
0.93
0.00
0.27
0.38
0.16
0.05
0.15
0.00
0.06
0.94
0.36
0.64
0.04
0.94
0.02
0.01
0.58
0.42
0.18
0.08
0.75
38
Table 5. Relationship of allele number among central Chinese germplasm (CCG), the North
American cultivars (NAC), and southern Chinese germplasm (SCG).
Present number relative to the null number
Number of null alleles
CCG
NAC
SCG
CCG
Number of alleles
85
12
****
5
9
NAC
74
23
16
****
16
SCG
87
10
7
3
****
39
Table 6. Genetic distance matrix determined by RFLP among central Chinese germplasm (CCG),
the North American cultivars (NAC), and southern Chinese germplasm (SCG).
GD (X 100)
ID
CCG
NAC
SCG
CCG
NAC
****
88
72
13
****
SCG
75
33
29
****
40
Table 7. Origin and Maturity Group of NAC lines.
NAC lines
Origin
Maturity Group
A1564
China X Japan X Korea
I
A2943
China X Japan X Korea
II
A3127
China X Japan
III
B216
Unknown
II
Bonus
China X Japan
IV
BSR201
China
II
Dorman
China X Korea
V
Essex
China X Japan
V
Haberlandt
Korea
VI
Hood
China X Japan
VI
Hutcheson
China
V
Ogden
Tokyo X China
VI
Patoka
China
IV
Peking
China
IV
Pella
China X Japan
III
PI71506
China
IV
Ransom
China X Japan X Korea
VII
Williams
China
III
York
China X Korea
V
Kingwa
China
IV
Adams
China
III
PI54610-1
China
III
Clark
China
IV
Corsoy
China
II
Harosoy
China
II
williams
China
III
Perry
Japan
IV
Arksoy
Korea
VI
Tokyo1
Japan
VII
Roanoke
China
VII
Mandarin
China
I
Manchu
China
III
Mandarin Ottawa
China
I
Richland
China
II
AK Harrow
China
III
Mukden
China
II
Illini
China
III
Dunfield
China
III
CNS
China
VII
Manitoba Brown
Unknown
0
Tokyo2
Japan
VII
PI54610-4
China
IV
S100
China
V
Continued next page
41
Table 7. continued
Pagoda
China
0
Acme
China
0
Lincoln
China
III
Capital
China
0
Hawkeye
China
II
BlackHawk
China
I
Evans
China
0
McCall
China
0
Lee
China
VI
Century
China
II
Chippewa
China
I
Ford
China
III
Shelby
China
III
Merit
China
0
Kent
China X Japan
IV
Adelphia
China X Japan
III
Wayne
China
III
Amsoy
China
II
Hark
China
I
Hobbit
China X Japan X Korea
III
Beeson
China X Japan
II
Calland
China X Japan
III