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PLANT GENETIC RESOURCES

Association of Ecogeographical Variables and RAPD Marker Variation in Wild Potato Populations of the USA

^a Dep. of Horticulture, Univ. of Wisconsin-Madison, 1575 Linden Drive, Madison WI 53706
^b USDA-ARS, Vegetable Crops Research Unit, Inter-Regional Potato Introduction Station, 4312 Hwy. 42, Sturgeon Bay, WI 54235
^c International Potato Center, P.O. Box 1558, Lima 100, Peru

* Corresponding author (nr6jb{at}ars-grin.gov)

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The goal of germplasm conservation in genebanks is to maximize genetic variation. Collecting explorations would be more efficient if factors that predict areas and habitats associated with greater genetic differences and diversity could be identified. Therefore, the objective of this research was to investigate whether ecogeographical variables have significant associations with patterns of genetic variation in wild potato populations. This study examined 96 wild potato populations collected from the southwestern USA. These were 43 populations of Solanum fendleri (2n = 4x = 48) and 53 populations of S. jamesii (2n = 2x = 24). These species represent two of the most predominant breeding systems found among Solanum species: tetraploid inbreeders and diploid outcrossers, respectively. Random amplified polymorphic DNA (RAPD) markers were used to assess populations in two ways: determination of simple genetic difference between pairs of populations, and genetic diversity of a population based on the frequency of that population's RAPD markers in the whole set. Results from 2282 comparisons indicated that patterns of genetic differences were not associated with any differences in ecogeographical structure assessed. Remarkably, even geographical separation of populations, a parameter usually considered important when collecting germplasm, did not predict genetic differences very well. Latitude, longitude, and heat-related factors significantly predicted genetic diversity in S. fendleri but not in S. jamesii. This experiment revealed few associations between ecogeographic parameters and genetic variation in the wild. It follows, therefore, that one should collect many populations and incorporate a manageable subset into the genebank on the basis of empirical measurements of genetic diversity.

Abbreviations: DI, diversity estimate • GD, genetic distance • NRSP-6, Inter-Regional Potato Introduction Station

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
THE MAIN GOAL OF CROP GERMPLASM COLLECTIONS is to preserve potentially useful sources of genetic variation for future use (Cohen et al., 1991). Thus, research that addresses strategies to maximize the conservation of genetic diversity is fundamental. Altieri and Merrick (1987) and Brown et al. (1997) recognized that there are problems associated with inadequate sampling procedures during explorations and lack of representation of the total gene pool. However, the genebanks' resources to address these problems are usually limited.

Ferguson et al. (1998) recognized that sampling would be more efficient if collecting trips had clearly defined target areas and habitats. An approach to identify these areas is to undertake ecogeographic studies preceding explorations, since plant populations may be expected to exhibit structured genetic variation across their geographical range (Antonovics, 1971; Loveless and Hamrick, 1984). At the present time, the common and historic practice in explorations is to sample as many different environments as possible (Brown, 1978; Marshall and Brown, 1975).

Zoro et al. (1998) pointed out that although sampling methods have been designed using probability methods combined with population genetics theory (Crossa et al., 1993), these results can provide only rough predictions when basic information about a species reproductive biology is unstudied. Hamrick et al. (1991) stressed that there is considerable variation among plant species for ecological traits that influence the distribution of genetic variation, so a genetically effective management strategy for one species may not be effective for another. Therefore, detailed (and real) studies on ecology, population biology, genetics, and reproductive biology are essential to plan adequate sampling strategies (Hawkes, 1971; Yonezawa and Ichihashi, 1989).

Germplasm conservation is extremely important in the case of potato species. The cultivated potato, Solanum tuberosum L., is the most important tuber crop in the world and is one of the four most valuable crops worldwide. Germplasm with adaptation to different climatic and cultural conditions has been essential to the development of improved varieties (Ross, 1986). The U.S. Potato Genebank (NRSP-6) preserves nearly 5000 different accessions of potato and its wild relatives (Spooner and Bamberg, 1994). Solanum species have a wide range of ecological and geographic distributions in the Americas. Therefore, guidelines for relating environmental variables to genetic diversity would significantly enhance collection efficiency.

In recent years, molecular markers such as RAPD markers have been confirmed as efficient tools for estimating genetic variation among genotypes of any organism (Williams et al., 1990). These markers have the potential to measure variation with good coverage of the entire genome. RAPD markers have clearly resolved patterns of diversity of many diverse types of plant populations and germplasm collections (del Rio et al., 1997a,b; Link et al., 1995; Virk et al., 1995; Hormaza et al., 1994).

The main objective of the present study was to correlate genetic variation observed in natural wild potato populations in the USA with a series of different ecological, geographical, and reproductive variables. Since these species present two of the most important breeding systems observed in Solanum species, information from these comparisons may provide general insights for future germplasm explorations for other species.

	MATERIALS AND METHODS

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Plant Materials
Ninety-six wild potato populations, collected from 1992 to 1997, from different geographical regions of the southwestern USA, were examined. (Bamberg et al., 1996, 1997). To maintain genetic integrity, these populations were clonally maintained at the Inter-Regional Potato Introduction Station (NRSP-6) at Sturgeon Bay, WI. Plant Introduction (PI) numbers, collector codes, and collection-site coordinates are presented in Table 1. Solanum jamesii, a diploid (2n = 2x = 24) outcrossing species was represented by 53 populations; and S. fendleri, a tetraploid (2n = 4x = 48), self-pollinating species was represented by 43 populations (Bamberg and Martin, 1993). More detailed information on these populations such as habitat, collection dates, and special observations is available through the USDA National Plant Germplasm System and Germplasm Resources Information Network database.

RAPD Markers and PCR Amplification
Primers representing 10 random nucleotide sequences, obtained from Operon Technologies (Alameda, CA), were used in the RAPD assay. PCR amplifications were performed in 15-µL reaction volumes as described in del Rio et al. (1997a). All RAPD products were fractionated by electrophoresis in 1.5% (w/v) agarose gels and visualized by ethidium bromide staining 0.5 µg/mL in 1x TAE buffer.

Data Analysis
For each population, polymorphic RAPD markers were scored as present (1) or absent (0). Genetic variation of potato populations was measured as genetic distance (GD) between paired populations. GD coefficients were calculated as the complement of the simple matching coefficient: the number of loci with matching band status divided by the total number of loci assessed (Sneath and Sokal, 1973).

Genetic variation also was measured by means of a genetic diversity index (DI). This was designed to identify populations rich in rare genetic markers. Within each species, the frequency of each RAPD marker over all populations was determined. Each population was assigned a score for that marker equal to the frequency of the marker's presence or absence, according to whether it was present or absent in that population. Thus, a population having a marker which was present in 5% of the populations studied would be assigned a score for that marker of 0.05, while a population not having that marker would be assigned a score of 0.95.

The DI was calculated in two ways: The standard deviation of the marker score (SD_f), and the average of the marker score exponentially weighted (1/f²). Both, SD_f and 1/f² give high DI values to populations with relatively rare alleles. In the case of SD_f, the average of the scores of the most common allele among all populations was high (about 0.85) in both species. In 1/f², the exponential weighting of allele scores makes the contribution of each locus to the DI increase by the square of its decrease in score. However, when DI was calculated by this alternate method (as the average of 1/f²), no significant associations with ecogeographical parameters were detected. Thus, in the interest of space, no details of results with respect to this statistic are presented. Rather, all results presented are with respect to DI calculated as SD_f.

Climatic information for each collecting site was obtained from public databases of the National Climatic Data Center-National Oceanic and Atmospheric Administration (NCDC-NOAA) (see Table 2). Collection records provided information on habitat features such as type of surrounding vegetation, population and plant size, latitude, longitude, altitude, physical distance, etc. (records are available through NRSP-6 homepage). Additionally, information on soil type, geological age of the sites, drought and freeze potential, and rodent concentration also was obtained (Cockrum, 1960; Davis and Schmidly, 1994; Fitzgerald et al., 1994). Since most of these records were included as discrete data (i.e., presence vs. absence, soil types, etc.), they were only used to determine associations with diversity coefficients.

	RESULTS

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For S. jamesii, a total of 1378 pairwise comparisons of 123 polymorphic RAPD markers indicated that among all populations the mean distance was GD = 0.21. The highest GD detected between two populations was GD = 0.40 and the lowest was GD = 0.01.

A phenetic tree based on the distance matrix gives a graphic representation of the relationship among populations for each species (Fig. 1 and Fig. 2).

View larger version (21K): [in this window] [in a new window]   Fig. 1. UPGMA phenogram of genetic relationships among S. fendleri populations based on genetic distance coefficients determined by RAPD markers.

View larger version (25K): [in this window] [in a new window]   Fig. 2. UPGMA phenogram of genetic relationships among S. jamesii populations based on genetic distance coefficients determined by RAPD markers.

RAPD band frequencies among 53 populations of S. jamesii varied from 0.11 (6/53) to 0.96 (51/53) for a total of 123 RAPD loci evaluated. Mean DI was 0.26, and genetic diversity ranged from DI = 0.21 in 592410 to DI = 0.32 in 592398.

Association between Ecogeographical Variables and Genetic Distance, GD
Correlation and regression analysis between genetic distance and ecogeographical variables for S. fendleri and S. jamesii populations showed that genetic distance was not significantly related to any of the ecogeographical variables used in this study (Tables 2 and 3).

The multiple regression analysis including all variables revealed a maximum, but not significant R² = 0.39, P = 0.08 for cooling and heating degree day variables in S. fendleri populations. On the other hand, a maximum R² = 0.26, P = 0.38 was observed in S. jamesii for temperature variables (Table 3).

Association between Ecogeographical Variables and Genetic Diversity, DI
Solanum fendleri populations showed significant but low correlations between genetic diversity and latitude (0.39 P = 0.00), longitude (-0.41 P = 0.00), monthly average heating degree days (0.45 P = 0.00), the two sets of data of monthly and annual average rainfall (-0.32 P = 0.04, 0.34 P = 0.02, 0.33 P = 0.03, and 0.33 P = 0.03), and monthly and annual variation of temperature (0.42 P = 0.03 and 0.42 P = 0.03 respectively). The complete correlation analysis is presented in Table 4.

View this table: [in this window] [in a new window]   Table 4. Pearson's correlation coefficients between genetic diversity and ecogeographical variables for S. fendleri and S. jamesii populations.

In S. jamesii, no significant correlations were observed between genetic diversity and ecogeographical variables. Multiple regression analysis, including all variables showed no significant association (R² = 0.26, P = 0.76), stepwise analysis determined annual average temperature and annual average maximum temperature were most important predictors; however, these were very weak (R² = 0.09, P = 0.10).

	DISCUSSION

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Physical Proximity Not Associated with Genetic Relationships
RAPD markers determined that genetic differences exist among populations of S. jamesii and S. fendleri (Fig. 1 and Fig. 2). However, these patterns of genetic differentiation were not well associated with the ecogeographical variables assessed. Remarkably, even physical proximity of populations was not a very good predictor of genetic resemblance. This particular finding could be of importance in planning future explorations for collecting potato species. Marshall and Brown (1975) pointed out that guidelines for collecting emphasize the use of geographical separation for determining sampling sites. Chapman (1989) indicated that collection sites must be as diverse, geographically and ecologically, as possible. Under this scheme, populations closely located within an area may not be considered worth collecting because redundant genotypes are expected to be found. Some previous research in other species has found that genetic similarity is very closely associated with geographical proximity (Francisco-Ortega et al., 1993; Hormaza et al., 1994), while others failed to detect a good relationship (Fahima et al., 1999; Gallois et al., 1998). Loveless and Hamrick (1984) explained that populations in close neighborhoods tend to be uniform because genetic differentiation is often prevented by gene flow. Though some potato populations in close proximity were found to be highly related, i.e., S. fendleri 564030-564031 GD = 0.00, 564032-564033 GD = 0.05 and, S. jamesii 603051-603052 GD = 0.01, some were not, i.e., S. fendleri 592415-564042 GD = 0.31; 592412-564043 GD = 0.23, 592400-564045 GD = 0.23; S. jamesii 564048-592408 GD = 0.27, 564051-592413 GD = 0.23, 564053-592411 GD = 0.21, 564057-592407 GD = 0.21. Fahima et al. (1999) studying genetic variation in wild emmer wheat [Triticum dicoccoides (Koern. ex Asch. &Graebner) Aarons.] populations also found similar results. Moreover, they pointed out that quite often it was easier to find a greater genetic difference between close populations than among populations which are far apart.

Hamrick (1987) determined that characteristics of habitats may be quite heterogeneous within small areas. That concept can explain why genetic differentiation among populations is independent of distances. Cobb et al. (1994), Mopper et al. (1991), and Mitton et al. (1998) reported consistent patterns of microgeographic variation among pinyon populations at Sunset Crater, Arizona. Mitton et al. (1998), studying allozyme variation in pinyon (Pinus edulis Engelm.) populations, observed a repeated pattern of microgeographic variation between moist and dry sites. For instance, allele three of glycerate dehydrogenase (Gly-3) showed a higher frequency on dry sites. Fahima et al. (1999) reported that a sharp local differentiation, rather than a gradual change, in allele frequencies across the geographical range explains the lack of association between genetic distance and separation. The absence of a significant relationship between geographical separation and genetic distance might also indicate that microgeographic (local) variation is a significant factor of genetic differentiation (Antonovics and Bradshaw, 1970; Antonovics, 1971). Most often, potato populations in the USA, like those in Latin America (D.M. Spooner, 1999,personal communication), are very small (<100 plants) and isolated. For this reason, the actual microclimate differences relevant to populations may not be well represented by the data based on broader areas used here. Under such conditions, stochastic events may have extreme effect in modifying genetic structure of populations. Events such as environmental changes, demographic factors (i.e., chance differences among individuals in survivorship or fecundity), and genetic drift are likely to have greater repercussions.

Limits of Resolution Due to Sampling Errors
Genetic differences also may be explained by sampling error. Solanum jamesii is a diploid outcrosser with self-incompatibility. Populations having this type of breeding system are expected to produce highly heterozygous and heterogeneous populations (Loveless and Hamrick, 1984). Thus, many individuals (genotypes) should be pooled from each population to assure that a representative sample has been taken (del Rio and Bamberg, 1998). Previous results of the authors (del Rio et al., 1997b) however, show that this is not observed in wild S. jamesii since plants within samples of these populations are very homogeneous. It is possible that samples taken in different seasons tend to be different homogeneous subsets of an overall heterogeneous population, their genetic identities being determined by the particular time and conditions at collection. This is supported by the observation that "duplicate" samples (i.e., from exactly the same place but collected in different seasons) were not genetically identical (GD equals about 0.17 for both species).

Thus, imprecision in both the genetic and ecogeographical characterization associated with particular collection sites could have obscured real significant correlations between the two. This analysis, however, seeks practical conclusions, and such imprecision is currently a practical reality.

Significant Associations with Genetic Diversity
Although parameters of ecogeographical structure measured here did not explain genetic differentiation, a significant association of genetic diversity with ecogeographical variables was detected in S. fendleri populations when DI was calculated as SD_f.

For S. fendleri, associations with latitude and longitude suggest that populations from northeastern regions of the range tend to be more diverse than populations from other regions. This pattern of diversity was also associated with some climatic variables, suggesting that these regions have distinctive environments which promote the emergence of rare alleles.

When DI was calculated as the average of exponentially weighted marker scores (1/f²), no significant correlations with ecogeographical parameters were detected. This also held true when a variety of exponents other than 2 were used (data not presented). Although no method of calculating DI resulted in strong correlations with any ecogeographical parameter, the DI statistic still has practical use. A high DI identifies populations that merit special attention in the genebank. Their relative abundance of rare alleles means that they make a greater contribution to the overall genetic diversity of the collection than most populations do. Also, by studying the sites of origin of populations with high DI values, one might discover a previously unrecognized ecogeographical parameter the populations have in common, and thereby identify other sites with the same parameter. Such new sites could be the richest sources for collecting new diversity.

Immigration also may explain differences in diversity. Slatkin (1987) indicated that selection tends to adapt a population to local environmental conditions but that immigrants also contribute genes adapted to other conditions. Human intervention can account for immigration. Moeller and Schaal (1999) indicated that trade among Native Americans might have been responsible for the great morphological variation among maize accessions in the Great Plains. Wild potatoes were consumed by Native Americans and movement of tubers along the trade routes of the Anasazi may have occurred. Populations of S. fendleri might be in a transition process in adaptation to new environments, which would explain why they are rich in rare alleles.

These populations are assumed to have moved progressively northward by natural and artificial means. Migration to new locations mediated by birds or other animals could also foster differentiation. These locations may be rare oases where conditions favorable to potato survival exist. Thus, many distinct colonies could have been brought from far away, maintaining their genetic differences clonally. For example, birds may have come from diverse sites and converged in one area (i.e., attracted by water or food), "planting" several potato genotypes.

In summary, assessment of interpopulation genetic distances in both species showed no associations with ecogeographical variables, suggesting that genetic differentiation is predicted by factors more subtle than those assessed here. However, significant associations between diversity and ecogeographical variables in S. fendleri indicate that collecting across a specific range of geographical coordinates may result in maximizing the acquisition of representative samples of the gene pool for this species.

	ACKNOWLEDGMENTS

 
The authors express their thanks to the University of Wisconsin Peninsular Agricultural Research Station program and staff for their cooperation. We also thank Mr. Lixin Han, Ms. Rebecca Hozak, and Mr. Peter Crump for their assistance in the statistical analysis.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Reported use of brand name products does not imply an endorsement by USDA.

Received for publication May 1, 2000.

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TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 

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