RESEARCH ARTICLE

Diversity of Oxalis tuberosa Molina: a comparisonbetween AFLP and microsatellite markers

Lauren J. Moscoe • Eve Emshwiller

Received: 21 February 2014 / Accepted: 22 July 2014

� Springer Science+Business Media Dordrecht 2014

Abstract Traditional crops contribute to food secu-

rity and agroecological sustainability, but their diver-

sity is increasingly threatened by complex interplays

of local and global sociocultural and economic

change. Molecular markers are powerful tools to

measure and characterize this diversity, and compar-

isons among different molecular marker systems are

necessary to assess their appropriateness in different

research contexts. Using a common sample set, we

compare amplified fragment length polymorphism

(AFLP) and microsatellite (simple sequence repeats;

SSRs) techniques to assess their utility in research on

the Andean tuber crop oca (Oxalis tuberosa Molina,

Oxalidaceae). We find that 26 of 27 individuals have

distinct AFLP genotypes, and all 27 individuals have

distinct SSR genotypes. Both markers systems cluster

samples in agreement with morphotype groups and

separate clusters with similar strength, but more

variation occurs within AFLP-based clusters than

within SSR-based clusters. In addition, correlation

between marker systems of pairwise distances is

positive and significant (R = 0.831, p = 0.001).

Ultimately, we discuss each system’s advantages and

disadvantages for future oca diversity research.

Keywords Agricultural biodiversity � Andean

crops � Clonal propagation � Molecular markers �Oxalis tuberosa

Introduction

Traditional crops are important contributors to agro-

ecosystems (Jarvis et al. 2008) and nutrition (Johns

2003), but their diversity is increasingly threatened by

complex interplays of local and global sociocultural

and economic change (Jarvis et al. 2007). Crop

diversity is shaped by a multitude of factors, and many

methods exist to describe and measure it, including

cultural, molecular, and morphological. Though no

single technique is entirely comprehensive on its own,

molecular markers are especially powerful because

they are highly heritable, available in high numbers,

and are often polymorphic enough to distinguish

among closely related genotypes (Archak et al.

2003). Appropriate molecular marker systems maxi-

mize the possibility of recognizing alleles, contain loci

that maximally span the genome, and generate highly

reliable and replicable data (Albertini et al. 2003).

Molecular marker techniques of microsatellites

(simple sequence repeats; SSRs) and amplified length

polymorphism (AFLP) have been used to detect

variation among closely related taxa since the early

1980s and mid 1990s, respectively (Vos et al. 1995;

Ellegren 2004). Microsatellites are rapidly evolving,

L. J. Moscoe (&) � E. Emshwiller

University of Wisconsin-Madison, 430 Lincoln Drive,

Madison, WI 53706, USA

e-mail: moscoe@wisc.edu

123

Genet Resour Crop Evol

DOI 10.1007/s10722-014-0154-x

non-coding regions of DNA with high levels of length

polymorphism often due to replication slippage (Elle-

gren 2000; Kuchma et al. 2011). The microsatellite

procedure involves amplification of SSR loci and

scoring of alleles based on fragment length. The AFLP

procedure consists in digestion of the genome with

restriction enzymes and selective amplification of the

resulting fragments. These fragments (i.e., bands) are

then scored as present or absent. Both techniques have

been reviewed extensively in the literature, and their

relative advantages and disadvantages discussed (e.g.,

Russell et al. 1997; Pejic et al. 1998; Ellegren 2000,

2004; Mariette et al. 2001, 2002; Campbell et al. 2003;

Guichoux et al. 2011). However, despite numerous

such reviews, investigations about the appropriateness

of each technique with respect to particular research

questions and study systems are warranted.

We compare AFLP and SSR utility for investi-

gations of diversity in oca (Oxalis tuberosa Molina,

Oxalidaceae), a tuber crop native to the Andean

region. Oca is an excellent model for studies of

crop molecular diversity because it exemplifies

characteristics shared among many threatened tra-

ditional crop species. First, Andean farmers main-

tain many folk cultivars (i.e., varieties recognized

and maintained by farmers; e.g., up to 20 folk

cultivars in a single household in Cusco Depart-

ment, Peru; Moscoe, unpublished data). Second,

like many other agriculturally significant species

(Zeven 1980), oca is a polyploid (mostly

2n = 8x = 64; reviewed in Emshwiller 2002; with

a few now inferred to be 2n = 4x = 32; Bradbury

2014), which presents unique challenges to genetic

research. Finally, like many crops around the

world, oca tubers are predominantly clonally prop-

agated, thus affecting overall genetic diversity and

population structure. We expect that our assess-

ments of the different marker systems are likely to

be useful for studies of other vegetatively propa-

gated crops, and/or other plants with varying ploidy

levels.

Until the recent development of primers flanking

polymorphic microsatellite loci in oca (Turumaya

2011; Guaraguara 2013), genetic studies of oca have

been carried out using anonymous molecular tech-

niques such as inter-simple sequence repeats (ISSRs;

Pissard et al. 2006, 2007a, b; Malice et al. 2007,

2010) and amplified fragment length polymorphism

(AFLP; Emshwiller 2006; Emshwiller et al. 2009).

Results indicate that oca folk cultivars are geneti-

cally heterogeneous (Pissard et al. 2006), that there

is not always a one-to-one relationship between oca

morphotypes and genotypes (Emshwiller 2006), and

that oca use-categories consistently comprise two

distinct genetic clusters (Emshwiller 2006; Emshw-

iller et al. 2009). Oca use-categories include (1) folk

cultivars with higher levels of oxalic acid, which are

processed to produce a dehydrated storable food

product and (2) folk cultivars with lower levels of

oxalic acid, which are consumed without such

processing (Hermann 1992; Bradbury and Emshw-

iller 2011). In Cusco Department, Peru, the source

of samples for our study, Quechua farmers recog-

nize one folk cultivar (p’osqo) in the first category

and many folk cultivars (e.g., kusipata, misitu) in

the second category. AFLP separation of oca use-

categories (Emshwiller 2006; Emshwiller et al.

2009) is consistent with the finding that some

p’osqo accessions are tetraploid, rather than octo-

ploid (Bradbury 2014).

We compare AFLP and SSR data to explore what

each marker system reveals about similarities and

differences within a common set of O. tuberosa

samples, especially emphasizing how AFLP and SSR

data distinguish the two use-categories described

above. Because some p’osqo clones are tetraploid

(Bradbury 2014), while most oca clones are octoploid

(reviewed in Emshwiller 2002), AFLP and/or SSR

data may provide evidence to suggest differences in

ploidy within the sample set. Such ploidy differences

may be revealed by clustering patterns or by differing

numbers of SSR alleles per locus or AFLP bands, as

potatoes (Solanum spp. L.) with lower ploidy have

been shown to display fewer AFLP bands (Kardolus

et al. 1998). Specifically, we ask the following

questions: Do average band/allele counts differ

between use-categories? How finely does each marker

system distinguish among samples? How does each

marker system group samples? How is diversity

hierarchically partitioned by each marker system?

Are pairwise differences between samples correlated

between marker systems? Asking these particular

questions will help us assess the appropriateness of the

aforementioned markers for research aimed at defin-

ing oca clones (i.e., individuals originating from the

same zygote), investigating intra-clonal heterogeneity

(e.g., due to somatic mutations), and elucidating the

evolutionary history of oca’s two use-categories.

Genet Resour Crop Evol

123

Materials and methods

Sampling

Twenty-seven O. tuberosa samples for which AFLP

data were previously generated (Emshwiller 2006)

were selected for the present study (Table 1). All

samples were collected from three communities in

Pisac District, Cusco, Peru (Emshwiller 2006). These

samples were selected to represent all major neighbor-

joining clusters that were found in the previous study

and which correspond to morphotypes identified by

Emshwiller (2006). We maintain Emshwiller’s (2006)

morphotype names based on the folk cultivar name

most often applied to that morphotype by Pisac

farmers. One exception, ‘‘yellow’’, refers to samples

from Emshwiller’s ‘‘yellow’’ cluster, comprising five

farmer-named folk cultivars, all with predominantly

yellow surface color (Emshwiller 2006).

Molecular procedure

AFLP

DNA isolation and AFLP procedure, using one primer

combination (EcoRI-AC and MseI-CAC), are described in

Emshwiller (2006).

SSR

Each sample was amplified at ten SSR loci: OT28001,

OT34142, OT25246, OT07355, OT43595, OT04636,

OT22713, OT21842, OT11876, OT24976 (Turumaya

2011; Guaraguara 2013; described in Bonnave et al. 2014).

Loci OT43595, OT04636, and OT21842 were

amplified jointly using the Qiagen Multiplex PCR Kit

(Valencia, CA, USA) following the manufacturer’s

recommendations for a final volume of 10 lL with 1 lL

of undiluted DNA extraction product. The thermal

cycling protocol included an initial incubation at 95 �C

for 15 min; 35 cycles of 30 s at 95 �C, 1 min 30 s at

59 �C, and 1 min at 72 �C; and a final 60 �C for 30 min.

Amplification products were diluted in 99 parts water,

and 20 lL diluted amplification product was mixed with

10 lL deionized formamide (Hi-Di, Applied Biosys-

tems, Foster City, CA, USA) plus 625 ROX-labeled size

standard (Geneflo, Chimerx, Milwaukee, WI, USA).

Loci OT28001, OT34142, OT25246, OT07355,

OT22713, OT11876, and OT24976 were each ampli-

fied independently with 20 lL reactions containing

10.2 lL water, 2 lL 109 buffer (10 mM Tris–HCl,

50 mM KCl, 1.5 mM MgCl2, pH = 8.3 @ 25 �C),

1.6 lL 2.5 mM dNTPs, 1 lL 10 mg/mL BSA, 1 lL

fluorescent labeled forward primer at 1 lM concen-

tration, 1 lL reverse primer at 1 lM concentration,

0.2 lL Taq polymerase at 5,000 units/mL (New

England Biolabs, Ipswich, MA, USA), and 1 lL

undiluted DNA extraction product. The thermal

cycling protocol included an initial incubation at

94 �C for 5 min; 30 cycles of 30 s at 94 �C, 1 min 30 s

at 60 �C (OT28001, OT34142, OT11876, OT24976),

60.3 �C (OT25246), or 67.9 �C (OT07355,

OT22713), and a final 72 �C for 30 min. Amplifica-

tion products were diluted in 9 (OT11876, OT24976),

Table 1 Oxalis tuberosa samples

Sample Morphotype

97:31-08 Damaso

97:02-05 Kusipata

97:14-02 Kusipata

97:31-04 Kusipata

97:44-03 Kusipata

97:17-03 Misitu

97:20-03 Misitu

97:46-05 Misitu

97:46-14 P’osqo

97:46-15 P’osqo

97:49-02 Puka chiliku

97:02-04 Puka panti

97:06-02 Puka panti

97:19-03 Puka panti

97:49-04 Puka panti

97:13-02 Puka p’osqo

97:14-04 Puka p’osqo

97:15-06 Puka p’osqo

97:25-02 Puka p’osqo

97:40-02 Puka p’osqo

97:50-02 Senorita

97:14-07 Ushpa

97:19-01 Ushpa

97:47-06 Ushpa

97:11-01 Yellow

97:21-06 Yellow

97:48-02 Yellow

Sample numbers and morphotype names assigned by

Emshwiller (2006)

Genet Resour Crop Evol

123

14 (OT25246, OT07355, OT22713), or 29 (OT28001,

OT34142) parts water, and 3 lL diluted amplification

product was mixed with 10 lL deionized formamide

(Hi-Di, Applied Biosystems, Foster City, CA, USA)

plus 625 ROX-labeled size standard (Geneflo, Chi-

merx, Milwaukee, WI, USA). The following amplified

loci were co-loaded: OT28001 and OT34142,

OT11876 and OT24976, and OT25246 and

OT22713. Locus OT07355 was loaded independently.

Genotyping was performed on an ABI 3730XL

capillary sequencer at the University of Wisconsin

Biotechnology Center.

AFLP data

The original dataset describing presence/absence of

each AFLP band for all original samples (Emshwiller

2006) was edited as follows. Only the 27 O. tuberosa

samples used in this study were retained, and only

bands that displayed variable presence in these 27

samples were included.

SSR data

Alleles from polymorphic loci were autoscored on

GeneMarker (SoftGenetics, State College, PA, USA)

and manually edited. Because oca is octoploid, alleles

were scored as present or absent, but dosage was not

estimated. If the presence/absence of an allele was

ambiguous for any sample, that allele was eliminated

from the dataset.

Data analysis

We counted AFLP bands and SSR alleles and

computed averages for all samples, for all samples

excluding the two p’osqo samples, and for only the

two p’osqo samples to explore potential ploidy

differences between use-categories.

We assessed each marker system’s strength in

differentiating among individual samples by compar-

ing the number of unique genotypes revealed by each

combination of primer pairs.

We visualized grouping patterns revealed by each

marker system through nonmetric multidimensional

scaling (NMDS) based on Jaccard distances. Jaccard

distances were calculated for each binary dataset

representing presence/absence of AFLP bands or SSR

alleles. For SSR data we used the sum of Jaccard

distances for each locus. Distance calculations and

NMDS analyses were performed using the software

package ecodist for R (Goslee and Urban 2007).

The hierarchical partitioning of diversity was

investigated through analyses of molecular variance

(AMOVA; Excoffier et al. 1992) using the software

package GenAlEx 6.5 for Excel (Peakall and Smouse

2006). Analyses excluded samples from morphotypes

represented by only a single sample (97:31-08, 97:49-

02, 97:50-02). Significance of AMOVA results was

calculated based on 999 permutations.

We explored correlations between AFLP and SSR

pairwise differences first through linear regression in

R. We shaded points to highlight three groups: sample

pairs within the same morphotype; sample pairs from

different non-p’osqo morphotypes; and sample pairs

from different morphotypes, one of which being

p’osqo (i.e., pairs that include both use-categories).

We then quantified significance of the correlation

between AFLP and SSR pairwise distances through a

Mantel test (Mantel 1967) performed using the

software package GenAlEx 6.5 for Excel (Peakall

and Smouse 2006). The Mantel test compared genetic

pairwise distance matrices of AFLP data and SSR

data, using Jaccard distances calculated in R as

described above. Significance of Mantel test r value

was calculated based on 999 permutations.

Results

Band and allele counts

When considering all samples, AFLP data revealed 66

variably present bands, with an average of 28.19 bands

present per sample among all samples; 29.16 bands per

sample excluding p’osqo samples; and 16.00 bands

per sample among only p’osqo samples (10 bands for

Table 2 Number of variably present AFLP bands and

averages

AFLP

primer

pair

Number

variably

present

bands

Avg.

number

bands

present

per sample

Avg. number

bands present

per sample

(excluding

p’osqo)

Avg. number

bands present

per sample

(only p’osqo)

EcoRI-AC/

MseI-CAC

66 28.19 29.61 16.00

Genet Resour Crop Evol

123

97:46-14 and 22 bands for 97:46-15; Table 2). SSR

data revealed 75 alleles across all polymorphic loci,

averaging 3.66 alleles per locus; 3.74 alleles per locus

excluding p’osqo samples; and 2.55 alleles per locus

among only p’osqo samples (2.6 alleles for 97:46-14

and 2.5 alleles for 97:46-15; Table 3). Locus specific

values are displayed in Table 3.

Differentiation

Of the 27 samples analyzed, we observed 26 distinct

AFLP genotypes and 27 distinct SSR genotypes.

Within the AFLP dataset, there was a single pair of

samples with identical genotypes: 97:02-05 and 97:44-

03, both belonging to the kusipata morphotype. These

two samples, however, differed in three SSR alleles at

locus OT07355 and one SSR allele at locus OT04636.

We calculated the number of distinct genotypes

revealed by each SSR locus and by each combination

of multiple loci. Figure 1 shows that as more loci are

included, the mean number of distinct genotypes

increases. However, some loci distinguish among

samples more finely than others, meaning that certain

combinations of fewer than ten loci maximize differ-

entiation (i.e., distinguish among all 27 individuals).

Locus OT22713, comprising 9 different alleles in this

dataset, reveals the greatest variability among samples

and on its own distinguishes among 25 unique

genotypes.

Because the 66 AFLP bands were amplified in a

single assay, we could not analyze the number of

0 2 4 6 8 10

0

5

10

15

20

25

30

# loci

# di

stin

ct g

enot

ypes

Fig. 1 Number of distinct genotypes revealed by different

numbers and combinations of SSR loci. Each point represents

the number of distinct genotypes revealed (Y) through each

combination of SSR loci (X). Gray points are means and bars

indicate standard deviation. As more loci are included, the mean

number of distinct genotypes increases

-0.4 -0.2 0.0 0.2 0.4

-0.6

-0.4

-0.2

0.0

0.2

0.4

NMDS1

NM

DS

2

A

K

A

Y

OK O

U

O

M

UA

M

Y

OK

D

O

K

M

P

P

U

Y

CA

S

Fig. 2 Results of nonmetric multidimensional scaling (NMDS)

analysis of AFLP data, with stress = 0.23. Samples are

represented according to Emshwiller’s (2006) morphotype

assignments. A = puka panti, C = puka chiliku, D = damaso,

K = kusipata, M = misitu, O = puka p’osqo, P = p’osqo,

S = senorita, U = ushpa, Y = yellow. Bold letters represent

entirely overlapping data points

Table 3 Number of alleles per SSR locus and averages (SSR

loci developed by Turumaya 2011 and Guaraguara 2013 and

described in Bonnave et al. 2014)

SSR

locus

Number

alleles

Avg.

number

alleles

per

sample

Avg. number

of alleles per

sample

(excluding

p’osqo)

Avg.

number of

alleles per

sample

(only

p’osqo)

OT28001 8 3.30 3.40 2.00

OT34142 7 2.63 2.60 3.00

OT25246 8 3.30 3.40 2.00

OT07355 7 4.19 4.24 3.50

OT43595 6 2.59 2.64 2.00

OT04636 8 4.67 4.80 3.00

OT22713 9 4.07 4.16 3.00

OT21842 9 3.89 4.04 2.00

OT11876 7 3.74 3.80 3.00

OT24976 6 4.19 4.36 2.00

All loci 75 3.66 3.74 2.55

Genet Resour Crop Evol

123

genotypes distinguished by varying numbers and

combinations of primers.

Ordination

NMDS plots reveal similar clustering of samples, and the

relative orientation of clusters (i.e., which clusters

neighbor which other clusters) is also similar (Figs. 2, 3).

In both plots, clusters form in agreement with

Emshwiller’s (2006) morphotype assignments. This is

consistent with Jaccard distances: the average Jaccard

distances within morphotypes are almost always less than

the average Jaccard distances between morphotypes

(Tables 4, 5). The p’osqo morphotype, which clustered

most loosely on both NMDS plots, is the exception

because the within-p’osqo morphotype distance (0.6667

for AFLP and 3.6000 for SSR) is greater than some

between-morphotype distances (Table 4). Both NMDS

plots reveal that the second most loosely clustered

morphotype is misitu and that the single senorita sample

nearly overlaps with it (Figs. 2, 3). These observations

can also be seen by the relatively high average distance

among misitu samples and relatively low average

distance between misitu and senorita samples (Table 4).

Both NMDS plots appear to distinguish among-

morphotype groups with remarkably similar strength

(Figs. 2, 3). This is supported by data on Table 5, which

show that the mean average among-morphotype distance,

expressed as a ratio to the average pairwise distance, is

1.0679 for AFLP data and 1.0646 for SSR data.

The NMDS plot based on SSR data (Fig. 3)

clusters morphotypes more tightly than the NMDS

plot based on AFLP data (Fig. 2). This is can also be

seen through distance measures. The mean average

within morphotype distance, expressed as a ratio to

the average pairwise distance, is 0.4631 for AFLP

data and 0.2617 for SSR data (Table 5). In other

words, samples within the same morphotype are more

similar to each other with respect to SSR data than

with respect to AFLP data.

Pairwise difference correlation

The Mantel test revealed significant correlation

between Jaccard pairwise distance matrices, based

on R = 0.831 and p = 0.001 (Table 6). This means

that 69.1 % (R2) of the variation in AFLP distances is

predicted by the linear relationship between AFLP

distances and SSR distances, as depicted in Fig. 4.

Three clusters form in the scatterplot assessing the

correlation between marker systems (Fig. 4). The cluster

at the lower end of the regression line is comprised of

intra-morphotype pairs (black points, Fig. 4). The cluster

in the center is comprised of inter-morphotype pairs, with

neither sample being p’osqo (gray points, Fig. 4). The

three exceptions in this middle cluster are two misitu/

misitu pairs (lower black points in middle cluster, Fig. 4)

and one p’osqo/p’osqo pair (higher black point in middle

cluster, Fig. 4). The highest cluster is comprised of inter-

morphotype pairs, with one sample in each pair being

p’osqo (white points, Fig. 4).

Hierarchical partitioning of diversity

Both marker systems reveal greater diversity among

morphotypes than within morphotypes. Based on AFLP

data, 73 % of molecular variation is derived from

among-morphotype diversity and 27 % from within

-3 -2 -1 0 1 2 3

-6

-4

-2

0

2

NMDS1

NM

DS

2

A

K

AY

O

K

OU

O

M

U

A

M

Y

O

K D

O

KM

P

P

U

Y

C

A

S

Fig. 3 Results of nonmetric multidimensional scaling (NMDS)

analysis of SSR data, with stress = 0.22. Samples are repre-

sented according to Emshwiller’s (2006) morphotype assign-

ments. A = puka panti, C = puka chiliku, D = damaso,

K = kusipata, M = misitu, O = puka p’osqo, P = p’osqo,

S = senorita, U = ushpa, Y = yellow. Bold letters represent

entirely overlapping data points

Genet Resour Crop Evol

123

morphotype diversity (/pt = 0.730, p = 0.001). Based

on SSR data, 81 % of variation is derived from among-

morphotype diversity and 19 % from within morpho-

type diversity (/pt = 0.807, p = 0.001).

0 2 4 6 8

0.0

0.2

0.4

0.6

0.8

SSR pairwise Jaccard distance

AF

LP p

airw

ise

Jacc

ard

dist

ance

Fig. 4 Scatterplot of SSR pairwise Jaccard distances versus

AFLP pairwise Jaccard distances with regression line

(R2 = 0.691). Black points indicate sample pairs within the

same morphotype. Gray points indicate sample pairs from

different non-p’osqo morphotypes. White points indicate sample

pairs from different morphotypes, one of which being p’osqo

Table 4 Average Jaccard distance measures between pairs of morphotypes for AFLP (italicized font) and SSR (normal font) data

Damaso Kusipata Misitu Puka panti P’osqo Puka chiliku Puka p’osqo Senorita Ushpa Yellow

Damaso – 0.3583 0.4136 0.3588 0.7980 0.3235 0.4739 0.3889 0.2812 0.3229

Kusipata 3.8958 0.0869 0.4960 0.4540 0.7428 0.5406 0.4603 0.5905 0.4798 0.3591

0.4889

Misitu 3.5825 4.7208 0.2405 0.4862 0.8788 0.5301 0.5796 0.4362 0.4751 0.4720

1.8841

Puka panti 4.8000 4.9738 3.5143 0.1304 0.7843 0.3973 0.6104 0.5026 0.4870 0.4699

0.7000

P’osqo 5.8333 6.0750 6.9790 6.7601 0.6667 0.8521 0.8186 0.8861 0.8365 0.8086

3.6000

Puka chiliku 2.5333 3.7708 3.6714 3.9792 6.5333 – 0.5539 0.4857 0.3425 0.5662

Puka p’osqo 3.7576 4.2438 4.5465 4.5267 6.6576 4.5010 0.1783 0.5509 0.4329 0.5132

0.2700

Senorita 3.6500 4.3905 2.1698 3.0250 7.0310 2.8667 3.8233 – 0.4474 0.4717

Ushpa 3.4667 4.6905 4.1810 3.9417 6.7087 4.3500 3.4000 3.5500 0.1359 0.4641

0.1333

Yellow 2.7143 4.1377 4.2392 4.2492 6.7611 3.6798 3.8181 3.6881 2.9048 0.1789

0.4667

Within morphotype distances for morphotypes with a single individual are left blank (–)

Table 5 Summary of Table 4, including within- and between-

morphotype average distances as proportion of average pair-

wise distance for each dataset

Average distance AFLP SSR

Within morphotypes 0.2311 1.0776

Within morphotypes compared to average

pairwise

0.4631 0.2617

Between pairs of morphotypes 0.5329 4.3843

Between pairs of morphotypes compared to

average pairwise

1.0679 1.0646

Between pairs of individuals 0.4990 4.1184

Table 6 Results of Mantel test performed on AFLP and SSR

pairwise Jaccard distance matrices

SSx SSy SPxy Rxy P (rxy-rand C rxy-data)

869.622 10.764 80.423 0.831 0.001

Genet Resour Crop Evol

123

Discussion

In the broadest sense, AFLPs and SSRs reveal similar

clustering patterns, consistent with Emshwiller’s

(2006) morphotype assignments. This three-way

agreement reinforces our confidence in each system’s

ability to accurately characterize oca germplasm, with

molecular markers offering some advantages over

classification based solely on morphology.

One advantage is that molecular markers can reveal

whether folk cultivars that are somewhat morpholog-

ically heterogeneous are made up of a single clone or

multiple clones. Emshwiller (2006) referred to such

groups as ‘‘complex varieties’’ when the tubers varied

in color or shape, yet were grouped by farmers under a

shared name.

Molecular data revealed that some such morphotypes

(e.g., misitu) are comprised of multiple clones. Whereas

many Pisac farmers referred to all tubers from the misitu

group simply as misitu, some farmers attached addi-

tional adjectives describing the color or shape of the

tubers (e.g., yana misitu and q’ellu misitu, black and

yellow, respectively). The molecular data suggested that

all the misitu tubers were not a single clone (i.e., that

they were separated by at least one cycle of sexual

recombination; Emshwiller 2006). In this case of

inconsistent folk classification, both kinds of molecular

data validated that at least some of the subtle morpho-

logical differences (particularly tuber shape) that were

discerned by a few knowledgeable farmers do indeed

seem to reflect differences among clones.

Alternatively, molecular markers may confirm

clonality among individuals that do not appear iden-

tical. For example, Pisac farmers refer to a morpho-

logically diverse group of tubers as ushpa, and these

also were sometimes given modifiers based on their

range of tuber colors (yana ushpa, puka uphpa, or

yuraq ushpa, for black, red, or white tubers,

Emshwiller 2006). In this case, both kinds of molec-

ular markers support that the ushpa samples in this

study belong to a single clone. Unlike the case of

misitu, this is an example where both folk classifica-

tion and molecular markers combine diverse morpho-

types into a single folk cultivar or clone, respectively.

These examples support the utility of molecular

markers, and comparisons between the results gener-

ated by AFLP and SSR makers are merited to evaluate

practical concerns and to infer evolutionary forces

responsible for observed patterns.

General observations

Distinguishability of individuals

Microsatellites distinguish all 27 samples as unique

genotypes (with as few as three loci), and AFLP

variation reveals 26 unique genotypes. Though both

marker systems were able to distinguish most or all

samples, distinguishability may wane if more samples

are introduced. Figure 1 suggests that the average

number of distinct genotypes revealed by microsatel-

lites might asymptote soon after ten loci. Therefore,

using Bonnave et al. (2014) seven additional SSR

primer pairs, which were published after completing

our lab work for this analysis, would likely strengthen

distinguishability when additional samples are used.

In addition, distinguishability could also be achieved

by increasing the number of AFLP primer combina-

tions, especially considering that one additional AFLP

primer combination could potentially double the

number of variably present bands for analysis.

Distinguishability of clones

Arnaud-Haond et al. (2007) suggest that a bimodal

distribution of pairwise genetic distances may indicate

the presence of clonal relationships in a sample set. In

our data, the combination of AFLP and SSR distances

more clearly identifies clonal pairs (lowest cluster,

Fig. 4) than either AFLP distances or SSR distances

alone. This is because clusters in Fig. 4 are best

separated by diagonal lines perpendicular to the

regression line rather than by horizontal or vertical

lines alone, which would represent separation with

respect to AFLP or SSR data, respectively. However,

it is worth noting that, if forced to choose between only

vertical separation (i.e., using SSRs) or only horizontal

separation (i.e., using AFLPs), then clonal pairs are

better separated from non-clonal pairs by a vertical

line (between black and gray clusters, Fig. 4) than by a

horizontal line, thus lending support for the use of

SSRs in this context. The three black points within the

middle cluster in Fig. 4 represent pairs within ‘‘com-

plex varieties’’ (supporting the idea that these less

homogeneous morphotypes are made up of multiple

clones; Emshwiller 2006). That the distribution of data

in Fig. 4 is trimodal, including a third cluster

containing only inter-morphotype pairs including

p’osqo (white points), suggests that there is an

Genet Resour Crop Evol

123

additional layer of relationships consistent with

differing use-categories (described above) and possi-

bly different ploidy levels (described below). In other

words, all non-p’osqo individuals are more different

from p’osqo individuals than they are from any other

individual within their same use-category.

Ploidy inferences

Recent findings by Bradbury (2014) show that some

p’osqo clones are tetraploid and that these clones

display fewer SSR alleles than octoploid clones.

Furthermore, in a previous dataset, Emshwiller

observed that tetraploid O. picchensis R. Knuth and

some p’osqo samples displayed significantly fewer

AFLP bands than other oca samples (unpublished

data). Kardolus et al. (1998) also found that potato

species with lower ploidy showed fewer AFLP bands.

We therefore hypothesize that tetraploid p’osqo sam-

ples will display fewer AFLP bands or SSR alleles

than octoploid samples, with the number of SSR

alleles per tetraploid locus never exceeding four. That

the average number of AFLP bands in p’osqo samples

(16.00; Table 2) is nearly half that of non-p’osqo

samples (29.61; Table 2) suggests that one or both of

these p’osqo samples may be tetraploid. According to

AFLP band counts, sample 97:46-14 is more likely to

have a lower ploidy (avg. AFLP bands = 10) than

97:46-15 (avg. AFLP bands = 22). The average

number of SSR alleles in p’osqo samples (2.55;

Table 3) was also less than the average number of SSR

alleles in non-p’osqo samples (3.74; Table 3), and the

two p’osqo samples did not differ greatly in number of

SSR bands. Furthermore, neither p’osqo sample ever

displayed greater than four alleles per SSR locus, but

all non-p’osqo samples did display greater than four

alleles at one or more loci. Though our lack of living or

dried tissue precludes our ability to measure the

samples’ ploidy directly, these AFLP and SSR results

do suggest the possibility of ploidy differences

between p’osqo and non-p’osqo samples and that

these ploidy differences may be revealed by both

molecular marker systems.

Correlation between marker systems

That AFLP- and SSR-derived distances were statisti-

cally significantly positively correlated, but imper-

fectly (r2 = 0.691), is consistent with results from

similar studies (e.g., Pejic et al. 1998). Various reasons

may explain imperfect and sometimes insignificant

(e.g., Skrede et al. 2009) correlations between AFLP

and SSR measures of diversity. First, correlation may

be low or insignificant because mutations affecting

AFLP band patterns and SSR alleles occur at different

rates (Mariette et al. 2001). Second, weak correlation

might result from low sampling of highly heteroge-

neous genomes (Alacs et al. 2010). This is supported

by simulations that showed increased correlation

between markers when more loci were included in

analyses (Mariette et al. 2002). Finally, lower corre-

lations between markers were found in simulated

populations that had recently diverged or had not

reached equilibrium (Mariette et al. 2002). This last

point is supported by evidence that correlations

between markers within populations (i.e., more

recently diverged) are often weaker than correlations

between markers among populations (i.e., more

anciently diverged; e.g., Pejic et al. 1998; Skrede

et al. 2009). However, this trend over time toward

equilibrium may not apply to oca because of its

predominantly clonal mode of propagation.

Inter- and intra-morphotype diversity findings

Our ongoing research is concerned with defining oca

clones and quantifying intra-clonal heterogeneity, so

an ideal molecular marker would clearly differentiate

among clones and reveal differences among samples

originating from a single zygote and undergoing

subsequent somatic mutation. In this study, AFLP

and SSR techniques separate morphotype clusters with

similar strength, but more variation occurs within

AFLP-based morphotype clusters than within SSR-

based morphotype clusters (Table 5; AMOVA

results). This observation may be explained by

different effects of evolutionary forces on different

markers, leading to different levels of diversity within

loci; differences in marker sensitivity, leading to

different levels of diversity across loci; and/or con-

tamination and scoring error. Each of these potential

explanations is described below.

Evolutionary forces

Some evolutionary forces (e.g., drift, migration) have

genome-wide effects, while others (e.g., mutation,

selection) affect different loci differently. One might

Genet Resour Crop Evol

123

therefore infer that different markers reveal different

diversity patterns among samples when differences

arose due to mutation or selection (Mariette et al.

2001). Given this, differences in intra-morphotype

variability between markers could suggest that sam-

ples within morphotypes have diverged through

somatic mutations among individuals of the same

clone. Veasey et al. (2008) and Mkumbira et al. (2003)

suggest that observed intra-varietal AFLP heteroge-

neity in sweet potato (Ipomoea batatas (L.) Lam.) and

manioc (Manihot esculenta Crantz), respectively,

resulted from somatic mutations.

This reasoning and the added assumption that

SSRs mutate at faster rates than AFLPs, has led

some to expect SSRs to be more sensitive to recent

mutation-derived divergences than AFLPs (Alacs

et al. 2010). If we assume that lineages within

morphotypes diverged after initial divergences of

morphotypes, however, our results do not support

this expectation (i.e., average SSR distances are not

greater than average AFLP distances within oca

morphotypes). However, other research has indeed

found SSRs to be more sensitive to recent diver-

gences. For example, Alacs et al. (2010) found that

SSRs distinguished both inter- and intra-regional

(mainland/island) populations of studied quokka

macropods (Setonix brachyurus Quoy et Gaimard

1830), while AFLPs only revealed structure between

regions. They attribute this to the weakness of

AFLPs at sensing differences among recently frag-

mented mainland populations. Mariette et al. (2001)

also attribute the higher within-population diversity

of Pinus pinaster Ait., as revealed by SSRs versus

AFLPs, to evolutionary factors, like mutation, that

do not affect the entire genome equally.

We should note, however, that Alacs et al. (2010)

and Mariette et al. (2001) both investigated sexually

reproducing organisms, in which heritable SSR muta-

tions likely occur through different mechanisms (e.g.,

unequal crossing over during meiosis) than mutations

spread through clonal reproduction (e.g., replication

slippage in cells from which asexual progeny are

derived). This difference—coupled with heterogene-

ity in SSR mutation processes among species, SSR

repeat types, and chromosomal location (Ellegren

2000)—complicates assumptions of levels of variation

detected in SSR versus AFLP loci and may at least

partially explain the deviation of our results from the

literature.

Sensitivity

While the above point refers to differences in

AFLP versus SSR diversity within loci, other

explanations address differences in AFLP and

SSR diversity across loci. Greater intra-morphotype

heterogeneity revealed by AFLPs may be explained

by AFLPs’ arguably greater sensitivity to slight

variation. Because the AFLP technique generally

amplifies more loci than a suite of SSR primers,

many have argued that this genome breadth allows

for finer differentiation among close individuals

(e.g., Campbell et al. 2003; Skrede et al. 2009). For

example, using simulations, Campbell et al. (2003)

found that AFLPs show higher population assign-

ment success and greater differences among popu-

lations than do SSRs. In addition, Skrede et al.

(2009) show that AFLP markers revealed greater

phylogeographic structuring of three circumpolar

plant species (Draba subcapitata Simmons, D.

nivalis Liljeblad, and D. fladnizensis Wulfen) than

did SSRs. With 66 AFLP loci and 10 SSR loci (75

SSR alleles) used in our study, it is possible that

AFLPs were more sensitive to finer-scale variation

than SSRs.

Error

It is also possible that our observed higher intra-

morphotype AFLP variation is not due to true

differences between AFLP and SSR patterns, but

rather the result of contamination or scoring error.

First, AFLPs, being non taxon-specific, are vulnerable

to amplification of fungal or parasitic DNA, thus

contributing variation to banding patterns separate

from sample variation (Dyer and Leonard 2000;

Pfeiffer et al. 2011). Second, AFLP scoring confidence

is made difficult by the anonymity of AFLP loci, thus

allowing for potentially greater scoring error. Though

reproducibility of AFLP scoring has been generally

recognized as ‘‘high’’ (‘‘Marker Technologies’’), this

may not be the case for finer-scale patterns. For

example, Emshwiller et al. (2009) found morphotype

assignments (i.e., clustering) based on seven AFLP

primer combinations to be consistent with assignments

based on one AFLP primer combination (Emshwiller

2006), but they found that relationships among

samples within clusters were not consistent. On the

other hand, Pfeiffer et al. (2011) found that SSRs were

Genet Resour Crop Evol

123

both more reliable and more precise than AFLPs when

genotyping Mercurialis perennis L., also a polyploid

plant for which allele dosage was not quantified.

AFLP and SSR in clonal crop research

This study contributes toward filling the gap in

research that uses molecular markers to investigate

diversity of vegetatively propagated crops. Within the

few studies that have been carried out, and across a

wide array of vegetatively propagated crops, previous

research generally corroborates our findings of clear

varietal clusters and varying levels of intra-varietal

heterogeneity. For example, SSR-based clusters cor-

respond to varietal clusters in sweet potato (I. batatas;

Veasey et al. 2008), cultivated grape (Vitis vinifera L.;

Emanuelli et al. 2013), manioc (M. esculenta; Mkum-

bira et al. 2003; Elias et al. 2004), potato (Solanum

spp.; Gavrilenko et al. 2013), and banana/plantain

(Musa spp. L.; de Jesus et al. 2013). Intra-varietal

diversity patterns are less frequently discussed, often

because sample sizes include too few representatives

of each variety to infer patterns. However, intra-

varietal SSR diversity was observed in sweet potato,

manioc, and bananas, and hypothesized explanations

for this diversity include farmer varietal perception,

somatic mutations among clones, and sexual repro-

duction events (Mkumbira et al. 2003; Veasey et al.

2008; de Jesus et al. 2013).

Our future research will include a large sample size

(approx. 600 O. tuberosa accessions), with many

accessions per morphotype, and additional SSR loci to

more intentionally investigate inter- and intra-clonal

diversity patterns. Our decision to employ microsatel-

lites is motivated by their non-anonymous and taxon-

specific amplification properties, as well as our findings

that they more finely distinguished among individuals

(Fig. 1) and more clearly distinguished clonal pairs

from inter-clonal pairs (Fig. 4). Meanwhile, unpub-

lished work by Emshwiller and colleagues includes

nearly 1,000 samples analyzed with 3 AFLP primer

combinations. As we continue investigations with each

molecular marker technique, greater sample sizes and

methods in place to assess typing error rate will allow us

to continue to explore questions examined in this paper

and move toward a better understanding of oca diversity

patterns among and within morphotypes.

Acknowledgments The authors would like to thank Cecile

Ane, Bret Larget, and Rebecca Shirk (UW-Madison) for help

with statistical analyses and Sarah Friedrich for help with

figures. EE thanks the people and community authorities of the

Communities Amaru, Sacaca, and Viacha; INRENA for

collection permits for Peru in 1996–1997; and funding from a

Student Fulbright Research Grant, and National Science

Foundation (USA) Doctoral Dissertation Improvement Grant

#DEB9623227 to Jeff J. Doyle and EE.

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