Source verification of mis-identified Arabidopsis thaliana accessions

TECHNICAL ADVANCE

Source verification of mis-identified Arabidopsis thalianaaccessions

Alison E. Anastasio1,†, Alexander Platt2,†, Matthew Horton1, Erich Grotewold3, Randy Scholl3, Justin O. Borevitz1,

Magnus Nordborg2,4 and Joy Bergelson1,*

1Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA,2Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA,3Arabidopsis Biological Resource Center, Ohio State University, Columbus, OH 43210, USA, and4Gregor Mendel Institute, 1030 Vienna, Austria

Received 9 March 2011; revised 3 April 2011; accepted 6 April 2011; published online 16 June 2011.*For correspondence (fax 773-702-9740; e-mail [email protected]).†These authors contributed equally to this work.

SUMMARY

A major strength of Arabidopsis thaliana as a model lies in the availability of a large number of naturally

occurring inbred lines. Recent studies of A. thaliana population structure, using thousands of accessions from

stock center and natural collections, have revealed a robust pattern of isolation by distance at several spatial

scales, such that genetically identical individuals are generally found close to each other. However, some

individual accessions deviate from this pattern. While some of these may be the products of rare long-distance

dispersal events, many deviations may be the result of mis-identification, in the sense that the data regarding

location of origin data are incorrect. Here, we aim to identify such discrepancies. Of the 5965 accessions

examined, we conclude that 286 deserve special attention as being potentially mis-identified. We describe

these suspicious accessions and their possible origins, and advise caution with regard to their use in

experiments in which accurate information on geographic origin is important. Finally, we discuss possibilities

for maintaining the integrity of stock lines.

Keywords: Arabidopsis thaliana, contamination, long-distance dispersal, stock center, natural variation,

population structure.

INTRODUCTION

The Arabidopsis community has been interested in natural

variation for decades (reviewed by Alonso-Blanco and

Koornneef, 2000; Meyerowitz, 2001). In 1937, Friedrich

Laibach began collecting local ecotypes (Laibach, 1943;

Meyerowitz, 2001; Koornneef and Meinke, 2010). His per-

sonal collection and compilation of natural accessions from

other collectors constituted the first standardized set of seed

stock used by researchers (Robbelen, 1965). As the popu-

larity of Arabidopsis increased, researchers focused on a few

standard lines (e.g. Ler-0, Col-0), from which mutants, and

later recombinant inbred lines, were derived. Researchers

also continued to collect new ecotypes from natural popu-

lations, notably Laibach in Europe, Ivo Cetl in Moravia, and

Albert Kranz in Germany. Several narrowly distributed

papers described phenotypic variation in these accessions,

but, with the advent of the Arabidopsis Information Service

(AIS) in 1964, descriptions of natural accessions and their

ecology were shared among a growing community (Cetl,

1965; Cetl et al., 1965; Robbelen, 1965; Effmertova and Cetl,

1966; Effmertova, 1967). Soon after, the AIS Seed Stock

Center was established to house ecotypes and mutant lines

(Somerville and Koornneef, 2002). Currently, three stock

centers supply Arabidopsis researchers around the world

with seeds and DNA for their work: the Arabidopsis Biolog-

ical Resource Center at Ohio State University, Columbus,

OH, USA (http://abrc.osu.edu), the Nottingham Arabidopsis

Stock Centre (NASC) at the University of Nottingham, UK

(http://arabidopsis.info), and the Biological Resource Center,

554 ª 2011 The AuthorsThe Plant Journal ª 2011 Blackwell Publishing Ltd

The Plant Journal (2011) 67, 554–566 doi: 10.1111/j.1365-313X.2011.04606.x

part of the RIKEN Organization (formerly Sendai Arabidopsis

Seed Stock Center), in Japan (http://www.brc.riken.go.jp/lab/

epd/Eng/catalog/seed.shtml).

The availability of so many natural accessions means that

scientists can exploit natural genetic variability within the

species to identify gene functions that mutagenesis fails

to uncover (Tonsor et al., 2005), illuminate traits that are

important in native habitats and are targets of selection

(Mauricio and Rausher, 1997; Stahl et al., 1999; Alonso-

Blanco and Koornneef, 2000; Johanson et al., 2000; Hauser

et al., 2001; Tian et al., 2002; Mauricio et al., 2003; Goss and

Bergelson, 2006; Mitchell-Olds and Schmitt, 2006; McKay

et al., 2008), test evolutionary theory (Bergelson et al., 2001;

Nordborg et al., 2005; Bakker et al., 2006; Ehrenreich and

Purugganan, 2006; Shindo et al., 2007; Novembre and

Slatkin, 2009), and understand the extent to which popula-

tion structure is important in natural populations (Bergelson

et al., 1998; Nordborg et al., 2005; Beck et al., 2008; Bom-

blies et al., 2010; Platt et al., 2010). A. thaliana has a global

distribution and is found in a variety of habitats. Consider-

able variation in ecologically relevant traits has been

uncovered using accessions from disparate geographical

areas (Aranzana et al., 2005; Banta et al., 2007; Shindo et al.,

2007; Bouchabke et al., 2008). This natural variation is

especially useful in the study of adaptation. Indeed, conclu-

sions about historical demographic events have been drawn

using these accessions and information regarding their

specific geographical locations (Nordborg et al., 2005; Beck

et al., 2008; Pico et al., 2008). These conclusions rely on the

association between genotypic and geographic information

being correct.

A way to validate the identity of accessions used for

research is to genotype a large collection of individuals,

characterize the population structure across the species

range, and identify individuals that appear to be out of place.

This is especially easy when a species has strong population

structure, such as in species that show clear isolation by

distance. In this case, misplaced individuals may either be

the result of recent long-distance migration or human error

(i.e. mislabeling or contamination of stocks). In general, we

do not expect outliers to be the result of true long-distance

migration. The mere fact that they are identifiable as outliers

means that long-distance migration is exceedingly rare;

furthermore, we would not expect to find such individuals in

studies with limited sample size. Exceptions include situa-

tions where migration rates have recently increased (as has

clearly happened in humans), or where genotypes with a

predisposition for migration are strongly favored by selec-

tion. However, outliers can usually be explained by misla-

beling or other human error.

Using a data set of 139 SNPs in almost 6000 common

laboratory strains and natural accessions, Platt et al. (2010)

revealed that A. thaliana conforms to a pattern of isolation

by distance at several scales, and that the scales differ

regionally. Although genetically identical individuals can be

found across North America, identical individuals are rarely

separated by more than 1 km in continental Eurasia. Given

the clear pattern of isolation by distance across large

geographical areas, there is an expectation of the degree

to which plants within a region are related. In preparing data

for our previous paper (Platt et al., 2010), we came across

a number of accessions from both our collections and the

stock centers that are outliers. Plants that are almost

identical to geographically distant plants stand out as

potentially mis-identified. In cases where local collections

are not especially diverse, a single distantly related outlier

(perhaps also found in another location) is also suspicious.

Importantly, the inverse is true as well. Plants that are closely

related to close neighbors and only distantly related to

distant neighbors are almost certainly correctly identified.

RESULTS

Based on pairwise comparisons between individuals, we

identified three categories of accessions, ranging from well-

corroborated to highly suspicious. Geographic information

for individuals on the ‘green list’ was corroborated by

neighbors. These accessions are genetically similar to their

neighbors, and genetically dissimilar from accessions found

farther away. The green list included 70% of accessions

(Table S1). Accessions on the ‘red list’ are genetically

differentiated from their neighbors, genetically similar to

geographically distant individuals, and often reveal other

characteristics strongly suggestive of contamination, as

discussed below. Just under 5% of the dataset (286 acces-

sions) were included on the red list: all of these are geneti-

cally similar to geographically distant accessions and

dissimilar to their geographic neighbors (Table 1). The

‘yellow list’ contained the remaining 1472 accessions

(almost 25%). These include 131 accessions for which there

are insufficient nearby collections for corroboration, and

1341 accessions whose only long-distance, genetically close

relatives were on the red list.

The 286 accessions on the red list were further divided

into four categories. The first category comprised 71 acces-

sions that were the sole member of their haplogroup. The

second category comprised 78 accessions that belonged

to haplogroups that included commonly used laboratory

strains and were extensively over-dispersed (Col-0, Kin-0,

Kondara, Ler-0 and Ws-2) (all members of such haplogroups

were included on the red list). The third category comprised

18 haplogroups in which one or more far-flung individuals

came from a different geographical area than the majority of

members in the group; 30 accessions fitted this description

and were the only members of their haplogroup placed on

the red list (although, accordingly, the remaining members

were on the yellow list). For instance, the notable Midwest

US haplogroup hg8115, known as the ‘Heartland’ haplo-

group, contains 1041 accessions, one of which is not

Mis-identified A. thaliana accessions 555

ª 2011 The AuthorsThe Plant Journal ª 2011 Blackwell Publishing Ltd, The Plant Journal, (2011), 67, 554–566

Tab

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556 Alison E. Anastasio et al.


Tab

le1

(Co

nti

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(km

)

172

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315

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339

67.1

370

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5880

3.64

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56.3

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on

nam

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le1

(Co

nti

nu

ed)

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870

77.1

284

03W

a-1

174

4.6

472

50C

S28

804

Wa-

1K

ran

z73

94P

OL

52.3

2172

52.1

372

50C

S28

491

174

4.6

472

50W

a-1

Wa-

1K

ran

z84

03P

OL

52.3

2172

52.1

372

50C

S28

491

174

4.6

474

29C

S28

455

Li-3

:3K

ran

z72

25G

ER

50.3

833

8.06

6668

28.9

274

29C

S28

496

141

0.94

474

29C

S28

496

Mr-

0K

ran

z74

29IT

A44

.15

9.65

9739

.57

7225

CS

2845

51

410.

944

7429

CS

2849

7M

r-0

Kra

nz

7522

ITA

44.1

59.

6513

25.3

183

38M

r-0

10

482

65B

lh-1

Blh

-1K

ran

z82

65C

ZE

4819

1667

.59

7117

CS

2822

80.

993

715.

664

8265

CS

2822

8E

l-0

Kra

nz

7117

GE

R51

.510

59.

6825

397

0.25

8265

Blh

-10.

993

715.

664

8280

CS

2820

6D

i-M

Viz

ir70

95FR

A47

524

05.6

482

80C

t-1

197

7.91

482

80C

S28

233

En

-1K

ran

z71

18G

ER

508.

520

29.4

482

80C

t-1

182

7.18



Tab

le1

(Co

nti

nu

ed)

Cat

ego

ryH

aplo

gro

up

Acc

essi

on

nam

eN

ativ

en

ame

Co

llect

or

Acc

essi

on

nu

mb

erC

ou

ntr

yLa

titu

de

Lon

git

ud

e

Max

imu

md

ista

nce

tore

lati

ve(k

m)

Mo

stre

late

dac

cess

ion

nu

mb

er

Mo

stre

late

dac

cess

ion

nam

eS

imila

rity

Geo

gra

ph

icd

ista

nce

(km

)

482

80C

S28

230

En

-DK

ran

z71

20G

ER

508.

520

29.4

482

80C

t-1

182

7.18

482

80E

n-1

En

-1K

ran

z82

90G

ER

508.

520

29.4

482

80C

t-1

182

7.18

482

80C

S28

196

Ct-

1K

ran

z69

10IT

A37

.315

2166

.15

8280

Ct-

11

04

8280

CS

2819

5C

t-1

Kra

nz

7067

ITA

37.3

1521

12.2

482

80C

t-1

10

482

80C

t-1

Ct-

1K

ran

z82

80IT

A37

.315

2166

.15

7118

CS

2823

31

827.

184

8341

CS

2848

8M

a-0

Kra

nz

7245

GE

R50

.816

78.

7667

1561

.25

8341

Mt-

01

1501

.97

483

41C

S28

502

Mt-

0K

ran

z72

49LI

B32

.334

22.4

626

51.9

8341

Mt-

01

04

8341

Mt-

0M

t-0

Kra

nz

8341

LIB

32.3

422

.46

1501

.97

7245

CS

2848

81

1501

.97

483

59U

KID

1U

KID

1H

olu

b57

08U

K56

.8)3

.959

00.2

983

59P

na-

171

5816

.74

483

59U

KID

23U

KID

23H

olu

b57

30U

K55

.3)1

.860

76.3

583

59P

na-

171

5993

.87

483

62C

S28

654

Pu

2-7

Cet

l69

56C

ZE

49.4

216

.36

1080

.56

8362

Pu

2-7

10

483

62P

u2-

7P

u2-

7C

etl

8362

CZ

E49

.42

16.3

611

46.1

557

27U

KID

201

1096

.69

483

62U

KID

20U

KID

20H

olu

b57

27U

K51

.31.

111

19.2

983

62P

u2-

71

1096

.69

483

82C

S28

744

Sp

r1-2

No

rdb

org

6964

SW

E56

.316

6283

.52

8382

Sp

r1-2

10

483

82S

pr1

-2S

pr1

-2N

ord

bo

rg83

82S

WE

56.3

1662

83.5

269

64C

S28

744

10

483

82U

KID

85U

KID

85H

olu

b57

90U

K51

.30.

454

78.7

883

82S

pr1

-21

1073

.59

483

86S

r:5

Sr:

5C

etl

8386

SW

E58

.911

.282

7.66

8423

Ho

v2-1

127

0.62

483

86H

ov2

-1H

ov2

-1N

ord

bo

rg84

23S

WE

56.1

13.7

467

86.6

483

86S

r:5

127

0.62

483

89C

S28

753

Ta-

0K

ran

z73

49C

ZE

49.5

14.5

8508

.283

89T

a-0

10

483

89T

a-0

Ta-

0K

ran

z83

89C

ZE

49.5

14.5

8508

.273

49C

S28

753

10

483

89C

S28

754

Tac

-0M

itch

ell-

Old

s73

50U

SA

47.2

413

)122

.459

8773

.53

8389

Ta-

01

8508

.24

8394

CS

2878

3T

u-0

Kra

nz

7375

ITA

457.

598

02.9

669

72C

S28

782

198

02.9

64

8394

Tu

-0T

u-0

Kra

nz

8395

ITA

457.

597

79.2

973

75C

S28

783

10

483

94C

S28

782

Tsu

-1H

olu

b69

72JP

N34

.43

136.

3198

02.9

673

75C

S28

783

198

02.9

64

8394

CS

2878

1T

su-1

Ho

lub

7374

JPN

34.4

313

6.31

9802

.96

7375

CS

2878

31

9802

.96

483

94T

su-1

Tsu

-1H

olu

b83

94JP

N34

.43

136.

3197

79.2

973

75C

S28

783

0.99

397

79.2

94

8399

Uo

d-7

Uo

d-7

Ko

ch83

99A

UT

48.3

14.4

563

8.44

6425

Zd

rI1-

241

149.

074

8399

Zd

rI1-

24Z

drI

1-24

Rel

ich

ova

6425

CZ

E49

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316

.254

454

6.39

8399

Uo

d-7

114

9.07

492

25C

S75

565

Pir

in-1

7B

eck

9224

BU

L41

.595

623

.548

314

46.5

692

25C

S75

566

126

1.5

492

25C

S75

566

Del

-1B

eck

9225

SR

B44

.944

421

.182

811

76.4

992

24C

S75

565

126

1.5

492

60C

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620

An

d-2

Bec

k92

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EL

50.8

54.

2833

380

7.32

9278

CS

7561

91

04

9260

CS

7562

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nd

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eck

9280

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L50

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4.28

333

807.

3292

79C

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620

10

492

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S75

622

An

d-4

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k92

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50.8

54.

2833

323

27.5

392

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601

155

5.85

492

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An

d-1

Bec

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EL

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54.

2833

380

7.32

9279

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7562

01

04

9260

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bil-

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eck

9260

DE

N56

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5891

8584

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9280

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11

555.

85

AR

M=

Arm

enia

,A

UT

=A

ust

ria,

AZ

E=

Aze

rbai

jan

,B

EL

=B

elg

ium

,B

UL

=B

ulg

aria

,C

AN

=C

anad

a,C

PV

=C

ape

Ver

de

Isla

nd

s,C

RO

=C

roat

ia,

CZ

E=

Cze

chR

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blic

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EN

=D

enm

ark,

ES

P=

Sp

ain

,E

ST

=E

sto

nia

,FI

N=

Fin

lan

d,

FRA

=Fr

ance

,G

EO

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eorg

ia,

GE

R=

Ger

man

y,IN

D=

Ind

ia,

IRL

=Ir

elan

d,

ITA

=It

aly,

JPN

=Ja

pan

,K

AZ

=K

azak

hst

an,

KG

Z=

Kyr

gyz

stan

,LI

B=

Lib

ya,

LTU

=Li

thu

ania

,M

AR

=M

oro

cco

,N

ED

=N

eth

erla

nd

s,N

OR

=N

orw

ay,

NZ

L=

New

Zea

lan

d,

PO

L=

Po

lan

d,

PO

R=

Po

rtu

gal

,R

OU

=R

om

ania

,R

US

=R

uss

ia,

SR

B=

Ser

bia

,S

UI

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wit

zerl

and

,S

WE

=S

wed

en,

TJK

=T

ajik

ista

n,

TU

R=

Tu

rkey

,U

K=

Un

ited

Kin

gd

om

,U

KR

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krai

ne,

US

A=

Un

ited

Sta

tes

of

Am

eric

a,U

ZB

=U

zbek

ista

n



corroborated by its neighbors and was thus placed on the

red list. The fourth category comprised 107 members of 39

over-dispersed haplogroups for which it was impossible to

determine which individuals were out of place. All members

in these haplogroups were placed on the red list. Finally,

outside verification was used in several cases where knowl-

edge of the literature exonerated accessions from the red

list.

DISCUSSION

Patterns of isolation by distance are slow to emerge, and are

generally the result of many generations of low but steady

migration, genetic drift, and local recombination (Lew-

andowska-Sabat et al., 2010; Platt et al., 2010). Such patterns

are unlikely to occur accidentally as a result of incorrectly

associating a natural accession with an arbitrary sampling

location. The vast majority of the accessions we examined fit

this pattern of isolation by distance, and thus are almost

certainly properly identified.

Of the remainder, our analysis focused primarily on

haplogroups that were geographically over-dispersed,

indicating potential contamination. In regions such as

continental Europe where A. thaliana has been established

for thousands of years, local haplotype diversity is high,

long-distance migration is rare, much of the available

habitat is already colonized, and outcrossing happens

relatively frequently (Bomblies et al., 2010; Platt et al.,

2010). Naturally sampling two genetically identical plants

more than 10 km apart is extremely unlikely. First, a long-

distance migration event would have to have taken place.

The source population would have to be included in the

sample, and the individual genotype that migrated would

have to be sampled within it. The recipient population

would also have to be included in the sample, and even

though the newly entered genotype would be at very low

frequency in the new population, it too would have to be

represented in the collected sample. If sufficient time had

passed for the migrant haplotype to have increased to

appreciable frequency in its new region, it would also

have had the opportunity to backcross with the estab-

lished population, reducing the probability that it would

be identified as an outlier. A widespread haplotype that is

unrelated to the other individuals sampled in one or more

of its purported sites (particularly when those individuals

tend to be closely related to each other) is thus almost

certainly not a naturally occurring haplotype at those

sites.

A contaminant, on the other hand, need only have been

sampled once from the wild, and may spread easily from pot

to pot, vial to vial, or spreadsheet row to spreadsheet row.

It is surely no accident that many of the widely spread

haplogroups involve the most commonly used laboratory

strains. While it could be imagined that this is because

scientists have spread them worldwide, this over-estimates

our importance as dispersal agents. For example, according

to our results, Col-0 (an accession originally from Central

Europe) is found in ‘natural’ samples from the very north of

Sweden to the south of Spain. This seems unlikely com-

pared to the alternative explanation, which involves con-

tamination in Arabidopsis research facilities, practically all

of which are known to contain vast quantities of Col-0.

Similarly, accessions identical to Ws-2 (Wassilewskija, in

Russia) are found in Germany, Sweden and the UK; acces-

sions identical to Ler-0 are found in the Czech Republic,

France, Sweden and the UK. In all of these cases, many

different collectors were involved (including some of the

authors), suggesting that several laboratory collections are

probably contaminated. Error may also have happened at

the level of a single collector. We found several instances

where distant accessions sampled by the same collector

apparently shared a haplotype. The case for better growth

practices and record-keeping is clear.

Putative mislabeling events can be found when examining

genetic information for similarly named but geographically

distant accessions. For example, the nominally ‘Japanese’

Figure 1. Proportion of suspicious accessions (a) from each collection and (b)

from large and small local populations.



accession Tsu-0 appears to be identical to the nominally

‘Italian’ accession Tu-0, and the ‘American’ accession Tac-0 is

identical to the ‘Czech’ accession Ta-0. Such mix-ups have

been noted previously: C24 was historically referred to as

Columbia, but Torjek et al. (2003) suggested that it was

clearly a different accession both phenotypically and genet-

ically. The case for barcode-based labeling when collecting,

sowing seed and transplanting is also clear.

As expected if discrepancies occurred due to handling,

older collections are often affected. For example, in the Kranz

collection, the ‘German’ accession Li-1 appears to be iden-

tical to the ‘Spanish’ accession Bla-4 and the ‘Russian’

accession Rsch-4. Which origin is the original one (if any) is

very difficult to ascertain. Older collections also suffer from

the well-known problem that ‘ecotypes’ were originally

propagated in bulk, and then gradually developed into

individual lines. Thus, for example, our collection contains

ten different samples from ‘Ws’ (Wassilewskija, from Russia),

with ten different stock center IDs, but only six abbreviated

names (Ws, Ws-0, Ws-1, Ws-2, Ws-3 and Ws-4). Based on our

marker data, these appear to be two distinct genotypes: the

three accessions called Ws-0 were different from all the rest, a

finding that has also been described by Aukerman et al.

(1997). This result is not surprising given that the latter four

accessions (Ws-1–Ws-4) were all derived very recently from a

single line in the B. Griffing collection. This line has itself has

been separated from the Kranz set for decades, traversing

from Laibach to Langridge and Griffing in Australia, and then

to the Griffing laboratory at Ohio State University, while Ws-0

came directly from the Kranz collection.

We recommend that researchers do not use accessions

from our red list when geographic data are relevant, because

it appears unlikely that they are the same accessions that

were originally collected at these locations. Although we

have no specific reason to question the integrity of the

accessions on the yellow list, their location and genetic

information is not corroborated as well as those on the green

list. The relative sizes of the green and red lists suggest that

over 93% of accessions on the yellow list are likely to be

accurate. However, whether or not these accessions should

be included in analyses will need to be decided on a case-by-

case basis. The quality of the data accompanying the

accessions on the green list is generally excellent. However,

it is possible that the green list contains accessions that have

inadvertently been outcrossed during propagation. It is not

possible to distinguish such an accession from a properly

placed one given the level of genetic resolution in our dataset.

We must hope that such events are rare. In general, it is much

easier to definitively corroborate or question the identity of

accessions sampled in the general vicinity of other acces-

sions (Figure 1b). It is difficult to comment with any degree of

certainty on accessions sampled singly from remote regions.

Individual accessions that lack neighbors with which to be

compared are therefore most likely to end up on the yellow

list. Those on the red list should be treated with caution until

corroborating samples are collected. The practice of collect-

ing larger population samples provides researchers with

greater power to verify the integrity of a collection.

In our own collections, we found a 3% error rate in the

assigned origin of accessions (Figure 1a), demonstrating

that it is quite easy to introduce error over even a small

number of generations in the laboratory. The error rate in the

stock center collection was threefold higher (14%), although

these accessions have been propagated for several decades.

Given the increasing number of submissions to the stock

center and the increasing number of laboratories utilizing

these accessions, it is no surprise that mix-ups occur. The

early collections were even maintained through extraordi-

narily difficult conditions during the Second World War

(Maarten Koornneef, Max Plank Institute for Plant Breeding

Research (Cologne), Department of Plant Breeding and

Genetics, personal communication; Reinholz, 1965; Redei,

1992). We strongly urge researchers to be diligent in their

greenhouse and labeling practices for both laboratory-

specific and community-wide collections. Fast and cheap

fingerprinting of accessions, if widely used, could ensure

that mis-identification errors of this kind are unlikely to occur

in the future. For instance, primer information for assays

of the 149 SNPs on which these samples were genotyped is

publicly available through the Bergelson laboratory website

at the University of Chicago (http://bergelson.uchicago.edu/

a.thaliana-resources). In the near future, sequencing costs

will be low enough that even small laboratories will be able

to cheaply confirm the accuracy of geographic information.

There are several ways that Arabidopsis stock centers

could ensure the integrity of propagated lineages: genetic

fingerprinting of current stocks and submitted accessions,

provision of accurate GPS coordinates, and allocation of a

unique barcode to accessions at the time of collection or

initial propagation, to avoid use of the same name abbrevi-

ations for distant populations. Finally, in the interest of

maintaining a robust and informative collection of ecotypes

for use in future geographical and ecological studies, it would

be helpful if location attributes were collected along with

samples in the field. Clear photographs of an individual plant

and its surrounding habitat could be included, together with

altitude, land use, soil type and plant community data as

appropriate. These additional data and safeguards will

ensure that the hard work that goes into collecting and

maintaining stocks for use in the community results in a

trustworthy, informative and increasingly valuable resource.

EXPERIMENTAL PROCEDURES

Dataset

We used the dataset from Platt et al. (2010), described in detail athttp://arabidopsis.usc.edu/. It contains 4942 accessions collectedfrom natural populations (not all of which are available from thestock centers) and 1023 accessions from the Arabidopsis Biological



Resource Center. The accessions obtained from the ArabidopsisBiological Resource Center were as a leaf from a single referenceplant such that the distributed seed matched the genotype in thisstudy. Notably, many of the accessions were donated decades agoand have been perpetuated via single seed descent in growthchambers (or greenhouses, in the case of common laboratorystrains) at stock center facilities. The collection spans 42 countriesand four continents. These accessions were genotyped at 149 SNPmarkers using the Sequenom MASSArray technology (SequenomInc., http://www.sequenom.com). As in Platt et al. (2010), weexcluded individuals that lacked a large number of genotype calls(more than 50 of 149), as this indicates poor quality of the genomicDNA. We also excluded information from 10 SNPs due to poorlyperforming genotype assays. However, we did retain accessionsthat were deemed geographically or genetically suspicious by Plattet al. (2010), leaving a dataset of 5965 accessions.

How haplogroups were created

Accessions were grouped by haplotype by Platt et al. (2010), andwe used the same groups. Briefly, haplogroups were createdusing a modified quality threshold (QT) clustering algorithm(Heyer et al., 1999). Haplogroups are maximal collections ofaccessions for which the observed full SNP genotype of eachaccession in the haplogroup is expected to be consistent beyondthe variation attributable to genotyping error. The actual numberof discrepancies allowed between an observed genotype and thepresumed common haplotype is a function of the size of the ha-plogroup. For all values of N, the accession with the Nth mostdiscrepant genotype is expected to have fewer discrepancies thanthe Nth most discrepant genotype in 95% of hypothetical samplesof identical individuals (where genotyping errors occur at 0.5% ofmarkers and are independently distributed). In rare cases involv-ing highly heterozygous accessions that are likely to be recentcrosses between other sampled accessions, haplogroups may bemore diverse than desired. No accessions were included on thered list solely because of this effect, although some accessions onthe red list were affected by high heterozygosity. Heterozygotesare indicated using standard nomenclature in the ‘genetic finger-print’ column of Table S1.

Algorithm

Accessions were divided into two major groups based on theirsimilarity to geographic and genetic neighbors. Broadly, thoseaccessions whose identity was corroborated by nearby individu-als and without geographically distant genetic relatives wereincluded on the green list. The vast majority of accessions fellinto this category. Only when individuals were genetically similarto geographically distant accessions and whose identity was notcorroborated by geographically close neighbors were theyincluded in the red or yellow lists. A number of further classifi-cations were used to categorize the types of accessions on thered list.

Accessions were initially considered for inclusion on the greenlist when at least ten accessions were sampled within 10 km andeither a corroborating accession existed or the accession sharedalleles at more than 70% of its markers with all other accessionsfound within 10 km. A corroborating accession is either anotheraccession in the same haplogroup found within 10 km or anaccession, found within 100 km, that shares alleles at more than70% of markers. Accessions from North America or the UK wereincluded in the green list unless an accession sharing alleles atmore than 70% of markers was found on another continent or inanother country, respectively. Accessions from other regions

were included when no other members of their haplogroup werefound more than 10 km away and no accessions sharing allelesat more than 70% of markers were found more than 100 kmaway.

Accessions that were not from North America or the UK wereinitially considered for the red list if another accession in thesame haplogroup was found more than 10 km away or anaccession sharing alleles at more than 70% of markers was foundmore than 100 km away. For accessions from North America orthe UK to be considered for the red group, we required anaccession sharing alleles at more than 70% of markers to befound on another continent or in another country, respectively.An accession was included in the red list unless it was part of ahaplogroup of at least four accessions where another accessionwithin 10 km shared alleles at more than 70% of markers, andmore than half of the members of the haplogroup also hadaccessions within 10 km sharing alleles at more than 70% ofmarkers. This avoided inclusion on the red list of an entire large,well-supported haplogroup on the basis of a small number ofdubious members.

Once accessions had been relegated to the red list (because theywere not similar to neighbors and were similar to one or moreaccessions from a great distance), we further sub-divided the listinto four categories. In category 1, the flagged accession was thesole member of the haplogroup (long-distance migrants areexpected to be found here. In category 2, all accessions in ahaplogroup were flagged because the group contained a commonlyused laboratory strain that was a likely contaminant. In category 3,one or more accessions in a large haplogroup were flagged for notlooking like the others. Finally, category 4 comprised all accessionsin a haplogroup when it was not clear which (if any) had accurategeographic information.

Accessions that did not qualify for the green or red lists wereplaced on the yellow list.

ACKNOWLEDGEMENTS

We would like to thank Luz Rivero (Ohio State University, ABRC) forcomments and discussion, and two anonymous reviewers forimprovements to the manuscript. This work was primarily sup-ported by US National Science Foundation grant DEB-0519961 toJ.B. and M.N.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the onlineversion of this article:Table S1. Complete dataset of 5965 accessions used in our analysis.Please note: As a service to our authors and readers, this journalprovides supporting information supplied by the authors. Suchmaterials are peer-reviewed and may be re-organized for onlinedelivery, but are not copy-edited or typeset. Technical supportissues arising from supporting information (other than missingfiles) should be addressed to the authors.

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Source verification of mis-identified Arabidopsis thaliana accessions

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