Fu et al. 1
Supplemental Materials Online
Yong-Bi Fu, Preeya P. Wangsomnuk, Benjawan Ruttawat (2014) Thai elite cassava
genetic diversity was fortuitously conserved through farming with different sets of
varieties. Conservation Genetics (DOI 10.1007/s10592-014-0631-y)
Table S1 List of 16 Thai cassava landraces and varieties assayed in this study, along
with the year of release, type, pedigree, and farm use survey
Variety or
landrace
Rayong 1
Rayong 3
Hanatee
Munsuan
Rayong 2
Rayong 60
Rayong 90
Sri Racha 1
Kasetsart 50
Rayong 5
Rayong 72
Huay Bong 60
Rayong 7
Rayong 9
Huay Bong 80
Rayong 11
a
Year of
release a
1975
1983
1983
1983
1984
1987
1991
1991
1992
1994
1999
2003
2005
2005
2008
2010
Cassava
type a
bitter
bitter
sweet
sweet
sweet
bitter
bitter
bitter
bitter
bitter
bitter
bitter
bitter
bitter
bitter
bitter
Pedigree a
UNK
MMex 55 x MVen 307
UNK
UNK
MCol 113 x MCol 22
MCol 1684 x Rayong 1
CMC 76 x V 43
(Mcol 113 x Mcol 22) x Rayong 1
Rayong 1 x Rayong 90
27-77-10 x Rayong 3
Rayong 1 x Rayong 5
Rayong 5 x Kasetsart 50
CMR30-71-25 x OMR29-20-118
CMR31-19-23 x OMR29-20-118
Rayong 5 x Kasetsart 50
Rayong 5 x OMR29-20-118
Farm
cultivationb
4
1
38
6
10
14
1
1
Label
R1
R3
H
M
R2
R60
R90
SR
K50
R5
R72
H60
R7
R9
H80
R11
Related information for these landraces or varieties was obtained from Sarakarn et al.
(2001; 2007) and Ratanawaraha et al. (2001). The landraces Hanatee and Munsuan were
cultivated before 1983 according to Rojanaridpiched (1988). UNK=unknown. b The
number of farms with records of planting the variety. There were 20 farms planting
Rayong series of cassava varieties with no specific names and three farms planting
multiple, unspecified cassava varieties
Fu et al. 2
Table S2 Variation of 35 SSR markers detected in 282 cassava samples
Linkage Number of Size range
Marker a
Type a group a
alleles
(base pair)
MAF b
eDC b
GA5
G
Q(2)
21
125-239
0.585
4.93
(2)
GA12
G
Nd
15
125-185
0.619
2.13
GA21
G
Nd(2)
12
104-150
0.533
3.19
GA126
G
K(2)
9
180-225
0.654
1.75
(2)
GA127
G
K
13
220-266
0.587
3.22
GA131
G
G(2)
9
110-135
0.634
2.09
(2)
GA134
G
Nd
16
250-325
0.560
4.46
GA136
G
Nd(2)
12
150-280
0.556
3.18
(2)
GA140
G
Nd
6
170-185
0.624
1.48
SSRY3
G
D(1)
10
115-250
0.550
2.89
(1)
SSRY5
G
J
6
120-254
0.465
1.53
SSRY8
G
I(1)
11
250-289
0.477
3.42
SSRY11
G
Nd(1)
12
193-414
0.752
2.41
(1)
SSRY13
G
N
11
175-185
0.607
2.70
SSRY28
G
U(1)
10
164-214
0.506
2.55
(1)
SSRY34
G
M
13
281-312
0.753
0.99
SSRY40
G
D(1)
18
245-455
0.560
4.04
(1)
SSRY43
G
U
12
230-345
0.632
2.48
SSRY143
G
O(1)
16
165-285
0.593
3.89
(1)
SSRY161
G
E
12
175-210
0.600
2.94
SSRY164
G
H(1)
14
148-216
0.548
3.90
SSRY186
G
Nd(1)
13
223-336
0.637
3.48
(1)
SSRY235
G
G
18
180-368
0.504
5.27
SSRY324
G
Nd(1)
15
175-320
0.760
2.93
(3)
EME164
E
3
10
170-230
0.474
3.09
EME171
E
6(3)
4
150-165
0.613
0.81
(3)
EME212
E
10
8
193-250
0.630
0.78
EME240
E
6(3)
5
180-210
0.579
0.98
(3)
EME254
E
2
3
250-257
0.994
0.02
EME445
E
18(3)
3
255-260
0.541
1.00
(3)
EME637
E
9
2
185-189
0.887
0.21
EME189
E
2(3)
3
195-230
0.645
0.58
MeESSR15
E
Nd(4)
7
150-228
0.589
0.43
(4)
MeESSR19
E
Nd
10
208-363
0.611
0.81
MeESSR29
E
Nd(4)
6
170-185
0.446
1.85
Total or mean
365
0.609
2.35
a
Information on markers, type and linkage group was obtained from (1) Mba et al.
(2001); (2) Chavarriaga-Aguirre et al. (1998); (3) Kunkeaw et al. (2010); and (4) Raji et
al. (2009). Genomic (G) and expressed sequence tag-derived (E) marker types are
specified. Nd =not determined. b MAF=mean allelic frequency for all the alleles detected
by the primer pair. eDC=entropy-based diversity content per locus calculated following
Reyes-Valdes and Williams (2005)
Fu et al. 3
Table S3 Additional AMOVA results on the genetic differentiations of 282 cassava
samples representing bitter vs sweet cassavas, soil types, farms with variable farming
practices, and the 16 reference varieties based on the data of 365 SSR alleles with two
approaches and under various models of genetic structure
Approach/model /
Sum of
Variance
Percent of
source
df
squares
component
variation
p-value
Prior defined grouping
Cassava type
Between types
1
131.25
Within types
264
14992.23
Soil type
Among soil types
20
2529.90
Within soil types
245
12593.59
Model-based grouping
Clusters inferred by STRUCTURE
Among clusters
10
4188.67
Within clusters
271
11924.43
a
Model for farm use
Cassava variety use
Among groups
2
237.79
Within groups
263
14885.69
Other crop use
Among groups
2
366.29
Within groups
263
14757.20
Fertilizer use
Among groups
3
432.39
Within groups
262
14691.09
Hormone use
Between groups
1
175.63
Within groups
264
14947.85
a
3.25
56.79
5.41
94.59
<0.0006
6.03
51.40
10.50
89.50
<0.0001
14.80
44.00
25.17
74.83
<0.0001
1.20
56.60
2.08
97.92
<0.0001
2.01
56.11
3.47
96.53
<0.0001
1.49
56.07
2.59
97.41
<0.0001
1.06
56.62
1.83
98.17
<0.0001
The cassava samples from the 80 farms were formed as various farm use groups
depending on the counting for a farm of the number of historical cassava varieties, other
crops, and different fertilizers used or if a farm used hormone or not. For example, the
assayed clones from all the farms using hormone in cassava cultivation were formed as a
hormone-use group and those from the farms without hormone use as a non-hormoneuse group. A farm using more than four fertilizers, more than three historical cassava
varieties, or more than three other crops, was treated as a farm with the use of four
fertilizers, three historical varieties or three other crops, respectively
Fu et al. 4
Table S4 Additional results on allelic counts and tests for significance of allelic
difference for various groups of cassava sample with respect to farm, district, cassava
type, and farm use of historic variety, other crop, fertilizer and hormone
Farm vs cultivar
Size SeDC NA
2
Farm group(1)
266 0.306 365 0c
Cultivar group(2)
16 0.307 365
District
2
3 4 5 6
7
8 9 10 11
Kosum Phisai(1)
9 0.310 338 -8b -14c 24a 13 21b 22b
1 17 13 12
Borabue(2)
11 0.303 330
-6a 32c 21b 29c 30c 9a 25c 21c 20c
Wapi Pathum(3)
13 0.307 324
38c 27c 35c 36c 15c 31c 27c 26c
Khukhan(4)
37 0.307 362
-11c -3 -2 -23a -7a -11 -12a
Nong Bun Nak(5)
29 0.307 351
8c 9c -12 4a 0a -1
M ancha Khiri(6)
16 0.301 359
1 -20b -4b -8a -9b
Ban Phai(7)
17 0.310 360
-21b -5b -9a -10b
Sai Ngam(8)
10 0.312 339
16 12 11
M ueang-Kam(9)
27 0.307 355
-4 -5
M ueang-Kan(10)
16 0.312 351
-1
Sai Yok(11)
18 0.305 350
Prachantakham(12)
16 0.307 350
Kabin Buri(13)
16 0.294 343
Watthana Nakhon(14)
9 0.302 339
Wang Nam Yen(15)
10 0.299 331
Khao Chakan(16)
12 0.308 345
Cultivar group(17)
16 0.307 365
Cassava type
2
3
Bitter type(1)
254 0.307 365 -48c 0c
Sweet type(2)
12 0.291 317
48c
Cultivar group(3)
16 0.307 365
Historic variety use
2
3 4
One variety(1)
201 0.305 365 -1 -19b 0c
two varieties(2)
51 0.308 364
-18b 1c
Three varieties(3)
14 0.313 346
19c
Cultivar group(4)
16 0.307 365
Other crop use
2
3 4
one crop (1)
176 0.305 365 -4c -19a 0c
two crops(2)
78 0.308 361
-15 4c
Three crops(3)
12 0.312 346
19c
Cultivar group(4)
16 0.307 365
Fertilizer use
2
3 4 5
One fertilizer(1)
91 0.303 365
0 -2 -14 0c
two fertilizers(2)
111 0.309 365
-2 -14 0c
three fertilizers(3)
54 0.305 363
-12 2c
four fertilizers(4)
10 0.308 351
14
Cultivar group(5)
16 0.307 365
Hormone use
2
3
Non-use group(1)
185 0.305 365 -1 0c
Use group(2)
81 0.308 364
1c
Cultivar group(3)
16 0.307 365
12
12
20c
26c
-12a
-1
-9b
-10b
11
-5
-1
0
13 14 15 16 17
5
1 -7a
7 27c
13a 9b
1 15c 35c
19c 15c 7b 21c 41c
-19c -23 -31c -17a 3c
-8 -12 -20a -6 14c
-16c -20a -28c -14b 6
-17c -21a -29c -15b 5
4
0
-8
6 26c
-12a -16 -24b -10 10c
-8a -12 -20b -6 14c
-7 -11 -19a -5 15c
-7a -11 -19b -5 15c
-4 -12
2 22c
-8a
6 26c
14a 34c
20c
Note that size=sample size for the group; SeDC=group-specific entropy-based diversity
content per sample. NA=the number of alleles detected for a group. The significance test
of allelic difference between groups was made using permutation method of Fu (2010).
The level of significance was labeled for the allelic difference with a, b, or c for p<0.05,
0.01, or 0.001, respectively
Fu et al. 5
Table S5 Soil series, number of farms (NF) and type-specific entropy-based diversity
content per sample (SeDC) obtained for farm clones associated with soil series
Soil series a
Soil classification a
NF
SeDC
Chiang Rai series
Don Rai series
Hang Dong series
Hin Son series
Kabin Buri series
Khamphaeng Phet
series
Khorat series
Fine, Kaolinitic, Isohyperthermic, Plinthic Paleaquults
Fine-Loamy, Kaolinitic, Isohyperthermic, Typic Kandiustults
Fine, Mixed, Semiactive, Isohyperthermic, Typic Endoaqualfs
Fine-Mixed, Isohyperthermic, Lithic Haplustalfs
Clayey-Skeletal, Kaolintitic, Isohyperthermic, Typic Kandiustox
Fine-Silty, Mixed, Isohyperthermic, Oxyaquic (Ultic) Haplustalfs
4
5
1
5
3
0.311
0.307
0.302
0.305
0.293
5
0.308
9
7
5
2
1
0.312
0.309
0.310
0.304
0.297
7
1
4
2
2
5
0.304
0.317
0.297
0.312
0.308
0.302
6
2
3
0.299
0.303
0.312
1
0.301
Nam Phong series
Phimai series
Roi Et series
Cha-am series
Renu series
Si Songkhram series
Surin series
Thap Khwang series
Takhli series
Tha Yang series
Ubon series
Wattana series
Warin series
Yasothon series
Fine-Loamy, Siliceous, Isohyperthermic, Typic (Oxyaquic)
Kandiustults
Loamy, Siliceous, Isohyperthermic, Grossarenic Haplustalfs
Very-fine, Smectitic, Isohyperthermic, Ustic Endoaquerts
Fine-Loamy, Mixed, Subactive, Isohyperthermic, Aeric Kandiaquults
Very-fine, Mixed, Semiactive, Isohyperthermic, Sulfic Endoaquepts
Fine-Loamy, Mixed, Semiactive, Isohyperthermic, (Aeric) Plinthic
Paleaquults
Fine, Mixed, Subactive, Isohyperthermic, Ustic Endoaquerts
Clayey-Skeletal, Kaolinitic, Isohyperthermic, Typic Rhodustalfs
Fine, Mixed, Isohyperthermic, Ultic Paleustalfs
Loamy-Skeletal, Carbonatic, Isohyperthermic, Entic Haplustolls
Loamy-Skeletal, Siliceous, Isohyperthermic, Kanhaplic Haplustults
Loamy, Siliceous, Semiactive, Isohyperthermic, Aquic Grossarenic
Halpustalfs
Fine, Smectitic, Isohyperthermic, Ustic Endoaquerts
Fine-Loamy, Siliceous, Isohyperthermic, Typic Kandiustults
Fine-Loamy, Siliceous, Semiactive, Isohyperthermic, Typic
Paleustults
a
Related information on soil series was obtained from Office of Soil Survey and Land
Use Planning (2004a,b,c) and Office of Soil Resources Survey and Research (2011a,b)
Table S6 The significant Pearson correlation matrix of farm cassava genetic diversity
estimates and farming variables a
Variable
Sample size
Allelic count (AC)
SeDC
Cluster count (CC)
Farm age (FA)
Farm size (FS)
Cassava plantation age (PA)
Cassava planting area (CpA)
Variety count (CuC)
Other crop count (OC)
Fertilizer count (FC)
Hormone count (HC)
a
AC
0.39c
SeDC
-0.36c
CC
0.50c
0.46c
FA
FS
PA
CpA
0.39c
-0.36b
0.44c
CuC
OC
FC
HC
-0.28a
-0.28a
0.30b
0.34b
0.28a
0.23a
SeDC=farm-specific entropy-based diversity content per sample. The letter (a, b, or c)
following a Pearson correlation coefficient stands for the level of significance test
observed at p <0.05, 0.01, or 0.001, respectively
Number of SSR alleles detected
Fu et al. 6
60
50
40
30
20
10
0
0.05 0.25 0.45 0.65 0.85
Frequency of occurrence in all accessions
Fig. S1 Number of polymorphic SSR alleles detected by all 35 SSR primer pairs in
relation to their frequencies of occurrence in 282 cassava samples
Fu et al. 7
A: 8 provinces
C: 80 farms
B: 16 districts
Fig. S2 The distograms displaying the genetic distances measured by pairwise
proportional SSR variations between cassava samples representing reference varieties,
province (A), district (B), and farm (C). The farm label ending with the letter s
represents a significant SSR variation between the related farm clones and reference
varieties
Fu et al. 8
A: 16 districts
B: 8 provinces
KosumPhisai1
MahaSarakham1
Borabue1
WapiPathum1
SiSaKet2
Khukhan2
ManchaKhiri4
N-Ratchasima3
BanPhai4
NongBunMak3
KhonKaen4
SaiNgam5
WatthanaNakhon8MW
KamphaengPhet5MW
MueangKam5
KamphaengPhet5
MueangKan6
WatthanaNakhon8
Kanchanaburi6
WangNamYen8
KhaoChakan8
PrachinBuri7
Prachantakham7
KabinBuri7
SaKaeo8
SaiYok6
0.00
0.05
0.09
0.14
0.18
0.00
The proprotional SSR variation
0.03
0.06
0.09
0.12
The proportional SSR variation
Fig. S3 Genetic structure of 266 cassava clones representing (A) 16 districts and (B) 8
provinces in Thailand, as illustrated in the dendrograms based on pairwise genetic
distances estimated from AMOVA. The number following a province or district is the
province label. N-Ratchasima=Nakhon Ratchasima
B: ∆K from STRUCTURE
A: ln(pr(Data/K))
3.0
-20000
2.5
-40000
2.0
-60000
1.5
-80000
1.0
-100000
0.5
0.0
2
4
6
8
K
10
12
14
2
4
6
8
10
12
14
K
Fig. S4 Support for the 11 optimal clusters of 282 cassava samples inferred using
STRUCTURE. A: The log-likelihood profiles for models with K=2 to 15. B: The rates
of change in log-likelihood for models with K=2 to 15
Genetic distance (Phi statistic)
Fu et al. 9
y=0.00009x+0.1703
R2=0.0118 p=0.016
y=0.0003x+0.1791
R2=0.0227 p=0.001
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0
100
200
300
400
500
-100
600
0
Genetic distance (Phi statistic)
Geographic distance (km)
200
Elevational difference (m)
y=0.0144x+0.1924
R2=0.0109 p=0.018
y=-0.0002x+0.1862
R2=0.0346 p=0.001
0.6
0.6
0.4
0.4
0.2
0.2
0.0
-300
0.0
-200
-100
0
100
300 -3
200
Difference in annual rainfall (mm)
Genetic distance (Phi statistic)
100
-2
-1
0
1
2
3
2
Difference in cassava plantation size (m /1000)
y=0.6895x+0.1891
R2=0.01 p=0.049
y=-0.0058x+0.1957
R2=0.0056 p=0.035
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
-10
-5
0
5
10 -0.08
Genetic distance (Phi statistic)
Difference in cassava production (kg)
-0.04
0.00
0.04
0.08
2
Difference in farm size (km )
y=0.0013x+0.1959
R2=0.011 p=0.048
0.6
0.4
0.2
0.0
-40
-20
0
20
Difference in farm age (year)
Fig. S5 Significant associations of pairwise farm genetic distance estimates of the
cassava clones with pairwise farm geographic distances and with differences of farm
elevation, annual rainfall, cassava plantation size, farm size or farm age. The prediction
equation was obtained from a linear regression, while R2 and significance of test were
generated from a Mantel test