sharof s. egamberdiev, sukumar
TRANSCRIPT
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GeneticaAn International Journal of Genetics andEvolution ISSN 0016-6707 GeneticaDOI 10.1007/s10709-016-9898-x
Comparative assessment of genetic diversityin cytoplasmic and nuclear genome ofupland cotton
Sharof S. Egamberdiev, SukumarSaha, Ilkhom Salakhutdinov, JohnieN. Jenkins, Dewayne Deng & IbrokhimY. Abdurakhmonov
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Comparative assessment of genetic diversity in cytoplasmicand nuclear genome of upland cotton
Sharof S. Egamberdiev1 • Sukumar Saha2 • Ilkhom Salakhutdinov1 •
Johnie N. Jenkins2 • Dewayne Deng2 • Ibrokhim Y. Abdurakhmonov1
Received: 8 October 2015 / Accepted: 7 April 2016! Springer International Publishing Switzerland (outside the USA) 2016
Abstract The importance of the cytoplasmic genome formany economically important traits is well documented in
several crop species, including cotton. There is no report on
application of cotton chloroplast specific SSR markers as adiagnostic tool to study genetic diversity among improved
Upland cotton lines. The complete plastome sequence
information inGenBank provided us an opportunity to reporton 17 chloroplast specific SSRmarkers using a cost-effective
data mining strategy. Here we report the comparative anal-
ysis of genetic diversity among a set of 42 improved Uplandcotton lines using SSR markers specific to chloroplast and
nuclear genome, respectively. Our results revealed that low
to moderate level of genetic diversity existed in both nuclearand cytoplasm genome among this set of cotton lines.
However, the specific estimation suggested that genetic
diversity is lower in cytoplasmic genome compared to thenuclear genome among this set of Upland cotton lines. In
summary, this research is important from several perspec-tives. We detected a set of cytoplasm genome specific SSR
primer pairs by using a cost-effective data mining strategy.
We reported for the first time the genetic diversity in thecytoplasmic genome within a set of improved Upland cotton
accessions. Results revealed that the genetic diversity in
cytoplasmic genome is narrow, compared to the nucleargenome within this set of Upland cotton accessions. Our
results suggested that most of these polymorphic chloroplast
SSRs would be a valuable complementary tool in addition tothe nuclear SSR in the study of evolution, gene flow and
genetic diversity in Upland cotton.
Keywords Cytoplasmic genome ! Chloroplast specificSSR markers ! Nuclear SSR markers ! Genetic diversity !Upland cotton
Introduction
Breeders normally select genotypes based on morphologi-
cal characters, primarily regulated by nuclear genome
because gene flow took place predominantly throughnuclear genome via pollination and subsequently via fer-
tilization of male and female gametes. However, cyto-plasmic genome including mitochondria and chloroplast
genomes play a critical role to perform many important
biological functions (Han et al. 2007; Allen et al. 2005;Karaca et al. 2004); therefore, it is important to know the
genetic diversity associated with cytoplasmic genome in a
plant species.Cytoplasmic genome is distinguished from nuclear
genome by the characteristics of non-recombinant, mater-
nally uniparental inheritance, haploid and highly conser-vative nature due to low mutation rate (Wendel 1989; Cato
Sharof S. Egamberdiev and Sukumar Saha equally credited as the firstauthor for their contribution in this research.
Disclaimer Mention of trademark or proprietary product does notconstitute a guarantee or warranty of the product by the United StatesDepartment of Agriculture and does not imply its approval to theexclusion of other products that may also be suitable.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s10709-016-9898-x) contains supplementarymaterial, which is available to authorized users.
& Sukumar [email protected]
1 Center of Genomics and Bioinformatics, Academy ofSciences of Uzbekistan, Tashkent, Uzbekistan 111215
2 Crop Science Research Laboratory, Genetics and SustainableAgriculture Research Unit, USDA-ARS, Mississippi State,MS 39762, USA
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DOI 10.1007/s10709-016-9898-x
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and Richardson 1996; Ferris et al. 1998; Marchelli et al.
1998; Fineschi et al. 2002; Arroyo-Garcia et al. 2002). Thenuclear genes are normally inherited in a Mendelian and
quantitative inheritance pattern following the recombina-
tion events between the male and female gametes. Thedifferent characteristics and mode of genetic inheritance of
cytoplasmic genome from nuclear genome provides com-
plementary useful information to study gene flow, evolu-tion and population history in a plant species.
The economical importance of cytoplasmic genome iswell documented by the severity and wide range of
Southern corn leaf blight epidemic (SCLB) caused by
Helminthosporium maydis, race T, infecting corn plantswith Texas male sterile cytoplasm from inbred Tcms in
1970 (Ullstrup 1972; Shurtleff 1980). This inbred cyto-
plasm was used in about 85 % of the hybrid seed cornproduced in the United States, and the impact of this epi-
demic was well-reflected by rapid increase of corn price
from about $1.35 per bushel to $1.68 within 3 months inChicago grain market, the largest market dealing in this
commodity in the United States (Ullstrup 1972).
Normally, it is very difficult to detect the genetic effectof cytoplasmic genome because of its uniparental inheri-
tance pattern and it is only possible to discern the differ-
ence in the event of reciprocal crosses. Sometimes, thepresence of multiple copies of the organelle genome in an
organism makes it difficult to identify new cytoplasmic
mutants because its effect can be masked by other non-mutant organelles in the plant cells (Allen et al. 2005).
Sometimes, the presence of similar sequence in both
nuclear and cytoplasmic genome makes genetic studies ofcytoplasmic genome very challenging.
There are very few reports on the genetic diversity in the
cytoplasmic genome in Upland cotton (Li et al. 2014). Thisprompted us to mitigate the problem of detecting diversity
in the cytoplasmic genome of cotton by developing
chloroplast specific SSR markers using data mining strat-egy from public sequence databases of GenBank (https://
www.ncbi.nlm.nih.gov/genbank/). This cytoplasmic geno-
type-based profiling is faster and more economical thanphenotypic based profiling of the genotypes using recip-
rocal crosses of the parents for detecting the effect of
cytoplasmic genome.In many crops including cotton, simple sequence repeats
(SSRs) or microsatellites are considered to be one of the
markers of choice because they are (1) PCR-based, (2)usually co-dominant, (3) usually multiallelic and hyper-
variable, and (4) randomly dispersed throughout the gen-
ome normally the chloroplast genome contains a singlecircular chromosome with two single copy regions (Lar-
ge:LSC and Small:SSC), separated by two inverted repeat
regions of about 10–76 kbp with an average 20–30 kbp inmost species (Hamza 2010). Recently, the advanced
sequencing technologies provided a scope to sequence
chloroplast genome from several tetraploid and diploidGossypium species (Lee et al. 2006; Ibrahim et al. 2006;
Xu et al. 2012). The complete plastome sequence infor-
mation in GenBank provided us an opportunity to developcytoplasm specific SSR marker in cotton. We followed the
overall strategy of our previous study detecting for the first
time EST-SSR markers in cotton (Qureshi et al. 2004).There are several studies on the genetic diversity in the
nuclear genome of cotton using SSR markers (Abdu-rakhmonov et al. 2008, 2009; Khan et al. 2009; Lacape
et al. 2007; Bertini et al. 2006; Tyagi et al. 2014). How-
ever, here we present for the first time the report on the useof cytoplasmic SSR markers in a comparative analysis of
genetic diversity of the cytoplasmic genome with the
nuclear genome in a set of improved Upland cotton lines.
Materials and methods
Materials
We have selected a set of 42 diverse Upland cotton lines
including improved populations from wild race stocks,
obsolete non-transgenic cultivars, and germplasm linesreleased by public breeders.
Chloroplast specific SSR primer pairs
The overall strategy of developing cpSSR markers were
based on our previous study on EST-SSR (Qureshi et al.2004). We downloaded published chloroplast genome
sequences for G. hirsutum and G. barbadense (Lee et al.
2006; Ibrahim et al. 2006; Xu et al. 2012) and performedsearching for simple sequence repeat regions following the
overall method of Qureshi et al. (2004).
We searched G. hirsutum chloroplast for the presence ofmicrosatellite motifs from about 160, 301 bp sequence
length from public sequence databases of GenBank
(https://www.ncbi.nlm.nih.gov/genbank/). Sequences con-taining at least four di-, tri, tetra-, penta- or hexanucleotide
repeats were detected using Perl script (Buyyarapu et al.
2011). Primers were designed for the flanking regions ofthe SSR using the web-based software, ‘‘Primer3’’ program
(Rozen and Skaletsky 2000) which was based on the cri-
teria of 50 % GC content, a minimum melting temperatureof 50 "C, and absence of secondary structure. Primers were
designed ranging from 18 to 27 nucleotides in length and
amplified products of 100–400 bp following the overallmethod of Qureshi et al. (2004). The primers were syn-
thesized by Life Technologies Corp., Carlsbad, CA, USA.
We used only polymorphic cpSSR primer pairs in our finalresult analysis.
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DNA extraction
We used about two mg of fresh leaf samples of individualline for DNA extraction using a QIAGEN DNeasy Plant
Maxi kit (QIAGEN Inc., CA) and/or with a QIAGEN
DNeasy Plant Mini kit following the manufacturer’s pro-tocol. DNA solutions were diluted to a working concen-
tration of 10 ng ll-1 and stored at 4 "C until PCR
amplification.
PCR method with universal labeled primerfor cpSSR
PCR reactions were performed in 10 ll volumes containing
10 ng of cotton template DNA, 19 GeneAmp PCR Goldbuffer from Applied Biosystems, (109, 150 mM Tris–HCl,
PH 8.0, 500 mM KCl), 1 mM MgCl2, 0.2 mM dNTPs,
0.5 lM of forward and reverse primer mixtures, 0.35 ll ofAmpliTaq Gold, (Applied Biosystems). Dye-labeled chro-
mosome specific forward and unlabeled reverse primers
were used in all PCR reactions. We used a modified PCRprotocol to be cost effective by using a universal Fluores-
cent labeled HPLC Purified T13 primer. To use universal
primer, we added a 19 bp long sequence of CAGTTTTCCCAGTCACGAC to the 50 left end of each forward
primer, and a 4 bp short sequence of GTTT to the 50 left
end of the respective reverse primer (CAGTTTTCCCAGTCACGAC). The modified forward and reverse SSR
primers were dissolved in water respectively to make a
100 lM stock solution. Then forward and reverse SSRprimers are combined to make a diluted 5 lM working
solution. PCR reaction was carried out in 10-ll reactionscontaining 2.5 ll of DNA, 10x Gold Taq Buffer, 20 mMMgCI2, 10 mM dNTPS, 0.3 of each 3 primers, 1 unit of
Taq polymerase and 4.4 milliQ water. The PCR amplifi-
cation profile consisted of an initial denaturation of DNA at95 "C for 3 min, followed by 95 "C for 1 m, 60 "C for
1 m; GOTO 2:1 time, 95 "C for 30 s, 60 "C for 30 s, 68 "Cfor 30 s; GOTO 5; 26 times and a final extension of 4 m at68 "C.
PCR method with nuclear SSR primer pairs
We selected 56 nuclear SSR primer pairs covering com-
plete nuclear genome covering all of the chromosomes toscreen the same set of 42 diverse Upland cotton lines from
CMD web page (Blenda et al. 2006). The nuclear SSR
primer pairs were selected based on the previous studiesconsidering their presence across the whole genome and
association with important fiber traits (Abdurakhmonovet al. 2008, 2009; Guo et al. 1997; Qin et al. 2008; Wu et al.
2009; Yu et al. 2012; Zhang et al. 2013; Fang et al. 2013).
PCR-amplifications were performed in a 8 ll reaction mix
containing 0.8 ll 109 PCR buffer, 0.2 ll dNTPs (10 mM
each), 0.72 ll 25 mM MgCl2, 0.2 ll 5 pM labeled primers(FAM, HEX, VIC, PET), 0.07 ll AmpliTaq Gold DNA
polymerase (Applied Biosystems, USA), and 15 ng geno-
mic DNA. PCR amplification was carried out using a PTC-225 DNA Engine Tetrad thermocycler (MJ Research,
USA) with first denaturation at 95 "C for 10 m, followed
by 10 cycles of 94 "C for 1 m, 60 "C for 1 m (decreases of0.5 "C in each cycle) and 72 "C for 2 m; 33 cycles of
94 "C for 15 s, 55 "C for 30 s, and 72 "C for 1 m. A final6 m extension at 72 "C was performed. Our final result
analysis was based on the screening of 42 Upland cotton
lines with 17 cpSSR and 65 nuclear SSR primer pairs inthis study of genetic diversity (Tables 1, 2, 3).
Gel electrophoresis
The PCR products were diluted 1:20 before loading into
capillaries and run in a denaturing capillary electrophoresisin an ABI 37309l with a 96-capillary system using POP-7
polymer (Applied Biosystems, USA) following the overall
methods of Abdurakhmonov et al. (2008, 2009). The sizeof amplified products was detected using GeneMapper 3.7
(Applied Biosystems, USA) as well as confirmed by visual
corrected for appropriate sizes. The nuclear SSR productsizes were also confirmed based on the available SSR
amplicon product sizes where available in the panel of
CMD web page (Blenda et al. 2006).
Data scoring
We followed the overall method of Bertini et al. (2006) for
dendogram construction and power maker software for data
analysis including PIC value estimation (Liu and Muse2005). Nei’s (1978) genetic distance and phylogenetic
analyses of cotton accessions was calculated using
PAUP*4.0 b. We used Cluster analysis using unweightedpair group method of UPGMA and the dendrogram
resulting from these estimations was drawn using STA-
TISTICA program (StatSoft Inc., http://www.statsoft.com/).Since G. hirsutum is an allopolyploid with reticulated
germplasm resources, nuclear SSR primer pairs often
yielded multiple PCR-products in our cotton accessions.There is a great risk of false allele calling for multiple-band
SSR markers when wide germplasm resources with
unknown pedigree information are genotyped, unless onlysingle-band loci are selected for genotyping. We scored
both the cpSSR and nuclear SSR data as a dominant marker
to avoid ambiguous scoring for allelic relationship withoutpedigree data and considering our primary goal to compare
the genetic diversity of the haplotype markers specific to
the cytoplasmic genome with nuclear genome in a set ofUpland cotton lines. We scored the SSR data like a
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dominant marker type with ‘‘1’’ for absent, ‘‘2’’ for present
state, or ‘‘0’’ for the occasional non-amplification ormissing data state, taking each band as an independent
marker locus with a clear size band separation (Abdu-
rakhmonov et al. 2008, 2009) to avoid assignment ofincorrect allelic relationships.
Since G. hirsutum is an allopolyploid with reticulated
germplasm resources, nuclear SSR primer pairs oftenyielded multiple PCR-products in our cotton accessions.
We scored both the cpSSR and nuclear SSR data as adominant marker to avoid ambiguous scoring for allelic
relationship without pedigree data and considering our
primary goal to compare the genetic diversity of the
haplotype markers specific to the cytoplasmic genome with
nuclear genome in a set of Upland cotton lines. The markerdata were analyzed to estimate genetic similarity between
cultivars based on the simple matching coefficient (SI)
using the overall method of Bertini et al. (2006). The SIestimated the similarity between genotypes for each culti-
var by awarding a score to each microsatellite and created
the dissimilarity coefficient index data for individual line.The genetic diversity of each microsatellite locus and
polymorphism information content (PIC) was calculatedusing PowerMarker software (Liu and Muse 2005). The
gene frequency of the microsatellite marker was estimated
based on PIC value.
Table 1 List of the SSR primer pairs specific to the cotton plastid
# Primer name Forward primer (50-30) Reverse primer (50-30) GenBank no
1 cpSSR04 CAGTTTTCCCAGTCACGACggggtcagtcaaacttct GTTTttcagggcgattttatca DQ345959.1
2 cpSSR 15 CAGTTTTCCCAGTCACGACgcaacgatttctatcagtca GTTTcttgttctagcaagagtgtt HQ901196.1
3 cpSSR 19 CAGTTTTCCCAGTCACGACcacatggatacaatctaaatggacg GTTTgaatgattcccatttcagtcg HQ901196.1
4 cpSSR 20 CAGTTTTCCCAGTCACGACgcgccattctaggattcc GTTTtaatggcttggctcgtgga HQ901196.1
5 cpSSR 21 CAGTTTTCCCAGTCACGACtcaaaatcggcagggtat GTTTattgaaaggcaagtcttacg DQ345959.1
6 cpSSR 22 CAGTTTTCCCAGTCACGACatctcacactaagccggt GTTTtgcaatgaattgtttcaaggcc HQ901196.1
7 cpSSR 23 CAGTTTTCCCAGTCACGACggggtcagtcaaacttct GTTTttcttcagttcagggcga HQ901200.1
8 cpSSR 26 CAGTTTTCCCAGTCACGACtcaccttcaacaagcgtaga GTTTacagagatggtgcgatttg HQ901196.1
9 cpSSR 27 CAGTTTTCCCAGTCACGACagcgaaatcgactgaagga GTTTctcgtcgaaacttccaattaggg HQ901196.1
10 cpSSR 29 CAGTTTTCCCAGTCACGACtatgggtctccgatagagacga GTTTaccaatttcgccatatcccc HQ901196.1
11 cpSSR 30 CAGTTTTCCCAGTCACGACcatttcagggccgaattacgc GTTTtgtatggcgcaacctgat DQ345959.1
12 cpSSR 33 CAGTTTTCCCAGTCACGACcgagttattgtcgcggga GTTTaattggagcttgaacccg HQ901196.1
13 cpSSR 36 CAGTTTTCCCAGTCACGACttggaaatgccctttctctc GTTTaagactatgccttcgcca HQ901196.1
14 cpSSR 37 CAGTTTTCCCAGTCACGACaggtctgaattctccaatgga GTTTgactgagaaggttgactcaag HQ901196.1
15 cpSSR 40 CAGTTTTCCCAGTCACGACtagcaacggaaccggggaaagta GTTTcgccaacagttaatcacggaaga HQ901196.1
16 cpSSR 41 CAGTTTTCCCAGTCACGACgcagcaccttaggatggc GTTTggaatctccggatctacgc HQ901196.1
17 cpSSR 45 CAGTTTTCCCAGTCACGACaaaggactcactgagccg GTTTccgagatcctttcgacga HQ901196.1
Table 2 Comparative analysis of nuclear and chloroplast specific SSR markers
SSRmarkerstype
Total numberof primerpairs used
Number ofmarkers/primer pair
Range ofmajor allelefrequency
Average of themajor allelefrequency
Range ofgenediversity
Averageof genediversity
Rangeof PICvalue
Averageof PICvalue
Marker withhighest PICvalue
NuclearspecificSSR
56 4 0.50–1.00 0.88 0–0.50 0.16 0–0.38 0.14 BNL0569_143
ChloroplastspecificSSR
14 5 0.65–1.00 0.94 0–0.46 0.10 0–0.35 0.09 CRSSR40_214
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Table 3 Coefficient of dissimilarity index among the lines using SSR markers (N nuclear SSR markers, CP Chloroplast specific SSR markers)
AcalaUltima Arkot9304b Arkot9308 Arkot9314 Arkot9506 Arkot9513 Coker315 DP90
N CP N CP N CP N CP N CP N CP N CP N CP
AcalaUltima 0 0 0.28 0.2 0.21 0.18 0.2 0.11 0.22 0.14 0.37 0.18 0.22 0.09 0.23 0.23
Arkot9304b 0.28 0.2 0 0 0.28 0.23 0.26 0.09 0.26 0.07 0.37 0.18 0.23 0.18 0.26 0.09
Arkot9308 0.21 0.18 0.28 0.23 0 0 0.22 0.2 0.21 0.23 0.36 0.27 0.25 0.27 0.22 0.25
Arkot9314 0.2 0.11 0.26 0.09 0.22 0.2 0 0 0.22 0.02 0.37 0.09 0.21 0.09 0.22 0.14
Arkot9506 0.22 0.14 0.26 0.07 0.21 0.23 0.22 0.02 0 0 0.28 0.11 0.19 0.11 0.26 0.16
Arkot9513 0.37 0.18 0.37 0.18 0.36 0.27 0.37 0.09 0.28 0.11 0 0 0.37 0.09 0.37 0.18
Coker315 0.22 0.09 0.23 0.18 0.25 0.27 0.21 0.09 0.19 0.11 0.37 0.09 0 0 0.26 0.18
DP90 0.23 0.23 0.26 0.09 0.22 0.25 0.22 0.14 0.26 0.16 0.37 0.18 0.26 0.18 0 0
DE119xT1388F6F11 0.25 0.11 0.3 0.11 0.25 0.2 0.26 0.02 0.27 0.05 0.34 0.07 0.28 0.07 0.29 0.11
DPL90xT1388F6 0.2 0.25 0.25 0.07 0.2 0.27 0.19 0.16 0.23 0.14 0.37 0.2 0.23 0.2 0.19 0.02
DPL90xT239BC3F8 0.17 0.43 0.24 0.41 0.2 0.3 0.17 0.39 0.22 0.41 0.38 0.41 0.2 0.39 0.18 0.36
FM966 0.22 0.16 0.3 0.16 0.24 0.25 0.24 0.07 0.24 0.09 0.36 0.07 0.25 0.07 0.23 0.16
LA1110004okra su 0.23 0.2 0.23 0 0.23 0.23 0.22 0.09 0.23 0.07 0.36 0.18 0.21 0.18 0.18 0.09
LA1110017 0.16 0.55 0.24 0.48 0.17 0.41 0.19 0.5 0.19 0.48 0.35 0.52 0.21 0.5 0.2 0.48
M1388 2 0.24 0.11 0.29 0.11 0.26 0.2 0.25 0.02 0.26 0.05 0.4 0.07 0.26 0.07 0.22 0.11
M237 3 0.24 0.16 0.28 0.25 0.26 0.34 0.26 0.16 0.27 0.18 0.39 0.16 0.3 0.11 0.25 0.25
M239 7 0.25 0.14 0.3 0.14 0.22 0.23 0.24 0.05 0.26 0.07 0.37 0.05 0.28 0.09 0.23 0.14
M240 0.21 0.18 0.28 0.27 0.23 0.36 0.22 0.18 0.24 0.2 0.37 0.18 0.24 0.14 0.22 0.27
MD52ne 0.29 0.11 0.36 0.11 0.31 0.2 0.29 0.02 0.32 0.05 0.39 0.07 0.31 0.07 0.29 0.11
MD65 11 0.35 0.2 0.37 0.02 0.36 0.23 0.34 0.11 0.34 0.09 0.47 0.16 0.35 0.16 0.32 0.07
MD90ne 0.32 0.18 0.36 0.05 0.33 0.2 0.3 0.09 0.36 0.11 0.47 0.14 0.35 0.14 0.23 0.05
MD9ne 0.26 0.14 0.31 0.11 0.24 0.16 0.25 0.16 0.3 0.18 0.38 0.23 0.28 0.23 0.19 0.14
Miscot7803 52 0.19 0.11 0.28 0.11 0.22 0.2 0.2 0.02 0.23 0.05 0.38 0.07 0.25 0.07 0.24 0.11
Miscot7918 0.27 0.14 0.32 0.07 0.23 0.23 0.28 0.02 0.25 0 0.32 0.11 0.27 0.11 0.31 0.16
MiscotT8 27 0.2 0.14 0.25 0.14 0.2 0.23 0.2 0.05 0.23 0.07 0.39 0.05 0.25 0.05 0.25 0.14
PMHS26 0.24 0.11 0.29 0.11 0.25 0.2 0.25 0.02 0.26 0.05 0.36 0.07 0.22 0.07 0.26 0.11
Prymaid 0.19 0.11 0.27 0.11 0.21 0.2 0.2 0.02 0.23 0.05 0.39 0.07 0.21 0.07 0.22 0.11
PSC355 0.21 0.25 0.27 0.18 0.22 0.07 0.18 0.16 0.24 0.18 0.4 0.2 0.22 0.2 0.18 0.18
SG747 0.16 0.45 0.26 0.43 0.21 0.32 0.19 0.41 0.23 0.43 0.37 0.41 0.21 0.41 0.21 0.39
ST474 0.17 0.09 0.22 0.18 0.18 0.27 0.16 0.09 0.22 0.11 0.36 0.14 0.19 0.05 0.16 0.18
ST825 0.34 0.18 0.35 0.27 0.33 0.36 0.34 0.18 0.24 0.2 0.18 0.14 0.33 0.09 0.35 0.27
T 2318USSR 0.31 0.45 0.37 0.52 0.27 0.57 0.3 0.48 0.3 0.5 0.39 0.52 0.29 0.48 0.29 0.52
T 2319USSR 0.45 0.5 0.48 0.57 0.46 0.64 0.44 0.52 0.4 0.55 0.41 0.52 0.4 0.48 0.45 0.57
T 2320USSR 0.21 0.57 0.28 0.52 0.26 0.59 0.22 0.48 0.25 0.5 0.42 0.48 0.24 0.52 0.26 0.52
TAM182 33ELS 0.21 0.18 0.23 0.05 0.22 0.2 0.2 0.09 0.22 0.11 0.37 0.14 0.22 0.14 0.18 0.05
TAM88G 104 0.24 0.14 0.25 0.11 0.24 0.23 0.24 0.02 0.24 0.05 0.38 0.07 0.22 0.11 0.17 0.16
TAM96WD 18 0.27 0.18 0.34 0.16 0.27 0.27 0.28 0.07 0.3 0.09 0.36 0.16 0.27 0.16 0.25 0.16
TAM98D 99ne 0.28 0.25 0.3 0.16 0.3 0.07 0.27 0.14 0.32 0.16 0.36 0.23 0.29 0.23 0.29 0.2
TAMWD69 s 0.18 0.2 0.25 0.18 0.19 0.3 0.18 0.09 0.23 0.11 0.36 0.14 0.23 0.18 0.22 0.23
TM 1 0.25 0.27 0.34 0.23 0.29 0.34 0.28 0.27 0.28 0.3 0.42 0.27 0.3 0.23 0.27 0.23
TTU077433 0.16 0.14 0.29 0.07 0.21 0.23 0.2 0.02 0.23 0 0.39 0.11 0.22 0.11 0.22 0.16
TTU0808161 0.17 0.18 0.28 0.16 0.21 0.27 0.2 0.07 0.25 0.09 0.38 0.16 0.19 0.16 0.23 0.2
Average 0.23 0.21 0.29 0.18 0.24 0.26 0.24 0.14 0.25 0.16 0.36 0.18 0.25 0.18 0.24 0.2
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Table 3 continued
DE119xT1388F6F11 DPL90xT1388F6 DPL90xT239BC3F8 FM966 LA1110004okra su LA1110017
N CP N CP N CP N CP N CP N CP
AcalaUltima 0.25 0.11 0.2 0.25 0.17 0.43 0.22 0.16 0.23 0.2 0.16 0.55
Arkot9304b 0.3 0.11 0.25 0.07 0.24 0.41 0.3 0.16 0.23 0 0.24 0.48
Arkot9308 0.25 0.2 0.2 0.27 0.2 0.3 0.24 0.25 0.23 0.23 0.17 0.41
Arkot9314 0.26 0.02 0.19 0.16 0.17 0.39 0.24 0.07 0.22 0.09 0.19 0.5
Arkot9506 0.27 0.05 0.23 0.14 0.22 0.41 0.24 0.09 0.23 0.07 0.19 0.48
Arkot9513 0.34 0.07 0.37 0.2 0.38 0.41 0.36 0.07 0.36 0.18 0.35 0.52
Coker315 0.28 0.07 0.23 0.2 0.2 0.39 0.25 0.07 0.21 0.18 0.21 0.5
DP90 0.29 0.11 0.19 0.02 0.18 0.36 0.23 0.16 0.18 0.09 0.2 0.48
DE119xT1388F6F11 0 0 0.23 0.14 0.23 0.36 0.29 0.05 0.27 0.11 0.26 0.48
DPL90xT1388F6 0.23 0.14 0 0 0.16 0.39 0.25 0.18 0.21 0.07 0.19 0.45
DPL90xT239BC3F8 0.23 0.36 0.16 0.39 0 0 0.21 0.41 0.21 0.41 0.16 0.18
FM966 0.29 0.05 0.25 0.18 0.21 0.41 0 0 0.26 0.16 0.19 0.52
LA1110004okra su 0.27 0.11 0.21 0.07 0.21 0.41 0.26 0.16 0 0 0.21 0.48
LA1110017 0.26 0.48 0.19 0.45 0.16 0.18 0.19 0.52 0.21 0.48 0 0
M1388 2 0.31 0 0.23 0.14 0.21 0.36 0.27 0.05 0.22 0.11 0.23 0.48
M237 3 0.28 0.14 0.24 0.27 0.22 0.39 0.27 0.18 0.26 0.25 0.22 0.5
M239 7 0.27 0.02 0.25 0.16 0.2 0.36 0.23 0.07 0.27 0.14 0.21 0.48
M240 0.26 0.16 0.23 0.3 0.2 0.39 0.21 0.2 0.25 0.27 0.19 0.55
MD52ne 0.33 0 0.3 0.14 0.27 0.36 0.26 0.05 0.32 0.11 0.28 0.48
MD65 11 0.43 0.09 0.34 0.05 0.33 0.39 0.39 0.14 0.34 0.02 0.34 0.45
MD90ne 0.38 0.07 0.3 0.07 0.3 0.36 0.32 0.11 0.32 0.05 0.32 0.48
MD9ne 0.28 0.16 0.23 0.16 0.21 0.45 0.27 0.2 0.27 0.11 0.24 0.55
Miscot7803 52 0.26 0 0.23 0.14 0.21 0.36 0.25 0.05 0.26 0.11 0.18 0.48
Miscot7918 0.27 0.05 0.28 0.14 0.27 0.41 0.3 0.09 0.3 0.07 0.26 0.48
MiscotT8 27 0.26 0.02 0.19 0.16 0.19 0.39 0.2 0.02 0.24 0.14 0.18 0.5
PMHS26 0.23 0 0.25 0.14 0.2 0.36 0.27 0.05 0.25 0.11 0.21 0.48
Prymaid 0.25 0 0.2 0.14 0.2 0.36 0.19 0.05 0.24 0.11 0.17 0.48
PSC355 0.27 0.14 0.2 0.2 0.17 0.23 0.25 0.18 0.21 0.18 0.21 0.34
SG747 0.26 0.39 0.2 0.41 0.14 0.25 0.21 0.43 0.2 0.43 0.18 0.16
ST474 0.25 0.07 0.15 0.2 0.14 0.36 0.2 0.11 0.19 0.18 0.18 0.48
ST825 0.35 0.16 0.34 0.3 0.35 0.45 0.34 0.11 0.33 0.27 0.32 0.52
T 2318USSR 0.28 0.5 0.28 0.55 0.29 0.66 0.28 0.55 0.33 0.52 0.3 0.73
T 2319USSR 0.46 0.55 0.45 0.59 0.47 0.68 0.47 0.55 0.45 0.57 0.47 0.75
T 2320USSR 0.25 0.5 0.2 0.55 0.19 0.7 0.24 0.5 0.25 0.52 0.21 0.77
TAM182 33ELS 0.28 0.07 0.17 0.07 0.18 0.36 0.23 0.11 0.22 0.05 0.19 0.48
TAM88G 104 0.29 0.05 0.23 0.18 0.18 0.39 0.23 0.09 0.2 0.11 0.22 0.5
TAM96WD 18 0.32 0.09 0.29 0.18 0.26 0.36 0.3 0.14 0.28 0.16 0.27 0.5
TAM98D 99ne 0.33 0.16 0.32 0.23 0.26 0.25 0.26 0.2 0.3 0.16 0.28 0.36
TAMWD69s 0.23 0.11 0.18 0.25 0.16 0.41 0.23 0.16 0.23 0.18 0.18 0.52
TM 1 0.32 0.25 0.28 0.25 0.22 0.39 0.27 0.3 0.3 0.23 0.27 0.5
TTU077433 0.26 0.05 0.21 0.14 0.19 0.41 0.22 0.09 0.23 0.07 0.2 0.48
TTU0808161 0.27 0.09 0.2 0.23 0.19 0.43 0.22 0.14 0.22 0.16 0.19 0.55
Average 0.28 0.14 0.24 0.21 0.22 0.38 0.25 0.17 0.25 0.18 0.22 0.48
Genetica
123
Author's personal copy
Table 3 continued
M1388 2 M237 3 M239 7 M240 MD52ne MD65 11 MD90ne MD9ne
N CP N CP N CP N CP N CP N CP N CP N CP
AcalaUltima 0.24 0.11 0.24 0.16 0.25 0.14 0.21 0.18 0.29 0.11 0.35 0.2 0.32 0.18 0.26 0.14
Arkot9304b 0.29 0.11 0.28 0.25 0.3 0.14 0.28 0.27 0.36 0.11 0.37 0.02 0.36 0.05 0.31 0.11
Arkot9308 0.26 0.2 0.26 0.34 0.22 0.23 0.23 0.36 0.31 0.2 0.36 0.23 0.33 0.2 0.24 0.16
Arkot9314 0.25 0.02 0.26 0.16 0.24 0.05 0.22 0.18 0.29 0.02 0.34 0.11 0.3 0.09 0.25 0.16
Arkot9506 0.26 0.05 0.27 0.18 0.26 0.07 0.24 0.2 0.32 0.05 0.34 0.09 0.36 0.11 0.3 0.18
Arkot9513 0.4 0.07 0.39 0.16 0.37 0.05 0.37 0.18 0.39 0.07 0.47 0.16 0.47 0.14 0.38 0.23
Coker315 0.26 0.07 0.3 0.11 0.28 0.09 0.24 0.14 0.31 0.07 0.35 0.16 0.35 0.14 0.28 0.23
DP90 0.22 0.11 0.25 0.25 0.23 0.14 0.22 0.27 0.29 0.11 0.32 0.07 0.23 0.05 0.19 0.14
DE119xT1388F6F11 0.31 0 0.28 0.14 0.27 0.02 0.26 0.16 0.33 0 0.43 0.09 0.38 0.07 0.28 0.16
DPL90xT1388F6 0.23 0.14 0.24 0.27 0.25 0.16 0.23 0.3 0.3 0.14 0.34 0.05 0.3 0.07 0.23 0.16
DPL90xT239BC3F8 0.21 0.36 0.22 0.39 0.2 0.36 0.2 0.39 0.27 0.36 0.33 0.39 0.3 0.36 0.21 0.45
FM966 0.27 0.05 0.27 0.18 0.23 0.07 0.21 0.2 0.26 0.05 0.39 0.14 0.32 0.11 0.27 0.2
LA1110004okra su 0.22 0.11 0.26 0.25 0.27 0.14 0.25 0.27 0.32 0.11 0.34 0.02 0.32 0.05 0.27 0.11
LA1110017 0.23 0.48 0.22 0.5 0.21 0.48 0.19 0.55 0.28 0.48 0.34 0.45 0.32 0.48 0.24 0.55
M1388 2 0 0 0.26 0.14 0.22 0.02 0.23 0.16 0.31 0 0.35 0.09 0.31 0.07 0.26 0.16
M237 3 0.26 0.14 0 0 0.25 0.11 0.25 0.07 0.3 0.14 0.42 0.23 0.35 0.2 0.28 0.3
M239 7 0.22 0.02 0.25 0.11 0 0 0.21 0.14 0.28 0.02 0.41 0.11 0.34 0.09 0.25 0.18
M240 0.23 0.16 0.25 0.07 0.21 0.14 0 0 0.23 0.16 0.37 0.25 0.32 0.23 0.24 0.32
MD52ne 0.31 0 0.3 0.14 0.28 0.02 0.23 0.16 0 0 0.45 0.09 0.38 0.07 0.32 0.16
MD65 11 0.35 0.09 0.42 0.23 0.41 0.11 0.37 0.25 0.45 0.09 0 0 0.31 0.02 0.36 0.11
MD90ne 0.31 0.07 0.35 0.2 0.34 0.09 0.32 0.23 0.38 0.07 0.31 0.02 0 0 0.26 0.09
MD9ne 0.26 0.16 0.28 0.3 0.25 0.18 0.24 0.32 0.32 0.16 0.36 0.11 0.26 0.09 0 0
Miscot7803 52 0.27 0 0.23 0.14 0.22 0.02 0.17 0.16 0.27 0 0.4 0.09 0.34 0.07 0.28 0.16
Miscot7918 0.33 0.05 0.32 0.18 0.27 0.07 0.26 0.2 0.33 0.05 0.46 0.09 0.4 0.11 0.29 0.18
MiscotT8 27 0.23 0.02 0.27 0.16 0.24 0.05 0.22 0.18 0.29 0.02 0.36 0.11 0.35 0.09 0.28 0.18
PMHS26 0.27 0 0.26 0.14 0.24 0.02 0.23 0.16 0.29 0 0.4 0.09 0.38 0.07 0.3 0.16
Prymaid 0.24 0 0.26 0.14 0.22 0.02 0.19 0.16 0.32 0 0.37 0.09 0.34 0.07 0.26 0.16
PSC355 0.21 0.14 0.25 0.27 0.26 0.16 0.2 0.3 0.29 0.14 0.31 0.16 0.28 0.14 0.22 0.23
SG747 0.25 0.39 0.25 0.34 0.22 0.36 0.19 0.41 0.28 0.39 0.35 0.41 0.3 0.39 0.24 0.48
ST474 0.21 0.07 0.24 0.11 0.21 0.09 0.18 0.14 0.27 0.07 0.34 0.16 0.27 0.14 0.22 0.23
ST825 0.36 0.16 0.38 0.16 0.35 0.14 0.33 0.18 0.34 0.16 0.43 0.25 0.43 0.23 0.36 0.3
T 2318USSR 0.34 0.5 0.36 0.5 0.3 0.48 0.32 0.52 0.36 0.5 0.41 0.55 0.33 0.52 0.28 0.55
T 2319USSR 0.46 0.55 0.45 0.55 0.47 0.52 0.48 0.57 0.5 0.55 0.47 0.59 0.5 0.57 0.46 0.59
T 2320USSR 0.27 0.5 0.25 0.59 0.25 0.48 0.23 0.61 0.27 0.5 0.38 0.55 0.34 0.52 0.25 0.57
TAM182 33ELS 0.25 0.07 0.25 0.2 0.25 0.09 0.23 0.23 0.29 0.07 0.33 0.02 0.3 0 0.25 0.09
TAM88G 104 0.24 0.05 0.25 0.14 0.2 0.02 0.22 0.16 0.26 0.05 0.32 0.14 0.29 0.11 0.22 0.18
TAM96WD 18 0.28 0.09 0.3 0.23 0.28 0.11 0.27 0.2 0.35 0.09 0.39 0.18 0.35 0.16 0.27 0.23
TAM98D 99ne 0.35 0.16 0.3 0.3 0.31 0.18 0.27 0.32 0.29 0.16 0.44 0.18 0.39 0.16 0.34 0.23
TAMWD69 s 0.24 0.11 0.24 0.16 0.21 0.09 0.21 0.18 0.3 0.11 0.36 0.2 0.34 0.18 0.25 0.25
TM 1 0.28 0.25 0.27 0.11 0.29 0.23 0.26 0.18 0.32 0.25 0.38 0.2 0.37 0.18 0.27 0.27
TTU077433 0.22 0.05 0.23 0.18 0.24 0.07 0.21 0.2 0.3 0.05 0.36 0.09 0.33 0.11 0.26 0.18
TTU0808161 0.23 0.09 0.24 0.23 0.22 0.11 0.22 0.25 0.26 0.09 0.37 0.18 0.33 0.16 0.27 0.23
Average 0.26 0.14 0.27 0.22 0.26 0.15 0.24 0.25 0.3 0.14 0.37 0.18 0.33 0.16 0.27 0.23
Genetica
123
Author's personal copy
Table 3 continued
Miscot7803 52 Miscot7918 MiscotT8 27 PMHS26 Prymaid PSC355 SG747 ST474
N CP N CP N CP N CP N CP N CP N CP N CP
AcalaUltima 0.19 0.11 0.27 0.14 0.2 0.14 0.24 0.11 0.19 0.11 0.21 0.25 0.16 0.02 0.17 0.16
Arkot9304b 0.28 0.11 0.32 0.07 0.25 0.14 0.29 0.11 0.27 0.11 0.27 0.18 0.26 0 0.22 0.14
Arkot9308 0.22 0.2 0.23 0.23 0.2 0.23 0.25 0.2 0.21 0.2 0.22 0.07 0.21 0 0.18 0.14
Arkot9314 0.2 0.02 0.28 0.02 0.2 0.05 0.25 0.02 0.2 0.02 0.18 0.16 0.19 0.14 0.16 0.27
Arkot9506 0.23 0.05 0.25 0 0.23 0.07 0.26 0.05 0.23 0.05 0.24 0.18 0.23 0.39 0.22 0.34
Arkot9513 0.38 0.07 0.32 0.11 0.39 0.05 0.36 0.07 0.39 0.07 0.4 0.2 0.37 0.07 0.36 0.11
Coker315 0.25 0.07 0.27 0.11 0.25 0.05 0.22 0.07 0.21 0.07 0.22 0.2 0.21 0.16 0.19 0.16
DP90 0.24 0.11 0.31 0.16 0.25 0.14 0.26 0.11 0.22 0.11 0.18 0.18 0.21 0.55 0.16 0.55
DE119xT1388F6F11 0.26 0 0.27 0.05 0.26 0.02 0.23 0 0.25 0 0.27 0.14 0.26 0.5 0.25 0.5
DPL90xT1388F6 0.23 0.14 0.28 0.14 0.19 0.16 0.25 0.14 0.2 0.14 0.2 0.2 0.2 0.5 0.15 0.59
DPL90xT239BC3F8 0.21 0.36 0.27 0.41 0.19 0.39 0.2 0.36 0.2 0.36 0.17 0.23 0.14 0.07 0.14 0.2
FM966 0.25 0.05 0.3 0.09 0.2 0.02 0.27 0.05 0.19 0.05 0.25 0.18 0.21 0.05 0.2 0.14
LA1110004okra su 0.26 0.11 0.3 0.07 0.24 0.14 0.25 0.11 0.24 0.11 0.21 0.18 0.2 0.43 0.19 0.18
LA1110017 0.18 0.48 0.26 0.48 0.18 0.5 0.21 0.48 0.17 0.48 0.21 0.34 0.18 0.16 0.18 0.48
M1388 2 0.27 0 0.33 0.05 0.23 0.02 0.27 0 0.24 0 0.21 0.14 0.25 0.39 0.21 0.07
M237 3 0.23 0.14 0.32 0.18 0.27 0.16 0.26 0.14 0.26 0.14 0.25 0.27 0.25 0.34 0.24 0.11
M239 7 0.22 0.02 0.27 0.07 0.24 0.05 0.24 0.02 0.22 0.02 0.26 0.16 0.22 0.36 0.21 0.09
M240 0.17 0.16 0.26 0.2 0.22 0.18 0.23 0.16 0.19 0.16 0.2 0.3 0.19 0.41 0.18 0.14
MD52ne 0.27 0 0.33 0.05 0.29 0.02 0.29 0 0.32 0 0.29 0.14 0.28 0.39 0.27 0.07
MD65 11 0.4 0.09 0.46 0.09 0.36 0.11 0.4 0.09 0.37 0.09 0.31 0.16 0.35 0.41 0.34 0.16
MD90ne 0.34 0.07 0.4 0.11 0.35 0.09 0.38 0.07 0.34 0.07 0.28 0.14 0.3 0.39 0.27 0.14
MD9ne 0.28 0.16 0.29 0.18 0.28 0.18 0.3 0.16 0.26 0.16 0.22 0.23 0.24 0.48 0.22 0.23
Miscot7803 52 0 0 0.26 0.05 0.22 0.02 0.23 0 0.19 0 0.24 0.14 0.22 0.39 0.19 0.07
Miscot7918 0.26 0.05 0 0 0.28 0.07 0.23 0.05 0.27 0.05 0.31 0.18 0.3 0.43 0.26 0.11
MiscotT8 27 0.22 0.02 0.28 0.07 0 0 0.26 0.02 0.19 0.02 0.21 0.16 0.18 0.41 0.18 0.09
PMHS26 0.23 0 0.23 0.05 0.26 0.02 0 0 0.23 0 0.25 0.14 0.23 0.39 0.22 0.07
Prymaid 0.19 0 0.27 0.05 0.19 0.02 0.23 0 0 0 0.19 0.14 0.18 0.39 0.16 0.07
PSC355 0.24 0.14 0.31 0.18 0.21 0.16 0.25 0.14 0.19 0.14 0 0 0.18 0.25 0.13 0.2
SG747 0.22 0.39 0.3 0.43 0.18 0.41 0.23 0.39 0.18 0.39 0.18 0.25 0 0 0.13 0.39
ST474 0.19 0.07 0.26 0.11 0.18 0.09 0.22 0.07 0.16 0.07 0.13 0.2 0.13 0.39 0 0
ST825 0.34 0.16 0.29 0.2 0.36 0.14 0.35 0.16 0.37 0.16 0.35 0.3 0.36 0.45 0.31 0.14
T 2318USSR 0.32 0.5 0.35 0.5 0.27 0.52 0.33 0.5 0.29 0.5 0.3 0.57 0.3 0.64 0.25 0.48
T 2319USSR 0.49 0.55 0.39 0.55 0.45 0.52 0.42 0.55 0.48 0.55 0.45 0.64 0.48 0.68 0.45 0.48
T 2320USSR 0.22 0.5 0.29 0.5 0.2 0.48 0.25 0.5 0.22 0.5 0.25 0.57 0.22 0.68 0.2 0.57
TAM182 33ELS 0.2 0.07 0.3 0.11 0.21 0.09 0.26 0.07 0.2 0.07 0.19 0.14 0.18 0.39 0.14 0.14
TAM88G 104 0.22 0.05 0.29 0.05 0.26 0.07 0.25 0.05 0.24 0.05 0.2 0.18 0.21 0.39 0.19 0.11
TAM96WD 18 0.28 0.09 0.3 0.09 0.29 0.11 0.32 0.09 0.29 0.09 0.24 0.23 0.27 0.45 0.24 0.16
TAM98D 99ne 0.28 0.16 0.33 0.16 0.3 0.18 0.26 0.16 0.3 0.16 0.3 0.02 0.26 0.27 0.23 0.23
TAMWD69s 0.21 0.11 0.25 0.11 0.17 0.14 0.24 0.11 0.2 0.11 0.21 0.25 0.19 0.45 0.18 0.18
TM 1 0.28 0.25 0.35 0.3 0.28 0.27 0.3 0.25 0.29 0.25 0.27 0.27 0.26 0.34 0.24 0.23
TTU077433 0.24 0.05 0.29 0 0.22 0.07 0.24 0.05 0.18 0.05 0.19 0.18 0.2 0.43 0.18 0.11
TTU0808161 0.2 0.09 0.26 0.09 0.22 0.11 0.24 0.09 0.17 0.09 0.21 0.23 0.2 0.48 0.16 0.16
Average 0.25 0.14 0.29 0.16 0.24 0.15 0.26 0.14 0.24 0.14 0.24 0.21 0.23 0.35 0.21 0.21
Genetica
123
Author's personal copy
Table 3 continued
ST825 T 2318USSR T 2319USSR T 2320USSR TAM182 33ELS TAM88G 104
N CP N CP N CP N CP N CP N CP
AcalaUltima 0.34 0.05 0.31 0.18 0.45 0.02 0.21 0.11 0.21 0.09 0.24 0.18
Arkot9304b 0.35 0.02 0.37 0.16 0.48 0 0.28 0.09 0.23 0.07 0.25 0.16
Arkot9308 0.33 0.02 0.27 0.16 0.46 0 0.26 0.09 0.22 0.07 0.24 0.16
Arkot9314 0.34 0.16 0.3 0.3 0.44 0.14 0.22 0.16 0.2 0.14 0.24 0.23
Arkot9506 0.24 0.36 0.3 0.41 0.4 0.39 0.25 0.41 0.22 0.39 0.24 0.48
Arkot9513 0.18 0.09 0.39 0.14 0.41 0.07 0.42 0.16 0.37 0.14 0.38 0.23
Coker315 0.33 0.14 0.29 0.18 0.4 0.16 0.24 0.25 0.22 0.23 0.22 0.3
DP90 0.35 0.52 0.29 0.57 0.45 0.55 0.26 0.59 0.18 0.57 0.17 0.59
DE119xT1388F6F11 0.35 0.48 0.28 0.52 0.46 0.5 0.25 0.55 0.28 0.52 0.29 0.55
DPL90xT1388F6 0.34 0.48 0.28 0.61 0.45 0.5 0.2 0.55 0.17 0.52 0.23 0.57
DPL90xT239BC3F8 0.35 0.09 0.29 0.23 0.47 0.07 0.19 0.02 0.18 0 0.18 0.09
FM966 0.34 0.02 0.28 0.16 0.47 0.05 0.24 0.14 0.23 0.11 0.23 0.18
LA1110004okra su 0.33 0.27 0.33 0.52 0.45 0.57 0.25 0.52 0.22 0.05 0.2 0.11
LA1110017 0.32 0.52 0.3 0.73 0.47 0.75 0.21 0.77 0.19 0.48 0.22 0.5
M1388 2 0.36 0.16 0.34 0.5 0.46 0.55 0.27 0.5 0.25 0.07 0.24 0.05
M237 3 0.38 0.16 0.36 0.5 0.45 0.55 0.25 0.59 0.25 0.2 0.25 0.14
M239 7 0.35 0.14 0.3 0.48 0.47 0.52 0.25 0.48 0.25 0.09 0.2 0.02
M240 0.33 0.18 0.32 0.52 0.48 0.57 0.23 0.61 0.23 0.23 0.22 0.16
MD52ne 0.34 0.16 0.36 0.5 0.5 0.55 0.27 0.5 0.29 0.07 0.26 0.05
MD65 11 0.43 0.25 0.41 0.55 0.47 0.59 0.38 0.55 0.33 0.02 0.32 0.14
MD90ne 0.43 0.23 0.33 0.52 0.5 0.57 0.34 0.52 0.3 0 0.29 0.11
MD9ne 0.36 0.3 0.28 0.55 0.46 0.59 0.25 0.57 0.25 0.09 0.22 0.18
Miscot7803 52 0.34 0.16 0.32 0.5 0.49 0.55 0.22 0.5 0.2 0.07 0.22 0.05
Miscot7918 0.29 0.2 0.35 0.5 0.39 0.55 0.29 0.5 0.3 0.11 0.29 0.05
MiscotT8 27 0.36 0.14 0.27 0.52 0.45 0.52 0.2 0.48 0.21 0.09 0.26 0.07
PMHS26 0.35 0.16 0.33 0.5 0.42 0.55 0.25 0.5 0.26 0.07 0.25 0.05
Prymaid 0.37 0.16 0.29 0.5 0.48 0.55 0.22 0.5 0.2 0.07 0.24 0.05
PSC355 0.35 0.3 0.3 0.57 0.45 0.64 0.25 0.57 0.19 0.14 0.2 0.18
SG747 0.36 0.45 0.3 0.64 0.48 0.68 0.22 0.68 0.18 0.39 0.21 0.39
ST474 0.31 0.14 0.25 0.48 0.45 0.48 0.2 0.57 0.14 0.14 0.19 0.11
ST825 0 0 0.38 0.52 0.42 0.52 0.39 0.57 0.31 0.23 0.33 0.16
T 2318USSR 0.38 0.52 0 0 0.43 0.14 0.26 0.14 0.3 0.52 0.32 0.45
T 2319USSR 0.42 0.52 0.43 0.14 0 0 0.45 0.23 0.47 0.57 0.46 0.5
T 2320USSR 0.39 0.57 0.26 0.14 0.45 0.23 0 0 0.23 0.52 0.23 0.45
TAM182 33ELS 0.31 0.23 0.3 0.52 0.47 0.57 0.23 0.52 0 0 0.19 0.11
TAM88G 104 0.33 0.16 0.32 0.45 0.46 0.5 0.23 0.45 0.19 0.11 0 0
TAM96WD 18 0.31 0.25 0.31 0.5 0.42 0.52 0.33 0.5 0.25 0.16 0.25 0.09
TAM98D 99ne 0.37 0.32 0.36 0.55 0.53 0.61 0.3 0.55 0.28 0.16 0.29 0.16
TAMWD69s 0.33 0.23 0.26 0.52 0.46 0.55 0.22 0.52 0.2 0.18 0.23 0.07
TM 1 0.38 0.27 0.33 0.52 0.46 0.57 0.25 0.61 0.28 0.18 0.27 0.25
TTU077433 0.38 0.2 0.31 0.5 0.46 0.55 0.23 0.5 0.23 0.11 0.22 0.05
TTU0808161 0.34 0.25 0.3 0.5 0.45 0.45 0.21 0.5 0.2 0.16 0.21 0.09
Average 0.34 0.24 0.31 0.43 0.44 0.43 0.25 0.43 0.24 0.19 0.24 0.2
Genetica
123
Author's personal copy
Table 3 continued
TAM96WD 18 TAM98D 99ne TAMWD69s TM 1 TTU077433 TTU0808161
N CP N CP N CP N CP N CP N CP
AcalaUltima 0.27 0.02 0.28 0.07 0.18 0 0.25 0.02 0.16 0.02 0.17 0.16
Arkot9304b 0.34 0 0.3 0.05 0.25 0.02 0.34 0 0.29 0 0.28 0.14
Arkot9308 0.27 0 0.3 0.05 0.19 0.02 0.29 0 0.21 0 0.21 0.14
Arkot9314 0.28 0.14 0.27 0.18 0.18 0.16 0.28 0.14 0.2 0.14 0.2 0
Arkot9506 0.3 0.39 0.32 0.43 0.23 0.41 0.28 0.39 0.23 0.39 0.25 0.25
Arkot9513 0.36 0.07 0.36 0.11 0.36 0.09 0.42 0.07 0.39 0.07 0.38 0.2
Coker315 0.27 0.16 0.29 0.2 0.23 0.14 0.3 0.16 0.22 0.16 0.19 0.3
DP90 0.25 0.55 0.29 0.55 0.22 0.52 0.27 0.55 0.22 0.55 0.23 0.64
DE119xT1388F6F11 0.32 0.5 0.33 0.5 0.23 0.52 0.32 0.5 0.26 0.5 0.27 0.57
DPL90xT1388F6 0.29 0.5 0.32 0.5 0.18 0.48 0.28 0.5 0.21 0.5 0.2 0.57
DPL90xT239BC3F8 0.26 0.07 0.26 0.11 0.16 0.09 0.22 0.07 0.19 0.07 0.19 0.14
FM966 0.3 0.05 0.26 0.05 0.23 0.07 0.27 0.05 0.22 0.05 0.22 0.18
LA1110004okra su 0.28 0.16 0.3 0.16 0.23 0.18 0.3 0.23 0.23 0.07 0.22 0.16
LA1110017 0.27 0.5 0.28 0.36 0.18 0.52 0.27 0.5 0.2 0.48 0.19 0.55
M1388 2 0.28 0.09 0.35 0.16 0.24 0.11 0.28 0.25 0.22 0.05 0.23 0.09
M237 3 0.3 0.23 0.3 0.3 0.24 0.16 0.27 0.11 0.23 0.18 0.24 0.23
M239 7 0.28 0.11 0.31 0.18 0.21 0.09 0.29 0.23 0.24 0.07 0.22 0.11
M240 0.27 0.2 0.27 0.32 0.21 0.18 0.26 0.18 0.21 0.2 0.22 0.25
MD52ne 0.35 0.09 0.29 0.16 0.3 0.11 0.32 0.25 0.3 0.05 0.26 0.09
MD65 11 0.39 0.18 0.44 0.18 0.36 0.2 0.38 0.2 0.36 0.09 0.37 0.18
MD90ne 0.35 0.16 0.39 0.16 0.34 0.18 0.37 0.18 0.33 0.11 0.33 0.16
MD9ne 0.27 0.23 0.34 0.23 0.25 0.25 0.27 0.27 0.26 0.18 0.27 0.23
Miscot7803 52 0.28 0.09 0.28 0.16 0.21 0.11 0.28 0.25 0.24 0.05 0.2 0.09
Miscot7918 0.3 0.09 0.33 0.16 0.25 0.11 0.35 0.3 0.29 0 0.26 0.09
MiscotT8 27 0.29 0.11 0.3 0.18 0.17 0.14 0.28 0.27 0.22 0.07 0.22 0.11
PMHS26 0.32 0.09 0.26 0.16 0.24 0.11 0.3 0.25 0.24 0.05 0.24 0.09
Prymaid 0.29 0.09 0.3 0.16 0.2 0.11 0.29 0.25 0.18 0.05 0.17 0.09
PSC355 0.24 0.23 0.3 0.02 0.21 0.25 0.27 0.27 0.19 0.18 0.21 0.23
SG747 0.27 0.45 0.26 0.27 0.19 0.45 0.26 0.34 0.2 0.43 0.2 0.48
ST474 0.24 0.16 0.23 0.23 0.18 0.18 0.24 0.23 0.18 0.11 0.16 0.16
ST825 0.31 0.25 0.37 0.32 0.33 0.23 0.38 0.27 0.38 0.2 0.34 0.25
T 2318USSR 0.31 0.5 0.36 0.55 0.26 0.52 0.33 0.52 0.31 0.5 0.3 0.5
T 2319USSR 0.42 0.52 0.53 0.61 0.46 0.55 0.46 0.57 0.46 0.55 0.45 0.45
T 2320USSR 0.33 0.5 0.3 0.55 0.22 0.52 0.25 0.61 0.23 0.5 0.21 0.5
TAM182 33ELS 0.25 0.16 0.28 0.16 0.2 0.18 0.28 0.18 0.23 0.11 0.2 0.16
TAM88G 104 0.25 0.09 0.29 0.16 0.23 0.07 0.27 0.25 0.22 0.05 0.21 0.09
TAM96WD 18 0 0 0.34 0.2 0.24 0.11 0.32 0.34 0.28 0.09 0.26 0.11
TAM98D 99ne 0.34 0.2 0 0 0.28 0.23 0.34 0.3 0.3 0.16 0.28 0.2
TAMWD69s 0.24 0.11 0.28 0.23 0 0 0.27 0.27 0.19 0.11 0.18 0.14
TM 1 0.32 0.34 0.34 0.3 0.27 0.27 0 0 0.27 0.3 0.27 0.34
TTU077433 0.28 0.09 0.3 0.16 0.19 0.11 0.27 0.3 0 0 0.16 0.09
TTU0808161 0.26 0.11 0.28 0.2 0.18 0.14 0.27 0.34 0.16 0.09 0 0
Average 0.29 0.2 0.3 0.23 0.23 0.21 0.29 0.26 0.24 0.18 0.23 0.23
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Results
Chloroplast genome
We detected the presence of many simple sequence repeat
(SSR) motifs including many single nucleotide repeatmotif in chloroplast genome. The sequences were assem-
bled into large contigs for quality and length improvements
based on sequence homology using NCBI BLAST tool in alocal database for cpSSR analysis to minimize the redun-
dancy. Our sequence analysis revealed that the simple
repeats constituted the highest number of repeats within thechloroplast genome confirming the results of Xu et al.
(2012). We also found several indels and multiple SNPs in
the chloroplast genome which might be useful in devel-oping additional cytoplasmic markers.
We developed 17 cpSSR primer pairs from chloroplast
genome to detect the genetic variation in cytoplasmicgenome within a set of 42 improved Upland cotton
accessions based on the following criteria: (1) distributed
across whole chloroplast genome, (2) did not produce anyunambiguous PCR products, (3) had little risk of marker
size overlapping in PCR products and (4) markers hadabout 10 bp length of repeat motifs with potential to find
some polymorphism (Table 1). However, we discarded
results of three primer pairs (cpSSR 23, cpSSR 26 andcpSSR 30) from our result analysis because they did not
fulfill some of the above criteria.
Fourteen cpSSR primer pairs generated more than onefragments ranging from 2 to 8 amplicons/primer pair. The
primer pairs generated a total of 55 polymorphic SSR
markers ranging in amplicon sizes of 112–370 bp with anaverage of about five amplicons per primer pair (Table 2;
Supplementary Table 1). The PIC value for cpSSR markers
varied from 0 to 0.35 with an average of 0.09 and 58 %SSR markers were polymorphic (Table 2; Supplementary
Table 1). The most informative cpSSR marker was
SSRCP40_214 contributing the highest PIC value of 0.35(Supplementary Table 1). The frequency of the major
allele/cpSSR marker varied from 0.65 to 1. Some of the
cpSSR primer pairs amplified rare bands in some specificlines. For example, cpSSR20_257 had a unique band of
257 fragment size in the lines of T-2318 (USSR), T-2319
(USSR) and T-2320 (USSR), lines from USSR originsuggesting a similar source of maternal parent. However,
cpSSR04 had a fragment of 230 bp present only in
T2320USSR suggesting introgression of a different sourceof maternal cytoplasm in its origin than other USSR cotton
lines. Also cpSSR21_308 had a unique band of 308 frag-
ment size in T-2318 (USSR) and T-2320 (USSR) lines.Preliminary results revealed that cpSSR 27 primer pair
produced a unique band of 367 fragment size in Pima 3-79,
T-2318 (USSR), T-2319 (USSR) and T-2320 (USSR)
suggesting the possibilities of some introgression of G.barbadense cytoplasm as maternal source during the
development of these lines (unpublished information).
The average coefficient of dissimilarity, estimating thegenetic distance among the 42 accessions based on the
cpSSR markers, varied from 0.14 to 0.53 with an overall
average of 0.22 (Table 3). However, the dissimilaritycoefficients of few lines such as T2318USSR,
T2319USSR, T2320USSR, LA1110017 and SG 747 werevery high ranging from 0.40 to 0.53 compared to the other
cotton lines (Table 3). We also observed that Arcot 9308
had a very high dissimilarity coefficient compared to otherArcot lines suggesting possibly a different source of
maternal parent in its origin, although these lines were
originated from the same breeders.The genetic distance in the phylogenetic estimation was
based on the dissimilarity coefficient of SSR makers
among the lines. The results from the phylogenetic analysisusing cpSSR markers grouped the relationship among the
42 cultivars into three broad groups (A, B, C) and seven
minor groups (G1 to G7) at a threshold level of aboutgenetic distance coefficient 0.55 in UPGMA clustering
(Fig. 1). Results revealed further that the cultivars more or
less tended to cluster within their breeding or geographicsources of origin suggesting possibly similar maternal
parental germplasms were used in developing these lines.
For example, G1 minor group at a threshold level of 19 %of genetic distance coefficient for UPGMA clustering had
three lines originated from Former Soviet Russia suggest-
ing similar source of maternal cytoplasm. The broad threegroups could further be subdivided into seven sub groups
(G1–G7) as per the phylogram. Thirty six of the total 42
lines (86 %) were grouped into C group at about 0.21 as thethreshold level for UPGMA clustering and these group
could be further classified under five minor clusters G3 to
G7 suggesting the presence of more genetic variation incytoplasm genome within these lines. About 52 % of the
cultivars were clustered in the minor group G6 at a dis-
imilarity coefficient of about 0.15. It is important to notethat Acala Ultima and T2320USSR lines were located at
the two extreme end of the UPGMA clustering suggesting
the maximum diversity existed in the maternal genomebetween these two lines.
Nuclear genome
We selected 56 nuclear SSR primer pair based on the
following specific criteria: (1) they are associated withsome important fiber QTLs as per previous published
information (Abdurakhmonov et al. 2008, 2009; Guo et al.
1997; Qin et al. 2008; Wu et al. 2009; Zhang et al. 2013;
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Fang et al. 2013), (2) they are located to different chro-
mosomes covering most part of the genome, (3) they pro-duced very clear reproducible PCR band for scoring and
the marker represented at least at the 5 % allele frequency
threshold for the total data set without any rare or uniqueband in the lines (Abdurakhmonov et al. 2008, 2009; Khan
et al. 2009; Lacape et al. 2007; Tyagi et al. 2014). Our
results showed 75 % of these primer pairs were polymor-phic. The PIC values of the primer pairs ranged from 0 to
0.38 with an average PIC value of 0.14 (Table 2; Supple-
mentary Table 2). BNL 0569_143 had the highest PICvalue of 0.38. Fifty-six nuclear SSR primer pairs produced
1–10 fragments with an average of about four amplicons/
primer pair ranging in fragment sizes of 100–371 bp. Themajor allele frequency ranged from 0.50 to 1.00 with an
average of 0.88 (Table 3). Sixty-six percent of the nuclear
SSR markers had a frequency of higher than 0.90 for themajor allele. Results from coefficient of dissimilarity based
on nuclear SSR profile index estimating the genetic dis-
tance of 42 accessions varied 0.22–0.44 with an overallaverage of 0.27 (Table 3). T 2319 USSR line had the
highest genetic dissimilarity coefficient of 0.44. Arcot 513,
MD 6511 and ST 825 had genetic dissimilarity coefficientsof 0.37, 0.36 and 0.34, respectively, from other lines.
The dendrogram based on genetic distance coefficient
classified 42 cotton lines into four major groups (A, B, Cand D) and six minor groups (G1–G6) considering a
threshold level at 35 % level based on nuclear SSR profile
(Fig. 2). The results revealed that the G6 minor group
contains 34 out of 42 lines (81 %) at a threshold level ofgenetic distance coefficient 0.27. T2319USSR and Acala
Ultima were located at the two extreme end of the den-
drogram suggesting the presence of maximum geneticdistance between these two lines in the nuclear genome. It
is interesting to note that some of the individual line
showed distinct differences in the genetic profile betweenmaternal and paternal genome based on the dendrogram
position. For example, T2320USSR, a line at the extreme
end of the cpSSR dendrogram, clustered with majorityother lines in G6 minor groups of nuclear SSR dendrogram
suggesting the presence distinct difference between the
paternal and maternal genome in the origin of this line.However, the reader must be aware that most the nuclear
SSR markers were pre-selected which might not be dis-
tributed at random across the genome and might have someeffects on the outcome of the results.
Discussion
Genetic variation for desirable alleles and the accuratecharacterization of the variability among breeding lines are
the foundation for any successful breeding program.
Complete cotton chloroplast genome sequences provided avaluable source to identify and use chloroplast specific
SSR markers in this study (Lee et al. 2006; Ibrahim et al.
Fig. 1 Genetic relationshipamong the Upland cotton linesbased on cpSSR markers
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2006; Xu et al. 2012). Recently, the only other study
reported on the use of chloroplast specific 75 SSR markersusing a similar strategy to study genetic diversity within
different species of Gossypium accessions to understand
cotton phylogeny and its evolution. (Li et al. 2014). Nor-mally, it is difficult to find variation in chloroplast genome
due to the low mutation rate. However, chloroplast
microsatellites, or simple sequence repeats, detect higherlevels of polymorphism and conserved primers for the
amplification of chloroplast microsatellites have been used
for genetic studies in conifers (Vendramin et al. 1996),gramineae (Provan et al. 2004) and dicotyledons (Weising
and Gardner 1999).In principle, mutation rates for length variation in
microsatellites have been found to be higher than point
mutations rates (Li et al. 2002). This could result in thesame genetic state of individuals in two different
microsatellite lineages evolving through two different
independent mutational events, an incident commonlyknown as homoplasy. Homoplasy within CPSSRs has been
considered in some cases as a potential problem for its use
as a genetic marker in population studies (Provan et al.2001). However, such effect of homoplasy in another
similar study was considered as moderate and disregarded
its potential to confound the results in genetic study(Cuenca et al. 2003). Therefore, we should be aware of
some limitations in the studies of genetic variation using
cpSSR markers such as: (1) the presence of sequences
differences in the similar size of amplicons (i.e., anamplicon size homoplasy) between the genotypes (Navas-
cues and Emerson 2005; Wheeler et al. 2014), (2) some-
times occurrence of bi-parental inheritance leading toheteroplasmy in some species (Hansen et al. 2007), and (3)
cytoplasmic introgression from another species due to
interspecific hybridization of the studied taxa (Lee et al.1998; Provan et al. 2001; Currat et al. 2008; Ebert and
Peakall 2009, Wheeler et al. 2014). With the continuous
declining costs and rapid development of the sequencingtechnologies some of these limitations could be overcome
by more detail analysis of genetic results using sequencingmethods.
Our primary objective in this paper was to study the
genetic diversity in cytoplasmic genome within 42improved Upland improved cotton lines (G. hirsutum) from
diverse genetic backgrounds. Several studies suggested that
recent cotton yield stagnation, declining fiber quality, aswell as increasing genetic vulnerability to biotic and abiotic
stresses are primarily due to narrow genetic base in the
nuclear genome of Upland cotton. This could be attributedto over reliance on crosses among closely-related elite
domesticated genotypes or reselection within existing cul-
tivars for high yield and superior fiber quality in cottonbreeding program (Van Esbroeck et al. 1999). However,
there is a critical need to study the genetic diversity within
Fig. 2 Genetic relationshipamong the Upland cotton linesbased on nuclear SSR markers
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the cytoplasmic genome of Upland cotton because almost
95 % of all cotton produced around the world is accountedfor by Upland cotton (G. hirsutum) because of its improved
agronomic characteristics including high yield.
Chloroplasts replicate independently of nuclear divisionwithout any genomic recombination, contrary to nuclear
genomes where normally recombination provided the
opportunity to create new variation. Normally, chloroplastsare inherited uniparentally from maternal parent in most of
the angiosperm (Sears 1980; Whatley 1982; Muir andFilatov 2007). Wendel (1989) documented the maternal
inheritance of chloroplast genome in cotton suggesting that
an A genome diploid with a chloroplast genome similar toG. arboreum and G. harbaceum was the possible donor of
the cytoplasm for all of the tetraploid cotton species in
evolution. These reports justify the use of cpSSR markersas the diagnostic tool representing the cytoplasmic genome
in cotton in the study of genetic variation.
It has been reported that the genetic variation in cyto-plasmic genome caused significant differences in several
phenotypes of maize, wheat, rice, potato and barley (Scotti
et al. 2004; Tao et al. 2004; Goloenko et al. 2002; Rao andFleming 1978; Allen et al. 2005; Leigh et al. 2013). Pre-
vious studies documented that the cytoplasm had a sig-
nificant effect on important QTLs and phenotypes in cotton(Han et al. 2007; Karaca et al. 2004). Cytoplasm has been
reported to have significant genetic effects on boll number,
lint percentage and fiber length in tetraploid cotton (Hanet al. 2007). Our previous study recorded that a plastid
mutant caused in the production of yellow virescent leaf in
young cotton seedlings (Karaca et al. 2004). A recentreview on 99 whole chloroplast genome from different
plant species using GenBank data confirmed the abundant
presence of hypervariable cpSSRs in the noncodingcpDNAs of plants with a variation frequency of 86 (median
value) in 81 vascular plants justifying the merit of cpSSR
as a tool in genetic studies (Ebert and Peakall 2009).Development of microsatellite or SSR (simple sequence
repeats) markers specific to chloroplast genome provides a
tool for analysis of phylogenetic relationships, cytoplasmicdiversity, inheritance of plastids, determination of precise
direction of cross between different genotype and moni-
toring gene flow and to study the history of domestication(Arroyo-Garcia et al. 2002; Bowers et al. 1999; Provan
2000; Provan et al. 2001).
Previous reports on 13 Gossypium species cotton plas-tome sequences including tetraploid cotton species docu-
mented the presence of large number of SSR loci including
the number of mononucleotide C 8 bp, dinucleotide C8 bp, trinucleotide C 9 bp, tetranucleotide C 12 bp, and
pentanucleotide C 15 bp (Lee et al. 2006; Xu et al. 2012).
They reported that different SSRs motifs were present indifferent frequencies with mononucleotide repeat motif as
the most abundant type ranging from 50 to 58 %. Among
the mononucleotides A or T was present as the predomi-nant type covering 95–97 % of the mononucleotide repeat
motifs. Li et al. (2014) obtained 100 mononucleotide and
16 dinucleotide cpSSRs, with lengths of 8–16 and 10–14nt, respectively, from 12.6 kb chloroplast region.
Comparing pair wise sequences of polymorphic SSR
loci among 13 chloroplast genomes revealed great varia-tion in sequences ranging from a minimum 2 loci (G.
barbadense race yuanmou and G. barbadense racekniyuam) to more than 115 loci (between tetraploid spe-
cies and D-genome species). The previous study showed
the presence of the most of the direct repeats in plastidsequences within intergenic spacer regions, intron
sequences and ycf2, an essential hypothetical chloroplast
gene and a 72 bp-long direct repeat was present in thepsaA and psaB genes, whereas a 34-bp forward repeat
was present within the rrn23 gene and a 32 bp-long direct
repeat in two serine transfer-RNA (trnS) genes that rec-ognize different codons; trnS-GCU and trnS-UGA (Xu
et al. 2012). Our analysis on cotton plastid sequences
further confirmed this report and provided us a scope todevelop chloroplast specific SSR markers in cotton using
following the overall method of our previous study in the
discovery of cotton EST-SSR using a cost-effective datamining strategy from public databases (Qureshi et al.
2004).
We developed 17 cpSSR primer pairs producing on anaverage four amplicons ranging in size 112–383 bp with
average major allele frequency 0.93, and PIC value 0.09
ranging from 0 to 0.35 with 58 % polymorphic in G. hir-sutum (Table 2). Li et al. (2014) reported the 66 % of
cpSSR were polymorphic and PIC value ranged from 0.11
to 0.88 with an average of 0.60 in Gossypium species.The multiple amplicons from most of the individual
cpSSR primer pairs suggested the presence of duplicated
events in the chloroplast genome. However, it was not clearjust from the amplicon sizes whether the size variation by
the cpSSR markers were due to only SSR length variation
or some other changes in sequences in our study. However,the signature stutter bands associated with the most of the
SSR markers in ABI gel system suggested the potential of
these markers as SSR type. We observed the presence ofdeletion/insertion and SNPs in chloroplast genome of the
tetraplod cotton during our comparative sequence analysis.
Xu et al. (2012) reported that 1–3 bp and 5 bp indels werethe primary source of SSR polymorphism in Gossypium
species. BLAST results of our sequence analysis further
confirmed the presence of such indels in the tetraploidcotton plastome sequences. In a preliminary study, we also
observed that primers specific to these indels can also be
used as a useful tool to detect genetic diversity in thecytoplasm of the tetraploid cotton.
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The original collection of the sequences from cotton
chloroplast sequences using NCBI GenBank and ourBLAST search results showed that these primers pairs were
specific to the chloroplast sequences. Fifty-eight percent of
cpSSR markers were polymorphic among these set of 42Upland cotton lines. It is noteworthy to mention that pre-
vious study based on RFLP markers indicated extremely
low level of variation in plastid genome of tetraploid cottonsuggesting the merit of the cpSSR markers (Wendel 1989).
Normally SSR markers are highly polymorphic and thehigh level of polymorphism arises from the number of
repeat units probably due to slipped-strand mis-pairing
during replication of the SSRs by the DNA polymarase(Erisen 1999; Levinson and Gutman 1987). Therefore, SSR
loci often evolved by insertions or deletions of one or more
repeat elements thereby increasing or decreasing in thelength of the repeat units. Previous studies in wheat, pine,
and other species documented that in spite of the low
mutation rate in chloroplast DNA relative to nuclear DNA,the hyper-variable nature of cpSSR make them an ideal
tool for genetic analysis of cytoplasmic genome (Leigh
et al. 2013; Cato and Richardson 1996; Xu et al. 2012;Ebert and Peakall 2009).
The PIC value provides the estimation on the informa-
tiveness of individual marker. A comparative analysis ofPIC values, an estimation of the probability of a poly-
morphism between two random samples of the germplasm
(Chao et al. 2009), based on cpSSR marker versus nuclearSSR marker (average cpSSR PIC value 0.08 versus nuclear
SSR PIC value 0.13) revealed that cpSSR markers were
more conserved compared to the nuclear SSR markers. Arecent study with 120 genome wide nuclear SSR markers
among a set of 381 accessions comprising 378 Upland
(Gossypium hirsutum L.) and 3 G. barbadense L. acces-sions of the United States estimated the average PIC value
was 0.17 (Tyagi et al. 2014), whereas some other studies on
similar investigations using nuclear SSR markers estimatedaverage PIC value for cotton SSRs could range from 0.122
(Abdurakhmonov et al. 2008, 2011) to 0.80 (Zhang et al.
2011). Our results revealed that the major allele frequencywas higher in chloroplast genome compared to the nuclear
genome among these lines suggesting the narrow genetic
diversity among maternal parents compared to the nucleargenome originating from the fertilization and recombina-
tion of both maternal and paternal parents. However, our
pre-selection of the nuclear SSR markers might haveeliminated some informative markers that could have some
influence on the nuclear SSR results.
With a reasonable level of confidence results revealedthat low to moderate level of genetic diversity existed in
both nuclear and cytoplasm genome among these cotton
lines. The overall results of the genetic diversity based onnuclear SSR markers are concordant with previous studies
(Hinze et al. 2012; Zhang et al. 2005; Sapkal et al. 2011).
Since chloroplast SSR markers are haploid in nature andtransmitted maternally in cotton (Cronn et al. 2002), as
expected the results from cpSSR showed a different pattern
of variation in cytoplasmic compared to the variation innuclear genome produced by recombination of both
maternal and paternal parents (Provan and Campanella
2003). Previous study also documented that phylogenies ofchloroplast and nuclear markers differ significantly within
Gossypium sp. (Cronn et al. 2002). Genetic variation incotton cytoplasm is maintained normally by gene flow
through egg cells, where as diversity in the nuclear genome
is determined by the gene flow through pollen and eggcells. Our results on genetic diversity in nuclear and
cytoplasmic genome can perhaps be used to determine the
pedigree history and selection of appropriate parents tomaximize the benefits at both nuclear and cytoplasmic
genomes.
The dissimilarity coefficient represents how diversegenetic resources were used in creating these lines. Results
based on cpSSR dissimilarity coefficient average ranged
from 0.14 to 0.53 with an overall average of 0.22, whereasthe dissimilarity coefficient of nuclear SSR markers varied
from 0.22 to 0.44 with an overall average of 0.27 sup-
porting the idea that chloroplast genome are more con-served compared to the nuclear genome in cotton among
this set of accessions. Bertini et al. (2006) estimated
genetic distance ranging from 0.00 to 0.71 with average of0.40 based on the coefficient of dissimilarity among 53
Upland cultivars using nuclear SSR markers. However,
Tyagi et al. (2014) estimated that the average genetic dis-tance among a set of 378 G. hirsutum accessions was lower
(0.195) comparative to our results using nuclear SSR
markers. Our result also showed all of the accessionsgrouped under the threshold value of 0.25 dissimilarity
coefficient of cpSSR, whereas around 73 % of these
accessions were clustered under the same threshold valueof dissimilarity coefficient in nuclear SSR dendrogram,
suggesting a more conserved and narrow base in cotton
cytoplasmic genome compared to the nuclear genomeamong these sets of accessions.
Results from the cpSSR dendrogram showed that the
accessions in some cases from the similar breeding sourcesor geographic locations clustered together, suggesting the
use of a similar in-house gene pool as the maternal parents
in the breeding program. For example, G1 minor group at athreshold level of 0.19 dissimilarity coefficient in UPGMA
clustering grouped three lines (T2319 USSR, T2318 USSR
and T2320 USSR) originated from Former Soviet Russia,suggesting the potential of similar sources of maternal
cytoplasm from the same geographic location. This type of
information will be more valuable when specific pedigreeinformation is available; however, we should take into
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account under the consideration of evolutionary inferences
(Wheeler et al. 2014). It is important to note that T2319USSR, T2318 USSR and T2320 USSR were separated in
different clade based on 0.45, 0.3 and 0.23 dissimilarity
coefficient, respectively, in the nuclear SSR dendrogram. Itis thus tempting to speculate about the pedigree of these
materials that these lines originated in USSR using a
similar source of maternal parents at USSR. However,since their introduction in USA, breeders used these
materials as maternal parents in crosses with other Uplandcotton lines as paternal parents creating genetic variation
among these lines.
We also observed that some accessions from differentsources had similar genetic background in the cytoplasmic
genome. For example, it is interesting to note that Acalla
Ultima and LA110017 clustered together in a group verydistant from other cultivars, suggesting genetically these
two lines are similar in cytoplasmic genome, but very
diverse from other accessions as per the result of cpSSRdendrogram. Acala cotton was developed for high fiber
qualities by interspecific introgression from crosses of G
arboreum, G. thurberi and G. hirsutum using triple hybridbreeding method (Smith and Cothren 1999; Zhang et al.
2005). Our result is congruent with a previous study on
genetic diversity using AFLP markers (Badigannavar et al.2012). They reported that most of the Louisiana genotypes
are congruent with the appearance of Acala-type genotypes
in their pedigree. Results also showed that the AcalaUltima and LA110017 lines were distantly separated based
on genetic distance in nuclear SSR dendrogram. Acala
lines were used in the USA cotton breeding program pri-marily for the improvement fiber qualities. On the other
hand, breeders used other Upland cotton lines to improve
yield and wide adaptation in their programs. These twodivergent selection pressures might have caused by the use
of diverse genetic sources as paternal parents in the
breeding program, thus separated these two lines distantlyin nuclear SSR dendrogram.
It is interesting to note that our results on genetic rela-
tionships are more or less congruent with some of thepedigree histories of the accessions. For example, TTU
0774-3-3 ranked genetically very close to Acala Ultima as
per both cpSSR and nuclear SSR dendrogram results. TTU0774-3-3 was developed from a cross of TTU 202-1107B
with Acala 1517-95 for improved fiber quality and well
adaptability in Texas High Plains (Bechere et al. 2007).DPL90XT1338F6n and DPL90 were grouped in the same
clade on CRSSR dendogram, but differ distinctly in nuclear
SSR dendrogram suggesting DPL90 was used as maternalparent in the cross of DPL90XT1338F6 genetic similarity
among the accessions originating from different breeding
programs shows that different breeding programs in theUSA are targeted to meet some specific regional needs;
however, germplasm exchange and use of some common
elite cultivars especially in the selection of some commontraits as parents was not uncommon among different
breeding programs (Tyagi et al. 2014).
It is tempting to speculate that perhaps the differentmolecular identity of the conserved chloroplast genome
complements the dynamic changes in nuclear genome
through the union of maternal and paternal gametes andoffers a scope of phenotypic plasticity in evolution main-
taining its own genetic niche. It will be interesting toinvestigate if any of these cpSSR markers are associated
with important fiber and agronomic traits. This may shed
some new light on the importance of the cytoplasmicgenome associated with those traits and the use of these
markers for marker assisted selection program in
improvement of the traits. We are currently investigatingthis aspect.
In summary, this research is important from several
perspectives. We detected a set of cytoplasm genomespecific SSR primer pairs by using a cost-effective data
mining strategy. We reported for the first time the genetic
diversity in the cytoplasmic genome within a set ofimproved Upland cotton accessions. Results revealed that
the genetic diversity in the cytoplasmic genome is narrow
compared to the nuclear genome within this set of Uplandcotton accessions. We also observed that genetic relation-
ship among the lines showed a different pattern of variation
in cytoplasmic compared to the nuclear genome. Thecomparative results of this research on the variation of
cytoplasmic and nuclear genome of a set of Upland cotton
accessions will complement to understand the geneticdiversity and gene flow within this set of improved Upland
cotton accessions. This will help breeders develop a
breeding strategy to maximize the effects of geneticdiversity in the genetic improvement of Upland cotton.
Acknowledgments The authors thank the Office of InternationalResearch Programs, U. S. Department of Agriculture (USDA) forproviding funds for this study under research grant UZB2-31016-TA-09 and U.S. Civilian Research & Development Foundation (CRDF)and Cotton Incorporated, USA. We thank the Academy of Sciences ofUzbekistan for supporting this joint study within USDA-Uzbekistancooperative programs. Mention of trade names or commercial prod-ucts in this article is solely for the purpose of providing specificinformation and does not imply recommendation or endorsement bythe U. S. Department of Agriculture. The U. S. Department ofAgriculture is equal opportunity provider and employer. Weacknowledge joint publication of USDA/ARS, and MississippiAgricultural and Forestry Experiment Station, approved for publica-tion as Journal Article of the Mississippi Agricultural and ForestryExperiment Station. We thank all of the public and private cottonbreeders who provided seeds for this study. We would specificallylike to acknowledge the help of Dr. Jack C. McCarty, Dr. WayneSmith, Dr. Bill Meredith, Dr. Gerald O. Myers, Dr. Ted Wallace, Dr.Fred Bourland, Dr. Jack Jones, and Dr. Dick L. Auld for their help inthis study by providing seeds of their released germplasm. Withouttheir help and support we could not accomplished the goals of this
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research project. We also thank Dr. B. Todd Campbell and Dr.Mauricio Ulloa for their help in reviewing the manuscript.
References
Abdurakhmonov IY, Kohel RJ, Yu JZ, Pepper AE, Abdullaev AA,Kushanov FN, Salakhutdinov IB, Buriev ZT, Saha S, SchefflerBE, Jenkins JN, Abdukarimov A (2008) Molecular diversity andassociation mapping of fiber quality traits in exotic G. hirsutumL. germplasm. Genomics 92:478–487
Abdurakhmonov IY, Saha S, Jenkins JN, Buriev ZT, Shermatov SE,Scheffler BE, Pepper AE, Yu JZ, Kohel RJ, Abdukarimov A(2009) Linkage disequilibrium based association mapping offiber quality traits in G. hirsutum L. variety germplasm. Genetica136:401–417
Abdurakhmonov IY, Buriev ZT, Shermatov SE, Abdullaev AA,Urmonov K, Kushanov F, Egamberdiev SS, Shapulatov U,Abdukarimov A, Saha S, Jenkins JN, Kohel RJ, Yu JZ, PepperAE, Kumpatla SP, Ulloa M (2011) Genetic diversity inGossypium genus. In: Caliskan M (ed) Genetic diversity, InTech,Slavka Krautzeka 83/A. Open Access Publisher, Croatia,pp 313–336
Allen JF, Puthiyaveetil S, Strom J, Allen CA (2005) Energytransduction anchors genes in organelles. BioEssays27:426–435. doi:10.1002/bies.20194
Arroyo-Garcia R, Lefort F, de Andres MT, Ibanez J, Borrego J, JouveN, Cabello F, Martinez-Zapater JM (2002) Chloroplastmicrosatellite polymorphisms in Vitis species. Genome45:1142–1149
Badigannavar A, Myers GO, Jones DC (2012) Molecular diversityrevealed by AFLP markers in upland cotton genotypes. J CropImprov 26:627–640
Bechere E, Auld DL, Cantrell RG, Krifa EHM, Misra S, Smith CW(2007) Registration of TTU 0774-3-3 and TTU 0808-1-6-1upland cotton germplasm lines with improved fiber length andstrength. J Plant Regist 1:58–59
Bertini CM, Schuster I, Sediyama T, Barros EG, Moreira MA (2006)Characterization and genetic diversity analysis of cotton culti-vars using microsatellites. Gene Mol Biol 29(2):321–329
Blenda A, Scheffler J, Scheffler B, Lacape J, Yu JZ, Jung S, Staton M,Palmer M, Jesudurai C, Muthukumar S, Yellambalase P, FicklinS, Eschelman R, Ulloa M, Saha S, Feng D, Cantrell R, Main D(2006) CMD: a cotton microsatellite data base resource forGossypium genomics. BMC Genom 7:132
Bowers J, Boursiquot JM, This P, Chu K, Johansson M, Meredith C(1999) Historical genetics: the parentage of Chardonnay, Gamayand other wine grapes of northeastern France. Science285:1562–1565
Buyyarapu R, Kantety RV, Saha S, Yu J, Sharma GC (2011) Newcandidate gene and EST-based molecular markers in Gossypiumspecies. Int J Plant Genomics. doi:10.1155/2011/894598
Cato SA, Richardson TE (1996) Inter- and intraspecific polymor-phism at chloroplast SSR loci and the inheritance of plastids inPinus radiata D. Don. Theor Appl Genet 93:587–592
Chao S, Zhang W, Akhunov E, Sherman J, Ma Y, Luo MC,Dubcovsky J (2009) Analysis of gene-derived SNP markerpolymorphism in US wheat (Triticum aestivum L.) cultivars. MolBreed 23:23–33
Cronn RC, Small RL, Haselkorn T, Wendel JF (2002) Rapiddiversification of the cotton genus (Gossypium: Malvaceae)revealed by analysis of sixteen nuclear and chloroplast genes.Am J Bot 89:707–725
Cuenca A, Escalante AE, Pinero D (2003) Long-distance coloniza-tion, isolation by distance, and historical demography in arelictual Mexican pinyon pine (Pinus nelsonii Shaw) as revealedby paternally inherited genetic markers (cpSSRs). Mol Ecol12:2087–2097
Currat M, Ruedi M, Petit RJ, Excoffier L (2008) The hidden side ofinvasions: massive introgression by local genes. Evolution62:1908–1920
Ebert D, Peakall R (2009) Chloroplast simple sequence repeats(cpSSRs) technical resources and recommendations for expand-ing cpSSR discovery and application to a wide array of plantspecies. Mol Econ Res 9:673–690
Erisen JA (1999) Mechanistic basis of microsatellite instability. In:Goldstein DB, Schlotterer C (eds) Microsatellite evolution andapplication. Oxford University Press, Oxford, pp 34–48
Fang DD, Hinze LL, Percy RG, Li P, Deng D, Thyssen GA (2013)Microsatellite-based genome-wide analysis of genetic diversityand linkage disequilibrium in Upland cotton (Gossypium hirsu-tum L.) cultivars from major cotton-growing countries. Euphyt-ica 191:391–401
Ferris C, King RA, Vainola R, Hewitt GM (1998) Chloroplast DNArecognizes three refugial sources of European oaks and suggestsindependent eastern and western immigrations to Finland.Heredity (Edinb) 80:584–593
Fineschi S, Taurchini D, Grossoni P, Petit RJ, Vendramin GG (2002)Chloroplast DNA variation of white oaks in Italy. For EcolManage 156:103–114
Goloenko IM, Lukhanina NV, Shimkevich AM, Aksyonova EA,Danilenko NG, Davydenko OG (2002) The productivity char-acteristics of substituted barley lines with marked chloroplastand mitochondrial genomes. Cell Mol Biol Lett 7(2):4
Guo W, Zhang TZ, Pan JJ, Wang XY (1997) A preliminary study ongenetic diversity of Upland cotton cultivars in China. ActaGossypii Sin 9:19–24
Hamza NB (2010) Cytoplasmic and nuclear DNA markers aspowerful tools in populations’ studies and in setting conservationstrategies. Afr J Biotech 9(29):4510–4515
Han L, Yang J, Zhu J (2007) Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model fordiploid plants. J Genet Genome 34(6):562–568
Hansen AK, Escobar LK, Gilbert LE, Jansen RK (2007) Paternal,maternal, and biparental inheritance of the chloroplast genome inPassifl ora (Passifl oraceae): implications for phylogeneticstudies. Am J Bot 94:42–46
Hinze LL, Dever JK, Percy RG (2012) Molecular variation amongand within improved cultivars in the U.S. cotton germplasmcollection. Crop Sci 52:222–230
Ibrahim RI, Azuma J, Sakamoto M (2006) Complete nucleotidesequence of the cotton (Gossypium barbadense L.) chloroplastgenome with a comparative analysis of sequences among 9 dicotplants. Genes Genet Syst 81:311–321
Karaca M, Saha S, Callahan FE, Jenkins JN, Read J, Percy RG (2004)Molecular and cytological characterization of a cytoplasmic-specific mutant in Pima cotton (Gossypium barbadense L.).Euphytica 139:187–197
Khan AI, Fu Y-B, Khan IA (2009) Genetic diversity of Pakistanicotton cultivars as revealed by simple sequence repeat markers.Commun Biom Crop Sci 4:21–30
Lacape J-M, Dessauw D, Rajab M, Noyer J-L, Hau B (2007)Microsatellite diversity in tetraploid Gossypium germplasm:assembling a highly informative genotyping set of cotton SSRs.Mol Breed 19:45–58
Lee DJ, Blake TK, Smith SE (1998) Biparental inheritance ofchloroplast DNA and the existence of heteroplasmic cells inalfalfa. Theor Appl Genet 76:545–549
Genetica
123
Author's personal copy
Lee S-B, Kaittanis C, Jansen R, Hostetler J, Tallon L, Town C,Daniell H (2006) The complete chloroplast genome sequence ofGossypium hirsutum: organization and phylogenetic relation-ships to other angiosperms. BMC Genomics 7:61
Leigh FJ, Mackay I, Oliveira HR, Gosman NE, Horsnell RA, Jones H,White J, Powell W, Brown TA (2013) Using diversity of thechloroplast genome to examine evolutionary history of wheatspecies. Genet Res Crop Evol 60:1831–1842
Levinson G, Gutman GA (1987) Slipped-strand mispairing: a majormechanism for DNA sequence evolution. Mol Bio Evol4:205–221
Li Y-C, Korol AB, Fahima T, Beiles A, Nevo E (2002) Microsatel-lites: genomic distribution, putative functions and mutationalmechanisms: a review. Mol Ecol 11:2453–2465
Li P, Li Z, Liu H, Hua J (2014) Cytoplasmic diversity of the cottongenus as revealed by chloroplast microsatellite markers. GenetResour Crop Evol 61:107–119
Liu K, Muse SV (2005) PowerMarker: an integrated analysisenvironment for genetic marker analysis. Bioinformatics21:2128–2129
Marchelli P, Gallo L, Scholz F, Ziegenhagen B (1998) ChloroplastDNA markers reveal a geographical divide across argentineanSouthern Beech Nothofagus nervosa (Phil.) Dim. ET Mil.distribution area. Theor Appl Genet 97:642–646
Muir G, Filatov D (2007) A selective sweep in the chloroplast DNAof dioecious silene (Section Elisanthe). Genetics 177:1239–1247
Navascues M, Emerson BC (2005) Chloroplast microsatellites:measures of genetic diversity and the effect of homoplasy. MolEcol 14:1333–1341
Nei M (1978) Estimation of average heterozygosity and geneticdistance from a small number of individuals. Genetics89:583–590
Provan J (2000) Novel chloroplast microsatellites reveal cytoplasmicvariation in Arabidopsis thaliana. Mol Ecol 9:2183–2185
Provan J, Campanella JJ (2003) Patterns of cytoplasmic variation inArabidopsis thaliana (Brassicaceae) revealed by polymorphicchloroplast microsatellites. Syst Bot 28(3):578–583
Provan J, Powell W, Hollingsworth PM (2001) Chloroplastmicrosatellites: new tools for studies in plant ecology andevolution. Trends Ecol Evol 16:142–147
Provan J, Biss PM, McMeel D, Mathews S (2004) Universal 394primers for the amplification of chloroplast microsatellites ingrasses (Poaceae). Mol Ecol Notes 4:262–264
Qin H, Guo W, Zhang Y, Zhang T (2008) QTL mapping of yield andfiber traits based on a four-way cross population in Gossypiumhirsutum L. Theor Appl Genet 117:883–894
Qureshi SN, Saha S, Kantety RV, Jenkins JN (2004) EST-SSR: a newclass of genetic markers in cotton. J Cot Sci 8:112–123. http://www.jcotsci.org
Rao AP, Fleming AA (1978) Cytoplasmic–genetypic effect in the GT112 maize inbred with four cytoplasms. Crop Sci 8(4):935–937
Rozen S, Skaletsky H (2000) Primer3 on the WWW for general usersand for biologist programmers. In: Krawetz S, Misener S (eds)Bioinformatics methods and protocols. Humana Press, Towota,pp 365–386
Sapkal DR, Suter SR, Thakre PB, Patil BR, Paterson AH, WaghmareVN (2011) Genetic diversity analysis of maintainer and restoreraccessions in upland cotton (Gossypium hirsutum L.). J PlantBiochem Biotechnol 20(1):20–28
Scotti N, Monti L, Cardi T (2004) Organelle DNA variation in pa-rental Solanum spp genotypes and nuclear-cytoplasmic inter-
actions in Solanum tuberosum (?) S. commersonii somatichybrid–backcross progeny. Theor Appl Genet 108(1):87–94
Sears BB (1980) Elimination of plastids during spermatogenesis andfertilization in the plant kingdom. Plasmid 4:233–255
Shurtleff MC (ed) (1980) Compendium of corn diseases, 2nd edn.American Phytopathological Society, Minneapolis
Smith CW, Cothren JT (1999) Cotton: origin, history, technology, andproduction. Wiley, New York
Tao D, Hu F, Yang J, Yang G, Yang Y, Xu P, Li J, Ye C, Dai L(2004) Cytoplasm and cytoplasm–nucleus interactions affectagronomic traits in japonica rice. Euphytica 135(3):129–134
Tyagi P, Gore MA, Bowman DT, Campbell BT, Udall JA,Kuraparthy V (2014) Genetic diversity and population structurein the US Upland cotton (Gossypium hirsutum L.). Theor ApplGenet 127:283–295
Ullstrup JA (1972) The impacts of the southern corn leaf blightepidemics of 1970-1971. Ann Rev Phytopath 10:37–50
Van Esbroeck GA, Bowman DT, May OL, Calhoun DS (1999)Genetic similarity indices for ancestral cotton cultivars and theirimpact on genetic diversity estimates of modern cultivars. CropSci 39:323–328
Vendramin GG, Lelli L, Rossi P, Morgante M (1996) A set of primersfor the amplification of 20 chloroplast microsatellites inPinaceae. Mol Ecol 5(415):595–598
Weising K, Gardner RC (1999) A set of conserved PCR primers for416 the analysis of simple sequence repeat polymorphisms inchloroplast genomes of dicotyledonous angiosperms. Genome42:9–19
Wendel J (1989) New World tetraploid cotton contains Old Worldcytoplasm. Proc Natl Acad Sci 86(11):4132–4136
Whatley JM (1982) Ultrastructure of plasmid inheritance: green algaeto angiosperm. Bot Rev 57:527–569
Wheeler GL, Dorman HE, Buchanan A, Challagundla L, Wallace LE(2014) A review of the prevalence, utility, and caveats of usingchloroplast simple sequence repeats for studies of plant biology.Appl Plant Sci. 2:1400059
Wu J, Gutierrez OA, Jenkins JN et al (2009) Quantitative analysis andQTL mapping for agronomic and fiber traits in an RI populationof upland cotton. Euphytica 165:231–245
Xu Q, Xiong G, Li P, He F, Huang Y, Wang K, Li Z, Hua J (2012)Analysis of complete nucleotide sequences of 12 Gossypiumchloroplast genomes: origin and evolution of allotetraploids.PLoS ONE. doi:10.1371/journal.pone.0037128
Yu JZ, Kohel RJ, Fang DD, Cho J, Deynze AV, Ulloa M, HoffmanSM, Pepper AE, Stelly DM, Jenkins JN et al (2012) A highdensity simple sequence repeat and single nucleotide polymor-phism genetic map of the tetraploid cotton genome. G3-Genes/Genomics/Genetics 2:43–58
Zhang J, Lu Y, Cantrell RG, Hughs E (2005) Molecular markerdiversity and field Performance in commercial cotton cultivarsevaluated in the Southwestern USA. Crop Sci 45:1483–1490
Zhang Y, Wang XF, Li ZK, Zhang GY, Ma ZY (2011) Assessinggenetic diversity of cotton cultivars using genomic and newlydeveloped expressed sequenced tag-derived microsatellite mark-ers. Genet Mol Res 10:1462–1470
Zhang T, Qian N, Zhu X, Chen H, Wang S, Mei H, Zhang Y (2013)Variations and transmission of QTL alleles for yield and fiberqualities in Upland Cotton cultivars developed in China. PLoSONE. doi:10.1371/journal.pone.0057220
Genetica
123
Author's personal copy