Genetic mapping and QTL analysis for sugar yield-relatedtraits in sugarcane

Ram Kushal Singh • Sujeet Pratap Singh • Dinesh Kumar Tiwari •

Shraddha Srivastava • Surendra Bahadur Singh • Mukut Lal Sharma •

Rakesh Singh • Trilochan Mohapatra • Nagendra Kumar Singh

Received: 17 June 2012 / Accepted: 17 November 2012 / Published online: 16 December 2012

� Springer Science+Business Media Dordrecht 2012

Abstract Genetic improvement of sugar content in

sugarcane would benefit from the availability of

sufficient DNA markers and a genetic map. Genetic

linkage maps were constructed to identify quantitative

trait loci (QTLs) for seedling brix (SB), brix (B),

sucrose percent in juice (SUC), stalk number (SN),

stalk length (SL), stalk diameter (SD), internodes

(INT), number of green leaves (NGL), at three crop

cycles across seven environments in a segregating

population with 207 individuals derived from a bi-

parental cross of sugarcane elite cultivars. Linkage

analysis led to the construction of eight linkage groups

(LGs) for Co86011 and sixteen LGs for CoH70. The

combined length of the two linkage maps was

2606.77 cM distributed over 24 LGs. 31 QTLs were

identified: 2 for SB, 7 for B, 6 for SUC, 4 for SN, 1 for

SL, 3 for SD, 6 for INT and 2 for NGL at LOD scores

ranging from 2.69 to 4.75. 7 QTLs (22 %) had stable

effect across crop year and locations. Markers from

parents were found to be associated with both positive

and negative effect on all of the traits analyzed. The

most important QTLs intervals identified in this study

using single-dose marker, were qB2, qSUC2, qINT2

and qB2, qSUC2, qSL2, qINT2 located between SSR

markers UGSM31548 and UGSM31649. These QTLs

could be put into use in marker assisted breeding.

Keywords Sugarcane � Simple sequence repeats �Quantitative trait loci � Linkage map

Introduction

Sugarcane (Saccharum spp.) is not only important for

sugar production, but also it is used as raw material for

paper, alcohol, plywood, industrial enzymes, and

animal feed (Arencibia 1998). The genetics of modern

sugarcane cultivars (Saccharum spp. hybrids) is very

complex, due to their highly heterozygous and com-

plex aneuploid nature. This has also led to the presence

of a variable number of chromosomes (2n = 100–140)

in the cultivars belonging to the two ancestral genomes

(Piperidis and D’Hont 2001). Improving sugar content

in sugarcane is highly attractive from a commercial

perspective, since higher sucrose content in the stalks

increases revenue without an associated increase in

cane production, transport, and milling costs. An

understanding of the relationship between sugar

R. K. Singh (&) � S. P. Singh � D. K. Tiwari �S. Srivastava � S. B. Singh � M. L. Sharma

Centre for Sugarcane Biotechnology, Sugarcane Research

Institute, (UP Council of Sugarcane Research),

Shahjahanpur 242001, UP, India

e-mail: rks.upcsr@gmail.com

R. Singh

National Research Centre for DNA Fingerprinting,

NBPGR, New Delhi 110012, India

T. Mohapatra � N. K. Singh

National Research Centre on Plant Biotechnology,

IARI, New Delhi 110012, India

123

Euphytica (2013) 191:333–353

DOI 10.1007/s10681-012-0841-7

content and yield-related traits in different cane-

growing environments would help to enhance the

efficiency of selection for sugar yield. A high priority is

therefore placed on improving sugar content in Florida

sugarcane breeding program. Lingle et al. (2009)

concluded that recurrent selection for sucrose content

in sugarcane has altered the allocation of photosynth-

ates from growth to storage in the internodes. A plateau

for cane yield was detected since the early 1970s;

however there was no evidence of a plateau for sugar

content in the sugarcane breeding programs (Edme

et al. 2005).

Several studies have shown genetic correlations

between sugar content and brix % juice, from com-

paring early with late sugar accumulation in the stalks

(Singh and Singh 1994, 1998, 2000; Jackson 2005).

There have been a number of studies on DNA marker-

trait associations in sugarcane, but due to the complex

ploidy structure, a limited number of markers were

generated which resulted in poor genome map cover-

age (Piperidis et al. 2008). Another problem associ-

ated with sugarcane is the occurrence of multiple

alleles which segregate at key loci governing the sugar

traits (Pinto et al. 2010). Therefore, individual QTL

effects may be quite small compared with those

detected in diploid species, especially for traits that

have been selected for several generations (Ming et al.

2002).

Molecular markers have been used to develop

genetic maps in sugarcane to improve our understand-

ing of the genome structure, to locate genes of

agronomic importance and to identify quantitative

trait loci (QTL) associated with such traits in order to

facilitate marker-assisted selection (Aitken et al. 2008;

Pinto et al. 2010). QTL mapping can improve our

understanding of the relationship among the genes

influencing sugar content and can facilitate determin-

istic manipulation of these traits towards development

of an elite sugarcane variety (Paterson 1996). Molec-

ular markers can play a pivotal role in tracking

favorable alleles from wild species and in ascertaining

their introgression into the cultivated background

(Edme et al. 2006).

Better understanding of genotype-by-environment

(G 9 E) interactions is expected to provide a solid

foundation for genetic improvement of crop produc-

tivity. To overcome this problem, huge trials have to

be conducted across years and environments to

identify the most suitable genotypes. The productivity

of sugarcane clones varies from one location to

another, indicating the presence of an environment

effect. The importance of G 9 E interaction is a

widely recognized phenomenon in sugarcane clonal

selection trials (Kang et al. 1987; Jackson and Hogarth

1992; Kimbeng et al. 2002; Tiwari et al. 2011). The

main difficulty in QTL mapping and its stability is that

several genetic and environmental factors affect the

final phenotype expression. Thus the experimental

design utilized for this must involve measurements

and genotyping of a large number of segregating

genotypes in order to allow the necessary precision for

QTL identification (Paterson 1998).

Among the DNA marker systems available, micro-

satellite or simple sequence repeats (SSRs) are the

most widely used for plant genome analysis (Morgante

and Olivieri 1993). These markers are characterized

by their simplicity, abundance, co-dominance and

multiple alleles among genomes (Varshney et al.

2005). SSRs are ideal for genetic fingerprinting and

construction of linkage maps because they are ran-

domly distributed across the genome. Recent studies

have revealed that gene transcripts also contain SSRs

and the abundance of Expressed Sequence Tag (ESTs)

in the databases has become an attractive source of

microsatellite markers (Cordeiro et al. 2001; Parida

et al. 2009). Moreover, their presence in conserved

transcribed regions makes them valuable in a breeding

program (Singh et al. 2011).

Due to polyploidy, the development of a high-

density genetic map for sugarcane requires much more

work than for a diploid species. The estimation of

genetic linkage maps in sugarcane on track after the

development of single-dose markers (SDMs) (Wu

et al. 1992). In a bi-parental mapping population, an

SDM has either a single copy of an allele in one parent

only segregating in 1:1 (presence: absence) or a single

copy of the same allele in both parents segregating in

3:1 (presence: absence). For higher polyploids with

irregular chromosome pairing, the loci showing either

a 1:1 or a 3:1 segregation pattern are much more

informative for genetic map construction than those

showing more complex segregation pattern (Wu et al.

2002). Based on this method, partial genetic maps

have been produced for S. spontaneum (Aljanabi et al.

1993; da Silva et al. 1993, 1995; Ming et al. 1998),

S. officinarum (Mudge et al. 1996; Guimaraes et al.

1999; Aitken et al. 2006), interspecific hybrids

(Daugrois et al. 1996; Ming et al. 2001; Alwala et al.

334 Euphytica (2013) 191:333–353

123

2009) and modern cultivars of sugarcane (Hoarau et al.

2002; Edme et al. 2006; Garcia et al. 2006; Raboin

et al. 2006; Oliveira et al. 2007; Alwala et al. 2008;

Pastina et al. 2012). The earliest molecular linkage

maps of the progenitors of modern sugarcane were

developed for S. spontaneum using 216 RFLP markers

(da Silva et al. 1993) and 279 RAPD markers

(Aljanabi et al. 1993), for S. officinarum using RAPD

(Mudge et al. 1996) and in cultivated sugarcane are

using 408 RFLP markers (Grivet et al. 1996). AFLP

markers have been used to construct genetic linkage

maps of a commercial sugarcane cultivar R 570, using

1185 loci (Hoarau et al. 2001); Q165 using 1365 loci

(Aitken et al. 2005); LCP 85-384 using 1111 loci

(Andru et al. 2011) and an interspecific cross of

S. officinarum and S. spontaneum using 344 loci

(Alwala et al. 2008). Edme et al. 2006 developed a

S. spontaneum/S. officinarum map using 193 microsat-

ellite (SSR) loci. However, coverage of the genomes

surveyed in these maps is still incomplete. Hence,

enriching the existing sugarcane linkage maps with

more SSR markers is a valuable objective for the

sugarcane breeding community. Past reports on QTL

analysis of sucrose content in sugarcane have used

populations derived from crosses with S. spontaneum

(Ming et al. 2001, 2002), commercial cultivars (Pastina

et al. 2012) and arising from self pollination of a clone

(Grivet et al. 1996; Hoarau et al. 2002; Aitken et al.

2005). While such studies provide useful information,

they do differ from the practice normally used to

generate commercial cultivars in the breeding

programs.

The objective of this study was to take advantage of

the large number of recently published SSR markers

for Saccharum species (Parida et al. 2009, 2010) and

construct a genetic map of sugarcane. Also, to map the

QTLs control sugar yield-related traits in three loca-

tions and two consecutive crop years with 207

segregating population of a bi-parental cross involving

a high and a low-sugar commercial cultivars.

Materials and methods

Mapping population

The mapping population was derived from a bi-parental

cross involving a commercial sugarcane variety

(Co86011) as female parent, having genes for earliness,

high sugar content and low cane yield and CoH70 as the

male parent, having genes for low sugar content, high

cane yield and tolerance to a wide range of biotic/abiotic

stresses. The F1 population was comprised of 857

genotypes of which, 207 were randomly chosen for

mapping. The seedlings were transplanted in June 2005

at the Sugarcane Research Institute (SRI), Shahjahan-

pur, 90 cm apart and at a row-to-row distance of

100 cm. The parents (Co86011 and CoH70) were used

to validate the amplification and polymorphism of the

markers. The same population and parents were clonally

propagated in two 5 m long rows in the C1 (first clonal

multiplication) in 2007 and C2 (second clonal multipli-

cation) stages in 2008 at three locations, namely

Shahjahanpur, (Longitude 79�370E and latitude

27�350N) in sandy loam soil at the Sugarcane Research

Station (SRS) Sultanpur, (Longitude 82�040E and

Latitude 26�160N) on a loam soil, and at Balrampur,

(Longitude 82�150E and Latitude 27�250N) on a lateritic

soil on March 2006 and 2007. Phenotyping for all traits

were completed on each plot at plant cane on January

2007 in C1 and January 2008 in C2 clonal multiplication

trails at all three locations.

Phenotyping and field data analysis

The mapping population and two parents were

phenotyped for sugar content and yield-related traits.

Brix (B) measurements, taken as a measure of sugar

content in the seedling and C1 and C2 stages across

locations, were coded as SB and B respectively.

Sucrose percent in juice (SUC) was measured at the

Shahjahanpur location in both the C1 and C2 stages. In

addition, data on stalk number (SN), stalk length

(SL), stalk diameter (SD), number of internodes

(INT), internode length (INTL), number of green

leaves (NGL) and leaf length (LL) were recorded on

each individual plot during January at all three

locations in both C1 and C2 stages. SUC was

estimated by polarization (pol value; grams of sucrose

per 100 g of fresh cane) on January, 2007 and 2008.

Brix was measured in November with a hand-held

refractometer on the juice of a sampling punch taken

at half-height of the stalk. Five randomly chosen

stalks were used to estimate B, SB and SN. Five stalks

per plot were chosen at random to evaluate SL, SD,

INT, INTL, NGL and LL. The SL was measured from

ground level to the last visible dewlap. SD and INTL

were measured at mid-length of the stalk.

Euphytica (2013) 191:333–353 335

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Phenotypic data from each trial were analyzed for

QTL 9 E by using C1 as replication 1 and C2 as

replication 2 at each location. The same set of

segregating population with 207 individuals recorded

on markers is evaluated phenotypically in different

environments. The statistical design was laid out as an

augmented randomized complete block design with

two replications. Genotypes were planted in seven

blocks with 30 each in six blocks and 27 individuals in

7th block. Each group of individuals was augmented

by four standards (commercial cultivars CoS95255,

CoS96268, CoSe92423 and Co1148). Both parents

were also included in one of the groups, but not

considered in the analysis. The analysis of variance

and correlations among traits was performed by the

Multi QTL software. The essence of analysis of

variance is to compare variability within groups versus

variability among different groups, using the ratio of

the F-statistics. Analysis of variance (ANOVA) was

made in 6 trials with 2 replications (C1 2007-rep1 and

C2 2008-rep2) at three locations. For ANOVA,

partitioning of the total sum of squares (SST), is

achieved by calculating sums of squared differences

(i) between individual replicates and their group mean

and between group means and the overall sample

mean (SSA, the among-group sum of squares).The

phenotypic variance and the error variance obtained

from the analysis of variance were used to estimate the

broad sense heritability for each trait. The traits

processed were: (i) sugar-related traits: SB, B, SUC

and (ii) yield-related traits: SN, SL, SD, INT, INTL,

NGL and LL.

DNA extraction and genotyping

For genomic DNA extraction, disease-free, whorl and

young-immature leaves were collected from the SRI

farm at Shahjahanpur. The samples were freeze-dried

and then stored at -86�C. Genomic DNA was

extracted from 500 mg of lyophilized powder of

young leaf tissue using the CTAB method (Hoisington

et al. 1994), with minor modification for sugarcane.

Six types of SSR markers, viz. unigenes-derived

Saccharum microsatellites (UGSM), sugarcane enriched-

genomic microsatellites (SEGM), EST-derived micro-

satellites (STMS) from the public domain, sugarcane

microsatellite (SMS), sugarcane genomic microsatellite

(SGM) and sugarcane cDNA microsatellites (SCM) were

used for genotyping the parents and the 207 segregating

F1 clones. SMS and STMS referred to the primer

developed by Mohapatra et al. (2003). Five hundred and

seventy-six of the SSR markers used for parental

polymorphism survey were developed at the National

Research Centre on Plant Biotechnology, Indian Agri-

cultural Research Institute, in New Delhi, India under a

collaborative project involving SRI, Shahjahanpur and

Sugarcane Breeding Institute, Coimbatore. PCR reactions

were carried out in 10 ll volume containing 25 ng of

template DNA, 1.0 ll each of forward and reverse

primers, 100 mM of dNTPs, 0.5 U of Taq DNA

polymerase, 1.0 ll of 109 PCR buffer and 2.5 mM of

MgCl2. Amplifications were performed in a Peltier

thermal cycler (MJ Research) with initial denaturation

at 94 �C for 5 min followed by 25 cycles of denaturation

at 94 �C for 1 min, annealing at (50–58 �C) for 1 min

and extension at 72 �C for 2 min; a final extension at

72 �C for 7 min was added to the program. The amplified

products were stored at 4 �C until electrophoresis, which

was done within 7.5 % denaturing polyacrylamide gel in

0.59 TBE buffer. Bands were visualized by staining with

0.5 lg/ml ethidium bromide. Gel photographs were

taken under UV light using the Gel Doc system by

Alpha Innotech.

Marker analysis and annotation

All unambiguously segregating bands in the mapping

population were scored independently as a dominant

marker, and assigned A if present exclusively in one

parent and B if present exclusively in the other parent.

Since sugarcane is highly poly-aneuploid, markers in

pseudo test-cross configurations between the parents

(1:1 segregation) and heterozygous in both parents

(3:1 segregation) were used for map construction (Wu

et al. 2002). Each marker was tested against its

expected ratio using v2 tests. Single-dose markers are

present only once in the genome, either in a 1:1 ratio

(markers present once in one parental genome) or in a

3:1 ratio (marker present in both parents). All loci with

strong deviations from expected proportions were

discarded after Bonferroni correction. Five hundred

and seventy-six SSR primer pairs, including 72 STMS,

20 SMS, 210 UGSM (class I type), 192 SEGM (class I

type), 50 SGM (class I type) and 32 SCM primers were

used for screening of the parents and two bulks of 20

F1 individuals with of high and low sugar content from

the mapping population in order to determine poly-

morphism levels.

336 Euphytica (2013) 191:333–353

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Map construction

The linkage map from the cross Co86011 9 CoH70 was

constructed using MultiPoint software (www.multiqtl.

com). The male (CoH70) and the female (Co86011)

maps were constructed in two steps. An initial map was

made with the 1:1 markers. The bi-parental simplex 3:1

markers were then included and mapped to the first

matrix of 1:1 markers 9 207 genotypes. This approach

gives approximate locations for the bi-parental simplex

3:1 markers. The pseudo test-cross population setting

was selected and a maximum threshold recombination

fraction (rfs) value of 0.35 was used to initially group the

markers into clusters. The polymorphic bands were tes-

ted for 1:1 and 3:1 segregation ratios using v2 analysis at

p \ 0.01 after Bonferonni correction. Chi square tests

were performed using 1:1 and 3:1 segregating markers to

further confirm the mode of segregation in the two par-

ents based on a 5 % label of significance (da Silva et al.

1993). Markers within each linkage group (LG) were

then order by multipoint analysis using Kosambi map-

ping function. Multipoint linkage analysis of loci within

each cluster was then performed and marker order was

further verified through re-sampling for quality control

via jack-knifing (Mester et al. 2003; Ritter et al. 2008;

Parh et al. 2008). Markers that could be ordered with a

jack-knife value of 90 % or greater were included as

‘framework’ markers and any remaining markers caus-

ing unstable neighborhoods were initially excluded from

the map. Following a repeated multipoint linkage anal-

ysis with the reduced set of markers from each cluster to

achieve a stabilized neighborhood, the previously

excluded markers were attached by assigning them to the

best intervals on the framework map. The map was

constructed after deleting 0 cM distance and high v2

values ([10.0) with a LOD threshold[3.0 and an rfs of

0.35. Distances between markers in cM were estimated

using Kosambi function (Kosambi 1944). Marker names

and map distances (cM) are indicated on the left and right

sides of each linkage group, respectively.

QTL mapping

QTL mapping was carried out by using Composite

Interval Mapping (CIM). The main advantage of CIM is

that it is more precise and effective at mapping QTLs

compared to single-point analysis and interval mapping,

especially when linked QTLs are involved. CIM

analysis also increased the control of the genetic

background and resolution of QTL mapping. QTL

analysis was performed on the trait values from the three

locations over two crop years using the Windows QTL

Cartographer Version 2.5 software package (Wang et al.

2004). To confirm the location of these QTLs, compos-

ite interval mapping (CIM) was undertaken with all

default settings. A permutation (1,000 permutations)

based LOD threshold of 3.0 was used to declare

significant QTLs (Churchill and Doerge 1994). QTLs

were named based on the nomenclature suggested by

McCouch et al. (1997). In the proposed nomenclature,

‘‘q’’ stands for QTL followed by the corresponding trait

and linkage distance in centimorgan (cM) and a number

at the end of the locus name designating the linkage

groups (LG) onto which the QTL has been mapped. The

QTL effect was estimated by combining interval

mapping with multiple regressions.

Results

Phenotypic segregation of sugar content

and morphological traits in the mapping

population

The means and variance for sugar content and yield-

related traits of the parents and segregating F1progeny in

the seedling, C1 and C2 stages at the three locations are

shown in Table 1. The result has shown that substantial

differences of population statistics in the measured traits

existed in the F1 population derived from biparental

commercial cultivars. These 207 clones were true F1

because the bands could be scored for presence or

absence with origin traced in the two parents. The

parents showed differences in their phenotypic values

for sugar content and yield-related traits. Evaluated,

contributing to the phenotypic segregation observed in

progenies. The population showed a significantly wider

range of variation than the two parents for all traits

across locations (SB and SUC only at Shahjahanpur),

highlighting transgressive segregation in the progenies

(Fig. 1, Table 1). Transgressive segregation was pro-

nounced particularly for SB, B and SUC with progeny

values ranging from 13.0 to 22.3 % in the seedling stage,

from 9.8 to 21.4 % in the C1 stage and from 9.25

to 19.96 % in the C2 stage at Shahjahanpur. A range

that was about 55 % (SBR), 53 % (B) and 58 %

(SUC) wider than the difference between the parents

Euphytica (2013) 191:333–353 337

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(Co86011 = 21 % SBR, 20 % B and 18 % SUC;

CoH70 = 15 % SBR, 15 % B and 13 % SUC).

Analysis of variance, trait heritabilities

and Correlations between traits

Analysis of variance was conducted for sugar and

yield-related traits collected from the six trials of 2007

and 2008 at three locations with 207 genotypes. This

data allow a statistical estimate of G 9 E. The results

of the analyses of variance of all traits are presented in

Table 2. Genotype and genotype 9 location interac-

tion were significant for all traits. The relative large

genotype mean square indicated that cultivars differed

in their genotypic potential for all traits. Furthermore,

broad sense heritabilities were moderate to high,

indicating good control of within-trial for environ-

mental variation and experimental error (Table 2).

Heritability ranged from 0.43(NGL) to 0.88 (B) at

Shahjahanpur, from 0.42 (LL) to 0.86 (B) at

Table 1 Phenotypic data on 207 F1 progeny from the cross (Co68011 9 CoH70) for 10 traits including nine mature cane traits and

seedling Brix percent in juice (SB) recorded in seven trials at three locations and 3 years

Trait Population

statistics

Shahjahanpur Sultanpur Balrampur

2007 (C1) 2008 (C2) 2007 (C1) 2008 (C2) 2007 (C1) 2008 (C2)

Brix % juice

Seedling (SB)

(2005–2006)

Mean 18.18 – – – – –

Range 13.00–22.30 – – – – –

SD 1.65 – – – – –

Brix % juice (B) Mean 15.75 17.22 18.78 19.92 18.45 19.65

Range 9.80–21.40 11.6–23.00 13.40–22.00 13.4–23.20 13.40–21.80 15.2–23.20

SD 3.02 3.72 1.78 1.95 2.01 2.11

Sucrose % juice (SUC) Mean 13.81 13.85 – – – –

Range 9.25–19.96 9.00–19.50 – – – –

SD 2.99 2.45 – – – –

Stalk number (SN/m) Mean 18.23 16.32 9.77 10.6 9.76 9.21

Range 10–42 11–38 10–25 12–24 5.00–26.00 4–27

SD 9.29 10.12 3.84 4.23 4.98 3.47

Stalk length (SL in cm) Mean 214.50 203.57 182.91 151.2 195.33 197.6

Range 108–290 121–295 110–285 89–245 100–235 112–298

SD 28.98 34.13 23.21 24.6 17.76 19.8

Stalk diameter (SD in

mm)

Mean 20.88 14.24 18.18 19.64 17.70 20.28

Range 13–28 8.52–17.25 11–27 15–24 12.00–23.00 18–32

SD 2.84 1.86 2.55 2–12 2.19 1.89

No. of internodes (INT) Mean 21.54 22.29 18.16 17.98 17.19 17.08

Range 12–30 12–32 12–25 13–23 12.00–21.00 10–22

SD 3.37 3.73 1.91 2.12 1.54 1.68

Internodes length (INTL

in cm)

Mean 13.27 19.46 22.35 21.8 23.3 26.6

Range 9.6–17.9 8–25 17–28 15–27 12–27 16–29

SD 1.43 3.07 1.90 2.08 1.09 2.4

No. of green leaves

(NGL)

Mean 10.43 9.73 7.25 7.8 6.6 7.9

Range 2–16 3–16 4–12 4–14 4–11 4–13

SD 2.58 2.08 1.78 1.25 1.57 1.32

Leaf length (LL in cm) Mean 124.92 136.18 98.78 112 97.87 194

Range 65–188 84–186 60–170 70–190 60–140 68–225

SD 15.25 18.91 17.08 12.52 16.76 14.23

C1 First clonal multiplication, C2 Second clonal multiplication

338 Euphytica (2013) 191:333–353

123

Balrampur and from 0.40 (LL) to 0.84 (SD) at

Sultanpur (Table 2).

Pairwise correlation coefficients among the nine

phenotypic traits are shown in Table 3. Highly signif-

icant positive correlations were found between SB and

B (r = 0.98), SB and SUC (r = 0.98), and between B

and SUC (r = 0.97). Moderately significant positive

correlations were found between SN and LL (0.36,

0.19, 0.27), SL and SD (rp = 0.28, 0.27, 0.21), SL and

LL (rp = 0.29, 0.28, 0.14), SL and INTL (0.43, 0.28,

0.22), INT and B (0.27, 0.25, 0.17), INT and SUC

(0.26), INT and INTL (0.45, 0.18, 0.25), INTL and LL

(0.63, 0.38, 0.29), NGL and LL (0.25, 0.30, 0.37), at

the three locations in both crop cycles. Significant

negative correlations were observed between SL and B

(rp = -0.28, -0.10, -0.29) and between SD and B

(rp = -0.34, -0.27, -0.42) at the three locations in

both crop cycles. All sugar-related traits: SB, B and

SUC were highly correlated (p = 0.001) with each

other (Table 3).

Parental linkage maps

An SSR-based linkage map was constructed for

mapping QTLs for sugar content and yield-related

traits in an inter-varietal cross Co86011 9 CoH70

using a population of 207 F1 lines. From a total of 389

polymorphic loci identified (including distorted mark-

ers), 56.29 % showed a pseudo test-cross configura-

tion between the parents, segregating in a 1:1 ratio as

Fig. 1 Frequency distribution of Brix % (B), sucrose % (SUC)

and stalk diameter (SD) among the 207F1 progenies from the

Co86011 9 CoH70 population measured in November 2006

(Seedling), 2007 (C1) and 2008 (C2). P1 and P2 indicate the

parental values for these traits

Euphytica (2013) 191:333–353 339

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Table 2 Analysis of variance across three environments and heritability (h2) for sugar and yield related traits in 207 F1 genotypes

from Co68011 9 CoH70

Trait ANOVA F value h2

Genotypes Genotypes 9 locations SPN BALP SULP

Brix % juice (B) 3.48** 1.93** 0.88 0.86 0.82

Stalk number (SN/m) 2.98** 1.86** 0.65 0.58 0.67

Stalk length (SL in cm) 4.9** 2.8** 0.74 0.76 0.77

Stalk diameter (SD in mm) 2.05** 1.73* 0.85 0.83 0.84

Internode no (INT) 3.23** 1.93** 0.79 0.75 0.72

Internode length (INTL in cm) 4.1** 2.45** 0.46 0.43 0.47

No. of green leaves (NGL) 6.4** 1.95** 0.42 0.46 0.41

Leaf Length

(LL in cm)

2.64** 1.16 0.45 0.42 0.40

* Significant at p \ 0.05, ** p \ 0.01. SPN Shahjahanpur, BLP Balrampur, SUL Sultanpur

Table 3 Correlation between traits based on seven field experiments at three locations on 207 F1 progeny derived from the cross

between two cultivars Co86011 and CoH70

Traitsa Locations B (%) SUC (%) SN SL (cm) SD (mm) INT INTL (cm) NGL LL (cm)

SB(%) SPNp 0.98** 0.98** -0.13 -0.02 -0.26* 0.25 -0.27* 0.31* -0.34*

B

(%)

SPNp 0.97** -0.08 -0.28* -0.34* 0.27* -0.18 0.17 -0.16

BALP 0.03 -0.10 -0.27* 0.25* -0.29* 0.17 -0.04

SULP -0.18 -0.29* 0.42** 0.17 -0.11 0.32* -0.34*

SUC(%) SPNp 0.07 -0.12 -0.08 0.26* 0.26* 0.18 0.01

SN SPNp 0.08 0.18 0.29* 0.23 0.31* 0.36*

BALP 0.05 0.12 0.27 0.14 0.18 0.19

SULP -0.04 0.26* 0.22 0.18 0.22 0.27*

SL

(cm)

SPNp 0.28* 0.38* 0.43** 0.26* 0.29*

BALP 0.27* 0.21 0.28* 0.16 0.28*

SULP 0.21* 0.20 0.22 0.14 0.14

SD (mm) SPNp 0.24 0.01 0.01 0.19

BALP 0.16 0.05 0.20 0.15

SULP 0.28* -0.03 0.24 0.32*

INT SPNp 0.45** 0.46** 0.33*

BALP 0.18 0.26 0.17

SULP 0.25* 0.14 0.20

INTL (cm) SPNp 0.34* 0.63**

BALP 0.33* 0.38*

SULP 0.12 0.29*

NGL SPNp 0.25*

BALP 0.30*

SULP 0.37*

Significance levels are indicated by * (p \ 0.05) and ** (p \ 0.01). SPN Shahjahanpur, BLP Balrampur, SUL Sultanpur, SB seedling

brix % juice, B Brix % juice, SUC sucrose % juice, SN stalk number, SL stalk length, SD stalk diameter, INT internodes, INTLinternodes length, NGL number of green leaves, LL leaf lengtha Genetic correlations among traits in the plant crop of first and second clonal generation ((C1-2007, C2-2008) and SB % juice in

seedling crop (2006) are given above the diagonal

340 Euphytica (2013) 191:333–353

123

single-dose markers and 43.02 % segregated in a 3:1

ratio (referred to as double single—dose markers). The

markers that did not fit these expected segregation

ratios were considered as distorted or at a higher allele

dosage and hence were ignored from subsequent

analysis. The 389 polymorphic loci produced by 119

SSR primer pairs, including 74 UGSM, 19 SEGM, 14

SCM, 10 SGM and 2 STMS markers were investigated

for segregation distortion. Fifty-three (13.63 %) of

these markers deviated from the 1:1 and 3:1 Mende-

lian segregation ratios and were not used subsequent

analyses. The 119 SSR primer pairs amplified 389

scorable markers, with an average of 3.33 markers per

primer pair. Out of these markers, 208 (53.47 %)

showed polymorphism between the parents and

segregated in a 1:1 ratio expected for single-dose

markers, whereas 128 markers (32.90 %) were mono-

morphic between the parents and segregated in a 3:1

ratio in the mapping population. In present studies

three point analyses were performed at a log of odds

(LOD) score threshold of 5 and a recombination

fraction threshold of 0.35. Markers were tested by a v2

goodness-of-fit test at a significance level of 5 %.The

distribution of different classes of SSR markers across

the genetic map was examined in term of map

coverage and tendency to cluster. The distribution of

different types of SSR loci on LGs was variable, with

substantial clustering around various LGs. The major-

ity (64.6 %) of the 336 linked markers were unigene-

derived (UGSM) whereas 18.15, 15.18, 1.5 and

0.57 % were SEGM, SCM, SGM and STMS-derived,

respectively.

Only eight and sixteen linkage groups were con-

structed with 208 and 128 linked EST-SSR markers

found in the CoH70 and Co86011 maps, respectively.

These 336 markers were mapped on 24 linkage groups

(LGs). A total of 188 loci were grouped into 8 LGs on

sugarcane commercial cultivar Co86011, of which 122

(36.31 %) were polymorphic on Co86011 and 66 were

common from both parents (Co86011 & CoH70). 188

loci were assigned to 8 LGs, with between 2 and 86 loci

per group. LG 1was the most densely populated

linkage group with 86 markers covering a map distance

of 529.2 cM (Fig. 2). Eighty-six loci (25.6 %) were

polymorphic on CoH70 and 62 markers were common

from both parents (Co86011 & CoH70), forming 16

LGs. Thus, 148 loci were assigned to 16 LGs with

between 2 and 50 loci per group (Fig. 3). The LGs

ranged in length from 1.5 cM for LG 15 with two

markers to 654.6 cM for LG 2 with 50 markers. The

cumulative length of the Co86011 map was 1,502.9 cM,

while that of the CoH70 map was 1,103.87 cM. The 24

linkage groups of the map spanned a total length of

2,606.77 cM with an average distance of 7.75 cM

between loci along the linkage groups (Figs. 2, 3).

QTLs for sugar content and yield-related traits

A major objective of this study is to map QTLs

associated with sugar content (B) and yield-related

traits in sugarcane commercial cultivars. QTLs detec-

tion were carried out by CIM using QTL Cartographer

Version 2.5 software, forward step-wise regression

with five markers as cofactors to control genetic

background and a 10 cM genome-wide scan window,

were used for the detection of QTLs. As sugarcane is

poly-aneuploid up to 12 alleles were considered to be

segregating at any given locus, suggesting that only

the most significantly different alleles were likely to be

detected. Other alleles at that locus may also contrib-

ute to the traits resulting in many quantitative

traits alleles of small effect (Aitken et al. 2008).

A permutation (1,000 permutations) based LOD

threshold of 3.0 was used to declare putative QTLs

(Churchill and Doerge 1994).

The CIM analysis identified 24 chromosomal

regions harboring 31 QTLs for traits controlling sugar

content and yield-related traits. QTL analysis identi-

fied 31 single-dose markers on five linkage groups with

LOD scores ranging from 2.69 to 4.75. These 31

marker trait association (MTA) were found to be

significant at the 5 % threshold using permutation tests

with at least one trait; individually, they explained

from 1.4 to 19.0 % of the phenotypic variation across

locations. The CIM mapping identified nine, six, six,

four, three, two and one QTLs linked to EST-SSR

markers for B, SUC, INT, SN, SD, NGL and SL,

respectively (Table 4).

The 15 QTLs for sugar content, identified using

CIM analysis included 9 for B on LG 1, LG 2, LG3,LG

6, LG 10 and 6 for SUC on LG 1, LG 2, LG 3 (Table 4,

Figs. 2, 3). The QTLs for sugar content and yield-

related traits were located on linkage groups : LG 1

(qB1, qSUC1, qSD1); LG 2 (qB2, qSUC2, qSN2, qSL2,

qSD2, qINT2); LG 3 (qSB3, qSUC3, qSN3, qINT3);

LG 4 (qSD4); LG 5 (qINT5); LG 6 (qB6); LG 10

(qSB10, qNGL10). The LOD value of each QTL

ranged from 2.69 to 4.75. These QTLs were dispersed

Euphytica (2013) 191:333–353 341

123

Fig. 2 EST-SSR-based framework linkage map of sugarcane

cultivar Co86011using 207 F1 progeny from cross Co86011 9

CoH70. Map was constructed using 122 (1:1), and 66 (3:1) markers

after deleting markers showing distorted segregation (v2 [10.0)

with a LOD threshold[3.0 and a recombination fraction cutoff of

0.35.Distancesbetween markers are shownincM using theKosambi

mapping function. Marker names and the map distances (cM) are

indicated on left and right sides, respectively, of each linkage group

342 Euphytica (2013) 191:333–353

123

over 8 of the 24 identified linkage groups (Fig. 2, 3).

Overall, 17 marker trait associations (MTA) were

found at 5 % (p \ 0.05) of which 12 (70.5 %) found at

all locations, 5 found only at one location (Table 4).

These MTA corresponds to five associations (p\ 0.005)

found for B, two each for SN,SD,INT and one for SL

(Table 5).

Allelic contributions

In total for single-dose markers, 15 QTLs were

identified for the three sucrose related traits (SB, B,

SUC) studied by Cartographer. Out of these, 6 QTL

had a negative effect on the traits 9 had a positive

effect. The individual genotypes were grouped on the

basis of Brix content and the average number of

positive and negative QTLs determined within each

group (data not shown). The number of negative QTLs

decreased as the Brix increased and the converse was

true of positive QTLs (data not shown). All QTL

alleles had both positive and negative additive effects

indicating that polymorphic band was in association or

repulsion with the favorable QTL allele were derived

from both parents (Table 4, 5). However, the seven

QTL identified for SN, SD, INT had a negative

additive effect, indicating that polymorphic band was

in repulsion with the favorable QTL allele and were

derived from Co86011.

QTL analysis

Genomic positions and single markers significantly

associated with putative QTL in the single marker

were included in the CIM for the estimation of QTL

main effects and QTL crop year-location-specific

effects. The QTLs identified marker UGSM31548 on

LG2 had significant additive main effect. QTLs

detected on LG2 associated with marker UGSM33158

Fig. 2 continued

Euphytica (2013) 191:333–353 343

123

and UGSM4428 had significant QTL 9 crop year

interaction for SN and SD, respectively, indicating

that these QTLs showed the same behavior along the

three locations. The QTL identified on LG1 associ-

ated with marker UGSM21081 for B and SD,

UGSM5845 and UGSM31649 on LG2 for B and

INT and marker associated withUGSM17253 on LG3

for SN, had QTL 9 crop year 9 location interac-

tions, which means that the effects of each QTL are

significantly different for at least one crop year-

location combination (Table 5). The most important

QTLs intervals identified in this study, were qB2,

qSUC2, qSL2 and qINT2 located between SSR

markers UGSM31548 and UGSM31649 covering

6.1 cM map distance. This QTL interval was located

in Co86011 map on LG 2, with stable additive effects

of 34 % for B, 19 % for SUC, 28.3 % for SL, and

30.7 % for INT, respectively, across crop year and

locations. The variation explained by individual Brix

QTLs ranged from 2.4 % (UGSM39154 on LG 10:

qSB10) to 9 % (UGSM21081 on LG1: qB1, UGSM31548

on LG 2: qB2). The variation explained by individual

SUC QTLs ranged from 2.5 % (UGSM21081 on LG1:

qSUC1, UGSM2976 on LG3: qSUC3) to 8.9 %

(UGSM31548 on LG2: qSUC2). Phenotypic variation

explained (r2 %) by sugar yield-related QTLs, were

Fig. 3 EST-SSR-based

framework linkage map of

sugarcane cultivar CoH70

based on 207 F1 progeny

from the cross

Co86011 9 CoH70. Map

was constructed using 86

(1:1), and 62 (3:1) markers

after deleting markers

showing distorted

segregation (v2 [ 10.0)

with a LOD threshold[3.0

and a recombination fraction

cutoff of 0.35. Distances

between markers are shown

in cM using the Kosambi

mapping function. Marker

names and the map distances

(cM) are indicated on left

and right sides, respectively,

of each linkage group

344 Euphytica (2013) 191:333–353

123

ranged from 9 % (UGSM4428 on LG2: qSD2) to 19 %

(UGSM29413 on LG2: qSN2). These alleles increased

sugar content by about 11 % and explained 9 % of the

phenotypic variation (Tables 4, 5).

Discussion

The segregating population used in this work was

created with an inter-varietal cross to map genes for

sugar trait in sugarcane. Both the parents are com-

mercial hybrids with complementary agronomic traits,

developed for the tropical and subtropical regions of

India. The cross showed transgressive segregation for

the economic traits. The genotype 9 location inter-

action resulted from changes in the relative ranking of

the genotype or changes in the magnitude of

differences between genotypes from one environment

to another. Broad-sense heritability estimates for the

traits evaluated in this study were moderate to high

(40–88 %), reflecting that most of the phenotypic

variation observed could be attributed to differences at

the genotypic level. This has been reported repeatedly

for these traits using other sugarcane mapping popu-

lations (Aitken et al. 2006; Alwala et al. 2009). 207

progenies derived from elite clones have the advan-

tage of mapping QTLs associated with favorable

alleles for the traits of interest as these progenies have

passed through three selection cycles at three geo-

graphically different locations. Schon et al. (2004) had

reported that major limitations, being the number of

genotypes and number of environments under study.

Such limitations will be inflated for traits of low to

moderate heritability.

Fig. 3 continued

Euphytica (2013) 191:333–353 345

123

To assess the relationships between traits, correla-

tion coefficients were calculated from the measured

trait values. The strongest correlations were found

between SB and SUC with r-value of 0.98 and

between B and SUC with r-value of 0.97. SUC was

highly and positively correlated with B and SB.

Sucrose percent in juice (SUC), seedling Brix (SB)

and clonal Brix (B) are components of sugar content,

and this was confirmed by the significant positive

correlation among them. Similar association between

SUC and Brix (B or SB) had been identified previously

in the plant cane and ratoon crops (Gravois and

Milligan 1992). Sugar content traits such as SB, B and

SUC were highly significant among sugar-related

traits but have minor or no significant effect on sugar

yield contributing traits.

Among the yield-related traits, SD and SL were

negatively correlated with sugar content (B). Other

traits e.g. INT, INTL, NGL, LL,which were reported

to be associated with sugar content by Singh and Singh

(1994, 1998), showed no significant correlation with

sugar content in the present study, suggesting that the

Table 4 Quantitative trait locus (QTL) analysis by linkage

analysis in the sugarcane derived from Co86011 9 CoH70.

Cartographer was used to determine genomic location of the

log of odds (LOD) scores, QTL position (cM) closest marker,

additive effect, and phenotypic variation explained (PVE) by

each QTL (%)

Traits Name of QTLs LG LOD QTL position (cM) Nearest marker Additive effect PVE (r2 %)

SB qSB3 3 2.73 11.40 UGSM1752 -0.13 2.70

qSB10 10 2.73 33.50 UGSM39154 -0.21 2.40

B qB1 1 3.91 33.80 UGSM21081 -0.71 9.00

qB2 2 2.80 34.60 UGSM5845 0.51 7.00

qB2 2 3.10 6.10 UGSM31548 0.34 9.00

qB2 2 2.80 9.80 UGSM31649 0.72 7.00

qB2 2 2.91 19.50 UGSM48905 0.38 6.00

qB2 2 3.70 16.80 UGSM58273 0.45 8.00

qB6 6 2.69 20.00 UGSM39253 -0.41 6.00

SUC qSUC1 1 4.71 33.80 UGSM21081 -0.15 2.50

qSUC1 1 3.96 2.2 SCM16125 0.19 4.50

qSUC2 2 3.77 31.00 UGSM4428 -0.23 3.90

qSUC2 2 2.97 6.10 UGSM31548 0.19 8.90

qSUC3 3 4.75 6.40 UGSM2976 0.20 2.50

qSUC2 2 3.42 9.80 UGSM31649 0.34 4.50

SN qSN2 2 3.52 11.50 UGSM29413 23.67 19.00

qSN2 2 2.83 5.00 UGSM31104 -22.02 17.00

qSN2 2 3.13 13.90 UGSM33158 23.73 13.00

qSN3 3 3.07 6.50 UGSM17 253 -19.46 12.00

SL qSL2 2 3.42 6.10 UGSM31548 28.30 13.00

SD qSD1 1 2.69 33.80 UGSM21081 -21.4 11.00

qSD2 2 3.51 31.00 UGSM4428 -37.41 9.00

qSD4 4 3.21 22.00 UGSM601262 31.16 11.00

INT qINT1 1 3.24 33.80 UGSM21081 -66.66 10.00

qINT2 2 3.60 6.10 UGSM31548 30.70 14.00

qINT2 2 3.41 9.80 UGSM31649 29.53 13.00

qINT3 3 2.89 6.90 UGSM13201 -17.49 14.00

qINT3 3 2.92 10.80 UGSM17407 19.06 14.00

qINT5 5 3.48 10.91 UGSM131081 -21.52 15.00

NGL qNGL10 10 3.58 34.60 UGSM39154 22.41 11.00

qNGL10 10 3.04 33.50 UGSM31208 33.00 10.00

346 Euphytica (2013) 191:333–353

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earlier reported correlations may be pedigree-related.

Sugarcane breeders concentrate on both increasing the

sugar content and sugar yield by increasing the

biomass, as both enhance the final sugar yield. There

is evidence that breeders have been more successful in

increasing the biomass than sugar content (Jackson

2005). Genotypic correlations between the seven trials

for sugar-related traits indicated that crossover geno-

type by environment interaction was relatively low for

the traits analysed. Zhang et al. (2011) also reported

that genetic factors controlling positive and negative

correlations between the traits and QTL with major

effects are more likely to be stable across multiple

environments in Brassica. From a phenotyping per-

spective, the close relationship between sugar-related

traits in this study suggested that the slower and more

expensive measurements of sugar content are not

required as Brix, a simpler to measure trait, provided

similar QTL results.

The complex genetic structure of sugarcane

genome makes mapping a challenging task. The large

number of variable chromosomes in sugarcane indi-

cates that a large number of markers are required to

build a saturated genetic map. The molecular sugar-

cane map presented here is largely based on ESTs-

derived genic-SSR markers and also with inclusion of

some genomic-SSR markers. The value of genic-SSR

markers is enhanced due to their good transferability

across taxon boundaries, as reported in grape by

Decroocq et al. (2003) and in sugarcane by Cordeiro

et al. (2001); Oliveira et al. (2007). Furthermore,

genic-SSR markers are sometimes directly associated

with the trait of interest due to functional polymor-

phism. However, in terms of marker development and

detection of polymorphic genic-SSR markers are less

efficient than genomic SSRs, requiring considerably

more to map a genome. Development of a functionally

defined gene-based genetic map of sugarcane provides

Table 5 QTL effects estimated with the Cartographer software and the average standard error of all differences

Traits LG (effect) Markera QTL position

(cM)

Additive effect SE

L1-1 L1-2 L2-1 L2-2 L3-1 L3-2

SB 3(�b) UGSM1752 11.40 -0.13 -0.13 – – – – –

10(�b) UGSM39154 33.50 -0.21 -0.21 – – – – –

B 2(�a) UGSM5845 34.60 0.45 0.62 0.72 0.46 0.47 0.29 0.13

6(�b) UGSM39253 20.00 -0.41 -0.41 -0.43 -0.38 -0.36 -0.41 0.07

2(�a) UGSM31548 6.10 0.34 0.34 0.34 0.34 0.34 0.34 0.14

2(�a) UGSM31649 9.800 0.68 0.58 0.73 0.72 0.76 0.82 0.19

1(�b) UGSM21081 33.80 -0.74 -0.72 -0.67 -0.76 -0.55 0.78 0.26

SUC 1(�b) UGSM21081 33.80 -0.15 -0.15 – – – – –

2(�a) UGSM31548 6.100 0.19 0.19 – – – – –

2(�b) UGSM4428 31.00 -0.23 -0.23 – – – – –

SN 2(�a) UGSM33158 13.90 23.73 23.73 22.49 22.49 24.15 24.15 1.20

3(�b) UGSM17 253 6.50 -22.85 -20.86 -24.85 -19.85 -18.70 -12.76 0.78

SL 2(�a) UGSM31548 6.10 28.3 28.30 28.3 28.3 28.3 28.3 1.18

SD 1(�b) UGSM21081 33.80 -20.1 -24.5 -23.1 -21.1 -19.2 -26.1 2.76

2(�b) UGSM4428 31.00 -37.58 -37.58 -36.26 -36.26 -38.23 -38.23 1.78

INT 1(�b) UGSM21081 33.80 -66.5 -68.32 -62.9 -72.0 -68.6 -52.2 3.20

2(�a) UGSM31548 6.10 30.7 30.7 30.7 30.7 30.7 30.7 2.03

�a and �b are additive main effects on parent A (CoH70) and B (Co86011)

Location: L1 = Shahjahanpur, L2 = Balrampur, L3 = Sultanpur. Positive and negative signs of the estimate indicate allele

contributed by CoH70 and Co86011, respectively

SB seedling brix % juice, B Brix % juice, SUC sucrose % juice, SN stalk number, SL stalk length, SD stalk diameter, INT internodes,

SE standard errora Maker name starting with ‘UGSM’ represent unigene genomic Saccharum microsatellite markers

Euphytica (2013) 191:333–353 347

123

the basis for correlating molecular variation in func-

tional sequences with the trait QTLs.

About 56.29 % of the markers detected in this study

were single-dose. Similar levels of single-dose mark-

ers have been reported in other sugarcane mapping

studies using population sizes of 84 (Mudge et al.

1996), 88 (Da Silva et al. 1993) and 100 (Guimaraes

et al. 1999; Alwala et al. 2009). The overall level of

double single-dose polymorphism (38 %) achieved is

in agreement with those obtained by various sugarcane

segregating populations (Reffay et al. 2005; Raboin

et al. 2006). Double single-dose polymorphisms of 63

and 70 % have also detected for a cross between

cultivars SP80-180 9 SP80-4966 with RFLP, SSR,

AFLP markers (Garcia et al. 2006) and the selfed-

progeny of cultivar LCP 85-384 with AFLP, SSR,

TRAP markers (Andru et al. 2011). Despite the high

level of monomorphic markers between the mapping

parents, 45 and 3.8 % of double-single-dose markers

were reported in the progeny with SSR by Andru et al.

(2011). The markers segregating in 1:1 or 3:1 ratio

were used to establish linkage between the double

pseudo-testcross markers. This strategy uses SDMs

segregating in 1:1 ratio for each parent separately to

build two independent genetic maps (one for each

parent) for any cross between heterozygous parents

like sugarcane. Linkage analysis with single-dose

markers has been successfully adopted in mapping

highly polyploid, heterozygous parents of outcrossing

diploid and polyploid species for which inbred lines

cannot be readily developed to generate F2 mapping

population (Grattapaglia and Sederoff 1994). This

strategy has been widely exploited to map single-dose

markers in polyploid species like sugarcane (da Silva

et al. 1993; Edme et al. 2006, Alwala et al. 2009).

Independent of genomic complexity and of the ploidy

level, (2n = 4x, 6x, 8x and 10x), a population size of

75 was considered large enough to detect single-dose

loci at high confidence levels by Wu et al. (2002).

The total length of the Co86011 and CoH70 maps

were approximately 2,606.77 cM and were distributed

over twenty-four linkage groups. The efficiency of

detecting co-segregation groups differed between

Co86011and CoH70 maps (8 vs. 16). Modern culti-

vars, like Co86011 and CoH70, contain 100–130

chromosomes and are hybrids descendants of the two

most prominent early progenitors (S. officinarum and

S. spontanium) with 10–15 % of the genome contrib-

uted by the latter. Thus for a sugarcane map, the

number of linkage groups (LGs) should be expected to

be between 100 and 130, which would be consistent

with the approximate number of chromosomes in

hybrid sugarcane cultivars. On average, a genetic map

consists of four markers per linkage group on either

parent. The low number of linkage groups in present

study is due to the low number of markers generated.

A large number of chromosomes decrease the prob-

ability of linkages among markers compared to other

species with fewer chromosomes. Sugarcane chromo-

somes are extremely small and differ considerably in

size, which may be partially responsible for the

differential distribution of markers to the different

linkage groups observed in our study. The size of

linkage groups is related to recombination, which is

due to polymorphism levels. The majority of markers

detected in this study were SDM, suggesting that the

parents were related to each other and shared the same

markers. This reduced polymorphism levels and thus

low number of markers detected. Due to insufficient

markers, a partial linkage map was constructed in our

study. One of the explanations for this outcome could

be the origin of both parental genotypes. This induced

polyploidy constitution could have given rise to many

loci at a multiplex condition (i.e. no segregation),

reducing the efficiency for detecting linkages in some

genomic regions. Chang et al. (2009) also reported 8

and 12 linkage groups, respectively for reciprocal

crosses in highly polyploidy crop like sweet potato

(Ipomoea batatas) whereas Kriegner et al. (2003)

reported 90 and 80 linkage groups, respectively.

A complete map of the sugarcane can be achieved

by mapping additional markers in the current popu-

lations and by combining the results of this study with

those of independent studies on the same population.

Further aligning genes mapped in different mapping

populations is also possible using common markers as

anchors.

The current SSRs based maps length coverage

(2,606.77 cM) were less than that of the map length

reported by Hoarau et al. (2001), Aitken et al. (2005)

and Andru et al. (2011). Although our maps are far

from saturated, they have enabled us to initiate a

pedigree study on the male parents of the mapping

population. Co86011 has been involved in the ancestry

of many elite parents. Another possible explanation

for the small genome coverage in our map is that this

genetic map is from a cross between two commercial

cultivars, which are interspecific hybrids, with a very

348 Euphytica (2013) 191:333–353

123

complex genetic system. The number of unlinked

markers was smaller than the numbers obtained in the

previous genetic maps of such bi-parental mapping

populations (Reffay et al. 2005; Garcia et al. 2006;

Oliveira et al. 2007). Similar levels of distorted/

unlinked markers have been reported in other sugar-

cane mapping studies using progeny sizes of 200

(Hoarau et al. 2001; Aitken et al. 2005). However, it is

higher than that obtained in other map for 300 self

progenies of LCP 85-384 (Andru et al. 2011). High

percentages (60 %) of unlinked marker were also

reported by Alwala et al. (2008). Segregation distor-

tion may also be depending on the distance/divergence

of the two parents within regions experiencing selec-

tion (Woram et al. 2004).

Mapping population of 207 F1 segregating individ-

uals at three locations and 2 years, considered large

enough to detect QTL associated with the quantitative

traits of interest. Despite varietal selection of sugar-

cane based on quantitative traits is usually done with

measurements taken from series of field trials in

multiple locations and crop years, fitting alternative

variance–covariance structures for studying genetic

effect across locations and crop years is rarely pursued

(Smith et al. 2007; Pastina et al. 2012). An increase in

population size provides gains in statistical power,

estimates of gene effects and confidence intervals of

the locations of QTLs (Beavis 1998). Jiang and Zeng

(1995) had reported that mapping QTL and testing

QTL 9 environment interaction, when n1 = n2 = n

and n is large, the test statistic under this analysis have

more statistical power. Asins (2002) also reported that a

way of improving the power and accuracy in detection

of true QTLs is by increasing population sizes or by

multiplying the number of environments in which the

population is evaluated.

QTL-marker associations are of great interest to

breeders as they may be useful for molecular breeding

applications. None of these traits are simply inherited

and many genes are expected to control each of them.

Although, other approaches such as interval mapping

(Lander and Botstein 1994) and composite interval

mapping (Zeng 1994), have more power than single-

marker analysis (Liu 1998) to detect QTLs. The main

advantage of CIM is that it is more precise and effective

at mapping QTLs compared to single-point analysis

and interval mapping, especially when linked QTLs

are involved. The results from CIM-QTL method are

usually comparable to those obtained from multi QTL

analysis (Aljanabi et al. 2007). Nevertheless, a dense

map is necessary to better appreciate the number of loci

governing quantitative traits. The common crop-year

markers with positive effects could be regarded as

strong marker-QTL associations and could potentially

be useful for MAS. In a clonally propagated crop like

sugarcane once a strong marker-QTL association is

detected in a progeny population, it has an immediate

role in crop improvement via clonal propagation as

there is no further probability of cross-over between

the marker and the QTL.

The large differences in parental phenotypic values

observed of the segregating population provided the

basis for QTL mapping. Our population involves elite

sugarcane commercial cultivars which are likely to

have been enriched for superior alleles (Co86011) are

controlling high sugar content during various cycles of

breeding and selection, resulting in an enhanced allele

contrast between loci underlining the sugar traits. This

may be responsible for the transgressive segregation

observed for these traits and highlights the importance

of implementing marker-assisted selection in sugar-

cane improvement program. A bi-parental mapping

seems more suitable than a selfed population when

QTL detection is the ultimate objectives, especially if

the two parents have highly contrasting phenotypes for

the traits of interest (Raboin et al. 2006). The first QTLs

mapped for sugar yield and related traits in sugarcane

were reported using segregating populations derived

from two interspecific crosses (Ming et al. 2001, 2002).

Recently, QTLs have been detected for basic sucrose

content and yield contributing traits viz; sucrose

content, fibre percent, cane yield, sugar yield and

suckering using biparental crosses (Da Silva and

Bressiani 2005; Pinto et al. 2010; Pastina et al. 2012).

The majority of the QTLs affected more than one trait;

since all the sugar-related traits are highly correlated.

In all sugar-related traits studied, both positive and

negative effects were detected. These findings are

similar and in the same range as other studies that

detected QTAs in sugarcane (Hoarau et al. 2002; Ming

et al. 2002). Out of 17 QTLs identified for sugar

related traits, 70.5 % of QTLs were stable across all

crop year-location combinations; corroborating the

speculated fact raised by many breeders that sucrose %

in juice has reached the plateau of adaptability and

stability. Stability of QTLs across environments were

inferred based on their genotype by environment

interaction effect for Pol % in cane (Pastina et al.

Euphytica (2013) 191:333–353 349

123

2012). Some QTLs of different traits were identified in

common linkage groups or associated with common

markers. B, SUC, SL and INT had QTL mapped on LG

2 and associated with markers: UGSM31548 and

UGSM31649. As all the QTLs were close by, it is

possible that they are pleiotropic QTLs. In breeding

programs, special attention should be given to these

QTLs when simultaneous improvement is aimed for

sugar and yield-related traits.

The detection of significant associations between

markers in the same genomic region of sugarcane

commercial cultivar Co86011 provided independent

confirmation of the importance of this genomic region

for increasing sugar content. In addition, based on

phenotypic segregation data and QTL analysis, this

genomic region probably contains putative major

genes that control sugar accumulation/high sugar

content. Despite the significant association between

two SSR markers-determined using single-factor

ANOVA and sugar content on LG 2, all SSR markers

UGSM31548 and UGSM31649, were significant by the

CIM method on this linkage group. The CIM method

allowed more than one QTL to be mapped on the same

chromosome (Zeng 1994) if they are relatively far

apart. However, as both significant markers fell within

a distance of 6.1–9.8 cM on LG 2, the CIM peak

detecting a QTL on the marker with the highest

significance level. A validation study will have to use

these markers to confirm. The co-location of QTL

could be due to genes in these genomic locations that

affect a number of traits (pleiotropy). Alternatively,

these genomic regions may contain several genes,

each of which affects a different trait and the

co-location of QTL for different traits is simply a

result of linkage. Combined across both locations and

crop-years, the QTL detected on similar region were

consistent for B, SUC, SL and INT respectively.

The consistency of marker-QTL associations in

different populations across different locations and

years is the key to successful marker-assisted selection

(MAS) breeding. In this study, ten QTLs mapped for

sugar content, including two for SB, five for B and

three for SUC were consistent in 2006, 2007 and 2008.

However, the previous studies showed low numbers of

common QTLs across the crop-years which may be

due to genotype-year interactions as noticed for most

quantitative traits in sugarcane (Kang et al. 1987;

Jackson and Hogarth 1992). Nevertheless, the effects

of these common markers across crop-years were

remarkably in the same direction. Earlier sugarcane

studies have also reported consistency of QTL effects

for brix and sugar content (Pol) across location and

crop years (Hoarau et al. 2002; Aitken et al. 2006;

Pastina et al.2012).

Conclusions

Three hundred thirty-six (336) polymorphic marker

loci were mapped on 24 linkage groups (LGs),

spanning a total map distance of 2606.77 cM with

an average distance of 7.758 cM between adjacent

markers. As an increase in stalk sugar content is an

important objective in sugarcane breeding, the robust

co-locating sugar QTLs (qB2, qSUC2,) and yield-

related QTL (qSL2, qINT2, qSN2, qSD2) identified in

this study using single-dose markers. These QTLs

were stable across three locations and two crop years.

The association between EST-SSRs and sugar con-

tent-related traits with 207 segregating individuals in

seven environments to the same linkage group

confirms the role of these linkages in the trait

expression. Most linked markers were unique to a

specific parent and trait, and may be useful in crop

improvement programs employing MAS. MAS for

sugar-yield related traits can be efficiently conducted

by selecting individual that contain QTL-linked

markers, which would thus facilitate conventional

breeding using either CoH70 or Co86011 as a donor

parent. Future studies are planned to include more

markers to obtained higher genome coverage.

Acknowledgments The authors want to thank the anonymous

reviewers for their valuable suggestions. We are also thankful to

Department of Biotechnology (DBT), Government of India for

funding this research project. We acknowledge the help of

Mr. Sudhir Pratap Singh, Miss. Parul Singh, Pradeep Kumar and

Miss. Nidhi Subhanand in field and laboratory work.

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