Comparative genetics in sugarcane enables structured mapenhancement and validation of marker-trait associations

Nathalie Piperidis Æ Phillip A. Jackson Æ Angelique D’Hont Æ Pascale Besse ÆJean-Yves Hoarau Æ Brigitte Courtois Æ Karen S. Aitken Æ C. Lynne McIntyre

Received: 17 May 2006 / Accepted: 11 July 2007 / Published online: 21 September 2007

� Springer Science+Business Media B.V. 2007

Abstract As sugarcane is a complex polyaneup-

loid with many chromosomes, large numbers of

markers are required to generate genetic maps with

reasonable levels of genome coverage. Compara-

tive mapping was investigated as an approach for

both quantitative trait loci (QTL) validation and

genetic map enhancement in sugarcane. More than

1000 SSR and AFLP markers were scored in a bi-

parental Australian sugarcane population (Q3) that

was segregating widely for sugar content-related

traits. Two maps were constructed, one for each

parent. The Q117 (female) and MQ77-340 (male)

maps each contained almost 400 markers distrib-

uted onto approximately 100 linkage groups (LGs),

of which nearly half could be assigned to homol-

ogy groups (HGs) on the basis of SSRs. Then,

using common SSR and AFLP markers, the two

Q3 parental maps were aligned with the maps of

the French cultivar, R570, and of the Australian

cultivar, Q165A (A denotes variety covered by

Australian plant breeding rights). As a result of

comparative mapping, all ten HGs in the Q117

map, and all eleven HGs in the MQ77-340 map

could be re-assigned to seven of the expected eight

sugarcane HGs, revealing that one sugarcane HG

was not covered at all in either Q3 parental map,

and that other HGs were poorly represented. QTL

analysis in the Q3 population identified approxi-

mately 75 marker-trait associations (MTAs) from

approximately 18 chromosomal regions or putative

QTL in each map for three sugar content-related

traits. QTL location appeared to be consistent

between the 4 maps; two of the eight HGs were

observed to contain MTAs for brix in two or three

maps, strongly suggesting the location of sugar

content-related trait loci in these HGs.

Electronic supplementary material The online version ofthis article (doi:10.1007/s11032-007-9124-8) contains supple-mentary material, which is available to authorized users.

N. Piperidis � K. S. Aitken � C. L. McIntyre

CSIRO Plant Industry, Queensland Bioscience Precinct,

306 Carmody Rd., St Lucia, QLD 4067, Australia

N. Piperidis � P. Besse

UMR 53, Universite de la Reunion – CIRAD, St Denis,

Reunion, France

Present Address:N. Piperidis (&)

BSES Ltd., Mackay, QLD 4741, Australia

e-mail: npiperidis@bses.org.au

P. A. Jackson

Davies Laboratory, CSIRO Plant Industry, Townsville,

QLD 4814, Australia

A. D’Hont � B. Courtois

CIRAD, UMR 1098, DAP, TA 40/03, Avenue Agropolis,

34398 Montpellier Cedex 5, France

J.-Y. Hoarau

CIRAD, Station de Roujol, 97170 Petit-bourg,

Guadeloupe, France

123

Mol Breeding (2008) 21:233–247

DOI 10.1007/s11032-007-9124-8

Keywords Saccharum spp. � Comparative

mapping � QTL � Sugar � Sucrose � SSR and AFLP

Introduction

Modern sugarcane cultivars have a complex poly-

ploid, aneuploid genome constitution derived from

introgression between S. officinarum (2n = 80), the

major sucrose contributor, and S. spontaneum

(2n = 40–128), a wild species. Chromosome numbers

in sugarcane cultivars range between 100 and 130

with an estimated 80–85% derived from S. officina-

rum, 10–15% derived from S. spontaneum and 5–

15% recombinant chromosomes between the two

species (Piperidis and D’Hont 2001). The basic

chromosome numbers in the two ancestral species

are also different, with x = 10 in S. officinarum and

x = 8 in S. spontaneum (D’Hont et al. 1998).

Molecular markers have been used to develop

genetic maps in crops to improve our understanding

of genome structure, to localise genes of agronomic

importance, and to identify quantitative trait loci

(QTL) associated with such agronomic traits in order

to facilitate marker-assisted selection. This approach

is also being investigated in sugarcane with major

targets being sugar content-related traits and disease

resistance. However, the high level of polyploidy

combined with the irregular and high numbers of

chromosomes in cultivars make sugarcane one of the

most difficult crops for genome mapping. A further

frustration arises from the observation that only a

subset of polymorphic markers can be used for

mapping in sugarcane. As there are an estimated 8–14

copies of homologous/homoeologous chromosomes

in sugarcane, conventional population sizes of

approximately 300 progeny allow only simplex

(markers present in single dose in one parent only)

or duplex (two copies or double dose) markers to be

mapped (Wu et al. 1992). The inheritance of poly-

morphic markers present in triplex or higher dosage

cannot be accurately determined and therefore such

markers cannot be used for map construction. Sim-

plex markers have been generated with a wide range

of marker types, such as Restriction Fragment Length

Polymorphism (RFLPs) (Grivet et al. 1996; Ming

et al. 2001), Randomly Amplified Polymorphic DNA

(RAPDs) (Da Silva et al. 1993, 1995; Mudge et al.

1996), Radio-labelled Amplified Fragments (RAFs)

(Jordan et al. 2004; Aitken et al. 2005), Amplified

Fragment Length Polymorphisms (AFLPs) (Hoarau

et al. 2002; Rossi et al. 2003; Aitken et al. 2005;

Reffay et al. 2005), Simple Sequence Repeats (SSRs)

(Rossi et al. 2003; Aitken et al. 2005; McIntyre et al.

2005; Reffay et al. 2005) and, more recently, Single

Nucleotide Polymorphisms (SNPs) (McIntyre et al.

2006).

The AFLP technique is widely used in sugarcane

mapping as it provides a large number of markers per

primer pair and large numbers of markers are

essential in sugarcane to provide adequate genome

coverage of the 100–130 chromosomes. SSRs are

also frequently used in sugarcane mapping. Like

RFLPs, they detect alleles at homo(eo)logous loci on

all chromosomes within a homology group. Single

dose RFLP or SSR markers that are mapped in

sugarcane can be used to assign the large number of

linkage groups to the expected eight homology

groups (HGs) (x = 8 in S. spontaneum, D’Hont et al.

1996).

In sugarcane, the largest maps contain predomi-

nantly SSR and AFLP markers. The map of French

cultivar R570 developed by Hoarau et al. (2001) and

amended by Rossi et al. (2003) contains 1169

markers distributed onto 128 LGs that could be

assigned to 7 of the expected 8 HGs. The map of

Australian cultivar Q165A (Aitken et al. 2005) con-

tains 968 markers distributed onto 136 LGs that

could be assigned to 8 HGs. On the basis of common

SSR markers, 6 HGs can be tentatively aligned

between the two maps.

Despite the large number of markers mapped in

R570 and Q165A, both maps are estimated to cover

less than 50% of the sugarcane genome. This is due to

the low number of markers mapped relative to the

large number of chromosomes in sugarcane and

raises the problem of genome coverage efficiency in

sugarcane. Deployment of a similar number of

markers for mapping in species such as sorghum

(Menz et al. 2002) or wheat (Chalmers et al. 2001)

provide excellent genome coverage, due to the

smaller number of chromosomes and genome size.

Synteny has been demonstrated between members

of the Poaceae family, allowing alignment of the

genetic maps of rice, sorghum, sugarcane, wheat, and

maize (Moore et al. 1995; Devos and Gale 1997).

Using RFLPs, several groups have demonstrated the

high level of synteny between sugarcane and other

234 Mol Breeding (2008) 21:233–247

123

diploid members of the Andropogonae tribe of the

Poaceae family such as sorghum and maize (Hulbert

et al. 1990; Grivet et al. 1994; Dufour et al. 1996;

Guimaraes et al. 1997; Ming et al. 2001, 2002;

McIntyre et al. 2005). Ming et al. (2002) constructed

a consensus genetic map in Saccharum from their 4

fragmentary sugarcane maps using a sorghum map as

a template. While each map only covered approxi-

mately 39.5–46% of the Saccharum genome, the

consensus map was estimated to cover 70% of the

genome. Asnaghi et al. (2000) used the synteny

between sorghum and sugarcane to identify new

markers for fine mapping of a major sugarcane rust

resistance gene.

The purpose of our study was to investigate the

feasibility and usefulness of comparative mapping in

sugarcane by (i) comparing the map coverage of

parents of an Australian cross, Q3, with maps of the

French cultivar R570 (Hoarau et al. 2001; Rossi et al.

2003), and the Australian cultivar, Q165A, (Aitken

et al. 2005), using common SSR and AFLP marker,

and (ii) validating QTLs associated with sugar

content from the three populations through the

identification of common QTLs.

Material and methods

Plant material

Phenotypic and genotypic data for the Q3 population

Mapping analyses were conducted in the Q3 population,

which consists of 232 I1 progeny (I1 progeny result

from a cross between two non-inbred, heterozygous at

many loci, parents) resulting from a bi-parental cross

between Q117 as the female parent and MQ77-340 as

the male parent. Q117 is an Australian cultivar bred by

BSES Ltd. MQ77-340 is an elite sugarcane parental line

developed by CSR Ltd. The mapping population was

planted in July 1998 at Ayr in the North Queensland of

Australia and replanted in July 2001. Both fields are

located at approximately 147.4�E, 19.5�S. The trial

design in both experiments was a randomised complete

block design with 4 replicates.

Genotypic information was collected as described

in Reffay et al. (2005). DNA was extracted from

freeze-dried material following the CTAB-method

described by Hoisington (1992). Amplified fragment

length polymorphism (AFLP) was conducted accord-

ing to Vos et al. (1995) with some modifications

already described in Reffay et al. (2005) while the

SSR analysis were conducted according to Rossi

et al. (2003).

The SSR markers were identified by mSSCIR (for

markers, Saccharum spp. and CIRAD) followed by

the microsatellite number and a band number, while

the polymorphic AFLP markers were identified by six

letters representing the EcoRI and the MseI primers

followed by a number or a capital letter in order to

identify the band.

Due to the high level of polyploidy, only markers

likely to be simplex (or single dose) were scored;

higher dosage markers were ignored. In the absence

of segregation distortion, single dose markers are

expected to segregate 1:1 if present in one parent and

absent in the other, or 3:1 if present in both parents

(bi-parental simplex). A chi-squared test was per-

formed at p = 5% in order to exclude duplex or

multiplex markers. Bi-parental simplex markers were

given an ‘x’ as a prefix to the marker name.

Linkage analysis of the simplex markers was

conducted using MAPMAKER 3.0 (Lander et al.

1987). The Q117 and MQ77-340 maps were con-

structed separately using the 1:1 markers first. The

3:1 markers were then analysed with the same

program after being integrated into the 1:1 marker

matrix. Two point analyses were performed at a LOD

score threshold of 5 and a recombination fraction of

0.35. Linked markers formed LG and the markers

were ordered by multipoint analysis. Distances in cM

were determined with Haldane’s mapping function.

The LG of the Q117 and MQ77-340 maps were

assigned to HG on the basis of at least one common

SSR marker and/or two AFLP markers in common, as

determined by band size on polyacrylamide gels. The

present paper introduces the Q117 map (Supplemen-

tary Figure 1) of the Q3 population; the male map

(MQ77-340) was published in Reffay et al. (2005).

All gel analyses of SSR and AFLP markers

included a lane of R570 to enable co-migrating

markers to be identified between the Q3 and R570

populations. Markers generated by these common

primer pairs were used to compare the MQ77-340

and Q117 maps with the much larger map of R570

and to re-assign Q3 HGs so that they were consistent

with R570 HG nomenclature (the R570 HGs origi-

nally annotated in Hoarau et al. (2001) are indicated

Mol Breeding (2008) 21:233–247 235

123

by roman numerals inside square brackets). The

nomenclature of final Q3 HG followed the HG

nomenclature used in R570 (Hoarau et al. 2001;

Rossi et al. 2003) because the map of R570 was

published first. For SSR markers, a Q117 or MQ77-

340 HG was assigned the same R570 HG number

when SSR alleles were present on at least one LG in

the HG. For AFLP markers, the assignment was

based on either two co-migrating AFLP markers in

MQ77-340 or Q117 that were also present in R570 or

on the basis of one co-migrating AFLP marker only if

the HG was already assigned because it contained a

common SSR marker.

Phenotypic information was collected for three sugar

content-related traits, Brix (a measure of the soluble

solids in sugarcane juice), Pol (the polarisation index of

the primary juice which accounts for the optically active

component and is therefore essentially a measure of

sucrose) and CCS (commercial cane sugar), as

described in Reffay et al. (2005), from both early

(2000, 9 month old plant cane, and 2001, 12 month

ratoon plant cane) and late (2002, 9 month old plant

cane) harvested material. All three are estimates of

sucrose content and are routinely used as selection aids

in Australian sugarcane breeding programs.

Phenotypic and genotypic data for the R570

population

The two Q3 population parental maps were compared

to the map obtained for the ‘R570’ population devel-

oped and analysed by Hoarau et al. (2001) and Rossi

et al. (2003), using the criteria defined above. The

R570 map was constructed from approximately 1200

AFLP, SSR and RFLP markers scored on a selfed

population from R570. Phenotypic information (Brix)

was collected as described in Hoarau et al. (2002).

MTA analysis of the Q3 and R570 populations

In this paper, the term MTA is used rather than QTL.

This is because the sparseness of the two Q3 parental

maps makes it difficult to determine whether linked

markers that are associated with a trait are detecting

the same or different QTL. The term ‘‘chromosomal’’

region is used to describe a cluster of two or more

loosely linked markers and may equate to a QTL.

A single factor analysis was conducted using Map

Manager QTXb16 (Meer et al. 2002) to determine

associations between markers and traits of the 232

progeny of the Q3 and 112 progeny from the R570

population. Interval mapping was conducted using

QTL-Cartographer version 2.0 but gave very similar

results to those obtained by single factor analysis.

Thus only the single factor analysis results are

presented. All MTAs were reported at P \ 0.01 in

the Q3 and R570 populations to enable a broader

comparison of MTA locations between the popula-

tions. At this low stringency, some of the MTAs are

likely to be false but, given the complexity of the

sugarcane genome which enables only a subset of

marker alleles, the simplex alleles, to be mapped,

weakly associated MTAs may be homo(eo)logous to

more stringently associated loci that are not tagged by

simplex marker alleles.

Mapping and QTL analysis in Q165A

The two Q3 population parental maps and QTL data

were compared to the map and QTL data obtained for

the cultivar Q165A (Aitken et al. 2005) to identify

SSRs mapped in all three maps and SSRs detecting

MTAs in the three maps. Q117, the female parent of

the Q3 population is also a parent of Q165A. The

Q165A map was constructed from approximately

1000 SSR, AFLP and RAF markers scored on an I1

population developed from a cross between IJ76-514

and Q165A (Aitken et al. 2005). Brix, Pol and CCS

were measured on the I1 population and QTL

analysis undertaken, as described in Aitken et al.

(2006). In this paper (Aitken et al. 2006), markers

were reported when associated with a trait at

P \ 0.005 in at least one year, or at P \ 0.01 with

two year data.

Results

Map construction in the Q3 population

A map was constructed for each parent in the Q3

population after genotyping the 232 progeny with a

set of 28 AFLP primer pairs and 33 SSR primer pairs,

as previously reported in Reffay et al. (2005). The

MQ77-340 map contains 400 markers, comprising

342 simplex (1:1) markers and 58 bi-parental simplex

236 Mol Breeding (2008) 21:233–247

123

(3:1) markers distributed onto 101 linkage groups

(LGs) (Reffay et al. 2005). Fifty-two of the LGs were

assigned to 13 HGs initially on the basis of shared

SSR loci and then into 11 HGs after comparison with

the female Q117 map (Table 1, Supplementary

Figure 1). The length of the LGs in the MQ77-340

map varied from 1.4 to 159 cM with a total map

length of 3582 cM.

The female map (Q117) contains 376 markers,

comprising 334 simplex and 42 bi-parental simplex

markers, distributed onto 93 LGs (Supplementary

Figure 1). Thirty-eight of the LGs were assigned on

the basis of shared SSR loci into 12 HGs initially, and

into 10 HGs after comparison with the male MQ77-

340 map (Table 1, Reffay et al. 2005). LGs were

assigned to HGs on the basis of single shared loci.

The length of the Q117 LGs varied from 1.8 to

115 cM and the cumulative length of this map is

3167 cM (Supplementary Figure 1).

Comparison of the maps of R570, Q117 and

MQ77-340 using common SSR and AFLP

markers

The genetic map of R570 contains a total of 1169

markers (887 AFLP, 148 RGA and 134 SSR markers)

(Rossi et al. 2003) distributed onto 128 LGs for a

combined length of 5850 cM. The 887 AFLP and 134

SSR markers mapped in R570 were generated using 37

AFLP primer pairs and 55 SSR primer pairs (Hoarau

et al. 2001; Rossi et al. 2003). Sixty-six of the 128 LGs

have been assigned to 7 HGs on the basis of at least two

RFLP (RGA) or SSR markers in common, while 62

LGs remain unassigned (Rossi et al. 2003).

Thirty-one SSR and 18 AFLP primer pairs were

used to genotype progeny in both the Q3 and R570

populations. Using SSRs, HG II of Q3, which

contains mSSCIR14, 27, 52 and 53 was re-named

HG [I], while HGs IV, VI and IX of Q3 were

combined into one HG and re-named HG [II] on the

basis of mSSCIR 34, 35, 39 and 48 which are present

in R570 HG [II]. HG V and X of MQ77-340 and HG

V and VIII of Q117 were also combined in each map

and renamed HG [VII] (Table 1).

The 18 common AFLP primer pairs generated

many markers but only 40 were single dose AFLP

bands of identical size between R570, MQ77-340

and/or Q117. Of the 40 putatively common AFLP

markers, 14 (five were common between R570 and

Q117, eight between R570 and MQ77-340 and one

was common between R570 and a bi-parental sim-

plex marker) could be used to confirm the assignment

of a LG to an HG (Table 1), using the rules of

assignment defined above. Five putatively common

AFLP markers mapped to LGs already assigned to an

HG on the basis of an SSR marker, but the HG

assignment of the AFLP and SSR markers differed

between the Q3 and R570 maps (data not shown); the

remaining 21 putatively common AFLP markers

could not be assigned to any HGs as they were

located on unassigned LGs (data not shown).

The use of common SSR and AFLP markers also

enabled several previously unassigned LGs from both

MQ77-340 and Q117 maps to be assigned to HGs

(Table 1). An example of the re-assigning and re-

naming of MQ77-340 and Q117 LGs and HGs after

comparison with the larger map of R570 is shown in

Fig. 1.

Only one SSR mapped to different HGs in the two

populations, mSSCIR 19 (Table 1). LG 50 which

contains mSSCIR19 and mSSCIR46 was assigned to

former HG III in the MQ77-340 map. This MQ77-

340 HG equates to HG [VIII] of R570, because both

HG contain SSRs mSSCIR26 and mSSCIR46. How-

ever, mSSCIR 19 was mapped to HG [I] of R570 as it

was linked to mSSCIR 14, mSSCIR 27, mSSCIR 42,

mSSCIR52, and mSSCIR 53 in this population.

After comparison with the R570 map using common

SSR and AFLP markers, a total of 90 LGs (46%) out of

194 (52 of the 101 and 38 of the 93 LGs of MQ77-340

and Q117, respectively), have now been assigned to

HGs (Table 1). Ten of the 11 MQ77-340 HGs and all

10 Q117 HGs have been assigned to 7 HGs and

re-named to be consistent with the HG nomenclature

used in R570 (Table 1). MQ77-340 HG VII could not

be assigned with any R570 HG as this MQ77-340 HG

contains mSSCIR16 and no common AFLP markers,

and mSSCIR16 has not been mapped in R570.

Comparison of the maps of Q165A, Q117 and

MQ77-340 using SSR markers

After initially comparing the Q117 and MQ77-340

maps with the map of R570, a further comparison

was undertaken with the recently published Q165A

map. The genetic map of Q165A contains a total of

Mol Breeding (2008) 21:233–247 237

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Mol Breeding (2008) 21:233–247 239

123

967 markers generated from 40 AFLP primer pairs

and 72 SSR primer pairs. These markers are distrib-

uted onto 116 LGs, and 96 of the 116 LGs have been

assigned to 8 HGs (Aitken et al. 2005).

Thirty-three SSRs primer pairs were used to

generate markers in the R570 and Q165A maps.

Using the assignment of linked SSRs to HGs in the

two maps, six of the HGs can be tentatively identified

as the same HG (Table 1). Of the 33 SSRs, five have

been mapped to LGs which are assigned to different

HGs (Table 1). To confirm the assignment of the Q3

population parental maps, the Q117 and MQ77-340

maps were also compared with the map of Q165A.

Twenty-nine SSR primers pairs were used to geno-

type both the Q165A and Q3 populations. Of these 29

SSRs, all but three were also mapped in R570. This

comparison confirmed the assignment of Q3 LGs to

HGs and enabled assignment of the previously

unassignment MQ77-340 HG to Q165A HG 7.

After comparing the Q3 maps with the maps of

R570 and Q165A, a total of 90 out of 194 LGs have

now been allocated to seven of the eight HGs of

sugarcane. The eighth HG of Q165A contains only

one single-locus SSR that has not been mapped in

either the R570 or Q3 populations.

MTA analysis of the Q3 population

Marker-trait associations (MTA) were investigated

for the three sugar content-related traits for both the

early harvest (1999 and 2000) and late harvest (2002)

data. Tables 2 and 3, and Supplementary Tables 1

and 2, summarise the MTA detected for simplex

markers derived from Q117 and MQ77-340, respec-

tively, including the direction and the size of the

marker effect. Table 4 and Supplementary Table 3

summarise MTAs for the bi-parental simplex markers.

MTAs with both positive and negative effects on the

traits were observed (Tables 2–4). Individual marker

effects were small and explained from 3 to 7% of the

phenotypic variation (Supplementary Tables 1–3).

For the two early harvest measurements, 14, 13 and

15 MTAs, detected by 14 simplex markers (12 AFLP

and 2 SSR), were identified at P \ 0.01 for Pol, Brix,

and CCS respectively in Q117 (Table 2). Of the 14

simplex markers detecting MTAs, three independent

markers (aggcag2, aggcag3 and mSSCIR21-10) were

associated with all three traits over both years. The

MTAs detected by these three markers were amongst

the most significantly associated MTAs in Q117

(Table 2). Only one of these markers has been

Table 2 MTAs identified in Q117 for sugar content-related traits from early (1999, 2000) and late harvests (2002)

POL a,b BRIX a,b CCS a,b

[R570 Q117 Q117

HG] HG LG Marker 1999 2000 2002 1999 2000 2002 1999 2000 2002

[I] II 4 aggcag29 *** *** ***

[I] X 15A mSSCIR42-4 **

[VI] I 17 aggctc1 ** **

[VII] VIII 47A aggcag2 ** *** ***** *** ** **** *** *** *****

[VII] VIII 47A actctc1 ** ***** ** *** ** *****

[VII] VIII 47A agcctc30 **** *** *****

/ / 20 acccat23 ** *** ** *** ** ** **

/ / 53 actctc4 ** ** **

/ / ULc aggcta2 **** **** ***

/ / ULc acccac6 ** *** ** ***

/ / ULc aggctc30 *** ** ***

/ / ULc mSSCIR8-3 *** ** ***

/ / ULc mSSCIR21-10 *** *** ***** *** ** ***

/ / ULc aggcag3 ***** ***** ***** ***** **** *****

/ / ULc agcctc27 *** *** **** ***

a **P £ 0.01, ***P £ 0.005, ****P £ 0.001, *****P £ 0.0005b Markers with a positive effect are shaded dark grey. Markers with a negative effect are shaded light greyc UL: unlinked; /: Unassigned to a HG

240 Mol Breeding (2008) 21:233–247

123

assigned to a HG (HG [VII]); the remainder map to an

unassigned linkage group or are unlinked markers.

Three additional markers were associated only in

2000 for all three traits, while one marker was

associated only in 1999.

In MQ77-340, 14, 12 and 14 MTAs, detected by

18 different simplex markers (16 AFLP and 2 SSR),

were identified for the same traits (i.e. Pol, Brix and

CCS) at the same significance level (P \ 0.01) for

both early harvest measurements (1999, 2000)

(Table 3). Two independent markers (acccag3 and

mSSCIR21-10) were associated with all three traits

over both years. As reported for Q117, the MTAs

detected by these markers were amongst the most

significant associations for MQ77-340. These two

markers correspond to two different chromosomal

regions (Supplementary Figure 1), and they were

assigned to HGs [I] and [V].

Nine different bi-parental simplex markers

(6 AFLP and 3 SSR) detected six, four and seven

MTAs for the three sugar content-related traits (Pol,

Brix, CCS, respectively) at P \ 0.01 for both early

harvest measurements (1999, 2000) (Table 4).

For the late harvest measurements (2002), seven,

six, and seven MTAs were detected by seven

different simplex markers (six AFLP and one SSR)

for Pol, Brix and CCS, respectively, in Q117 at

P \ 0.01 (Table 2, Supplementary Table 1). Six of

the seven markers detected associations with all three

traits. These six markers map to at least two different

chromosomal regions on two different LGs; three of

the markers are unlinked. In MQ77-340, five, six and

Table 3 MTAs identified in MQ77-340 for sugar content-related traits from early (1999, 2000) and late harvests (2002)

POL a,b a,b bBRIX CCS

[R570 HG] HG LGc Marker 1999 2000 2002 1999 2000 2002 1999 2000 2002

[II] VI 3 aaccac19 ** **

[II] VI 3 aaccac4 *** ** ***

[IV] / 12A acgcta9 ** **

[VI] I 1A acgcta2 ** ** **

[VI] I 1A aaccta14 ** ** **

[VI] I 1A agccag4 ** ** **

[VI] I 1A aagctc54 **

[VI] I 4 aagctc1 ** ***

[VI] I 27 mSSCIR10-7 *** ***

[VI] I 27 aggctc19 *** ***

[VI] I NL mSSCIR10-11 ***** *** *** *** ***** **

[VII] V 17 aaccta4 ** ** ** *** ** **

/ / 6 actctc6 *** ** ** ***

/ / 21 acccac25 **

/ / 21 aggcag5 ** *** **

/ / 40A acccta41 ** **

/ / 40A actcac34 **

/ / 40A actcac5 **

/ / 44 accctg10 ** ** ***

/ / 110 aggctt11 **

/ / UL aaccacH *** ****

/ / UL aagcac27 **

/ / UL aagctcA ** **

a **P £ 0.01, ***P £ 0.005, ****P £ 0.001, *****P £ 0.0005b Markers with a positive effect are shaded dark grey. Markers with a negative effect are shaded light greyc UL: unlinked; /: Unassigned to a HG

Mol Breeding (2008) 21:233–247 241

123

four MTAs were detected by six different simplex

AFLP markers for the three sugar content-related

traits (Pol, Brix, CCS, respectively) for the late

harvest measurement (2002) at P \ 0.01 (Table 3,

Supplementary Table 2). Four of the six markers

detected associations with all three traits. The four

markers mapped to two different LGs in two HGs,

HGs [II] and [VI]. Four different bi-parental simplex

markers detected two, three and three MTAs for the

three sugar content-related traits Pol, Brix, CCS,

respectively at P \ 0.01 for the late harvest mea-

surement (2002). Two markers detected associations

in all three traits (Table 4, Supplementary Table 3).

Only one of the two markers was assigned to the

genetic map on HG [VI].

A comparison between MTAs identified in Q117

and MQ77-340 reveals several HGs in both maps that

contain linkage groups with MTAs for the three sugar

content-related traits as well as several map-specific

MTA locations (Tables 2, 3). Only Q117 contains

MTAs detected across all years for all traits in HG

[VII] while in M77-340 MTAs in HG [VI] and [VII]

are detected for all traits for 1999 and 2000. The

MTAs that map to HG [VII] in Q117 have a

predominantly negative effect (Table 2), whereas

the MTAs that map to the same HG in MQ77-340

have a predominantly positive effect on the three

traits (Table 3).

For the late harvest measurements (2002) of the

three sugar content-related traits, the more significant

MTAs seem to have a predominately negative effect

on the traits (Tables 2–4).

A comparison between MTAs identified in Q3,

R570 and Q165A based on common SSR markers

MTAs were detected in R570 for Brix over 2 years

(1995 and 1996) using SSR markers and the results are

presented in Supplementary Table 4 and Table 5. A

total of 15 MTAs of both positive and negative effect

were detected by 11 different SSRs at P \ 0.01 with

individual MTAs explaining from 5 to 13% of the

phenotypic variation in Brix (Supplementary Table 4).

Only four of the SSRs detected MTAs in both years.

These four SSRs mapped to three different HGs, HGs

[III], [VI] and [VII] (Rossi et al. 2003) (Table 5). Two

of the three HGs contain MTAs of negative effect; only

HG [VI] contains MTAs of positive effect.

QTLs for the Q165A-derived population were

identified for the two sugar content-related traits brix

and pol, measured over two years from both an early

and mid-season harvest, by Aitken et al. (2006).

QTLs in Q165A were identified when associated at

P \ 0.005 in at least one year, or at P \ 0.01 in both

years. Using these stringent criteria, a total of 28

Table 4 MTAs detected in the Q3 population for sugar content-related traits using bi-parental simplex markers

POLa,b BRIXa,b CCSa,b

[R570 Q117 Q117 [R570 MQd MQd

HG] HG LGc HG] HG LGc Marker 1999 2000 2002 1999 2000 2002 1999 2000 2002

[VI] I 72 / / UL xmSSCIR10-2 *** ** ***

[VII] VIII 47A / / UL xmSSCIR43-4 **

/ / 85 / / 89 xacccta49 ** ** ** **

/ / 90 / / 94 xaccctg33 **

/ / UL / / UL xaggctt8 *** ** ** ***

/ / UL / / UL xagccag13 ** **** ***** ** ***

/ / UL / / UL xaagctc38 ** ***

/ / UL / / UL xactctc12 ** **

/ / UL / / UL xmSSCIR14-3 *** ***

/ / ULc / / UL xmSSCIR21-7 **

a **P £ 0.01, ***P £ 0.005, ****P £ 0.001, *****P £ 0.0005b Markers with a positive effect are shaded dark grey. Markers with a negative effect are shaded light greyc UL: Unlinked; /: Unassigned to a HGd MQ: MQ77-340

242 Mol Breeding (2008) 21:233–247

123

QTLs were detected overall of which 23 were

detected from early harvest data and 21 from mid-

season harvest data. For the early harvest data, 11

QTLs, mapping to five different HGs, were detected

in both years data, while eight QTLs, mapping to four

different HGs, were detected in both years from mid-

season harvest data (Aitken et al. 2006). Each MTA

explained between 3 and 9 % of the phenotypic

variation. Ten of the 28 QTLs were detected by nine

SSRs of which four had also been mapped in R570

and/or Q3 (mSSCIR SSRs). These four SSRs mapped

to the equivalent of R570 HGs [II], [III] and [VII].

Three of the four SSRs detected QTLs in the early-

season or in the mid-season harvest data from Q165A,

and two of the four detected QTLs in both data sets.

The remaining 6 SSRs, while not mapped in either

Q3 or R570, mapped to HGs that could be aligned

R570 HGs [I] and [VIII] (data not shown).

MTAs were compared between the four maps

using both common SSR primers and AFLP markers.

None of the AFLP markers common to R570 and the

Q3 population were associated with any of the traits

analysed (data not shown). No attempt was made to

compare the AFLP markers between Q3 and Q165A.

Of the 24 SSR primers used to generate markers for

all four maps, 14 generated markers involved in

MTAs with sugar content-related traits at P \ 0.01

(Table 5). Of these 14 SSRs, none were involved in

MTAs in all four maps, and only one (mSSCIR8)

detected MTAs in three of the four maps. A further

two SSRs generated markers involved in MTAs in

two of the four maps (excluding the MTA detected by

the bi-parental simplex marker as it is present in both

Q3 parental maps). Of the remaining 11 SSRs, one

generated a bi-parental simplex marker while the

other 10 SSRs generated markers that detected MTAs

in one map only; eight in R570 and two in Q117.

These 14 SSRs mapped to six different HGs

(Table 5). MTAs for sugar content-related traits

mapped to four of the six HGs in two or more maps,

with MTAs mapping to HG [VII] in all 4 maps.

MTAs mapped to HGs [II] and [III] in R570 and

Q165A only and [VIII] in R570 only.

The SSRs producing markers most significantly

associated with sugar content-related traits in R570 or

Q165A produced markers that were not associated

(mSSCIR 4, 8, 54, 57, 64, 70) in the Q3 maps. The

converse was also true. mSSCIR10 generated

Table 5 Summary of SSRs generating markers associated with MTAs for Brix in R570, Q165A and the Q3 population

Q165A Q165A

R570a,b MQ77-340a,b a,b,c a,b,c a,b

a,b a,b

Q117 Bi-parental simplex early-season mid-season

HG SSR 1995 1996 1999 2000 2002 1999 2000 2002 1999 2000 2002 2001 2002 2001 2002

[I] mSSCIR42 **

[I] mSSCIR52 **

[II] mSSCIR34d ** **[II] mSSCIR34d ***[II] mSSCIR55 **

[III] mSSCIR64 ***** ***** **** ****[III] mSSCIR70 *** **

[VI] mSSCIR10 *** *** **

[VI] mSSCIR54d **

[VI] mSSCIR54d ***

[VI] mSSCIR57 ** **

[VII] mSSCIR21 ***** ***[VII] mSSCIR36 ***[VII] mSSCIR4 **

[VII] mSSCIR8d **** ** ** ** ***[VII] mSSCIR8d ******

[VIII] mSSCIR18 **

a **P £ 0.01, ***P £ 0.005, ****P £ 0.001, *****P £ 0.0005. Markers with a positive effect are shaded dark grey. Markers with a

negative effect are shaded light greyb 1999 and 2000 = early season harvest, 2002 = late season harvestc MTAs in the different maps on the same row of the table are not necessarily detected by the same SSR alleled Some SSRs generated more than one marker associated with an MTA so more than one allele of an SSR is associated with a trait

Mol Breeding (2008) 21:233–247 243

123

markers that were associated at low P values in

MQ77-340, but not associated in R570, Q165A or

Q117. Similarly, mSSCIR21 markers were associated

at low P values in Q117 but not associated in R570,

Q165A or MQ77-340 (Table 5).

Discussion

The Q3 maps and their alignment with R570 and

Q165A

High levels of genome coverage in a polyploid such

as sugarcane are difficult to obtain. With approxi-

mately 1000–1200 markers, the R570 and Q165A

maps are estimated to cover less than one-half of the

genome (Aitken et al. 2005); in the Q3 maps, with

approximately 400 markers on each parent map, only

20% of the genome is estimated to be covered.

Several comparative mapping studies have been

undertaken in sugarcane to assist in genome coverage

or in order to obtain a better understanding of the

constitution and alignment of chromosomes between

different species within a family. D’Hont et al.

(1994) undertook the first comparison between sug-

arcane and a relative, maize. Since then, sugarcane

maps have been compared to a variety of other

species, including sorghum, maize and rice (Grivet

et al. 1994; Dufour et al. 1996; Glaszmann et al.

1997; Guimaraes et al. 1997; Ming et al. 1998).

However, no study has yet reported using a compar-

ative mapping approach between sugarcane cultivar

maps of agronomical interest to increase genome

coverage and to validate QTLs.

The major benefit of the comparison of the Q3

maps with the maps of R570 and Q165A was the

assignment and re-naming of all of the Q3 HGs using

the two larger maps as reference. This assignment of

HGs, using the HG nomenclature of R570, provided

structure to the two fragmentary Q3 parental maps,

and indicated that two HGs ([IV] and [V]) were

apparently not covered at all (HG [IV] is assigned

only on the basis of common AFLP markers). Some

of the unassigned LGs in the two maps may originate

from these two HGs but until more SSR/RFLP/AFLP

markers are mapped in the Q3 population, these HGs

are effectively untagged. It should be noted, however,

that the two HGs not represented in the Q3 maps are

two of the smallest HGs in the R570 and Q165A

maps (Rossi et al. 2003; Aitken et al. 2005) and

hence alignment with these two HGs will always be

difficult. It is possible that these two HGs contain a

low frequency of simplex markers.

Most of the HG assignments were based on single

common SSR primers and hence HG assignments are

only tentative. It would have been more rigorous to

base the HG assignments on two or more SSR

primers; two or more common SSR primers would

also have enabled LGs to be aligned and oriented

correctly. However, the relatively small number of

common SSR primers and the large number of

sugarcane LGs prevented such a rigorous assignment.

All common SSR primers were used to assign and

align LGs and HGs in Q3 with R570 and the only

discrepancy observed was mSSCIR19 which mapped

to different HGs in R570 and MQ77-340. Interest-

ingly, it mapped to the corresponding R570 HG in

Q165A, suggesting that either it is mis-assigned in

MQ77-340 or that there is a chromosomal rearrange-

ment in MQ77-340 compared to R570 and Q165A. A

simple explanation for this could be that some SSR

are present as duplicated loci and that different loci

have been mapped in MQ77-340 compared with

R570 and Q165A. Several sugarcane SSRs have been

observed to map to multiple HGs already, including

mSSCIR 40 which maps to both HG [III] and [VI] in

R570 (unmapped in Q165A), mSSCIR 42 which

maps to HGs [I] and [III](and HG 3 in Q165A =

R570 HG [VI]) and mSMC292MS which maps to

every HG but HG8 in Q165A (Rossi et al. 2003;

Aitken et al. 2005). An alternative hypothesis could be

that there are minor structural rearrangements between

the sugarcane clones. Approximately 10–15% of the

sugarcane genome consists of recombinant chromo-

somes between S. officinarum and S. spontaneum

(Piperidis and D’Hont 2001). Little is known about the

chromosomes involved in these recombination events

and it is possible that they involve chromosomes from

different HGs (Aitken et al. 2005).

To increase the number of markers available for

map alignment, we also used AFLP markers to

compare the Q3 maps with the map of R570. AFLP

markers have been used to align genetic maps in

other species including maize. Vuylsteke et al. (1999)

analysed the distribution of AFLP markers in two

maize genetic maps and found numerous AFLP

markers that mapped at the same position on the two

maps.

244 Mol Breeding (2008) 21:233–247

123

A total of 40 simplex AFLP markers were

identical in size between R570 and Q117 and/or

MQ77-340. Fourteen (35%) of them were used for

HG assignment and mapped to a LG allocated to the

same HG in the two populations. Five other AFLP

markers mapped to LGs assigned to other HGs. It is

possible that these AFLP markers were DNA frag-

ments of the same size that came from different parts

of the genome in R570 and Q117/MQ77-340. Alter-

natively, as indicated above for SSRs, they could

indicate a minor structural rearrangement between

R570 and Q117/MQ77-340. Sequencing of these

AFLP markers could help to determine whether the

markers of identical size are the same marker or not.

So while the number of AFLP markers used to align

the R570 and Q117/MQ77-340 sugarcane maps is

limited, this approach appears useful and comple-

mentary to SSR primer-based alignment.

Identification and validation of new MTAs via

comparative mapping

Numerous MTAs were detected for the three sugar

content-related traits in the Q3 population. Each

MTA explained between 3% and 8% of the pheno-

typic variation and were comparable in size to MTAs

identified by Hoarau et al. (2002) and Aitken et al.

(2006) but were inferior in magnitude to MTAs

obtained by Ming et al. (2002) (up to 15%). This

difference may be due to the different genetic

structures of the selected populations. Ming et al.

(2002) analysed sugar content-related MTAs in two

F1 populations derived from crosses between two

wild Saccharum hybrids and two S. spontaneum

clones. These populations, due to their origin, are

likely to exhibit more extensive variation for sugar

content-related traits. The Q165A, R570 and Q3

populations involve elite sugarcane material which

are likely to have been enriched for superior alleles

controlling high sugar content during cycles of

breeding and selection, resulting in a reduced allele

contrast within loci underlying the target trait. The

difference between the phenotypic variations

explained by the MTAs could also be the result of

different statistical thresholds used in the analysis.

The identification of common QTLs in more than

one population is an important validation exercise

and has been used in many species for many different

traits. In our study, none of the SSR primers

generated markers involved in MTAs in all four

maps, and only one detected MTAs in 3 of the 4

maps. While MTAs were detected in the Q3 and

R570 populations at a similar P value, a higher

stringency was used in Q165A (Aitken et al. 2006);

this would have resulted in fewer common SSRs

detecting MTAs between Q165A and the other 3

maps. Q117 is a parent of Q165A (P.A. Jackson, pers.

comm.), and would be expected to contribute

approximately half of its chromosomes to Q165A;

the LG and HG of the Q117 map aligned equally well

with both R570 and Q165A. Only one of the common

SSRs (mSSCIR 8 on HG [VII]) detected MTAs in

both Q117 and its progeny Q165A. Nevertheless the

most significant sugar-content QTL in Q117 was

associated with mSSCIR28 in the same HG[VII] and

this HG also contains a major QTL in Q165A in a

similar region (Aitken et al. 2005). Other major

sugar-content QTL in Q165A have presumably been

inherited from the second parent of Q165A.

The association between markers generated by

three of the SSRs (mSSCIR8, 34 and 64) and sugar

content-related traits in two or three maps and the

localisation of MTAs to 3 HGs ([II], [III], [VII]) in

two or more maps validate the association between

these markers and traits and provide additional

evidence for the role of these chromosomal regions

on expression of these traits. These results support

previous results that suggest that the factors involved

in sugar production are distributed throughout the

sugarcane genome and this provides essential infor-

mation for implementation of future marker-assisted

selection projects.

Also of interest from an industry perspective was the

observed association between markers generated by

one SSR and mid-to late season harvest data in both the

Q3 population and in Q165A (Aitken et al. 2006). The

validation of that MTA in the two populations suggests

that different QTLs may be involved in the production

of sucrose early and late in the season, which may

allow some varieties to be harvested with high sucrose

content earlier or later in the season as previously

suggested by Aitken et al. (2006).

Of the SSRs mapped in all four maps, those that

were associated with sugar content-related traits in

the R570 map were also associated at high signifi-

cance levels in the Q165A map but were usually not

associated at all in the Q3 population. Similarly, the

Mol Breeding (2008) 21:233–247 245

123

most significantly associated SSRs in the Q3 popu-

lation were not associated with these traits in either

the R570 or the Q165A maps. There are several

possible explanations for these observations. Firstly,

a more stringent threshold was used to detect MTAs

in the Q165A map and hence SSRs involved in MTAs

in the other three maps may not have been reported as

associated in the Q165A map if the P value was

below the cut-off. Secondly, not all alleles of an SSR

will have been incorporated into these maps as only

simplex alleles can be mapped. A SSR allele may be

a simplex marker in one parental line and mapped,

but multi-dose in another population parental line and

not mapped. The weak association or absence of an

association between SSR alleles and the traits may

mean that different chromosomes, carrying different

SSR alleles and sugar content-related trait genes,

within a homology group have been tagged in each

map. Alternatively, the SSR markers may be tagging

the same chromosomes, but the alleles at these sugar

content-related trait genes may have different effects

on the amount of each sugar content-related trait in

the different sugarcane varieties. The SSR primers

that detected strong associations in one population

but not in the other two populations suggest that

further work should target these HGs and chromo-

somal regions for markers associated with these sugar

content-related traits. These SSR primers could also

be examined for the presence of multi-dose, and

hence unmapped, markers and their potential associ-

ation with sugar content-related traits evaluated.

These results have demonstrated the value of, and

also the difficulty associated with, comparative

mapping in sugarcane. After comparison with the

larger R570 and Q165A maps, all Q3 HGs and

several unassigned LGs in the smaller Q3 population

parental maps could be tentatively assigned to HGs.

This assignment confirmed the sparse genome cov-

erage of the Q3 population parental maps. It also

identified which sugarcane HGs were poorly covered

or not covered in these maps and hence which HGs

should be targeted, and which SSRs selected, to assist

in expanding genome coverage or to find markers

associated with traits of interest on those HGs. The

association between SSRs and sugar content-related

traits in more than one population and the observed

localisation of MTAs to the same HGs in more than

one population have also assisted in validating these

MTAs and confirming the role of these chromosomal

regions in the expression of these sugar content-

related traits.

Acknowledgements We are grateful for the PhD scholarship

to NP provided by Le Conseil Regional de la Reunion in La

Reunion, for travel support to NP, PB and CLM provided by

the Embassy of France in Australia, and to the Sugar Research

and Development Corporation, Australia, for funding this

research.

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