Journal of Experimental Botany, Vol. 61, No. 13, pp. 3553–3562, 2010doi:10.1093/jxb/erq192 Advance Access publication 13 July, 2010

RESEARCH PAPER

Characterization of root-yield-1.06, a major constitutive QTLfor root and agronomic traits in maize across water regimes

Pierangelo Landi*, Silvia Giuliani, Silvio Salvi, Matteo Ferri, Roberto Tuberosa and Maria Corinna Sanguineti

Department of Agroenvironmental Sciences and Technology, University of Bologna, Viale Fanin 44, I-40127 Bologna, Italy

* To whom correspondence should be addressed: E-mail: pierangelo.landi@unibo.it

Received 23 February 2010; Revised 4 June 2010; Accepted 10 June 2010

Abstract

A previous study on maize F2:3 families derived from Lo9643Lo1016 highlighted one QTL in bin 1.06 (hereafter named

root-yield-1.06) affecting root and agronomic traits of plants grown in well-watered (WW) and water-stressed (WS)

conditions. Starting from different F4 families, two pairs of near isogenic lines (NILs) were developed at root-yield-

1.06. The objective of this study was to evaluate root-yield-1.06 effects across different water regimes, genetic

backgrounds, and inbreeding levels. The NILs per se and their crosses with Lo1016 and Lo964 were tested in 2008

and 2009 near to Bologna, with the well-watered (WW) and water-stressed (WS) treatments providing, on average,

70 mm and 35 mm of water, respectively. For NILs per se, the interactions QTL3water regime and QTL3family were

negligible in most cases; the QTL additive effects across families were significant for several traits, especially rootclump weight. For NILs crosses, analogously to NILs per se, the interactions were generally negligible and the

additive effects across water regimes and families were significant for most traits, especially grain yield. A meta-

analysis carried out considering the QTLs described in this and previous studies inferred one single locus as

responsible for the effects on roots and agronomic traits. Our results show that root-yield-1.06 has a major

constitutive effect on root traits, plant vigour and productivity across water regimes, genetic backgrounds, and

inbreeding levels. These features suggest that root-yield-1.06 is a valuable candidate for cloning the sequence

underlying its effects and for marker-assisted selection to improve yield stability in maize.

Key words: Drought, inbreeding, near-isogenic lines, plant vigour, QTL validation, root traits, Zea mays.

Introduction

Among the morpho-physiological traits that were listed by

Ludlow and Muchow (1990) as advantageous for improvingdrought resistance, root features were considered of primary

importance in three of the four scenarios identified by the

combination of either intermittent or terminal stress environ-

ments with either subsistence or modern agriculture practices.

Therefore, in view of the fundamental role played by root

architecture and size in regulating the water balance of the

plant, hence drought avoidance, root traits are an interesting

target for programmes aimed at improving drought resistance.Although the investigation of root traits has traditionally

lagged behind the study of above-ground traits especially

when measured on adult plants grown under field con-

ditions, the development of molecular marker platformsthat allow us to detect and map quantitative trait loci

(QTLs) has encouraged the genetic dissection of variability

in root features of different crops (Tuberosa et al., 2003;

de Dorlodot et al., 2007; Bohn and Grift, 2009; Courtois

et al., 2009). In maize (Zea mays L.), a number of studies

described QTLs for root features of plants grown in

controlled environments (Lebreton et al., 1995; Kaeppler

et al., 2000; Tuberosa et al., 2002; Hund et al., 2004;Zhu et al., 2006; Trachsel et al., 2009) and/or in the field

(Guingo et al., 1998; Barriere et al., 2001; Landi et al., 2002;

Abbreviations: a, additive effect of root-yield-1.06; ASI, anthesis-silking interval; d, dominance effect of root-yield-1.06; EP, number of ears per plant; FAM, families;GY, grain yield; JV, juvenile vigour; KE, number of kernels per ear; KM, kernel moisture; KW, kernel weight; L-ABA, concentration of abscisic acid in the leaf; LP,number of leaves per plant; MAS, marker-assisted selection; NILs, near-isogenic lines; PH, plant height; PS, pollen shedding; QTL, quantitative trait locus; RC, rootclump; RWC, relative water content; SD, largest stalk diameter; SG, stay green; TS, testers; WS, water-stressed; WW, well-watered; Y, years.ª The Author [2010]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved.For Permissions, please e-mail: journals.permissions@oxfordjournals.org

at Sistem

a Bibliotecario d'A

teneo - UniversitÃ

  degli Studi di B

ologna on Decem

ber 21, 2010jxb.oxfordjournals.org

Dow

nloaded from

Chen et al., 2008). The importance of these studies is

emphasized by the fact that the co-location (overlap) of

QTLs for root features with QTLs for shoot features and

yield provides clues on the interpretation of the causal

relationships among such traits (Lebreton et al., 1995;

Tuberosa et al., 2002, 2003; Hochholdinger and Tuberosa,

2009) and can assist breeders in identifying the best QTL

alleles for implementing marker-assisted selection (MAS).An important prerequisite for a successful MAS programme

aimed at improving drought resistance is the identification

of QTLs consistently able to affect crop growth and

performance, particularly yield, across different water

regimes (Collins et al., 2008). Notwithstanding the plethora

of QTLs for yield and yield components that have been

described in maize (Tuberosa and Salvi, 2009), only a hand-

ful have shown a reasonably consistent effect across soilmoisture regimes (Frova et al., 1999; LeDeaux et al., 2006),

while the majority has shown a strong interaction with

water regimes (Messmer et al., 2009). An additional

constraint to a more widespread use of MAS to enhance

drought resistance is the influence of the genetic background

on the effect of each QTL (Collins et al., 2008), an aspect

that becomes increasingly relevant as the genetic complexity

of the target trait increases, clearly the case of yield and itscomponents, particularly under drought conditions.

Previous studies conducted in the maize population

obtained from the single cross Lo9643Lo1016 highlighted

the role of a major QTL on bin 1.06 that was shown to

influence root traits of seedlings grown in hydroponics

(Tuberosa et al., 2002) and of adult plants grown in the

field (measured as vertical root-pulling resistance at flower-

ing; Landi et al., 2002). These two studies also showed thatthe same QTL region on bin 1.06 influenced grain yield

under both well-watered (WW) and water-stressed (WS)

conditions, thus leading Landi et al. (2002) to hypothesize

that, at least partly, this QTL might concurrently be able to

affect root features and grain yield. Based on these results,

the QTL will hereafter be identified as root-yield-1.06.

Additionally, these findings prompted us to develop near-

isogenic lines (NILs) for root-yield-1.06, so as to havegenetic materials suitable for studies providing a more

thorough evaluation of the QTL and, eventually, to

positionally clone the sequence harbouring the relevant

functional polymorphism responsible for the QTL effects.

NILs that differ at a single QTL allow for a more precise

and accurate assessment of the effects of a target QTL, as

they can be assumed to share in homozygosity the rest of

the genome, or at least, a large portion of it. Additionally,NILs can be crossed to tester lines, so as to evaluate the

QTL effects not only in NILs per se but also in hybrid

combination, a valuable aspect for allogamous species like

maize, whose performance can be considerably biased by

inbreeding depression (Hallauer and Miranda, 1988). In

maize, the development of NILs for drought-related QTLs

has so far been quite limited because the procedure requires

a long-lasting effort, unless NILs are produced by startingwith lines having a high homozygosity level but still

heterozygous at the target QTL (Tuinstra et al., 1997;

Yamanaka et al., 2005). In this paper, the development of

NILs for root-yield-1.06 and their evaluation at varying

water regimes, genetic backgrounds, and inbreeding levels

are reported.

Materials and methods

Plant material (NILs development)

The development of NILs at root-yield-1.06 was undertaken byusing, as base material, F3:4 families from the cross between Lo964and Lo1016 (see also Supplementary Fig. S1 at JXB online). Bymolecular marker analyses, F3:4 families including heterozygousplants at the target QTL region were identified. Within each F3:4

family, 15 plants were screened with SSR markers umc1601 andumc1709 (indicated as m1 and m2, respectively, in SupplementaryFig. S1 at JXB online) that flanked root-yield-1.06. Plantsidentified as heterozygous for both flanking markers were selectedand selfed until the F5:6 generation; in the subsequent F6:7

generation, only the two homozygotes for the parental allelecombinations were selected and selfed, thus producing the pairof NILs (as F7:8). The absence of double recombination in thechromosome segment defined by the two markers was confirmedby testing the NILs with umc1281, an SSR located within thetarget interval; thus the NILs should be homozygous at the QTLeither for the plus (+) or the minus (�) parental allele. The (+)allele is the one increasing both root and grain yield values and isprovided by Lo1016 (based on Landi et al., 2002; Tuberosa et al.,2002); conversely, the (�) allele decreasing such values is providedby Lo964. Although the NILs development was started from sevenF3:4 families, it was completed for only two of them, i.e., thefamilies indicated as no. 1 and as no. 4. According to geneticdistance based on the reference maize map ‘Genetic 2008’(www.maizegdb.org), the length of the target region concerningroot-yield-1.06 was 29 cM.Finally, the two pairs of NILs were crossed to the two parental

inbreds Lo1016 and Lo964 in order to evaluate NILs not onlyper se but also as crosses.

Molecular characterization of the two families of NILs

The two families of NILs were analysed at the molecular level inorder to investigate whether the comparison within each familybetween NIL (+/+) and NIL (�/�) could possibly be influenced bydifferent portions of the parental genomes, outside the region ofroot-yield-1.06. In particular, the four NILs and the two parentallines were analysed with public SSR markers (www.maizegdb.org)defining the 32 chromosome regions that had been shown byLandi et al. (2002) and Tuberosa et al. (2002) to harbour QTLs forroot traits and/or grain yield. The following SSR markers wereused: bnlg176 (bin 1.03), phi001 (1.03), umc1013 (1.08), bnlg1671(1.10), umc1552 (2.02), mmc0231 (2.03), umc1635 (2.05), bnlg1329(2.07), umc2085 (2.08), umc1931 (3.00), umc2048 (3.09), bnlg1265(4.05), umc1808 (4.06), umc1940 (4.09), bnlg1606 (5.00), umc1705(5.03), umc1221 (5.04), bnlg1118 (5.04), umc1014 (6.04), bnlg2249(6.05), umc1859 (6.06), umc1585 (7.02), umc1660 (7.03), idp744(7.05), bnlg162 (8.05), bnlg240 (8.06), umc1933 (8.08), umc1804(9.06), umc1293 (10.01), bnlg1451 (10.01), umc2163 (10.04), andbnlg1518 (10.07). All SSRs were analysed using agarose oracrylamide gel-electrophoresis following standard procedures.

Field experiments

The NILs per se and their crosses with the two parental inbredswere tested for two years (2008 and 2009) in Cadriano (close toBologna, Po Valley, Northern Italy; 11� 24# E, 44� 33# N) ona loam soil (clay, 18%; sand, 37%; silt, 45%). At sowing, soilmoisture (as % of soil dry matter in the 0–35 cm layer) was 20.2%and 20.5% in 2008 and 2009, respectively. Testing was made at two

3554 | Landi et al. at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

irrigation volumes. One irrigation volume, corresponding to anaverage of 70 mm of water (64 mm and 76 mm in 2008 and 2009,respectively), was provided as sprinkler irrigations in four or fivetimes from the end of stem elongation to the beginning–mid stageof grain filling (i.e. from the V15–V16 to the R2–R3 stage,according to Ritchie et al., 1997). This irrigation volume waschosen as representative of the volumes most frequently used byfarmers in the Po Valley to attain the most favourable growingconditions (well-watered, WW). Irrigation was made on the dayssubsequent to the appearance of drought stress symptoms in theWW treatment, as revealed, in particular, by leaf rolling and lossof turgor. The other volume, corresponding to an average of35 mm of water (32 mm and 38 mm of water in 2008 and 2009,respectively), was provided at the same time as the previous ones;this volume was chosen to represent a condition of moderate waterstress (WS), mimicking the local constraints frequently present infarmers’ fields. Total rainfall from sowing to harvest was 263 mmin 2008 and 196 mm in 2009. Three trials were carried in 2008 (i.e.NILs under only WS because of seed shortage; NILs crosses underboth WS and WW) and four in 2009 (i.e. NILs and their crossesunder both WS and WW). In each year the trials were randomizedin the same field, with border rows on each side used to separateone trial from the other.For each trial, the field design was a randomized complete block

with three replications. Each plot was a single row 0.90 m wideand 3.56 m long, including 20 plants at a density of 6.25 plantsm�2. No border rows were used between genotypes of the sametrial, because the differences among such genotypes were expectednot to be particularly huge, even when significant, as NILs wereoriginated from the same single cross and the NILs crosses wereobtained using the two parental inbreds; in addition, the distanceof 0.90 m between rows was deemed to be wide enough to furtherattenuate the possible interactions among genotypes for bothbelow- and above-ground characteristics. The field techniqueusually adopted for maize in the Po Valley was applied. Trialswere hand-sown at the end of April and hand-harvested at the endof September; moreover, 250 kg ha�1 of N (half before sowing andhalf after thinning) and 100 kg ha�1 of P2O5 (all before sowing)were provided. In each trial, weeds were manually removed asnecessary.Data were collected on a single-plot basis for the following

traits: (i) juvenile vigour (JV; cm), estimated as distance from theground to the tip of the uppermost leaf (at the V12–V13 stage);(ii) days to pollen shedding (PS; d), as interval between sowingdate and PS date (assessed when 50% of plants had extrudedanthers); (iii) anthesis-silking interval (ASI; d), as differencebetween silking date (when 50% of plants had extruded silks) andPS date; (iv) concentration of leaf abscisic acid (L-ABA; ng g�1

DW) and (v) relative water content (RWC; %) both measured atthe end of pollen shedding on the third leaf from the top; (vi) plantheight (PH, cm), measured after pollen shedding at the flag leafcollar; (vii) number of leaves per plant (LP; no.), by first markingthe fourth leaf (at the V5–V6 stage) and then the ninth leaf (at theV10–V11 stage); (viii) largest stalk diameter (SD; mm) measuredon the second elongated internode; (ix) stay green [SG; as visualscore for green leaf surface, from 1 (no green) to 5 (completelygreen)] at the R5–R6 stage (i.e. dent-physiological maturity stage);(x) kernel moisture at shelling (KM; %); (xi) grain yield (GY;Mg ha�1); (xii) number of ears per plant (EP; no.); (xiii) meankernel weight (KW; mg); (xiv) number of kernels per ear (KE; no.)calculated as the ratio between grain yield per plant and theproduct between EP and KW; (xv) weight of the root clump (RCweight; kg plant�1) measured 5–6 d after harvesting (stalks werecut at ca. 25 cm from the soil, then clumps were uprooted by hand-pulling and weighed). RC weight was recorded only for NILsper se; at sampling, soil moisture was 13.7% (2008 WS trial), 13.1%and 13.6% (2009 WS and WW trials, respectively). L-ABA, RWC,and SG were investigated only in the 2009 trials. JV, L-ABA,RWC, PH, SD, and RC weight were recorded on five competitive

plants per plot (non-consecutive for RC); KW was measured on asample of 200 kernels per plot, while all other traits wereinvestigated at the whole-plot level, after discarding the first andthe last plant of each row. L-ABA was measured on duplicatesamples of a bulk of five leaves per plot; an ABA-specificmonoclonal antibody (Quarrie et al., 1988) was used as describedin Tuberosa et al. (1994). RWC was measured on the same leafsample used for measuring L-ABA; leaf discs were weighed andthen immersed in deionized water overnight at 4 �C to regainturgidity and finally weighed and dried. RWC was computed as(fresh weight–dry weight)/(turgid weight–dry weight)3100. BothGY and KW were adjusted to 15.5% moisture.

Statistical analyses

The analysis of variance (ANOVA) was first conducted withineach trial, then combined across trials for NILs per se as well asfor NILs crosses. As to NILs per se in the single trial (Table 1), the3 degrees of freedom (df) for NILs were partitioned into thefollowing sources with 1 df each: between families (FAM), betweenNILs (+/+) and (�/�) across families (estimating the variation dueto twice the QTL additive effect), and the corresponding FAM 3

[(+/+) versus (�/�)], estimating the interaction between theadditive effect and the genetic background. In the combinedANOVA across the three trials, the interaction trials (environ-ments) 3 NILs and its components were also investigated.As to the ANOVA for NILs crosses (Table 1), the 3 df for trials

were partitioned into the following sources with 1 df each: betweenyears (Y), between irrigation treatments (WW versus WS) and thecorresponding interaction. The 7 df for crosses were partitionedinto among NILs (3 df), between testers (TS, 1 df), and thecorresponding interaction; then, the among NILs was partitionedinto three components with 1 df each: between families (FAM),between NILs (+/+) and (�/�) across families, and the corre-sponding interaction. Two sources of variation were of greaterimportance, i.e. the between NIL (+/+) and (�/�) estimating thevariation due to the average effect of the QTL allele substitutionand the interaction TS3[(+/+) versus (�/�)] estimating the

Table 1. Main sources of variation in the ANOVA of trials

concerning NILs per se and NILs crosses

Main sources of variation Degrees of freedom

NILs per se

Trials 2

NILs 3

Families (FAM) 1

(+/+) versus (�/�) 1

FAM3[(+/+) versus (�/�)] 1

Trials3NILs 6

NILs crosses

Trials 3

Years (Y) 1

WW versus WS 1

Y3(WW versus WS) 1

Crosses 7

NILs 3

FAM 1

(+/+) versus (�/�) 1

FAM3[(+/+) versus (�/�)] 1

Testers (TS) 1

NILs3(TS) 3a

Trials3Crosses 21

a The partitioning of the 3 df is not presented, being the same asthat of NILs.

Root-yield-1.06, a major constitutive QTL for maize performance | 3555 at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

variation due to the QTL dominance effect. In particular, theaverage effect of the QTL allele substitution corresponds toa+d3(q�p) (Falconer and Mackay, 1996), where a and d are theadditive and dominance effects, respectively, and p and q are theaverage allelic frequencies over the two related testers. Becauseboth average allelic frequencies are equal to 0.5 ( p being equal to 1for one tester and equal to 0 for the other and vice versa for q), itfollows that the average effect of the QTL allele substitutioncorresponds to a. In the combined ANOVA across trials, theinteraction trials3crosses (21 df) and its components wereinvestigated.With respect to the calculations of the a effect of root-yield-1.06,

different procedures were followed for the NILs per se and for theNILs crosses. For the NILs per se, a was calculated as the halveddifference between NIL (+/+) and NIL (�/�). For the NILscrosses, a was calculated as the difference between the mean value[Lo10163NIL(+/+)+Lo9643NIL(+/+)]/2 and the mean value[Lo10163NIL(�/�)+Lo9643NIL(�/�)]/2. The d effect was calcu-lated as the difference between the mean value [Lo10163NIL(�/�)+Lo9643NIL(+/+)]/2 and the mean value [Lo10163NIL(+/+)+Lo9643NIL(�/�)]/2. To have an estimate of the relativeimportance of the d effect, the d/a ratio was calculated.

QTL meta-analysis

Meta-analysis was applied to test whether the single QTLs fordifferent traits identified on the Lo9643Lo1016 population grownin different environments corresponded to the same meta-QTL(Goffinet and Gerber, 2000; Khowaja et al., 2009). For thispurpose, information was collected on the peak position, support-ing interval, nearest molecular markers, proportion of phenotypicvariance explained, and population size for QTLs mapped at ornear bin 1.06, based on our previous studies (Landi et al., 2002;Tuberosa et al., 2002; P Landi, unpublished results) involving thecross Lo9643Lo1016. The position of 15 bin 1.06-QTLs (threeQTLs for root traits in hydroponics, two for vertical pullingresistance, four for plant height, two for stay green, one fordrought tolerance index, and three for grain yield) was projectedon the maize reference map ‘Genetic 2008’ (www.maizegdb.org/map) following the homothetic approach (Chardon et al., 2004).The ‘Genetic 2008’ map was chosen as reference because itprovides the typically high genetic resolution with distancesexpressed in standard cM, therefore facilitating the comparison ofmarker distances or confidence intervals. Following projection,QTL meta-analysis was carried out using BioMercator 2.1 (Arcadeet al., 2004).

Results

Molecular characterization of the NILs

For the 32 chromosome regions that were surveyed with

SSR markers, the two NILs within each family always

shared the same genetic make-up, except for the QTLregion (ca. 20 cM on the original map) on bin 3.09 that

influenced primary root weight in hydroponics. Therefore,

these data clearly indicate that within each NIL pair the

comparison between NIL (+/+) and (�/�) was carried out

in a highly isogenic genetic background and that the

estimate of root-yield-1.06 effects should not be much

affected by loci fixed at random in one NIL and not in the

other of the same family.

Evaluation of NILs per se

The ANOVA (not shown) indicated significant differences

among the three trials for all investigated traits except PS,

thus indicating that the four NILs were tested across

a broad range of growing conditions. The mean values of

the three trials for the investigated traits are given in

Table 2. For most traits the 2009 WS trial showed the

lowest means, whereas the 2009 WW trial showed the

highest means. In particular, as to the comparison between

the two trials carried out in 2009, a marked decrease for GY

(–60%) and all its components, as well as for RC weight(–46%), was detected under WS conditions. On the other

hand, higher mean values for the WS trial were noticed for

ASI and L-ABA, i.e. for those traits which are known to

increase under conditions of water shortage. Although the

two trials of 2009 showed similar PS values, the WS trial

showed lower means for SG and KM, clearly indicating

that the water shortage led to a premature senescence and

to a shortening of the grain-filling period (as confirmed bythe lower mean for KW). For most traits, the mean of WS

2008 was intermediate between those of WW and WS 2009,

indicating that the stress conditions in 2008 were less severe

than those of 2009.

Despite the large differences among the three trials, the

interaction trials3NILs was significant only for KM and

RC weight; also the interaction FAM3[(+/+) versus (�/�)]

was significant only for few traits (i.e. JV, LP, and KM).Therefore, given the negligible magnitude of these interac-

tions, only the mean values of NILs (+/+) and (–/–) across

the three trials and the two families are presented and

discussed. The comparison between these two means

(Table 3) was significant for several traits (JV, PS, PH, LP,

SD, SG, KM, KW, and RC weight), with the NILs (+/+)

Table 2. NILs per se: mean values of the three trials across the

two families for the investigated traits

Trait 2008 2009

WS WW WS a

Juvenile vigour (JV, cm) 180 162 154 b

Pollen shedding (PS, d)c 69.8 68.4 68.2 ns

Anthesis-silking interval (ASI, d) 4.9 1.3 2.0 **

Leaf-ABA (L-ABA, ng g�1 DW) – 438 522 *

Relative water content (RWC, %) – 89.9 84.7 *

Plant height (PH, cm) 188 199 174 **

Leaves per plant (LP, no.) 18.9 20.4 19.6 **

Stalk diameter (SD, mm) 29.0 23.7 20.4 **

Stay green (SG, 1-5 score)d – 3.2 2.2 **

Kernel moisture (KM, %) 23.9 25.5 20.5 **

Grain yield (GY, Mg ha�1) 3.35 5.79 2.31 **

Ears per plant (EP, no.) 0.68 0.93 0.74 **

Kernel weight (KW, mg) 190 239 177 **

Kernels per ear (KE, no.) 411 417 281 **

Root clump (RC) weight (kg plant�1) 2.74 2.37 1.29 *

a Comparison between WW and WS trials of 2009: * and **,significant at P <0.05 and P <0.01 level, respectively; ns, notsignificant. The comparison among the three trials was significant atP <0.01 for all traits except PS (ns).

b Meaningless comparison because irrigation was applied after themeasurements of the trait.

c As interval from sowing.d Score from 1 (no green) to 5 (completely green).

3556 | Landi et al. at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

showing higher values than the NILs (–/–). Therefore, the

additive effect, when significant, was always positive, in-

dicating that the QTL allele increasing the value of the traits

was always the (+) allele provided by Lo1016. In particular,

the results indicated that the (+) allele, as compared to the

(�) allele, conferred greater juvenile vigour, later flowering

and maturity, and a larger size for the above-ground traits

and for root mass (as estimated by RC weight). Moreover,the greater lateness conferred by the (+) allele was not

indicative of a slower growth, being just due to the greater

LP number, hence a delayed switch from the vegetative

to the reproductive phase. In fact, when considering as

an estimate of growth rate the ratio between the number

of leaves/plant and the growing degree days from field

emergence to PS date, the two NILs homozygous for the

(+) allele averaged a value very similar to that of the othertwo NILs homozygous for the (–) allele (data not shown).

For most traits, the a effect ranged from 3% to 8% as

referred to the overall mean. Interestingly, an a effect of 8%

was noted for GY, but the effect was not significant because

of the large experimental error of this trait (as confirmed by

the fact that the coefficient of variation was 22.7% for GY

versus 5–10% for the other traits). A sizeable a effect (30%

as referred to to the overall mean) was noted for RC weight;to have a better insight of this trait, some representative

RCs of the four NILs are shown in Fig. 1. It is noteworthy

that RC weight is a complex trait arising from the

contribution of the shallowest roots, the adhering soil, and

the base of the stalk.

Evaluation of NILs crosses

The ANOVA (not shown) indicated that most of the

variation among the four trials was due to the watering

regimes (WW and WS), whereas the difference between

years and the interaction between these two factors were of

minor importance. The WS treatment showed a mean value

significantly lower than that of WW for most traits

(Table 4). The greatest decrease of WS versus WW was

exhibited by GY (–25%); in particular, the decrease in GY

was greater in 2009 (–29%) than in 2008 (–21%). Significant

decreases were also detected for RWC, PH, LP, SG, andKM as well as for the three yield components; the only trait

for which the WS treatment showed a higher mean value

Table 3. NILs per se: mean values across the two families and the

three trials of the (+/+) and (–/–) NILs and additive (a) effects for the

investigated traits

Trait (+/+) (–/–) aa b

Juvenile vigour (JV, cm) 173 157 8 *

Pollen shedding (PS, d)c 69.6 68.1 0.8 **

Anthesis-silking interval (ASI, d) 2.9 2.6 0.2 ns

Leaf-ABA (L-ABA, ng g�1 DW) 489 471 9 ns

Relative water content (RWC, %) 87.1 87.5 –0.2 ns

Plant height (PH, cm) 198 176 11 **

Leaves per plant (LP, no.) 20.1 19.2 0.5 **

Stalk diameter (SD, mm) 25.4 23.4 1.0 **

Stay green (SG, 1–5 score) d 3.8 1.6 1.1 **

Kernel moisture (KM, %) 24.6 21.9 1.4 **

Grain yield (GY, Mg ha�1) 4.11 3.53 0.29 ns

Ears per plant (EP, no.) 0.81 0.75 0.03 ns

Kernel weight (KW, mg) 219 185 17 **

Kernels per ear (KE, no.) 362 378 –8 ns

Root clump (RC) weight (kg plant�1) 2.79 1.48 0.65 **

a Additive effect calculated as [(+/+) – (–/–)]/2.b Significance of the a effect: * and **, significant at P <0.05 and

P <0.01 level, respectively; ns, not significant.c As interval from sowing.d Score from 1 (no green) to 5 (completely green).

Fig. 1. Root clumps of the two NILs families (no. 1 and no. 4)

grown under water-stressed (WS) conditons.

Table 4. NILs crosses: mean values over two years and two

families of well-watered (WW) and water-stressed (WS) treatments

and significance of their differences

Trait WW WS a

Juvenile vigour (JV, cm) 192 192 b

Pollen shedding (PS, d)c 66.7 66.2 ns

Anthesis-silking interval (ASI, d) 2.2 2.4 ns

Leaf-ABA (L-ABA, ng g�1 DW) 456 680 **

Relative water content (RWC, %) 93.0 86.6 *

Plant height (PH, cm) 212 205 *

Leaves per plant (LP, no.) 19.5 19.2 *

Stalk diameter (SD, mm) 24.3 24.2 ns

Stay green (SG, 1–5 score)d 3.8 3.1 **

Kernel moisture (KM, %) 25.6 23.6 **

Grain yield (GY, Mg ha�1) 9.56 7.15 **

Ears per plant (EP, no.) 0.98 0.93 **

Kernel weight (KW, mg) 259 228 **

Kernels per ear (KE, no.) 604 542 **

a Comparison between WW and WS trials: * and **, significant atP <0.05 and P <0.01 level, respectively; ns, not significant.

b Meaningless comparison because the irrigation treatments werestarted after the measurements of the trait.

c As interval from sowing.d Score from 1 (no green) to 5 (completely green).

Root-yield-1.06, a major constitutive QTL for maize performance | 3557 at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

was L-ABA, consistently with the expected increased stress

conditions experienced by the plants because of water

shortage. These data are thus in accordance with those for

the NILs per se and indicate that the irrigation treatments

were also adequate in determining different growing con-

ditions for NILs crosses.

As to the analysis of the different interactions (data not

shown), it should be underlined that (i) the variance due toyears3crosses, even if significant for some traits, was always

smaller than the variance due to crosses; (ii) the (WW

versus WS)3crosses interaction was not significant for any

trait and also the detailed analysis of the components of this

interaction provided the same type of information, and

(iii) the interactions involving the families were also of

negligible importance. Collectively, these results indicate

that the effects of root-yield-1.06 were not much influencedby the climatic conditions of the two years, the two

irrigation treatments, and the two genetic backgrounds

(families). Therefore, only the overall mean values of the

four crosses are presented in Table 5.

The differences among crosses were significant for all

traits and largely due to the differences between the two

testers; in particular, Lo1016 showed a higher GY across

NILs mainly due to the contribution of the KE component.The QTL a effect was significant for JV, PH, LP, SG, KM,

GY, KW, and KE; for all these traits the a effect was

positive. This latter finding is consistent with the results of

the NILs per se evaluation, indicating that Lo1016 always

contributed the QTL allele increasing traits’ value. Analo-

gously to what was noted in the NILs per se, the a effect of

almost all traits ranged from 3 to 8% of the overall means;

in particular, the additive effect for GY (0.63 Mg ha�1,

P <0.05) reached the same percentage (8%) as in the NILs

evaluated per se. On the other hand, the a effect was not

significant for traits such as ASI, L-ABA, and RWC, i.e.

those traits more directly influenced by the plant water

status. The QTL d effect was significant for SG and GY,

with a d/a ratio equal to 0.7 (thus indicating partial

dominance) for SG and equal to 1.0 (complete dominance)for GY.

QTL meta-analysis

By means of QTL meta-analysis, the hypothesis was tested

that the QTL effects on root morphology and pullingresistance, stay-green, plant height, drought-tolerance in-

dex, and grain yield that were given for bin 1.06 in our

previous studies (see Supplementary Table S1 at JXB

online) correspond to a single segregating locus in the

Lo9643Lo1016 population. A total of 15 QTLs were

projected on the maize reference map and considered for

QTL meta-analysis. The best fit model supported the

presence of only one segregating meta-QTL located atposition 130 cM (95% confidence interval: 127–133), slightly

south of the bin 1.06 anchor marker umc67a (Fig. 2).

Discussion

A deeper understanding on how crops can mitigate the

negative effects of water shortage on growth and yield will

only be possible through a better knowledge and targeted

Table 5. NILs crosses: overall mean values (2 years32 irrigation treatments32 families) of NILs (+/+) and (–/–) combined with testers

Lo1016 and Lo964 and QTL additive (a) and dominance (d) effects

Trait Lo1016 Lo964 Effectsa

(+/+)b (–/–)c (+/+)b (–/–)c ad de

Juvenile vigour (JV, cm) 204 202 186 177 5 * 3 ns

Pollen shedding (PS, d)f 65.5 64.8 67.5 67.9 0.1 ns –0.5 ns

Anthesis-silking interval (ASI, d) 2.9 2.5 1.8 1.9 0.2 ns –0.2 ns

Leaf-ABA (L-ABA, ng g�1 DW) 531 578 584 578 –21 ns 27 ns

Relative water content (RWC, %) 90.6 90.0 89.7 89.0 0.7 ns 0.1 ns

Plant height (PH, cm) 225 222 200 188 7 * 5 ns

Leaves per plant (LP, no.) 19.8 19.2 19.4 19.0 0.5 ** –0.1 ns

Stalk diameter (SD, mm) 23.0 22.7 26.3 24.9 0.8 ns 0.5 ns

Stay green (SG, 1–5 score)g 3.1 2.8 4.8 3.1 1.0 ** 0.7 **

Kernel moisture (KM, %) 25.1 24.3 25.5 23.4 1.4 * 0.7 ns

Grain yield (GY, Mg ha�1) 9.00 8.96 8.34 7.12 0.63 * 0.64 *

Ears per plant (EP, no.) 0.95 0.97 0.95 0.94 0.00 ns 0.01 ns

Kernel weight (KW, mg) 237 237 258 243 7 * 7 ns

Kernels per ear (KE, no.) 634 622 540 496 28 ** 16 ns

a Significance of effects: * and **, significant at P < 0.05 and P < 0.01 level, respectively; ns, not significant.b and c: NILs homozygous for the QTL allele provided by Lo1016 and Lo964, respectively.

d Additive effect (a) calculated as [Lo10163(+/+)+Lo9643(+/+)]/2–[Lo10163(�/�)+Lo9643(�/�)]/2.e Dominance effect (d) calculated as [Lo10163(�/�)+Lo9643(+/+)]/2–[Lo10163(+/+)+Lo9643(�/�)]/2.f As interval from sowing.g Score from 1 (no green) to 5 (completely green).

3558 | Landi et al. at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

manipulation of the genetic factors that limit plant func-

tions under such conditions (Tuberosa and Salvi, 2006;

Ribaut et al., 2009). To this end, the availability of NILs for

QTLs able to influence the adaptive response to drought

provides valuable opportunities to disentangle the func-

tional complexity behind such a response while providingclues on the selection strategy eventually to be adopted in

order to maximize yield across different water regimes.

Responses to WW and WS treatments of NILs per seand NILs crosses

The responses to water treatments of NILs per se and ofNILs crosses should be compared with caution because

these materials were tested in different trials and, as to NILs

crosses, because of the peculiar combining ability effects of

the two inbred testers. Nonetheless, it is noteworthy that

a more marked GY decrease from WW to WS conditions

was observed in 2009 for NILs per se than for NILs crosses,

thus indicating that the same WS treatment exerted

a different effect depending on the level of homozygosity ofthe investigated materials. These findings can be accounted

for by considering the positive role played by heterosis, as

compared with inbreeding depression, in alleviating the

negative effects of drought (Betran et al., 2003). All three

yield components contributed to the more marked decrease

of GY of NILs per se, with the largest contribution due to

the fitness-related trait KE.

Influence of water treatments on the QTL effects

The effects of the investigated QTL were stable across water

regimes for both the NILs per se and the NILs crosses. This

stability is consistent with previous findings (Tuberosa et al.,

2002), hence supporting the hypothesis that the QTL actsmainly constitutively rather than being water-stress respon-

sive (Landi et al., 2002), at least within the range of stress

conditions investigated in this and the previous studies.

According to Bruce et al. (2002), yield potential can play an

important role in determining the yield level under moderate

drought stress, whereas it may not when the yield level drops

below 50–60% because of a very severe drought stress. In

previous QTL studies conducted under both WW and WSconditions, a much larger number of QTLs were common to

the two water regimes in case of moderate WS (Frova et al.,

1999), as compared to more severe WS conditions (Ribaut

et al., 1997; Lu et al., 2006; Messmer et al., 2009). It is also

noteworthy that the characteristics determined by the (+)

allele (from Lo1016) are not consistent with the adaptive

features of the drought-stress-tolerant ideotype, such as

smaller stature, shorter anthesis-silking interval, and rootsystem poorly developed in the shallow layers, that have

been suggested by Ribaut et al. (2009). Collectively, our

findings strengthen the hypothesis that root-yield-1.06 is

not directly involved in the control of drought response.

Influence of the genetic background and inbreedinglevel on the QTL effects

The level of isogenicity of the two pairs of NILs was very

high, as shown by the fact that among the 32 chromosome

regions that were surveyed with SSR markers, only one

showed contrasting alleles within each NILs pair. Because

this latter region was previously shown to influence theweight of the primary seminal root but not other features of

root architecture as well as agronomic traits (Landi et al.,

2002; Tuberosa et al., 2002), its effects on the comparison

between NIL (+/+) and (�/�) for both families were most

likely marginal, consistently with the negligible importance

of the interactions involving families and root-yield-1.06

effects. In fact, the root-yield-1.06 effect for the majority of

the investigated traits did not substantially change from onefamily to the other for both NILs per se and NILs crosses,

thus indicating that the QTL is not subjected to important

epistatic interactions, at least in the two genetic back-

grounds considered in this study.

Moreover, the QTL additive effect did not show any

substantial difference between NILs per se and NILs crosses,

thus underlining also the stability of the effects of root-yield-

1.06 across materials with different inbreeding coefficients(F very close to 1 for NILs per se and, on average, equal to

0.5 for NILs crosses). In particular, the GY additive effect

(as referred to the mean) estimated in the NILs per se was

similar to that estimated in the NILs crosses; nonetheless, it

was significant only for the NILs crosses, a finding that

Fig. 2. Results of QTL meta-analysis of 15 QTLs for drought-

tolerance index (DTI), grain yield (GY), plant height (PH), vertical

root pulling resistance (VRPR), and root morphology in hydropon-

ics (adventitious root dry weight, R2W; primary root length, R1L;

primary root diameter, R1D) identified at bin 1.06 in the cross

Lo9643Lo1016. Full QTL details are provided in Supplementary

Table S1 at JXB online and were originally described in Landi et al.

(2002) and Tuberosa et al. (2002). Meta-analysis was carried out

using BioMercator 2.1 (Arcade et al., 2004). The only consensus

‘meta-QTL’ identified is indicated by the dashed line.

Root-yield-1.06, a major constitutive QTL for maize performance | 3559 at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

should be ascribed to the greater statistical power attained

in the analysis of this latter material (mainly because four

trials were conducted for NILs crosses as compared to three

trials for NILs per se and because of lower experimental

errors in the trials for NILs crosses). The high stability of

the root-yield-1.06 effect across materials with different

inbreeding coefficients is further confirmed by the fact that

the QTL additive effect for GY estimated in this experimentwas also consistent with the effect for GY estimated by

Tuberosa et al. (2002) using the F2:3 families (F equal to

0.75) derived from the same single cross (Lo9643Lo1016).

Therefore, the stability across genetic backgrounds (i.e. the

two families), inbreeding levels and, more importantly, also

water regimes makes root-yield-1.06 a particularly valuable

candidate for MAS programmes.

Characterization of the effects of root-yield-1.06 acrosswater regimes and genetic backgrounds

The QTL additive effect was notable for almost all

investigated traits concerning the size and the vigour ofboth root and above-ground plant traits. As to the root, the

RC weight (i.e. a trait that estimates the size of the root and

the strength of its soil anchorage; Thompson, 1968; Nass

and Zuber, 1971) was investigated. The positive additive

effect of root-yield-1.06 for RC weight was consistent with

the positive additive effect for both the weight of adventi-

tious seminal root traits of seedlings in hydroponics (Tuber-

osa et al., 2002) and vertical root-pulling resistance asevaluated on adult plants in the field (Landi et al., 2002).

Therefore, all these findings support the role of this QTL in

affecting root traits throughout the plant’s life cycle.

Positive additive effects of root-yield-1.06 were also found

for agronomic traits, especially grain yield, consistently with

what was previously reported (Landi et al., 2002; Tuberosa

et al., 2002). Such a consistency of concurrent effects of

root-yield-1.06 on the vigour of both root and shoot(agronomic) traits is partly unexpected because of the

increasing competition for photosynthates of a larger root

system against the shoot. Indeed, negative associations

between root strength (as evaluated by resistance to root

pulling or to root lodging) and above-ground plant traits

(mainly grain yield) have been reported in several studies

(Beck et al., 1988; Bolanos et al., 1993; Landi et al., 2007).

In particular, the significant improvements for agronomictraits and grain yield reported by Bolanos et al. (1993)

following recurrent selection for grain yield under drought

were accompanied by a reduction of vertical root-pulling

resistance, in turn due to a reduction of root biomass in the

upper soil layers. In all likelihood, the consistent association

among additive effects of root-yield-1.06 for root, shoot,

and agronomic traits found in our study as well as in

previous ones (Landi et al., 2002; Tuberosa et al., 2002) canbe accounted for by assuming that these traits are concur-

rently controlled by the same gene/s. This concurrent

control might arise because the underlying gene/s is/are

active at an early stage of plant development and then

control a sequence of functionally interrelated traits, in-

cluding grain yield as the final outcome. If the association

among additive effects were due only to linkage, inconsis-

tent effects (i.e. positive for some traits and negative for

others) should be found as well. Accordingly with this

hypothesis, the QTL meta-analysis carried out considering

15 QTLs for morpho-physiological traits previously

identified in the Lo9643Lo1016 background fully sup-

ported root-yield-1.06 as a single segregating QTL. Notably,the meta-analysis also allowed for a remarkable reduction

of the confidence interval of root-yield-1.06, which has been

mapped on the ‘Genetic 2008’ reference map within ca. 6

cM. On the other hand, a possible contribution of linkage

to the observed positive association among the investigated

traits can not be entirely dismissed, since tightly linked

genes in bin 1.06 controlling root and agronomic traits

could have been fixed in the coupling phase during theselection of the two parental inbreds Lo964 and Lo1016.

Also other studies conducted on different genetic materials

have shown that this QTL region influences root (Lebreton

et al., 1995; Kaeppler et al., 2000; Barriere et al., 2001;

Tuberosa et al., 2003; Liu et al., 2008) and shoot traits (e.g.

Koester et al., 1993; Pelleschi et al., 2006; Lauter et al.,

2008), as well as grain yield and its components (Ribaut

et al., 1997; Bohn et al., 2000; Hirel et al., 2001; Malosettiet al., 2008). Moreover, several studies pointed out that this

chromosome region affects important physiological and

biochemical traits, which, in turn, are somehow influenced

by root features, such as absorption and utilization

efficiency of nitrogen (Bertin and Gallais, 2001) and

phosphorus (Chen et al., 2008). On the contrary, the QTL

did not show appreciable effects on RWC, L-ABA, and ASI

(despite the effects of root-yield-1.06 on root mass), furthercorroborating the hypothesis that the nature of root-yield-

1.06 is mainly constitutive.

The QTL dominance effect was significant for SG and

GY, and, in both cases, it was positive, thus indicating that

the favourable (+) allele contributed by Lo1016 was the

dominant one. The importance of the dominance effects

(especially for GY) is consistent with the hypothesis pre-

viously discussed, i.e. that root-yield-1.06 could be involvedin the control of plant’s vigour and, hence, also in the

manifestation of heterosis, further contributing to the

maintainance of growth under a broad range of growing

conditions.

Conclusions

Collectively, our study shows that root-yield-1.06 affects

constitutively and consistently root features, agronomic

traits, and grain yield in maize across the soil moisture

regimes and the genetic backgrounds tested here, and

irrespective of the inbreeding level. In view of these features,root-yield-1.06 represents an interesting target for MAS

aimed at increasing and stabilizing yield in maize across

water regimes. Moreover, the effects of root-yield-1.06 on

simple and easily measurable root and shoot traits at an

early growth stage, as previously shown by Tuberosa et al.

3560 | Landi et al. at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

(2002), suggest the feasibility of its fine mapping and,

eventually, positional cloning to identify the underlying

gene(s) and to elucidate the genetic cause of the concurrent

effects of this QTL on root and agronomic traits. In this

respect, the availability of NILs for root-yield-1.06 repre-

sents an essential prerequisite for undertaking its fine

mapping and cloning (Salvi and Tuberosa, 2007). In

addition, the same NILs provide an opportunity for testingthe QTL effects across several testers obtained from

different germplasm pools and evaluating the interactions

of root-yield-1.06 with other QTLs for root traits and other

drought-related traits for which NILs are already or will

become available.

Acknowledgements

This research was funded by the University of Bologna

(Grant RFO). The Interdepartmental Centre for Biotechnol-

ogy, University of Bologna, Italy is gratefully acknowledged.

The authors thank Massimo Bellotti, Sandra Stefanelli, and

Stefano Vecchi for technical assistance throughout theproject, and Nicola Gaspari for the soil moisture analyses.

References

Arcade A, Labourdette A, Falque M, Mangin B, Chardon F,

Charcosset A, Joets J. 2004. BioMercator: integrating genetic maps

and QTL towards discovery of candidate genes. Bioinformatics 20,

2324–2326.

Barriere Y, Gibelin C, Argillier O, Mechin V. 2001. Genetics

analysis in recombinant inbred lines of early dent forage maize. I. QTL

mapping for yield, earliness, starch and crude protein contents from

per se value and top cross experiments. Maydica 46, 253–266.

Beck DL, Darrah LL, Zuber MS. 1988. Effect of sink level on root

and stalk quality in maize. Crop Science 28, 11–18.

Bertin P, Gallais A. 2001. Genetic variation for nitrogen use efficiency

in a set of recombinant inbred lines. II. QTL detection and

coincidences. Maydica 46, 53–68.

Betran FJ, Ribaut JM, Beck D, Gonzalez-de-Leon D. 2003.

Genetic diversity, specific combining ability, and heterosis in tropical

maize under stress and nonstress environments. Crop Science 43,

797–806.

Bohn M, Grift TE. 2009. The genetic analysis of maize root

complexity. HortScience 44, 986–986.

Bohn M, Schulz B, Kreps R, Klein D, Melchinger AE. 2000. QTL

mapping for resistance against the European corn borer (Ostrinia

nubilalis H.) in early maturing European dent germplasm. Theoretical

and Applied Genetics 101, 907–917.

Bolanos J, Edmeades GO, Martinez L. 1993. Eight cycles of

selection for drought tolerance in lowland tropical maize. III.

Responses in drought adaptive physiological and morphological traits.

Field Crops Research 31, 269–286.

Bruce WB, Edmeades GO, Barker TC. 2002. Molecular and

physiological approaches to maize improvement for drought tolerance.

Journal of Experimental Botany 53, 13–25.

Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L,

Murigneux A, Charcosset A. 2004. Genetic architecture of flowering

time in maize as inferred from quantitative trait loci meta-analysis and

synteny conservation with rice genome. Genetics 168, 2169–2185.

Chen J, Xu L, Cai Y, Xu J. 2008. QTL mapping of phosphorus

efficiency and relative biologic characteristics in maize (Zea mays L.) at

two sites. Plant and Soil 313, 251–266.

Collins NC, Tardieu F, Tuberosa R. 2008. Quantitative trait loci and

crop performance under abiotic stress: where do we stand? Plant

Physiology 147, 469–486.

Courtois B, Nourollah Ahmadi N, Farkhanda Khowaja F,

Price AH. 2009. Rice root genetic architecture: meta-analysis from

a drought QTL database. Rice, DOI 10.1007/s12284-009-9028-9.

de Dorlodot S, Forster B, Pages L, Price A, Tuberosa R,

Draye X. 2007. Root system architecture: opportunities and

constraints for genetic improvement of crops. Trends in Plant Science

12, 474–481.

Falconer DS, Mackay TFC. 1996. Introduction to quantitative

genetics, 4th edn. London: Longman.

Frova C, Krajewski P, Di Fonzo N, Villa M, Sari-Gorla M. 1999.

Genetic analysis of drought tolerance in maize by molecular markers. I.

Yield components. Theoretical and Applied Genetics 99, 280–288.

Goffinet B, Gerber S. 2000. Quantitative trait loci: a meta-analysis.

Genetics 155, 463–473.

Guingo E, Hebert Y, Charcosset A. 1998. Genetic analysis of root

traits in maize. Agronomie 18, 225–235.

Hallauer AR, Miranda JB. 1988. Quantitative genetics in maize

breeding, 2nd edn. Ames, USA: Iowa State University Press.

Hirel B, Bertin P, Quillere I, et al. 2001. Towards a better

understanding of the genetic and physiological basis for nitrogen use

efficiency in maize. Plant Physiology 125, 1258–1270.

Hochholdinger F, Tuberosa R. 2009. Genetics and genomics of

root architecture in maize. Current Opinion in Plant Biology 12,

172–177.

Hund A, Fracheboud Y, Soldati A, Frascaroli E, Salvi S,

Stamp P. 2004. QTL controlling root and shoot traits of maize

seedlings under cold stress. Theoretical and Applied Genetics 109,

618–629.

Kaeppler SM, Parke JL, Mueller SM, Senior L, Stuber C,

Tracy WF. 2000. Variation among maize inbred lines and detection of

quantitative trait loci for growth at low phosphorus and responsiveness

to arbuscular mycorrhizal fungi. Crop Science 40, 358–364.

Khowaja FS, Norton GJ, Courtois B, Price AH. 2009. Improved

resolution in the position of drought-related QTLs in a single mapping

population of rice by meta-analysis. BMC Genomics 10, 276.

Koester RP, Sisco PH, Stuber CW. 1993. Identification of

quantitative trait loci controlling days to flowering and plant height in

two near isogenic lines of maize. Crop Science 33, 1209–1216.

Landi P, Sanguineti MC, Darrah L, Giuliani M, Salvi S,

Tuberosa R. 2002. Detection of QTLs for vertical root pulling

resistance in maize and overlaps with QTLs for root traits in

hydroponics and for grain yield at different water regimes. Maydica 47,

233–243.

Root-yield-1.06, a major constitutive QTL for maize performance | 3561 at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from

Landi P, Sanguineti MC, Liu C, Li Y, Wang TY, Giuliani S,

Bellotti M, Salvi S, Tuberosa R. 2007. Root-ABA1 QTL affects root

lodging, grain yield, and other agronomic traits in maize grown under

well-watered and water-stressed conditions. Journal of Experimental

Botany 58, 319–326.

Lauter N, Moscou MJ, Habiger J, Moose SP. 2008. Quantitative

genetic dissection of shoot architecture traits in maize: towards

a functional genomics approach. Plant Genome 1, 99–110.

Lebreton C, Lazic-Jancic V, Steed A, Pekic S, Quarrie SA. 1995.

Identification of QTL for drought responses in maize and their use in

testing causal relationships between traits. Journal of Experimental

Botany 46, 853–865.

LeDeaux JR, Graham JI, Stuber CW. 2006. Stability of QTLs

involved in heterosis in maize when mapped under several stress

conditions. Maydica 51, 151–167.

Liu J, Li J, Chen F, Zhang F, Ren T, Zhuang Z, Mi G. 2008.

Mapping QTLs for root traits under different nitrate levels at the

seedling stage in maize (Zea mays L.). Plant and Soil 305, 253–265.

Lu GH, Tang JH, Yan JB, Ma XQ, Li JS, Chen SJ, Ma JC, Liu ZX,

Li-Zhu E, Zhang YR, Dai JR. 2006. Quantitative trait loci mapping of

maize yield and its components under different water treatments at

flowering time. Journal of Integrative Plant Biology 48, 1233–1243.

Ludlow MM, Muchow RC. 1990. A critical evaluation of traits for

improving crop yields in water-limited environments. Advances in

Agronomy 43, 107–153.

Malosetti M, Ribaut JM, Vargas M, Crossa J, van Eeuwijk FA.

2008. A multi-trait multi-environment QTL mixed model with an

application to drought and nitrogen stress trials in maize (Zea mays L.).

Euphytica 161, 241–257.

Messmer R, Fracheboud Y, Banziger M, Vargas M, Stamp P,

Ribaut JM. 2009. Drought stress and tropical maize: QTL-by-

environment interactions and stability of QTLs across environments for

yield components and secondary traits. Theoretical and Applied

Genetics 119, 913–930.

Nass HG, Zuber MS. 1971. Correlation of corn (Zea mays L.) roots

early in development to mature root development. Crop Science 11,

655–658.

Pelleschi S, Leonardi A, Rocher JP, Cornic G, de Vienne D,

Thevenot C, Prioul JL. 2006. Analysis of the relationships between

growth, photosynthesis and carbohydrate metabolism using

quantitative trait loci (QTLs) in young maize plants subjected to water

deprivation. Molecular Breeding 17, 21–39.

Quarrie SA, Whitford PN, Appleford NEJ, Wang TL, Cook SK,

Henson IE. 1988. A monoclonal antibody to (S)-abscisic acid: its

characterization and use in a radioimmunoassay for measuring

abscisic acid in crude extracts of cereal and lupin leaves. Planta 173,

330–339.

Ribaut JM, Betran J, Monneveux P, Setter T. 2009. Drought

tolerance in maize. In: Bennetzen JL, Hake SC, eds. Handbook of

maize: its biology. The Netherlands: Springer, 311–344.

Ribaut J-M, Jiang C, Gonzalez-de-Leon D, Edmeades GO,

Hoisington DA. 1997. Identification of quantitative trait loci under

drought conditions in tropical maize. 2. Yield components and marker-

assisted selection strategies. Theoretical and Applied Genetics 94,

887–896.

Ritchie SW, Hanway JJ, Benson GO. 1997. How a corn plant

develops. Iowa State University of Science and Technology Special

Report 48, 1–21 (reprinted).

Salvi S, Tuberosa R. 2007. Cloning QTLs in plants. In: Varshney RK,

Tuberosa R, eds. Genomics-assisted crop improvement, Vol. 1. New

York: Springer, 207–226.

Thompson DL. 1968. Field evaluation of corn root clumps. Agronomy

Journal 60, 170–172.

Trachsel S, Messmer R, Stamp P, Hund A. 2009. Mapping of

QTLs for lateral and axile root growth of tropical maize. Theoretical and

Applied Genetics 119, 1413–1424.

Tuberosa R, Salvi S. 2006. Genomics-based approaches to

improve drought tolerance of crops. Trends in Plant Science 11,

405–412.

Tuberosa R, Salvi S. 2009. QTL for agronomic traits in maize. In:

Bennetzen JL, Hake SC, eds. Handbook of maize: its biology. The

Netherlands: Springer, 501–542.

Tuberosa R, Salvi S, Sanguineti MC, Maccaferri M, Giuliani S,

Landi P. 2003. Searching for QTLs controlling root traits in maize:

a critical appraisal. Plant and Soil 255, 35–54.

Tuberosa R, Sanguineti MC, Landi P. 1994. Abscisic acid

concentration in the leaf and xylem sap, leaf water potential, and

stomatal conductance in drought-stressed maize. Crop Science 34,

1557–1563.

Tuberosa R, Sanguineti MC, Landi P, Giuliani MM, Salvi S,

Conti S. 2002. Identification of QTLs for root characteristics in maize

grown in hydroponics and analysis of their overlap with QTLs for grain

yield in the field at two water regimes. Plant Molecular Biology 48,

697–712.

Tuinstra MR, Ejeta G, Goldsbrough PB. 1997. Heterogeneous

inbred family (HIF) analysis: a method for developing near-isogenic

lines that differ at quantitative trait loci. Theoretical and Applied

Genetics 95, 1005–1011.

Yamanaka N, Watanabe S, Toda K, Hayashi M, Fuchigami H,

Takahashi R, Harada K. 2005. Fine mapping of the FT1 locus for

soybean flowering time using a residual heterozygous line derived from

a recombinant inbred line. Theoretical and Applied Genetics 110,

634–639.

Zhu J, Mickelson SM, Kaeppler SM, Lynch JP. 2006. Detection of

quantitative trait loci for seminal root traits in maize (Zea mays L.)

seedlings grown under differential phosphorus levels. Theoretical and

Applied Genetics 113, 1–10.

3562 | Landi et al. at S

istema B

ibliotecario d'Ateneo - U

niversità degli S

tudi di Bologna on D

ecember 21, 2010

jxb.oxfordjournals.orgD

ownloaded from