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a CIRAD, Avenue Agropolis, 34398 Montpellier, Cedex 5, France
b CIRAD, Projet Cône Sud, SHIS/QI15, Conjunto 15, Casa 3, Lago Sul, 71635-350, Brasilia DF, Brazil
* Corresponding author (marc.lacape{at}cirad.fr).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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This paper reports on an analysis of cotton fiber QTLs undertaken as part of a marker-assisted backcross introgression scheme (Lacape et al., 2000) based on a combined RFLP-SSR-AFLP genetic map of a G. hirsutum/G. barbadense backcross (Lacape et al., 2003). We choose a G. barbadense variety as the donor parent for superior fiber quality and a G. hirsutum variety as recurrent parent. The first (one set of phenotypic data) and second (two sets of data) backcrosses to the G. hirsutum recurrent parent were used for QTL detection.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Analysis of Fiber Quality
All fiber samples were analyzed at the Cirad laboratory at Montpellier. Measurements were made with an HVI line (Zellweger Uster 900, Uster Technologies, Switzerland) including fiber length components (mean length, ML, upper half mean length, UHML, and uniformity index, UI), strength (STR), elongation (ELO), color components (reflectance, Rd, and yellowness index, +b). Maturimeter parameters (FMT3, Shirley Dev Ltd, England) included fiber maturity ratio (MR), micronaire reading (IM), linear fineness (H), and standard fineness (Hs).
SSR/AFLP Analysis of the BC2 Population
The BC1 population was used to construct a genetic map as described in Lacape et al. (2003). With the new microsatellite markers subsequently developed by Nguyen et al. (2004), the updated version of this map consists of 1160 loci mapped along 5520 cM. The 200 individual BC2 plants were analyzed for a selected subset of SSR (81 primer pairs) and AFLP (45 EcoRI/MseI primer pairs) loci chosen on the basis of their representative distribution across the 26 chromosomes. SSR and AFLP protocols are described in Lacape et al. (2003). The allelic constitution of the 200 BC2 plants, either homozygous for G. hirsutum (Gh) alleles, Gh/Gh, or heterozygous for G. hirsutum and G. barbadense (Gb), Gh/Gb, was finally scored for 594 segregating loci.
Statistical Analysis
Heritability and Trait Correlation
Trait heritability was directly estimated from the parent/offspring regressions of 200 BC2–BC2S1. Pearson correlation coefficients were calculated for all trait combinations within each of the 3 phenotypic data sets.
Genetic Mapping
Additional markers scored only in the BC2 population were positioned on the BC1 map (Nguyen et al., 2004) after constructing a BC2 genetic map and aligning the two maps by common loci. The Mapmaker "group," (LOD 5.0 and 30 maximal recombination frequency) "order," and "sequence" commands were used to generate the BC2 linkage map from 594 segregating markers. Marker segregations in the BC2 were tested for deviation from the expected 3:1 (Gh/Gh: Gh/Gb) ratio by a 
2 test. All BC2 linkage groups were assigned to a corresponding BC1 group using loci in common between the 2 maps as bridges. Each additional BC2 locus was positioned on the BC1 map as a backbone reference map, by extrapolating its position from the positions of shared flanking loci. Hence, the same mapping data, i.e., those obtained in the BC1 generation, were used for QTL analysis in the BC1 and BC2 populations.
QTL Analysis
Excluding the cosegregating markers, 595 BC1 and 341 BC2 markers were used to conduct three individual QTL analyses using one phenotypic data set in BC1 and two phenotypic data sets (BC2 and BC2S1) in BC2. The association between phenotype and marker genotype was investigated through simple marker analysis (SMA), interval mapping (IM), and composite interval mapping (CIM) using the computer software QTL Cartographer 1.13 (Basten et al., 1999). For each variable, IM using multiple regression of phenotypic data on marker genotypic data over the whole genome was run with 1000 permutations to identify the minimum significant LOD score to be considered. For each individual test, permutation-based thresholds were considered at a 5% risk at the genome level. Composite interval mapping (Jansen and Stam, 1994; Zeng, 1994) was then performed by means of the markers preselected by stepwise regression as cofactors. The results of the QTL position, proportion of phenotypic variance explained (R2), and effect that are reported are those derived from CIM, after checking for their agreement with the SMA results. The QTL positions on the BC1 or BC2 map are reported on the BC1/BC2 consensus map and drawn by MapChart software (Voorrips, 2002).
The effects of QTLs are considered from a product transformation perspective. Therefore, decreases in fiber fineness (H), standard fineness (Hs), micronaire reading (IM), and yellowness index, were indicative of a "positive" contribution conferred by either parent.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Correlation coefficients between phenotypic traits calculated in each of the BC1, BC2, and BC2S1 populations are presented in Table 1. The strongest correlations consistently observed within the three data sets related ML to UHML (0.97 on average), IM to H (0.89) and to MR (0.83), and Rd to +b (–0.74), and (only in the BC2S1 data set) H to Hs (0.76). These were all expected as inherent to the measurement apparatus and/or physiological definitions of the trait concerned. Another group of correlations of lower magnitude relate length (ML or UHML), strength, and/or fineness components (IM or H), probably reflecting some genetic association of these characteristics as observed in the contrasting parents used (Fig. 1).
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QTL Analysis
The highest critical LOD thresholds after permutation tests varied between 3.2 and 4.0 for the various traits except for uniformity index in the BC2 and BC2S1 generations (4.3 and 5.7, respectively) and for color indices in BC2 generation (5.0 and 11.1 for Rd and +b respectively). Fifty QTLs met permutation-based LOD thresholds. The results are nevertheless reported for all the QTLs that showed a LOD superior to 2.5 in any of the generations, i.e., a total of 80 QTLs (Table 2). While QTLs detected above this LOD limit of 2.5 but below the permutation-based LOD threshold should be treated cautiously, i.e., putative QTLs, they enabled a more precise comparison of results between generations, between traits for a given chromosomal region, and between our data and other data sets for a given trait. For all but one (QTL for IM on c10) of the 50 QTLs meeting permutation-based threshold detected under CIM, and for 70 of the 80 putative QTL, we also obtained a significant marker-trait association (P > 0.001 of F statistics) using simple marker analysis.
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Percentages of phenotypic effects (Table 2) ranged between 4.8 and 14.8% for length, 3.1 and 17.9% for length uniformity, 4.4 and 21.3% for strength, 5.4 and 21.2% for elongation, 4.6 and 29.1% for fineness/maturity, and 4.8 and 15.6% for fiber color (yellowness index in BC2 discarded from comparisons).
As expected, the respective contributions of the Gh and Gb parents to fiber quality (Table 2) indicated that a majority of Gb parent alleles had a positive influence on fiber length (12 versus 3), strength (8 versus 4), and fineness QTLs (13 versus 8), and a majority of Gh parent alleles had a positive influence on fiber color QTLs (13 versus 3).
Altogether, the 80 QTLs mapped over 22 of the 26 chromosomes (a single QTL on two unassigned linkage groups, NL5 and NL8), with chromosomes 1, 2, 7, and 12 lacking any QTLs (Fig. 2). The QTLs that were accounted for by a positive effect of the Gb alleles on either fiber length, strength, or fineness, mapped to 15 different chromosomes. Cases of colocalization of QTLs exceeded cases of isolated positioning. Such cases of colocalization of QTLs for different fiber properties were often detected in different population data sets. Cases of across-population and across-trait colocalization are illustrated for c17 with a colocalization of a fiber elongation QTL detected in the BC1 population with a color QTL detected in the BC2, on A02 (fineness QTL in the BC1 and color QTL in the BC2S1), on D03 (color QTL in the BC1 and fineness QTL in the BC2S1), and on A03 (uniformity QTL in the BC1 and a strength QTL in the BC2).
Significant QTLs were identified for all six groups of fiber traits and in all three populations. After merging data from the three QTL analyses, the total of QTLs varied from six for length uniformity to 21 for the fineness–maturity complex.
For fiber length, nine QTLs met permutation-based thresholds, and six additional QTLs may be considered as putative. For 12 of these 15 QTLs, the positive allele contributing to increased fiber length derived from the Gb parent. The two length QTLs detected on c26 have peak LODs separated by 60 cM. The position and direction of three length QTLs on c5 (highest LOD in BC1), c26, and A01 (highest LOD in BC2S1) agreed with QTLs reported in the literature (Table 2).
Three of the six QTLs reported for fiber length uniformity met permutation-based thresholds. Gb and Gh alleles contributed equally at three QTLs each. Two QTLs, on c14 and A03, were detected at positions that have been previously reported.
Six fiber strength QTLs met permutation-based thresholds, and six others were detected at lower LOD values. Eight QTLs resulted from a positive contribution of the Gb parent, five of which (on c3, c25, c23, A01, and A03) mapped at comparable positions in the literature. In addition, we detected two QTLs of a Gh positive contribution on c18, mapping at similar positions to two QTLs described by Paterson et al. (2003), near loci pAR788 and P5-11, but with opposite phenotypic effects.
From a total of 10 elongation QTLs, eight met permutation-based thresholds. Six resulted from a positive contribution of the Gb parent. Two QTLs on c20 are positioned 40 cM apart. The fiber elongation QTLs on c15, c9, c23, and c10 agreed with QTLs from the literature.
Fourteen of the 21 reported QTLs relating to the maturity or fineness complex met permutation-based thresholds of either IM, MR, H, or Hs parameter, and, for most of them, QTLs were detected simultaneously for several traits: (i) two traits: IM and MR on c22 and c6, IM and Hs on c9, Hs and MR on c5 and c18, H and Hs on c6 and c16; (ii) three traits: IM, MR and H on c3 and A02, IM, H, and Hs on D08; and (iii) four traits: IM, MR, H, and Hs on D03.
Thirteen cases were found in which the contribution of the Gb parent to each trait was positive (lower IM, H, and Hs values, and higher MR value). Interestingly, the map locations and effects for seven of the QTLs described in this way, including the strongest ones detected in BC2 (c3) and BC2S1 (D03), corresponded to those for QTLs relating to IM values reported in the literature by Kohel et al. (2001) and Paterson et al. (2003). One QTL detected on D08 mapped at a similar position to a QTL reported in Paterson et al. (2003), near pGH225 (former c20 in the reference paper renamed to D08), but had the opposite effect.
In total, 16 QTLs were detected for the two color indexes, Rd and +b. Half of these QTLs explained variation observed for both indexes. In accordance with parental contributions (Fig. 1), 13 of the 16 detected QTLs resulted from a positive contribution of the Gh parent, conferring higher Rd and/or lower +b values. Five QTLs were reported in Paterson et al. (2003) for yellowness index, with similar map location and effect.
We observed a few cases of QTL-rich regions delimited along homeologous regions of the A- and D-subgenome chromosomes: fiber color QTLs on c6 and c25, both congruent with Paterson et al. (2003); fineness–maturity QTLs on the upper part of c6 and c25; fineness and elongation QTLs at central parts of c10 and c20; and lastly on the c5-D08 homeologous pair a concentration of "negative" QTLs for length, elongation, fineness, and color.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The quality of cotton fiber derives from several traits, the most important being length, fineness, and strength, each of which is the result of complex genetic architecture. This study confirmed the expected genetic complexity of individual cotton fiber components (Paterson et al., 2003; Shappley et al., 1998). Combining all the different traits, we detected a total of 80 QTLs using LOD2.5 as a threshold, of which 50 surpassed the permutation-based LOD thresholds (3.2–5.7). For most of the QTLs related to the most economically important traits, the favorable alleles came from the G. barbadense parent. However, the inferior parent, G. hirsutum, also contributed to fiber quality (Table 2): for length on c5 (length QTL of the highest LOD in BC1), strength on c18, and fineness/maturity on D08, c6, A02, and D03. Such observation supports the proposition that superior allele combinations may be obtained in interspecific progenies of mosaic genome constitution (Tanksley et al., 1996).
The distribution of QTLs between chromosomes or linkage groups assigned to A and D subgenomes shows a slight over-representation of QTLs mapping to the D-subgenome chromosomes (58%) as compared with the A-subgenome. This finding is in agreement with the idea that in AADD tetraploid cottons, the D-subgenome globally contributes to a higher level of trait variability than does the A-subgenome (reviewed by Paterson et al., 2003).
The cases of colocalization of QTLs for different traits (Fig. 2) confirmed the observed phenotypic correlations (Table 1); the presence of alleles of the Gb parent in some QTL-rich chromosome regions contributed simultaneously and positively to several properties among fiber strength, length, and fineness. Of significant interest are the localizations of "positive" QTLs related to traits of greater economic importance, i.e., fiber strength, length, and fineness. Chromosomes 3 and 23 are worth mentioning in this respect: c3 with the strongest length QTLs detected in BC1 (LOD 4.43) and in BC2S1 (LOD 4.99), colocalized with the second strongest strength QTL detected in BC1 (LOD 4.51), and -c23 with the strongest strength QTL detected in BC2 (LOD 4.78) and BC2S1 (LOD 4.01) colocalized with a length QTL in BC2 (LOD 2.80). Because fiber length, strength, and fineness may be considered as physiologically independent and relating to different spatial and temporal biological processes (Wilkins and Jernstedt, 1998), the colocalization of QTLs may be more indicative of linkage between different genes than of pleiotropy.
We observed a low consistency between the QTLs detected in the three populations (around 10% of QTLs in common between BC1 and BC2 and 20% between BC2 and BC2S1). Such apparent instability in QTLs detected under diverse environments is not uncommon (Ribaut et al., 1997; Saranga et al., 2001). In the present case, the factors that could explain such a difference between generations may involve the environmental conditions (greenhouse or field, Montpellier or Brazil), the genetic backgrounds of the BC1 and BC2 generation, or possible differences in the distribution of QTLs between the 75 plants constituting the BC1 population and the subset of 53 BC1 plants that effectively served as parents to the 200 BC2s. However, the colocalized QTLs also relate to various combinations of traits and data sets. Chromosomes 6 and 25, for example, contain a high density of QTLs corresponding to various trait or generation combinations: color in BC2 and fineness in BC1 on the upper part of c6; color in BC1, fineness in BC1 and/BC2S1, and length in BC2 in the central part of c6; length in BC2S1 and fineness in BC2 on the upper part of c25. Also noteworthy is the fact that the map position and sign of phenotypic effect for 26 (33%) of the 80 QTLs reported in this study confirm data reported from four other independent investigations reporting cotton fiber quality QTLs detected in interspecific G. hirsutum x G. barbadense populations (Jiang et al., 1998; Kohel et al., 2001; Paterson et al., 2003; Mei et al., 2004). To evaluate the consistency of QTLs across different populations, the genetic structure (backcross versus F2–derived populations) of these populations, their relationships (direct genetic relatedness in the case of our two populations), the range of molecular polymorphism (and therefore different genome coverage), as well as QTL x environment interactions must be taken into consideration (Melchinger et al., 1998; Paterson et al., 1991). Our observation that QTLs for cotton fiber quality show some stability across populations, however, provides the first insight on comparative QTL mapping in cotton. Knowledge of QTL-rich chromosome regions that are congruent between mapping population, generations, and locations is highly valuable from a breeding perspective. Such consistency in the detection of QTLs adds confidence in their reality and these QTL-rich chromosomes regions may serve as primary targets in future studies on cotton fiber quality. In particular, attention may be paid to the above-mentioned chromosomes 3 and 23: both are rich in strong QTLs in our study, Paterson et al. (2003) also found a fiber strength QTL in the lower part of c23, and chromosome 3 contained one of the four strength QTLs reported by Kohel et al. (2001) and a fineness QTL common between Kohel et al. (2001) and Paterson et al. (2003).
Among the 80 QTLs detected, the subset of "positive" fiber quality QTLs (i.e., of a G. barbadense positive allelic contribution) is distributed across 19 different chromosome segments, each bearing one of several QTLs, and is located on 15 different carrier chromosomes (Table 3). The 19 regions thus delimit a total length of 636 cM (20% of the carrier genome), or 11.5% of the total genome (Table 3, Fig. 3) . In the process of our marker-assisted backcross strategy, these carrier chromosomes and chromosomal regions are now considered as G. barbadense target regions (foreground genome) to be manipulated through successive backcrosses for an introgression into a G. hirsutum background genome. Given such a high number of chromosome regions, we are now concentrating efforts on BC progenies containing subsets of introgressed chromosome segments (generally two to four), with the view toward further genetic fixation and intercrossing of advanced BC progenies for QTL pyramiding. The ongoing development of near isogenic lines, NILs, differing only by the introgression of G. barbadense alleles at a given QTL (QTL–NILs) will prove useful for studying the effect of a single given QTL on the phenotypic value of a plant harboring it. Such material will also be useful for expression studies and for map-based cloning.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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