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Genetics, Vol. 175, 891-905, February 2007, Copyright © 2007
doi:10.1534/genetics.106.066423
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,2
* Laboratory of Genetics and Laboratory of Plant Physiology, Wageningen University, NL-6703 BD, Wageningen, The Netherlands, Centro Nacional de Biotecnología (Consejo Superior de Investigaciones Científicas), 28049 Madrid, Spain and Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
2 Corresponding author: Laboratory of Genetics, Wageningen University, Arboretumlaan 4, NL-6703 BD, Wageningen, The Netherlands.
E-mail: maarten.koornneef{at}wur.nl
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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QTL analyses make use of the natural variation present within species (ALONSO-BLANCO and KOORNNEEF 2000; MALOOF 2003) and have been successfully applied to various types of segregating populations. In plants, the use of "immortal" mapping populations consisting of homozygous individuals is preferred because it allows performance of replications and multiple analyses of the same population. Homozygous populations can be obtained by repeated selfing, like for recombinant inbred lines (RILs), but also by induced chromosomal doubling of haploids, such as for doubled haploids (DHs) (HAN et al. 1997; RAE et al. 1999; VON KORFF et al. 2004). Depending on the species one can in principle also obtain immortality by vegetative propagation, although this is often more laborious. RILs are advantageous over DHs because of their higher recombination frequency in the population, resulting from multiple meiotic events that occurred during repeated selfing (JANSEN 2003).
Another type of immortal population consists of introgression lines (ILs) (ESHED and ZAMIR 1995), which are obtained through repeated backcrossing and extensive genotyping. These are also referred to as near-isogenic lines (NILs) (MONFORTE and TANKSLEY 2000) or backcross inbred lines (BILs) (JEUKEN and LINDHOUT 2004; BLANCO et al. 2006). Such populations consist of lines containing a single fragment or a small number of genomic introgression fragments from a donor parent into an otherwise homogeneous genetic background. Although no essential differences exist between these populations, we use the term near-isogenic lines for the materials described here. A special case of ILs are chromosomal substitution strains (CSSs) (NADEAU et al. 2000; KOUMPROGLOU et al. 2002), where the introgressions span complete chromosomes.
All immortal populations except those that can be propagated only vegetatively share the advantage that they can easily be maintained through seeds, which allows the analysis of different environmental influences and the study of multiple, even invasive or destructive, traits. Statistical power of such analyses is increased because replicate measurements of genetically identical individuals can be done.
In plants, RILs and NILs are the most common types of experimental populations used for the analysis of quantitative traits. In both cases the accuracy of QTL localization, referred to as mapping resolution, depends on population size. For RILs, recombination frequency within existing lines is fixed and can therefore be increased within the population only by adding more lines (i.e., more independent recombination events). Alternatively, recombination frequency can be increased by intercrossing lines before fixation by inbreeding as homozygous lines (ZOU et al. 2005). In NIL populations resolution can be improved by minimizing the introgression size of each NIL. Consequently, to maintain genomewide coverage a larger number of lines are needed. Despite the similarities between these two types of mapping populations, large differences exist in the genetic makeup of the respective individuals and the resulting mapping approach. In general, recombination frequency in RIL populations is higher than that in equally sized NIL populations, which allows the analysis of less individuals. Each RIL contains several introgression fragments and, on average, each genomic region is represented by an equal number of both parental genotypes in the population. Therefore, replication of individual lines is often not necessary because the effect of each genomic region on phenotypic traits is tested by comparing the two genotypic RIL classes (each comprising approximately half the number of lines in the population). In addition, the multiple introgressions per RIL allow detection of genetic interactions between loci (epistasis). However, epistasis together with unequal recombination frequencies throughout the genome and segregation distortions caused by lethality or reduced fitness of particular genotypes may bias the power to detect QTL. Furthermore, the wide variation of morphological and developmental traits present in most RIL populations may hamper the analysis of traits requiring the same growth and developmental stage of the individual lines. When many traits segregate simultaneously, this often affects the expression of other traits due to genetic interactions. Moreover, large-effect QTL may mask the detection of QTL with a small additive effect.
In contrast to RILs, NILs contain only a single introgression per line, which increases the power to detect small-effect QTL. However, the presence of a single introgression segment does not allow testing for genetic interactions and thereby the detection of QTL expressed in specific genetic backgrounds (epistasis). In addition, because most of the genetic background is identical for all lines, NILs show more limited developmental and growth variation, increasing the homogeneity of growth stage within experiments. Nevertheless, lethality and sterility might sometimes hinder the obtaining of specific single introgression lines.
The choice of one mapping population over another depends on the plant species and the specific parents of interest. In cases where different cultivars or wild accessions are studied preference is often given to RILs. However, when different species or when wild and cultivated germ plasm are combined (ESHED and ZAMIR 1995; JEUKEN and LINDHOUT 2004; VON KORFF et al. 2004; BLAIR et al. 2006; YOON et al. 2006) NILs are preferred. For instance, in tomato the high sterility in the offspring of crosses between cultivated and wild species made the use of NIL populations (ESHED and ZAMIR 1995) preferable because genomewide coverage cannot be obtained with RIL populations due to sterility, etc. Furthermore, the analysis of agronomical important traits (such as fruit characters) cannot be performed when many genes conferring reduced fertility segregate. In Arabidopsis, the easiness to generate fertile RIL populations with complete genome coverage, due to its fast generation time, has led to their extensive use in mapping quantitative traits.
NILs have been developed in various studies using Arabidopsis to confirm and fine map QTL previously identified in RILs (ALONSO-BLANCO et al. 1998b, 2003; SWARUP et al. 1999; BENTSINK et al. 2003; EDWARDS et al. 2005; JUENGER et al. 2005a; TENG et al. 2005) for which also heterogeneous inbred families (HIFs) (TUINSTRA et al. 1997) have been used (LOUDET et al. 2005; REYMOND et al. 2006). A set of chromosomal substitutions of the Landsberg erecta (Ler) accession into Columbia (Col) has been developed to serve as starting material for making smaller introgressions (KOUMPROGLOU et al. 2002). In mice CSSs are widely used for mapping purposes and have proven to be a valuable complement to other population types (STYLIANOU et al. 2006). However, no genomewide set of NILs that allows mapping to subparts of the chromosome has been described in Arabidopsis and, to our knowledge, no empirical comparative study has been performed between the two population types within a single species.
In this study we aim to compare a RIL population with a NIL population in terms of QTL detection power and localization resolution. For that, we generated a new genomewide population of NILs using the same Ler and Cape Verde Islands (Cvi) parental accessions as used earlier to generate a RIL population (ALONSO-BLANCO et al. 1998a). The two experimental populations were grown simultaneously in the same experimental setup, including multiple replicates. QTL mapping analyses were performed on six different traits and the results of these analyses were compared in both populations.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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The second population consists of a set of 92 NILs. NILs were generated by selecting appropriate Ler/Cvi RILs and repeated backcrossing with Ler as recurrent female parent. A number of these lines have been described previously (ALONSO-BLANCO et al. 1998b, 2003; SWARUP et al. 1999; BENTSINK et al. 2003; EDWARDS et al. 2005; JUENGER et al. 2005a; TENG et al. 2005). The progeny of backcrosses was genotyped with PCR markers and lines containing a homozygous Cvi introgression into an otherwise Ler background were selected. The set of selected lines was then extensively genotyped by AFLP analysis using the same restriction enzymes and primer combinations as those used for the genotyping of the RILs (ALONSO-BLANCO et al. 1998a). The NILs will be made available through the Arabidopsis stock centers.
In both populations each line is almost completely homozygous and therefore individuals of the same line are genetically identical, which allows the pooling of replicated individuals and repeated measurements to obtain a more precise estimate of phenotypic values. For the RIL and NIL population 16 and 24 genetically identical plants were grown per line, respectively. Additionally, 96 replicates were grown for each parental accession Ler and Cvi. All plants were grown in a single experiment with four completely randomized blocks containing 4, 6, and 24 replicates per RIL, NIL, and parent, respectively.
Plant growing conditions:
Seeds were sown in petri dishes on water-soaked filter paper and incubated for 5 days in a cold room at 4° in the dark to promote uniform germination. Subsequently, petri dishes were transferred to a climate chamber (24°, 16 hr light per day) for 2 days before planting. Germinated seedlings were transferred to clay pots, placed in peat, containing a sandy soil mixture. A single plant per pot was grown under long-day light conditions in an air-conditioned greenhouse from July until October. Plants were fertilized every 2 weeks using a liquid fertilizer.
Quantitative traits:
A total of six developmental traits, which were known to vary within the populations for the number of QTL and heritability, were measured on all individuals. We quantified flowering time (FT); main inflorescence length at first silique (SL); total length of the main inflorescence (TL); basal branch number (BB), which is the number of side shoots growing out from the rosette; main inflorescence branch number (IB), which is the number of elongated axillary (secondary) inflorescences along the main inflorescence; and total number of side shoots (TB) (basal plus main inflorescence). Flowering time was recorded as the number of days from the date of planting until the opening of the first flower. All other traits were measured at maturity.
Quantitative genetic analyses:
For both populations and for each trait, total phenotypic variance was partitioned into sources attributable to genotype (VG; i.e., the line effect) and error (VE), using a random-effects analysis of variance (ANOVA, SPSS version 11.0) according to the model 
. Variance components were used to estimate broad sense heritability according to the formula 
, where VG is the among-genotype variance component and VE is the residual (error) variance component. Genetic correlations (rG) were estimated as 
, where cov1,2 is the covariance of trait means and VG1 and VG2 are the among-genotype variance components for those traits. The coefficient of genetic variation (CVG) was estimated for each trait as 
, where VG is the among-genotype variance component and 
is the trait mean of the genotypes.
QTL analyses in the RIL population:
To map QTL using the RIL population, a set of 144 markers equally spaced over the Arabidopsis genetic map was selected from the RIL Ler/Cvi map (ALONSO-BLANCO et al. 1998a). These markers spanned 485 cM, with an average distance between consecutive markers of 3.5 cM and the largest genetic distance being 11 cM. The phenotypic values recorded, except basal branch number, were transformed (log10(x + 1)) to improve the normality of the distributions and the values of 16 plants per RIL were used to calculate the means of each line for all traits. These means were used to perform the QTL analyses unless otherwise stated. The computer program MapQTL version 5.0 (VAN OOIJEN 2004) was used to identify and locate QTL linked to the molecular markers, using both interval mapping and multiple QTL mapping (MQM). In a first step, putative QTL were identified using interval mapping. Thereafter, a marker closely linked to each putative QTL was selected as a cofactor and the selected markers were used as genetic background controls in the approximate MQM of MapQTL. LOD statistics were calculated at 0.5-cM intervals. Tests of 1000 permutations were used to obtain an estimate of the number of type 1 errors (false positives). The genomewide LOD score, which 95% of the permutations did not exceed, ranged from 2.6 to 2.8 and chromosomewide LOD thresholds varied between 1.8 and 2.1 depending on trait and linkage group. The genomewide LOD score was then used as the significance threshold to declare the presence of a QTL in MQM mapping, while the chromosomewide thresholds were used to detect putative small-effect QTL. In the final MQM model the genetic effect (µB – µA) and percentage of explained variance were estimated for each QTL and 2-LOD support intervals were established as an 
95% confidence level (VAN OOIJEN 1992), using restricted MQM mapping.
Epistatic interactions between QTL were estimated using factorial analysis of variance. For each trait, the mean phenotypic values were used as a dependent variable and cofactors, corresponding to the detected QTL, were used as fixed factors. The general linear model module of the statistical package SPSS version 11.0 was used to perform a full factorial analysis of variance or analysis of main effects only. Differences in R2-values, calculated from the type III sum of squares, were assigned to epistatic interaction effects of detected QTL. Additionally we performed a complete pairwise search (P < 0.001, determined by Monte Carlo simulations) for conditional and coadaptive epistatic interactions for each trait, using the computer program EPISTAT (CHASE et al. 1997).
The effect of replication on statistical power was analyzed by performing MQM mapping on means of trait values from 1, 2, 4, 8, 12, and 16 replicate plants, respectively. Analyses were performed on 10 independent, stochastically sampled, data sets for each replication size and trait using automated cofactor selection (P < 0.02). Total explained variance, LOD score of the largest-effect QTL, and number of significant QTL were recorded for each analysis.
The effect of population size on statistical power was analyzed by performing MQM mapping on increasing population sizes. Analyses were performed on 10 independent, stochastically sampled, data sets for each population size. Subpopulations of increasing size, with a step size of 20 lines, were analyzed for each trait using automated cofactor selection (P < 0.02). Total explained variance, LOD score of the largest-effect QTL, and number of significant QTL were recorded for each analysis.
Statistical analyses of NILs:
Differences in mean trait values of Ler and NILs were analyzed by univariate analysis of variance, using the general linear model module of the statistical package SPSS version 11.0. Dunnett's pairwise multiple comparison t-test was used as a post hoc test to determine significant differences. For each analysis, trait values were used as a dependent variable and NILs were used as a fixed factor. Tests were performed two sided with a Bonferroni-corrected significance threshold level of 0.05 and Ler as a control category. To increase statistical power, similar analyses were conducted for bins (see RESULTS). For this, trait values of all introgression lines assigned to a certain bin were pooled and compared to values of the Ler parental line. Because each NIL can be a member of more than one bin the significance threshold was lowered to 0.001 to correct for multiple testing. The genetic effect of Cvi bins significantly differing from Ler was calculated as µB – µA, where µA and µB are the mean trait values of Ler and the Cvi bin, respectively. Explained variance was estimated from the partial 
2 of the univariate analysis of variance, where 2 is the proportion of total variance attributable to factors in the analysis. The total percentage of explained variance was then estimated by using trait values as a dependent variable and NILs as a fixed factor, where all NILs were included as subjects. The percentage of explained variance of individual QTL was estimated as a fraction of the total variation in the population (including all lines), using a single bin as a fixed factor and as a fraction of the total variation in a comparison of a single bin with Ler only.
To determine the effect of replicated measurements we calculated the power of detecting significant differences between Ler and NILs using various replicate numbers. For each trait we calculated the minimal relative difference in mean trait values that could still be significantly detected. Calculations were performed using a normal distribution two-sample equal variance power calculator from the UCLA department of statistics (http://calculators.stat.ucla.edu/). We first calculated for each trait the mean phenotypic value of 96 Ler replicate plants (µA) and for each line the standard deviation of 24 replicate plants. The mean line standard deviation of each trait was taken as a measure of variation (
) in all subsequent calculations. The significance level, the probability of falsely rejecting the null hypothesis (H0: µA = µB) when it is true, was set to 0.05 and power, the probability of correctly rejecting the null hypothesis when the alternative (H1: µA 
µB) is true, was set to 0.95. The sample size of Ler (NA) was always identical to the sample size of NILs (NB) and ranged from 2 to 24 individuals. For each trait and sample size the mean trait value (µB) for NILs was then calculated as the minimum value to meet the alternative hypothesis (H1: µA µB) in a two-sided test. These minimum values were then converted in a fold-difference value compared to the Ler value, calculated as (|µB – µA| + µA)/µA, to obtain a relative estimate independent of trait measurement units.
The effect of replication on statistical power was also analyzed by performing bin mapping using 2, 4, 8, 12, and 16 replicate plants, respectively. Analyses were performed on 10 independent, stochastically sampled, data sets for each replication size and trait and the number of significant QTL was recorded for each analysis.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Strong and similar genetic correlations were observed between traits in the two Ler/Cvi populations, indicating partial genetic coregulation (Table 2). Flowering time shows the highest correlation with the number of main inflorescence branches but is negatively correlated with basal branch number. Flowering time is also, but to a lesser degree, correlated with plant height. Correlations were also found between plant height and branching with again positive values with the number of main inflorescence branches and negative correlations with basal branch number. These results contrasted with those from UNGERER et al. (2002), who found negative correlations between flowering time, plant height, and branching in all pairwise comparisons, which is probably due to the different environmental setups in the two laboratories.
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To map QTL in the NIL population, all bins were tested individually by comparing the phenotypes of the NILs assigned to each bin with that of Ler. As shown in Figure 3 and Table 4, one to nine QTL were detected for each trait. The total explained variance for each trait ranged from 26.7% for basal branch number up to 87.7% for total plant height. Explained variances for the largest-effect QTL for each trait ranged from 19.3% for basal branch number to 91.9% for total plant height as calculated from a restricted ANOVA using only lines from the most significant bin and Ler. To show the relative effect of Mendelizing QTL with respect to the total population variance we calculated the explained variances also when all lines of the population were subjected to ANOVA analysis using the most significant bin as a fixed factor (Table 4). Relative effects of QTL were much lower in this unrestricted analysis because all other QTL in the population increase residual variation that is not corrected for, as is done in MQM mapping in the RIL population. Moreover, lines partly overlapping the QTL bin are not assigned to that bin but can still contain the QTL Cvi allele, further increasing the residual variation in the population.
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For a number of traits several QTL were found that could not be significantly detected in the RIL population. In total 12 of such small-effect QTL were detected for flowering time (3), length at first silique (5), total plant length (2), and basal branch number (2). None of those met the lower chromosomewide significance threshold for suggestive QTL in the RIL population. Although 2 were close to this threshold, 10 of them did not reach LOD scores >1.0 in the RIL population (supplemental Table 3 at http://www.genetics.org/supplemental/).
We defined the support interval in the NIL mapping population as the region spanned by consecutive bins, significantly differing from Ler (P < 0.001) and sharing the same direction of effect. The length of support intervals estimated in this way ranged from 1.4 (total plant length) to 85.3 cM (basal branch number) with an average of 30.9 cM. Alternatively, we also searched for QTL in the NIL population by comparing the phenotype of each NIL individually against Ler (supplemental Figures 2–7 at http://www.genetics.org/supplemental/). In this case, support intervals can be estimated as the length of the overlapping regions between the Cvi introgression fragments of NILs significantly differing from Ler in a particular genomic region. This second method increases the QTL localization resolution, but reduces statistical power. For each bin on average 116 plants could be tested against Ler whereas only 24 plants were available for analysis of individual NILs. Moreover, individual lines may contain multiple opposing-effect QTL, resulting in nonsignificant differences compared to Ler. Therefore, lines spanning the bin support interval were occasionally not significantly different from Ler. Likewise, lines bearing introgressions outside the bin support intervals sometimes differed significantly from Ler, probably due to multiple additive small-effect QTL. Together, the loss of power and the complexity of the traits under study hindered a confident estimation of a NIL support interval. Nevertheless, all QTL detected in the bin analysis could also be detected by analyzing individual NILs. As a compromise between the two methods of support interval estimation we recorded the position of the largest-effect bin within the bin support interval (Table 4). However, it must be noted that bin support intervals may contain multiple QTL of similar direction. The average size of these largest-effect bins was 4.6 cM. Within those bins, at least one individual NIL significantly differing from Ler was always found.
Power in RIL vs.NIL QTL mapping:
The power to detect a QTL at a specific locus basically depends on the difference in mean trait values between A and B genotypes for that particular locus. Although other parameters like trait heritability, genetic interactions, and genetic map quality should not be ignored. Because power increases when variance for mean values decreases, QTL analyses can benefit greatly from multiple measurements. In a RIL population this can be achieved in two ways. First, because segregation of both alleles occurs randomly and each locus is represented equally by the A and the B genotype, provided there is no segregation distortion (DOERGE 2002), increasing the number of RILs to be analyzed will increase the number of observations of each genotype at a given genomic position. A further advantage of increasing the RIL population size is that the number of recombination events increases, which can improve resolution. However, when the number of lines is fixed, more accurate trait values of lines can be achieved by measuring replicate individuals of the same line. In addition, accurate trait values based on replicate measurements improve the possibility of detecting smaller-effect QTL.
To test the effect of replicated measurements and population size on the QTL detection power of the two Ler/Cvi populations we analyzed the phenotypic data obtained in these populations by varying both parameters. For the RIL population we performed QTL analyses on different numbers of RILs (population size) and used mean line values obtained with different numbers of replicates (replicate size). The total explained variance in the population, the LOD score of the largest-effect QTL, and the number of detected QTL were then recorded for each trait (Figure 4). When the population size was kept constant (161 lines), the recorded statistics increased when increasing the replicate number from one to four but this increase leveled off rapidly when measuring more replicates (Figure 4, A–C). In contrast, when the number of replicates was kept constant (16 replicated measurements per RIL) and population size was increased, the QTL detection power improved more drastically. However, the total explained variance remained more or less constant over all population sizes (Figure 4D). This phenomenon is commonly known as the Beavis effect and is due to the fact that estimated explained variances of detected QTL are sampled from a truncated distribution because QTL are taken into account only when the test statistics reach a predetermined critical value (XU 2003). As a result, the expectations of detected QTL effects are biased upward. A second effect of increasing population size is the nearly linear increase of LOD scores, observed for all analyzed QTL (Figure 4E). Significance thresholds determined by permutation tests for each population size were steady around 2.7 LOD for population sizes >30 RILs and increased slightly with smaller population sizes (data not shown). The largest-effect QTL could be significantly detected at all population sizes for all traits except for basal branch number, whose largest-effect QTL could not be significantly detected in population sizes <80 RILs.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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A summary of the differences observed between the RIL and NIL populations derived from Ler and Cvi is shown in Table 5 and in supplemental Figure 8 at http://www.genetics.org/supplemental/. The total number of QTL detected did not differ much between the two populations. However, different loci were detected in both types of populations, showing their complementary properties. For both populations the detection of QTL was highly dependent on the trait under consideration and its genetic architecture (e.g., effect and position of QTL, epistasis). The power of the new NIL population to detect the large-effect loci was close to that of the existing RIL population since most large-effect loci were detected in both populations. However, a few relatively large-effect loci showing significant epistatic interactions could be detected only in the RIL population, but not in the NILs (supplemental Table 3 at http://www.genetics.org/supplemental/). Moreover, localization resolution was higher in the RIL population compared to the bin analysis of the NIL population, allowing separation of linked QTL. This was best illustrated by the two major QTL for flowering time detected in the RIL population on the top of chromosome 5, which not only are linked but also showed strong epistatic interaction. Consequently, these two QTL could not be separated in the NIL population. Nevertheless, the QTL resolution in the NIL population can be increased when analyzing individual lines, although this will be at the cost of mapping power. In total, 14 QTL detected in the RIL population could not be detected in the NIL population, of which 10 showed significant epistatic interaction with other QTL and all others were closely linked to another significant QTL.
In contrast, the average explained variance of single QTL was higher in the NIL population, increasing the power to detect small-effect QTL. This difference can be attributed to the level of transgression, which is stronger in the RIL population, thereby increasing total phenotypic variance. As a result, 13 small-effect QTL could be detected in the NIL population, which were not detected in the RIL population. Nevertheless, some of the small-effect QTL detected in the NILs were close to the significance threshold in the RIL population when using the lower chromosomal LOD thresholds (supplemental Table 3 at http://www.genetics.org/supplemental/). Expectedly, the power to detect small-effect QTL in the NIL population was higher for the more heritable traits (flowering time and plant height) than for those traits with low heritability (branching traits). The different power to detect small-effect QTL of the two populations is due to the effect of the segregation of multiple QTL in the RIL population, which increases the residual variance at each QTL under study.
The analyses of the RIL and NIL populations performed in this work were probably close to the maximum statistical power for the given population sizes since the number of detected QTL leveled off at higher replication sizes (Figures 4 and 5). The power analyses presented here could guide the making of decisions on the number of plants to be analyzed when experiments are costly, laborious, or time consuming and therefore may require the analysis of fewer plants. Overall, for RILs, the effect of population size on mapping power was larger than the effect of replicated measurements of individual lines. Therefore, to reduce the number of plants to be analyzed, it is preferable to first reduce the number of replicates per line, and only thereafter, if required, the number of lines. In our analyses major-effect QTL for most traits could still be significantly detected when only 50 lines were analyzed without replicates (data not shown). However, due to the Beavis effect (XU 2003) the explained variances obtained with small population sizes were strongly overestimated. In the NIL population, the number of replicated measurements has a larger impact on mapping power and at least five replicated plants should be analyzed to obtain enough statistical power (Figure 5). However, fewer lines can be analyzed as long as genomewide coverage is maintained. In this NIL population this can be achieved using a core set of 25 lines, although localization resolution was diminished. Nevertheless, most QTL detected in the full set could still be detected in the core set (supplemental Figure 9 at http://www.genetics.org/supplemental/). Once a QTL has been identified in a particular region, one can zoom in with a minimal set of lines carrying smaller introgressions defined by crossovers in the support interval of the QTL of interest (FRIDMAN et al. 2004).
The Ler/Cvi NIL population developed in this work provides a useful resource that will facilitate the genetic dissection of quantitative traits in Arabidopsis in various aspects. First, as shown here, it can be analyzed as an alternative segregating population to perform genomewide QTL mapping, with the particular advantage of detecting small-effect QTL. Second, this population can be used to confirm previously detected QTL in the Ler/Cvi RIL population. Third, individual lines of this population can serve as a starting point for the rapid Mendelization of particular QTL and for their fine mapping and cloning (PARAN and ZAMIR 2003). Finally, the single introgression lines of this population may also strongly facilitate the fine mapping of artificially induced mutant alleles in the common laboratory Ler genetic background (or transferred to this accession). The fine mapping of mutant loci affecting quantitative adaptive traits is often hampered by the confounding effects of QTL segregating in the mapping populations derived from crosses between the mutant and another Arabidopsis wild accession. Knowing the approximate genetic location of the mutant locus within a chromosomal arm, specific lines of this NIL population can be selected as carrying a single introgression spanning the map position of the locus of interest. These lines can then be used to derive the required monogenic mapping population, as has been illustrated with the flowering-time locus FVE (AUSIN et al. 2004). In conclusion, the elucidation of quantitative traits can benefit from the parallel analysis of both populations.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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FOOTNOTES |
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LITERATURE CITED |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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, flowering time; 
, length at first silique; 
, total plant length; x, main inflorescence branch number; 
, basal branch number; and +, total branch number. Error bars represent SEM of 10 independent analyses.