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Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms.

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Most traits of economic and evolutionary interest vary quantitatively and have multiple genes affecting their expression. Dissecting the genetic basis of such traits is crucial for the improvement of crops and management of diseases. Here, we develop a new resource to identify genes underlying such quantitative traits in Arabidopsis thaliana, a genetic model organism in plants. We show that using a large population of inbred lines derived from intercrossing 19 parents, we can localize the genes underlying quantitative traits better than with existing methods. Using these lines, we were able to replicate the identification of previously known genes that affect developmental traits in A. thaliana and identify some new ones. This paper also presents all the necessary biological and computational material necessary for the scientific community to use these lines in their own research. Our results suggest that the use of lines derived from a multiparent advanced generation inter-cross (MAGIC lines) should be very useful in other organisms.

The use of heterogeneous stocks (HS) improves the power to detect and localise QTL, and model genetic architecture more realistically. HS are the result of repeated crosses between multiple parental lines over many generations to produce a highly recombinant heterozygous outbred population. This strategy has been successfully used for fine-mapping QTL using eight parental strains in mice [29],[30] and Drosophila [31]. A disadvantage with HS is that each individual's genome is unique and heterozygous, and therefore the population must be genotyped at high density each time it is phenotyped. A related strategy, that avoids the need to re-genotype, is to generate RILs from multiple parents [32],[33], where the genomes of the founders are first mixed by several rounds of mating and then inbred to generate a stable panel of inbred lines. The name MAGIC (for multiparent advanced generation intercross) has been suggested for this type of population [33]. The large number of parental accessions increases the allelic and phenotypic diversity over traditional RILs, potentially increasing the number of QTL that segregate in the cross. The larger number of accumulated recombination events increase the mapping accuracy of the detected QTL compared to an F2 cross [34]. Thus, MAGIC lines occupy an intermediate niche between naturally occurring accessions and existing synthetic populations.

Here we present the first set of MAGIC lines. They are derived from an advanced intercross of Arabidopsis thaliana produced by intermating 19 natural accessions for four generations (as described in [35]) and then inbreeding for 6 generations. The resulting nearly homozygous lines form a stable panel of RIL that do not require repeated genotyping in each QTL study. We describe their construction and genomic structure, and demonstrate that these lines can be used for QTL fine-mapping using examples of developmental traits. Finally, we establish the statistical and computational tools and resources required for their analysis, and propose new candidate genes for germination date and bolting time.

Given the random mating design, each F4 family incorporates a variable number of accessions in their pedigree, with an average of 9.97 distinct founder accessions per F4 (the distribution is plotted in Figure 1); Table S1 lists the lines, the cross they were derived from and which accessions contribute to their pedigree. Although there are 1026 MLs in production, in this paper we focus on a subset of up to 527 lines for which genotype data is currently available (the exact number of lines phenotyped varies for each trait). The ML germplasm is being made available through the Arabidopsis stock centre ( ).

Extensive variation was observed for developmental quantitative traits (see Table 2). For these traits, we measured the heritability among MLs in two ways: (i) is the proportion of variation that is due to genetic differences between lines, using the phenotypic average of the replicates within each line. (ii) is an estimate of the genetic variance if only one replicate per line were phenotyped. Thus measures the true genetic variance between individual plants, while is the effective genetic variance in an experiment with replication (as is the case in this study). In all cases and increases with the number of replicates. These estimates are given in Table 2 along with the sample sizes for each trait. As highly inbred lines were used, within-line variability is almost entirely non-genetic, and hence the mean of each line was used for QTL mapping. Therefore is the upper bound of , the fraction of the variance that is due to mapped QTL; indicates how much genetic variability has been found by mapping.

The average SNP minor allele frequency in the MLs is also 0.22, and is distributed similarly to that in the founders (Figure 3), with the exception that there are fewer alleles with intermediate frequencies, as expected by drift. The extent of allele sharing between MLs was consistent with their breeding history. Cousin MLs, descended from the same F4 family, share 74% of alleles on average whilst those from different F4 families share 68% (Figure 3). Thus, as expected, cousins share slightly more alleles than the founders and non-cousins slightly less. We found 19 pairs of lines that share over 95% of their genotypes, and three pairs were identical. We believe this is most likely due to errors during breeding; these 38 lines were therefore omitted from the heritability analysis and QTL mapping.

The chromosome boundaries are marked by black lines. The intensity of the LD between SNPs at loci x,y is indicated by the colour in the corresponding x,y coordinate, using the scale indicated in the legend.

We also investigated the power and accuracy that would be achievable if the complete MAGIC population of 1026 lines were used, by simulating an instance of the full cohort. We found the power to detect a QTL that explains 5% of the variation increased to 79% and the median mapping resolution was reduced to 0.29 Mb. The corresponding figures for a QTL that explains 10% of the variation were 96% and 0.19 Mb.

Each curve is estimated from simulations as described in Materials and Methods. The width of the corresponding confidence interval is twice the mapping error. The horizontal dashed lines cut the distributions at the 50% (lower) and 90% (upper) points.

QTL heritability is defined as the fraction of variance accounted for by QTL, averaged across all sampled multiple-QTL models, and is given in Table 2. Overall, Table 2 indicates that, depending on the phenotype, up to 63% of the between-line heritability is accounted for by the mapped QTL. The sources of the missing heritability might include environmental interactions, undetected QTL with small genetic effects, and epistasis. Dominance effects should be negligible given the lines are effectively inbred.

We also mapped QTL using two alternative methods. The first, an Empirical Bayes linear mixed effects model, assesses the evidence for a QTL taking into account the expected population structure. This class of method has been shown to be effective at controlling for population structure in association mapping with natural accessions of A. thaliana and in other species [13],[24]. Our model assumed that mean trait values on lines descended from the same F4 are likely to be more similar than otherwise. We found that the QTL logP values produced by this method were slightly smaller than the fixed effects model described above, but that the difference was generally negligible. This result, suggests again that population structure does not have a strong impact.

We have described a new panel of genetically diverse and highly recombinant inbred lines of A. thaliana. Like other recombinant inbred lines they do not require repeated genotyping, and since unlimited replicates of each line can be grown, data for many traits can be accumulated, facilitating the study of trait correlations, genotype by environmental interactions, and the genetic basis of phenotypic plasticity. They represent a significant improvement over standard RILs descended from just two founders in that they capture more of the genetic and phenotypic variation present. Furthermore, they have a higher density of recombinants, which improves mapping resolution. We have shown how to take account of the increased genetic complexity in the analysis, and our results show that mapping accuracy and detection is much improved in the MLs when compared to traditional two-parent F2 and RIL mapping populations. Consequently, the MLs are an important new tool for the study of the genetic basis of plant growth and yield under multiple environments. Improved understanding of the genetic basis of such quantitative traits is important for the improvement of crop varieties, and to improve our basic knowledge of plant form, growth and development. 17dc91bb1f