GWAS analysis combined with QTL mapping identify CPT3 and ABH as genes underlying dolichol accumulation in Arabidopsis

Dolichols (Dols), ubiquitous components of living organisms, are indispensable for cell survival. In plants, as well as other eukaryotes, Dols are crucial for posttranslational protein glycosylation, aberration of which leads to fatal metabolic disorders in humans. Until now, the regulatory mechanisms underlying Dol accumulation remain elusive. In this report, we have analyzed the natural variation of the accumulation of Dols and six other isoprenoids between 120 Arabidopsis thaliana accessions. Subsequently, by combining QTL and GWAS approaches, we have identified several candidate genes involved in the accumulation of Dols, polyprenols, plastoquinone, and phytosterols. The role of two genes implicated in the accumulation of major Dols in Arabidopsis – the AT2G17570 gene encoding a long searched for cis-prenyltransferase (CPT3) and the AT1G52460 gene encoding an alpha-beta hydrolase (ABH) – is experimentally confirmed. These data will help to generate Dol-enriched plants which might serve as a remedy for Dol-deficiency in humans.


INTRODUCTION
In order to understand the genetic basis underlying the variation in polyisoprenoid content, and 121 to identify genes that are responsible for it, we used both a quantitative trait loci (QTL) mapping     Moreover, Est-1 and Col-0 are the parents of the advanced intercross recombinant inbred lines 220 (AI-RILs) mapping population (EstC), which is an excellent resource for QTL analyses due to 221 a large number of fixed recombination events and the density of polymorphisms 222 (Balasubramanian et al., 2009). For these reasons, the EstC population was selected for further 223 analyses in addition to the analysis of the natural accessions. Estimation of the heritability of isoprenoid levels 238 To identify the fraction of the observed variation that is genetically determined and whether it 239 can be potentially mapped into QTLs, we estimated the broad sense heritability (H 2 ) for each 240 isoprenoid (Table 1) as described in the Material and Methods section. In the AI-RIL population, 241 the broad sense heritability ranged from 0.33 (for Phytosterols) to 0.55 (for Pren and Dol) and  (Table 1).   Figure 5D) we identified three QTLs underlying the variation in carotenoid accumulation, as 280 the whole model explains together almost 24% of the PVE (Table 2). It should be underlined 281 that the QTL on chromosome 3 (for chlorophylls) and the QTL on chromosome 5 (for 282 carotenoids) were included in this analysis despite the fact that their LOD scores were slightly 283 below the threshold (below 3) ( Figure 5C and Figure 5D, respectively). Interestingly, two of 284 the QTLs identified for chlorophylls and carotenoids, localized on chromosomes 2 and 3, were

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Our search also revealed two small QTL regions for phytosterols (data not shown); however, 307 they were not analyzed further due to their statistical insignificance (LOD < 3.0). Despite the 308 large set of numerical data, no QTLs were identified for plastoquinone or tocopherols. This 309 might indicate that the mapping population used in this study was not appropriate for 310 investigating these metabolites.  (Table 2).

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As a result of the above-described procedure of selection and prioritization, we generated four    For phytosterols, 10 SNPs at 5 distinct genetic regions showed significant associations. One of 485 these is again the same SNP that has been reported above for plastoquinone and Dols.   Next, we conducted hierarchical clustering, in which the correlation matrix was used as a 593 measure of the distance between metabolites in the natural accessions and the mapping 594 population. This clearly showed relationships between metabolite levels (Figure 11), which 595 might reflect coupling(s) in their biosynthetic pathways ( Figure 12). Thus, chlorophylls and 596 carotenoids were the most closely related compounds (Figure 11), while phytosterols, 597 plastoquinone, and Prens formed a separate cluster, which was also attracting the Dol cluster.

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The Dol cluster was, however, much more distant from the three other metabolites. The most Despite the fact that no overlapping associations have been found for the GWAS and QTL 707 results, one can try, using the GWAS results, to prioritize candidate genes in the QTL interval.

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In the confidence interval of the detected QTL for Dol on chromosome 2, we could analyze 709 6,668 independent segregating polymorphisms with a minor allele frequency greater than 5%.     We would like to express our gratitude to Professor Maarten Koornneef for providing the AI-

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RILs seeds used in this study. We also would like to thank Dr Agata Lipko for initial 989 characterization of mutant lines and Rafał Banasiuk for help with preparing high-quality figures.

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Dr Marta Hoffman-Sommer is kindly acknowledged for help with preparation of the manuscript.