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Technical Note

Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Author to whom correspondence should be addressed.
Methods Protoc. 2020, 3(2), 28;
Submission received: 10 March 2020 / Revised: 1 April 2020 / Accepted: 2 April 2020 / Published: 4 April 2020


Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits. To date, a total of 313 QTL for 31 quantitative traits have been reported in 14 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, the scaffold sequences, or the pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but the most used ones were simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). To uniquely map the SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules, methods with several scripts and database files were developed to locate PCR- and SNP-based markers onto the same reference, co-locate QTL, and scan genome-wide candidate genes. Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.

1. Introduction

Most traits of importance in plant breeding are quantitative and controlled by polygenes with minor effects on phenotypes. Traditional quantitative genetics can estimate overall genetic effects or variances of polygenes for quantitative traits through dedicated genetic designs [1], providing a theoretical guide for plant breeding. With the development of molecular markers and high-throughput genotyping techniques, individual polygenic loci on chromosomes and their effects on phenotypes can be detected and estimated using statistical genomics approaches. Such polygenic loci on chromosomes are called quantitative trait loci (QTL). They are associated with phenotype variation of quantitative traits and are usually mapped in various populations using molecular markers such as simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). Generally, QTL can be identified by two main approaches: linkage mapping (LM) and association mapping (AM) or genome-wide association study (GWAS) [2]. LM uses bi-parental populations, such as F2, recombinant inbred line (RIL), doubled haploid (DH), and backcross (BC) populations, to identify loci responsible for trait variation between parents based on recombination-based genetic linkage maps [3]. AM relies on linkage disequilibrium (LD) between markers and QTL. AM uses a more diverse genetic panel to overcome the phenotypic diversity limitation of bi-parental populations. This diversity limitation may include natural germplasm collections, or, more often, panels including germplasm accessions and breeding lines, or multi-parent populations such as nested association mapping (NAM) [4,5,6] and multi-parent advanced generation intercross (MAGIC) populations [7,8,9,10]. QTL can be exploited for gene cloning, marker-assisted breeding, and genomic selection or prediction.
Cultivated flax (Linum usitatissimum L.) is a self-pollinating annual crop valued for its seed oil and stem fibre. Phenotypic selection remains a major conventional breeding approach to improve traits of agronomic importance in flax. To accelerate the application of molecular breeding, a large number of molecular markers [11,12,13,14] and genetic populations [15,16,17,18] have been developed to assist QTL identification in the last decade. Using these genetic resources, a total of 313 QTL for 31 traits (13 seed yield and agronomic traits, 11 seed quality traits, four fibre traits, and three disease resistance traits) were reported in 14 studies (Table 1 and Table 2). These QTL were identified mainly using SSR or SNP markers with LM or AM/GWAS (Table 2). The studies using LM were based on genetic maps [15,18,19,20,21,22,23,24], while those using AM or GWAS were based on the flax scaffold sequences [17,25,26], the early (hereafter pre-released) version of chromosome-scale pseudomolecules (PCPs) [27,28] or the most recent release of the chromosome-scale pseudomolecules (RCPs) [14,29] (Table 2). The use of different references in the QTL identification studies made it difficult to compare the results across studies, genome-wide QTL analysis, candidate gene prediction, and breeding applications. Thus, the objectives of this study were to develop methods and corresponding software tools to uniquely map the QTL identified in different studies onto the RCPs [29]. These methods and tools were designed to be applicable to studies in flax as well as other crops.

2. Materials and Methods

2.1. The Most Recent Release of the Chromosome-Scale Pseudomolecules

The chromosome-scale pseudomolecules for flax were recently released [29]. A total of 622 scaffolds from the flax reference genome [25] were sorted onto 15 chromosomes, totalling 316.2 Mb. Thus, the SNPs identified based on the scaffold reference sequences can be accurately mapped to the pseudomolecules. The 15 pseudomolecule sequences corresponding to 15 chromosomes were downloaded from the National Center for Biotechnology Information (NCBI) database. The accession numbers of the pseudomolecules for the 15 chromosomes are CP027619 (Lu1), CP027626 (Lu2), CP027627 (Lu3), CP027628 (Lu4), CP027629 (Lu5), CP027630 (Lu6), CP027631 (Lu7), CP027632 (Lu8), CP027633 (Lu9), CP027620 (Lu10), CP027621 (Lu11), CP027622 (Lu12), CP027623 (Lu13), CP027624 (Lu14), and CP027625 (Lu15). The chromosome sizes are listed in Table S1.

2.2. Marker Infomation of QTL in Flax

All 313 flax QTL reported in the 14 studies (Table 2) were identified from three types of markers: amplified fragment length polymorphisms (AFLPs), SSRs, and SNPs. PCR primer sequences of AFLPs and SSRs were retrieved from the literature [15,19,20,21,23,24]. For the SNPs named based on the scaffold sequences, their scaffold names and coordinates were collected directly from the publications [17,26]. For the SNPs identified without a reference [18], flanking sequences of the SNP markers were downloaded from the publication [18]. All available primer sequences of SSR markers and flanking sequences of SNP markers for the identified QTL are listed in Tables S2 and S3, respectively.

2.3. Mapping PCR-Based Markers to the Most Recent Release of the Chromosome-Scale Pseudomolecules

PCR primer sequences of markers were mapped onto the RCPs using the electronic PCR (E-PCR) tool [31]. A pipeline using E-PCR was developed. This pipeline includes two Perl scripts: (Program S1) and (Program S2). Program S1 is a script that creates a search database of the RCPs, outputting two files for the downstream analysis: *.famap and *.hash. Program S2 is a script that performs electronic PCR to map paired primers onto the RCPs, generating result files with coordinates of the primers on chromosomes and their amplicon sizes. No nucleotide mismatches or gaps were allowed. The instructions of these programs are described in User guide S1.
PCR primers designed from sequences of different genotypes could not always be accurately mapped to the RCPs using the E-PCR approach. In such cases, BLASTN searches were performed to ascertain their map positions.

2.4. Mapping SNPs to the Most Recent Release of the Chromosome-Scale Pseudomolecules

If SNPs are identified using the flax scaffold sequences [25], their coordinates can be accurately converted to the RCPs’ coordinates. The Perl script (Program S3) executes this conversion. A database file for the accurate relationship between the scaffolds and the RCPs (Table S4) is required to run this program. The instructions of this script are described in User guide S1.
For the SNPs identified without a reference sequence [18], the flanking sequences of the SNPs were searched against the RCPs using BLASTN at an E-value of 10−30. The alignment regions of top hits were used and manually verified.
For the SNPs based on the PCPs in two publications [27,28], their scaffold names and corresponding coordinates on the scaffolds were retrieved from the raw SNP data as these SNPs were initially identified from the scaffolds, followed by conversion to the RCPs using Program S3.

2.5. Grouping QTL to Clusters

QTL mapping software tools can detect multiple quantitative trait nucleotides (QTNs) from a small region that may be grouped into the same QTL or a QTN cluster based on the LD between markers [14]. QTNs detected in different populations cannot be grouped based on population-dependent marker LD. To provide a simple solution, we opted to group in a single QTL cluster all QTL located within a 200 kb window covering the 100 kb upstream and 100 kb downstream regions of the QTN position.

2.6. Candidate Gene Analysis Based on the Most Recent Release of the Chromosome-Scale Pseudomolecules

As the RCPs [29] were generated by sorting and refining the existing scaffold sequences [25], no changes were made to the original gene annotations on the scaffold sequences. However, the new coordinates of these genes on the RCPs were not previously released [29]. The RCPs contain 42,277 protein coding genes, of which 1,327 were predicted to be resistance gene analogs (RGAs) [29]. To facilitate genome-wide candidate gene analyses, the revised version of the script “” was used to convert the coordinates of the genes on the scaffolds onto the RCPs. All genes and RGAs and their coordinates on the RCPs are listed in Tables S5 and S6, respectively. These genes were mapped to orthologous genes of the model species Arabidopsis thaliana using BLASTP of flax protein sequences against A. thaliana protein sequences at an E-value of 10−10. A total of 15,323 unique A. thaliana genes were mapped. Then, the flax genes were searched against the NCBI non-redundant protein database (nr) at an E-value of 10−5, and functional annotations were generated using a custom script that integrates protein annotation information of top hits and the orthologous A. thaliana genes. The annotation results were added to the gene list. A genome-wide gene scan along chromosomes for QTL was performed to characterize the underlying genomic regions and identify candidate genes. The genes within a 200-kb window covering the 100 kb upstream and downstream regions of the QTN position were scanned. A Perl script was developed (Program S4) to scan potential candidate genes for given QTL based on the gene annotation database files in Table S5 (for all protein coding genes) and Table S6 (for RGAs only). The instructions for this program are described in User guide S1.

3. Results

3.1. Mapping QTL onto the Most Recent Release of the Chromosome-Scale Pseudomolecules

In all 14 publications reporting flax QTL, only 67 newly reported pasmo QTL and 46 QTNs associated with seed length, seed weight and 1000-seed weight were based on the RCPs [14,30]. Therefore, the mapping of the remaining 200 QTL onto the RCPs was performed. A total of 195 QTL uniquely mapped to the RCPs of 15 chromosomes, including 40 SSRs and 36 SNPs from genetic maps, 75 SNPs from the scaffolds, and 44 SNPs from the PCPs (Figure 1 and Table 3). Markers afB13 and afXR6 for two powdery mildew QTL were not mapped because their AFLP primer sequences were not available [24]. One QTL for branching score failed to map because its SSR marker Lu2067a could not be mapped to any region on the RCPs; this was likely because the marker was designed from a genotype different from the reference genome (cv CDC Bethune). Finally, the marker Lu8_185009 for QTL uq.C8–2 associated with plant height (PLH) and technical length (TL) [18] mapped to two different chromosomes (Chr 4 and Chr 7).
It is important to pinpoint that the SSRs/SNPs corresponding to a single marker or a pair of flanking markers from genetic maps were mapped to a genomic region on a pseudomolecule, while the SNPs from the scaffold sequences or the PCPs were anchored exclusively to single nucleotide positions representing their QTL peak locations.

3.2. Identical or Co-Located QTL

QTL that mapped to the same RCPs were comparable across studies, mapping populations, and traits. Based on the 200 kb upstream and downstream region rule, the 195 QTL/markers for the 26 traits mapped to the RCPs were grouped into 133 QTL clusters (Table 3). The QTL with the same numbers in the “Co-location” column in Table 3 were deemed to belong to the same QTL clusters, indicating identical or co-located QTL. QTL for 16 of the 29 traits were identified in two or more studies, of which 12 had one or more QTL located at the same positions or within the same QTL clusters (Table 1), thereby supporting the accuracy of the QTL through validation across studies.
Some QTL were validated in several studies that differed in marker types (SSRs or SNPs), populations (bi-parental population or diverse germplasm panel), or statistical methods used for QTL mapping (Table 1 and Table 2). For example, QTL-195 (QDTM-Lu4.1) and QTL-54 (QDm.BM.crc-LG4) on Chr 4 corresponded to the same QTL for days to maturity (DTM) identified in two different studies [20,28]. QTL-187 (QIOD-Lu7.2) and QTL-7 (QIod.crc-LG7) on Chr 7 for iodine value (IOD) [19,28], QTL-190 (QLIN-Lu7.2) and QTL-5 (QLin.crc-LG7) on Chr 7 for linolenic acid content (LIN) [19,28], QTL-6 (QLin.crc-LG16) and QTL-33 (QLin-LG12.3) on Chr 12 for LIN [19,23], and QTL-4 (QLio.crc-LG16) and QTL-30 (QLio-LG12.3) on Chr 12 for linoleic acid content (LIO) [19,23] were additional examples of the same QTL identified in different studies. Some QTL or QTNs were grouped into single QTL because their coordinates on chromosomes were close or identical and, historical recombinations may not have been present in the population; for example, QTL-144 (scaffold11-96400) and QTL-145 (scaffold11-96569) on Chr 1 for steric acid content (STE) [17], and QTL-155 (scaffold297-275131), QTL-100 (scaffold297_275113), and QTL-154 (scaffold297-275113) on Chr 1 for technical length (TL) corresponded to unique QTL (Co-location cluster No. 46 and 9 in Table 3) [17,26].
Some co-located QTL may lead to their pleiotropic effects on multiple traits. Thirteen genomic regions that had at least three identical or co-located QTL were observed (yellow highlights in Figure 1 and Table 3). For example, eight QTL—QTL-195 (QDTM-Lu4.1), QTL-168 (QYLD-Lu4.1), QTL-179 (QPLH-Lu4.3), QTL-49 (QCw.BM.crc-LG4), QTL-54 (QDm.BM.crc-LG4), QTL-52 (QSpb.BM.crc-LG4), QTL-50 (QSw.BM.crc-LG4), and QTL-53 (QYld.BM.crc-LG4)—were co-located between positions 13,170,489 and 15,040,682 bp on Chr 4 and had pleiotropic effects on phenotypes of six traits: DTM, YLD, PLH, cell wall content (%) (CEW), seeds per boll (SEB), and straw weight (STW). Thus, this is an important genomic region controlling seed yield and related agronomic traits. As noted and discussed previously [19,20,28], QTL-186 (QIOD-Lu4.1), QTL-189 (LIN-Lu4.1), and QTL-192 (QLIO-Lu4.1) were co-located at position 19,907,982 bp on Chr 4; QTL-193 (QLIO-Lu7.2), QTL-190 (QLIN-Lu7.2), QTL-187 (QIOD-Lu7.2), QTL-7 (QIod.crc-LG7), QTL-5 (QLin.crc-LG7), and QTL-3 (QLio.crc-LG7) were between positions 14,540,252 and 17,976,903 bp on Chr 7; QTL-188 (QIOD-Lu12.3), QTL-191 (QLIN-Lu12.3), and QTL-194 (QLIO-Lu12.3) located in the 489,561 and 2,981,562 bp interval on Chr 12; and QTL-6 (QLin.crc-LG16), QTL-33 (QLin-LG12.3), QTL-4 (QLio.crc-LG16), QTL-30 (QLio-LG12.3), and QTL-8 (QIod.crc-LG16) positioned between 2,036,216 and 3,802,807 bp on Chr 12. These four genomic regions contributed greatly to the genetic variation for LIO, LIN, and IOD in several flax populations [19,20,28].

3.3. Candidate Genes for QTL

The resolution of current QTL mapping or GWAS technologies is insufficient to pin QTL to accurate locations of genes or genetic features controlling traits. A simple approach for predicting candidate genes is to investigate the annotated genes in the vicinity of QTL, such as a window of 200 kb flanking the QTL [14,20]. Our ability to position most of the previously reported QTL to the RCPs makes it possible to perform an overall genome-wide candidate gene scan along chromosomes. Thus, all potential candidate genes of the 195 QTL listed in Table 3 were scanned. A total of 7,821 unique candidate genes co-located with the 133 QTL clusters (Table S7). These candidate genes can be further analysed and validated. For example, three QTL for powdery mildew resistance were identified [15] and mapped to chromosomes 1, 7, and 9 (Table 3, Figure 1). Some RGAs were found in the vicinity of the QTL, i.e., within the pre-defined window (Table 4). One nucleotide-binding-site (NBS) encoding gene (Lus10026765), one transmembrane coiled-coil (TM-CC) gene (Lus10023437), and several receptor-like protein kinase (RLK) genes co-located with these QTL.

4. Discussion

The RCPs, representing the first chromosome-scale flax reference sequence, were released to the NCBI database in 2018 [29]. This new flax genome reference has previously been adopted for genomic studies, such as QTL identification. Prior to this release, many QTL had been identified based on different reference sequence versions (Table 2); thus, it is necessary to re-map these QTL onto the most recent and comprehensive flax reference (RCP). In addition, some research groups have already adopted the scaffold-based reference to identify SNPs and have performed other genomic studies. Consequently, more current methods and software tools are required for this re-mapping. For this purpose, we developed several utility tools, including scripts for mapping PCR- and SNP-based QTL onto the RCPs, grouping QTL in terms of a predefined window size, and performing genome-wide candidate gene analysis. These tools were successfully used to map 195 out of 200 QTL onto the new reference. Only five QTL failed to map because of incomplete information. This demonstrates the reliability and robustness of the methods, especially those for mapping the scaffold-based SNPs to the new reference, which is unique to this study. No other methods were available because this conversion must be based on the accurate coordinates of the scaffolds on pseudomolecules that were generated by the authors of this article [29]. The QTL positioned onto the RCPs and their gene candidates can be further validated and analysed on a genome-wide basis. Comparability across different studies and genetic populations will facilitate their further evaluation for applications in flax breeding.
The methods and the computer scripts described here are not only suitable for flax, but are also applicable to other crops. In wheat, for example, a large number of PCR- and SNP-based markers have been developed from different genetic maps and many versions of reference sequences, which are deposited in genome databases such as GrainGenes ( and T3/Wheat ( However, the first version of the chromosome-based reference sequence (RefSeq v1.0) was just recently released by the International Wheat Genome Sequencing Consortium [32]. Thus, the re-mapping of existing markers onto the new wheat reference necessitates software tools. Program S1 and Program S2, which adopted the widely accepted E-PCR tool [31] to map PCR primers to a reference, can be directly used for the mapping of the existing PCR-based markers to the new reference. In addition, the basic methodology of Program S3 and Program S4 is useful for the development of new tools specifically based on the wheat reference and gene annotation databases.
It is noteworthy that the gene annotation information of the new flax reference was not available in the NCBI or in any other databases or publications. Although being reported through personal communications, this is the first release of the complete gene annotation of the chromosome-scale flax reference (Table S4). This information is presented in addition to the flax reference [29] to facilitate genome-wide candidate gene analysis of QTL along chromosomes and other genomic studies. The RGAs, a subset of the flax genes (Table S6), are also useful for candidate gene prediction of disease resistance QTL.

5. Conclusions

This article details the methods, software tools, and database files developed to uniquely map the QTL previously identified from different references onto the RCPs. The methodology can be used not only for flax, but also for other crops. Using the methodology described here, 195 out of 200 PCR- and SNP-based QTL markers that were not based on the RCPs were successfully sorted into the 15 chromosomes of the RCPs and grouped into 133 co-located QTL clusters, thereby demonstrating genomic regions associated with, and/or pleiotropic to, important agronomic and seed quality traits. These re-mapped chromosome-based QTL can be easily compared across studies and facilitate genome-wide QTL analysis, candidate gene prediction, and further validation for breeding applications.

Supplementary Materials

The following are available online at Table S1. Information related to the pseudomolecules of 15 chromosomes in the NCBI database. The downloaded sequences from NCBI are used as input for Program S1. Table S2. Primer sequences of SSR markers for the identified QTL. Table S3. Flanking sequences of SNP markers for the identified QTL. Table S4. Coordinates of flax scaffold sequences on the most recent release of the chromosome-scale pseudomolecules. This file is used as input for Program S2. Table S5. Coordinates and annotations of flax protein coding genes on the most recent release of the chromosome-scale pseudomolecules. This file is used as input for Program S4. Table S6. Coordinates and annotations of flax resistance gene analogs on the recently released chromosome-scale pseudomolecules. This file is used as input for Program S4. Table S7. Candidate gene prediction of the 200 QTL in Table 3. Program S1. A Perl script to prepare a search database of reference sequences for electronic PCR. Program file name: Program S2. A Perl script to perform electronic PCR, i.e., map a pair of PCR primer sequences to a reference sequence. Program file name: Program S3. A Perl script to convert coordinates of flax scaffold sequences onto the chromosome-scale pseudomolecules. Program file name: Program S4. A Perl script to extract all candidate genes and gene annotation information (protein-coding genes or specifically resistance gene analogs) within a genomic region of a QTL or a marker. Program file name: User guide S1. A user guide for executions of Programs S1, S2, S3, and S4. All programs are also available in the gitHub site:

Author Contributions

Conceptualization, F.M.Y.; methodology, F.M.Y.; software, F.M.Y.; validation, F.M.Y. and S.C.; formal analysis, F.M.Y. and S.C.; investigation, F.M.Y. and S.C.; writing—original draft preparation, F.M.Y.; writing—review and editing, S.C. All authors have read and agreed to the published version of the manuscript.


This research was funded by Agriculture and Agri-Food Canada, Projects J-001672 and J-002035.


The authors thank Zhen Yao for figure editing, Chunfang Zheng for the GitHub web site and Madeleine Lévesque-Lemay for editing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Distribution of 308 QTL associated with 29 traits mapped onto flax chromosomes. Of these QTL, 67 for pasmo resistance and 46 for thousand-seed weight, seed width and seed length have been previously mapped on the most recent release of the flax chromosome-scale pseudomolecules [14,30]. Two fusarium wilt QTL [24] were not included because of incomplete information. Co-located regions are highlighted in yellow. See Table 1 for the trait name abbreviations.
Figure 1. Distribution of 308 QTL associated with 29 traits mapped onto flax chromosomes. Of these QTL, 67 for pasmo resistance and 46 for thousand-seed weight, seed width and seed length have been previously mapped on the most recent release of the flax chromosome-scale pseudomolecules [14,30]. Two fusarium wilt QTL [24] were not included because of incomplete information. Co-located regions are highlighted in yellow. See Table 1 for the trait name abbreviations.
Mps 03 00028 g001
Table 1. Number of QTL associated with 31 traits in flax.
Table 1. Number of QTL associated with 31 traits in flax.
CategoryNoTraitAbbreviationTotal QTL IdentifiedTotal Unique QTLSource
Seed yield and agronomic traits1Seed yieldYLD54[20,22,28]
2Thousand seed weight (g)TSW4544[17,21,22,26,30]
3Seed length (mm)SL1010[30]
4Seed width (mm)SW1515[30]
5Seeds per bollSEB11[20]
6Fruit (boll) numberFN98[17,26]
7Branching scoreBSC11[21]
8Number of branchesNB1313[26]
9Days to floweringDTF11[21]
10Days to maturityDTM32[20,28]
11Plant height (cm)PLH3330[18,21,22,26,28]
12Technical length (cm)TL1713[17,18,22,26]
Seed quality14Iodine valueIOD87[19,20,23,28]
15Protein content (%)PRO22[20,28]
16Oil content (%)OIL1010[20,23,28]
17Oleic (%)OLE44[20,28]
18Palmitic (%)PAL75[17,19,20,28]
19Stearic (%)STE87[17,20,23,28]
20Linoleic (%)LIO119[17,19,20,23,28]
21Linolenic (%)LIN1210[17,19,20,23,28]
22Seed mucilage contentMC77[27]
23Seed hull contentHC44[27]
24Seed colourSC21[19]
Fibre25Straw weight (g)STW44[20,22]
26Fibre yield (g)FY22[22]
27Fibre content (%)FC44[17,22]
28Cell walls (%)CEW11[20]
Disease29Fusarium wilt ratingFW22[24]
30Powdery mildew ratingPM33[15]
31Pasmo ratingPAS6767[14]
Table 2. QTL identification studies in flax.
Table 2. QTL identification studies in flax.
PopulationPop SizeMarkersMethod 1Ref 2Total QTLNo. of QTL Identified/Trait 3Source
DH598 RFLPs, 213 AFLPsLMGM22/FW[24]
DH78113 SSRs, 5 SNPs, 4 genesLMGM92/LIO, LIN, IOD; 1/PAL; 2/SC[19]
F3-F4300143 SSRsLMGM33/PM[15]
Core collection390464 SSRsAMGM115/TSW; 1/DTF; 2/PLH; 1/BSC; 2/LDG[21]
Core collection390460 SSRsAMGM91/OIL; 1/STE; 3/LIO; 3/LIN; 1/IOD[23]
RIL243329 SNPs, 362 SSRsLMGM201/PAL; 3/STE; 3/OLE;2/LIO; 1/LIN; 2/IOD; 1/OIL; 1/PRO; 1/CEW; 1/STW; 1/TSW; 1/SEB; 1/YLD; 1/DTM[20]
2 RILs2334,497 SNPsLMGM2414/PLH; 10/TL[18]
F21122,339 SNPsLMGM121/PLH; 1/TL; 3/YLD; 3/STW; 2/FY; 2/FC[22]
Core collection224146,959 SNPsAMSS439/PLH; 3/TL; 13/NB; 8/FN; 10/TSW[26]
Core collection224584,987 SNPsAMSS232/PLH; 1/FN; 8/TSW; 3/TL; 1/PAL; 2/STE; 1/LIO; 3/LIN; 2/FC[17]
Core collection200771,914 SNPsAMPCPs117/MC; 4/HC[27]
2 RILs and 1 DH26017,288 SNPsAMPCPs331/YLD; 8/OIL; 5/PLH; 4/PAL; 3/IOD, LIN, LIO, 2/DTM; 2/STE; 1/PRO; 1/OLE[28]
Core collection370258,873 SNPsAMRCPs6767 PAS[14]
Germplasm collection200674,074 SNPsAMRCPs4610/SL; 15/SW; 21/TSW[30]
Pop: population. Ref: reference sequences or linkage maps for QTL identification. 1 LM: bi-parental population-based QTL mapping; AM: association mapping or genome-wide association study. 2 GM: genetic map; SS: scaffold-based reference sequences [25]; RCPs: recent release of the chromosome-scale pseudomolecules [29]; PCPs: pre-released version of the chromosome-scale pseudomolecules. 3 See Table 1 for trait name abbreviations.
Table 3. QTL mapped to the recently released chromosome-scale pseudomolecules.
Table 3. QTL mapped to the recently released chromosome-scale pseudomolecules.
QTL NoTraitQTL/Marker IDLG/ScaffoldFlanking MarkersChrCoordinates on chrCo-LocationSource
2 afXR610afXR6NANANA
4 QLio.crc-LG1616Lu206-Lu765B122036216-2041030109
6 QLin.crc-LG1616Lu206-Lu765B122036216-2041030109
8 QIod.crc-LG1616Lu206-Lu765B122038322-2038517109
11 Qb*.crc-LG2222Colour-Lu178814838877-1483910075
13 QPM-crc-LG77Lu2810-Lu283273817603-381786366
14 QPM-crc-LG99Lu1125a-Lu9329357191-35751083
15TSW 3Lu2164122948222-2294858013[21]
16 6Lu2555614948801-1494898665
17 7Lu25327661757-66202066
18 7Lu58a123802629-3802807111
19 9Lu52695936422-593669488
20DTF 1Lu943128800644-2880090216
21PLH 1Lu943128800644-2880090216
22 Lu316817106045-1710626679
23BSC 22Lu2067aNA NA
24LDG 6Lu2560613553559-1355377963
25 6Lu2564613620999-1362123463
29 QLio-LG5.25c30-s11_Lu164510600927-1060112547
30 QLio-LG12.312c306-s98_Lu765B122036216-2041030109
32 QLin-LG5.25c202-s39_Lu41107602629-806601894
33 QLin-LG12.312c306-s98_Lu765B122036216-2041030109
37 QSte.BM.crc-LG33Lu3-8415336/Lu21643726308728
38 QSte.BM.crc-LG1111Lu2128/Lu11-190009281116797707-16797907102
40 QOle.BM.crc-LG3-23Lu658/Lu3150324238080-2423842733
41 QOle.BM.crc-LG55Lu5-97284921511375006131
43 QLio.BM.crc-LG66Lu254568616550-861691961
46 QIod.BM.crc-LG66Lu6-2260313/Lu6-233025862018434-208857957
55PLHuq.C1–1 Lu1_39642816539309-65390893[18]
56 uq.C3–1 Lu3_693423325295008-2529480134
57 uq.C4–1 Lu4_300701419453432-1945370442
58 uq.C5–1 Lu5_850458681823-868201845
59 uq.C6–1 Lu6_63923662175711-217591157
60 uq.C8–2 Lu8_1850097 (4)6427466-6427621 (6238294-6238449)
61 uq.C8–3 Lu8_119488828706-2893872
62 uq.C9–1 Lu9_503128144498680-4498955122
63 uq.C11–1 Lu11_557617111276828-127714396
64 uq.C11–1 Lu11_4470481113338945-13339276100
65 uq.C12–1 Lu12_696508121004697-1004929108
66 uq.C12–1 Lu12_16359612351979-352221106
67 uq.C13–1 Lu13_367183138997700-8998007115
68 uq.C14–1 Lu14_2318531413485754-13486113126
69TLuq.C1–1 Lu1_69538915664124-56643302
70 uq.C2–2 Lu2_597057222508975-2250868321
71 uq.C5–1 Lu5_850458681823-868201845
72 uq.C6–1 Lu6_63923662175711-217591157
73 uq.C7–1 Lu7_781312718087445-1808773371
74 uq.C8–1 Lu8_646184820045574-2004581580
75 uq.C8–2 Lu8_1850097 (4)6427466-6427621 (6238294-6238449)
76 uq.C9–2 Lu9_618122143378716-3378969121
77 uq.C12–1 Lu12_696508121004697-1004929108
78 uq.C14–1 Lu14_2318531413485754-13486113126
79PLHMarker4371scaffold156 (LG1) 36019156-601949924[22]
80TLMarker747228scaffold2786 (LG8) 123620608-3620934110
81YLDMarker799956scaffold319 (LG10) 133856362-3856771114
82 Marker770415scaffold117 (LG12) 611929857-1193025362
83 Marker1073071scaffold27 (LG12) 68701939-870232461
84STWMarker326151scaffold33 (LG5) 822241866-2224222681
85 Marker2368217scaffold355 (LG15) 107140622-714098892
86 Marker614116scaffold355 (LG15) 107219061-721944593
87FYMarker2603286scaffold156 (LG1) 36573623-657402327
88 Marker1722134scaffold127 (LG11) 1310603161-10603485116
89FCMarker1051901scaffold680 (LG5) 821807786-2180814881
90 Marker1561746scaffold376 (LG11) 48748431-874879536
92 scaffold1491_318496scaffold1491scaffold1491_31849661400665163
93 scaffold31_1800846scaffold31scaffold31_18008463392993222
94 scaffold344_309662scaffold344scaffold344_3096621110082796
95 scaffold51_1349321scaffold51scaffold51_134932141053242437
96 scaffold59_572553scaffold59scaffold59_5725531100517094
97 scaffold156_641874scaffold156scaffold156_6418743590679123
98 scaffold147_367986scaffold147scaffold147_36798651128851748
99 scaffold859_123972scaffold859scaffold859_123972151939372129
101 scaffold361_14957scaffold361scaffold361_1495711672690410
102 scaffold273_68457scaffold273scaffold273_68457858511373
104 scaffold156_1203677scaffold156scaffold156_12036773646856226
105 scaffold1863_545scaffold1863scaffold1863_5458122369874
106 scaffold212_601171scaffold212scaffold212_6011716638049560
107 scaffold353_773806scaffold353scaffold353_77380651607789354
108 scaffold42_494571scaffold42scaffold42_4945711315861394117
109 scaffold464_754364scaffold464scaffold464_7543641415460919127
110 scaffold635_43971scaffold635scaffold635_4397182249454782
111 scaffold977_784147scaffold977scaffold977_7841471118799131104
112 scaffold212_216830scaffold212scaffold212_2168306599615459
113 scaffold359_282990scaffold359scaffold359_282990146711296124
114 scaffold359_289139scaffold359scaffold359_289139146705147123
115 scaffold977_469888scaffold977scaffold977_4698881118484872103
117 scaffold225_427119scaffold225scaffold225_42711981599415477
118 scaffold687_121617scaffold687scaffold687_1216171416813947128
119 scaffold156_761294scaffold156scaffold156_7612943602621124
120 scaffold413_1116527scaffold413scaffold413_111652741691422841
121 scaffold156_1203677scaffold156scaffold156_12036773646856226
122 scaffold413_388319scaffold413scaffold413_38831951491070952
123 scaffold687_123666scaffold687scaffold687_1236661416811898128
125 scaffold112_184204scaffold112scaffold112_18420411851404911
126 scaffold1143_190268scaffold1143scaffold1143_190268143759351
127 scaffold1155_171787scaffold1155scaffold1155_171787157690615130
128 scaffold123_1191347scaffold123scaffold123_119134711387581998
129 scaffold1317_154716scaffold1317scaffold1317_1547161515275145133
130 scaffold132_713877scaffold132scaffold132_71387712487731714
131 scaffold1491_58878scaffold1491scaffold1491_5887861426626964
132 scaffold15_1207948scaffold15scaffold15_120794851691498755
133 scaffold1519_272169scaffold1519scaffold1519_2721699102773984
136 scaffold51-598586scaffold51scaffold51-59858641128314239
137 scaffold51-598611scaffold51scaffold51-59861141128311739
138 scaffold51-699833scaffold51scaffold51-69983341118189538
139 scaffold261-925068scaffold261scaffold261-9250689641938580
140 scaffold373-545801scaffold373scaffold373-5458011317912691119
141 scaffold373-545816scaffold373scaffold373-5458161317912706119
142 scaffold107-300735scaffold107scaffold107-30073522240517720
145 scaffold11-96569scaffold11scaffold11-965695996514246
148 scaffold302-224377scaffold302scaffold302-22437751388942551
149 scaffold302-224395scaffold302scaffold302-22439551388944351
151 scaffold866-116645scaffold866scaffold866-1166456108324756
153 scaffold59-572553scaffold59scaffold59-5725531100517094
155 scaffold297-275131scaffold297scaffold297-2751311164358349
156 scaffold361-14957scaffold361scaffold361-1495711672690410
158 Lu3-255596003Lu3-2555960031764546129
159 Lu3-260333423Lu3-2603334231805803330
160 Lu3-73984873Lu3-73984873624625325
161 Lu5-38088785Lu5-38088785408734044
162 Lu7-132252947Lu7-1322529471204804068
163 Lu11-249830311Lu11-249830311275543997
165 Lu10-2155216110Lu10-215521614460946935
166 Lu12-526770612Lu12-5267706125160897112
167 Lu13-280322413Lu13-2803224132764903113
170 QOIL-Lu5.25Lu5-15704607-Lu5-15705039515703416-1570384853
171 QOIL-Lu6.36Lu6-4879632-Lu6-48796326487949358
172 QOIL-Lu6.46Lu6-13799180-Lu6-13970951613798861-1397063263
173 QOIL-Lu7.47Lu7-14209179-Lu7-1420917971420877269
174 QOIL-Lu10.510Lu10-6517448-Lu10-651744810651733991
175 QOIL-Lu12.612Lu12-4591214-Lu12-7491405124591134-7490902112
176 QOIL-Lu15.715Lu15-14665900-Lu15-154290551514665228-15428383132
178 QPLH-Lu1.21Lu1-20012490-Lu1-2001249012001181312
179 QPLH-Lu4.34Lu4-14305982-Lu4-15042104414304616-1504068240
180 QPLH-Lu13.413Lu13-17243884-Lu13-172438841317242916118
181 QPLH-Lu13.514Lu14-2320469-Lu14-2320469142320188121
183 QPAL-Lu5.25Lu5-13797851-Lu5-15668995513796740-1566780451
184 QPAL-Lu7.37Lu7-624461-Lu7-54236917624439-542360066
185 QPAL-Lu11.411Lu11-4417685-Lu11-4429424114417306-442904599
187 QIOD-Lu7.27Lu7-15346458-Lu7-17977459715346004-1797690370
188 QIOD-Lu12.312Lu12-489561-Lu12-298164212489561-2981562107
190 QLIN-Lu7.27Lu7-14540719-Lu7-17977459714540265-1797690370
191 QLIN-Lu12.312Lu12-489561-Lu12-298164212489561-2981562107
193 QLIO-Lu7.27Lu7-14540706-Lu7-17977459714540252-1797690370
194 QLIO-Lu12.312Lu12-489561-Lu12-298164212489561-2981562107
196 QDTM-Lu11.211Lu11-14768686-Lu11-147686861114767787101
198 QSTE-Lu9.29Lu9-20080531-Lu9-21636823920079433-2065452790
See Table 1 for additional note.
Table 4. Resistant gene analog (RGA) candidates near three QTL for flax powdery mildew resistance.
Table 4. Resistant gene analog (RGA) candidates near three QTL for flax powdery mildew resistance.
QTL No.QTLChrQTL Coordinates (bp)RGAGene Location on chr (bp)Gene Annotation
NBS: nucleotide binding site; RLK: receptor-like protein kinase; TM-CC: transmembrane coiled-coil.

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You, F.M.; Cloutier, S. Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax. Methods Protoc. 2020, 3, 28.

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