Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)


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Molecular Marker Systems in Plant Breeding and Crop Improvement : Gerhard Wenzel :

Other books in this series. Trees IV Professor Dr. Nuclei DNA was extracted from flower buds and leaf tissue following standard lab protocols [ 49 ]. In addition, two lines DH and Express underwent Illumina library preparation in order to compare the results from the two approaches. The entire genomic DNA product from this library preparation e.

The library fragment sizes were found to be to bp, which are well within the expected range. Illumina Paired End libraries Illumina Inc. The agarose gel excision was performed at base pairs bp to produce libraries with an approximate insert size of bp. The entire recovery product was used as template in the pre-hybridization library amplification via the Illumina sequencing adapters i. PCR cycling conditions were as follows: The mean library fragment size was found to be bp.

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Fragment sizes are well in range with the expected ones. A total of 47 genomic regions from the A and C genomes of B. This was conducted by selecting sequenced molecular markers that were located under QTL peaks and identifying their corresponding intervals in the 10 A genome haploid chromosomes of B. Since the genome sequence for B. The regions from the C genome that had the most compelling synteny with the A genome segments were selected.

Map integration was conducted according to common molecular markers and parental lines used in three different mapping studies [ 15 , 22 , 50 ]. QTL locations were inferred from relative map positions previously described [ 13 , 15 , 17 - 19 , 22 ] always using common sets of molecular markers and genetic stocks. The first step during the design of the sequence capture array was the development of a probe database set 70 to mer oligos to be placed on the sequence capture microarray device for the The second step involved the selection of probes using proprietary Roche NimbleGen software algorithms.

As a result, a set of probes was selected to provide a Sequence capture array hybridization for all genotypes was conducted by Roche NimbleGen Madison, WI following their proprietary protocol. Sequence reads were mapped to a reference dataset as described below. Three reference sequence datasets were developed to fully interrogate the capture design process. The capture design reference sequences was previously described. A second dataset containing the capture design sequences as well as their orthologous sequences was generated to increase the resolution of homoeologous sequences during the read mapping.

To identify the orthologue of each sequence used in the capture design, sequences were individually aligned to the pseudomolecules of the complementary genome using NUCmer MUMmer 3. Resulting delta files from the NUCmer alignments were passed through a Perl script to filter out the majority of the repetitive hits. The resulting files were plotted using mummerplot MUMmer 3. The third dataset contains de novo assembles of the Brassica A and C genome pseudomolecules representing The sequence reads for all ten genotypes were aligned to the each of the three reference data sets using the CLC Bio Genomics Server software version 4.

The mapped reads were then interrogated for sequence variation using the CLC Bio probabilistic variant calling tool with a minimum depth of 3 reads for the data, and 8 reads for the Illumina data. The SNPs were filtered according to the following criteria: Coordinates for genes and the corresponding coding sequences CDS were extracted from GFF annotation files using a Perl script and SNP positions were then compared to the list of gene coordinates.

For each of these sets the breakdown of transitions and transversions was determined using standard UNIX utilities to extract and count the SNP types. Coverage of the target and non-target regions of the sequence capture was compared using BEDTools [ 53 ] and custom Perl scripts. KASPar chemistry validation assay: The KASPar assay version 4. The assays were carried out in a B. In addition, 44 KASPar oligonucleotide sets were designed to detect polymorphisms in both populations at the same time. All the primer sets were designed using PrimerPicker KBioscience, with default parameters.

SNPs were assayed against a selection of B. Mapping of a subset of validated SNP markers: Linked loci were grouped using a LOD threshold of and a maximum recombination fraction of 0. After the original scores were rechecked, a final linkage map was constructed for each population. Map distances in centiMorgans cM were calculated using the Kosambi mapping function. To investigate meaningful DNA sequence variation, genomic regions of interest were selected for sequence capture that had shown consistent QTL locations in B.

Examples of consistent QTL findings for important agronomical and nutritional traits e. As a result, a total of 47 genomic regions comprising a total of Selected genomic sequences A summary of the obtained NGS results for each of the 10 B. Briefly, the average number of reads obtained for the Life Sciences and Illumina HiSeq chemistries were , and ,,, respectively. The average sequence read length was bp using the sequencer and 54 bp for the data obtained using the Illumina HiSeq sequencer Table 1.

Sequence quality parameters Q-scores were high for both NGS methods, with a 33 Q-score average for the data and a 40 Q-score average for the Illumina data Table 1. Sequenced reads were mapped to two reference sets, the capture design reference A and C Brassica genome reference sequences and the A and C genome pseudomolecules Parkin and Sharpe, unpublished data [ 51 ], using the CLC Bio Genomics Server read mapping algorithm Table 2. The discovered SNPs were classified according to type transition vs. A significant number of the discovered SNPs ,, Genic and intergenic SNPs were further broken down into transitions and transversions.

Transitions represent approximately The breakdown of each SNP type e. Of the , genic transitions, It also removes SNPs with a percentage of heterozygous calls over a threshold 0. Total SNP counts were classified by genomic location Intergenic vs. Genic and further separated into transitions and transversions. To assess the difference in sequencing platforms, the number of filtered SNPs unique to each sequencing type was determined for DH and Express.

Of these, 95, were unique to the Illumina sequencing data while 4, were unique to the Life Sciences sequencing data. This difference is largely due to the requirement that there are no flanking SNPs on at least one side of the target SNP for Infinium design. In order to analyze the efficacy of the sequence capture process, the depth of coverage was surveyed for both target and non-target regions. The original capture design was based on incomplete diploid Brassica genome sequence with a bias to the A genome Thus, to accurately assess the true target region, it was necessary to identify the orthologous sequence from the complementary genome for each of the sequences used in the design process.

As described in the methods each sequence was aligned to the pseudomolecules of the complementary genome using NucMER, which resulted in 1, orthologous sequences or Sequence reads from each of the ten B. Sequence read mapping is presented across all ten lines for both sequencing platforms for these three reference sequence sets in Table 4. A percentage increase of reads mapped over the previous set RMpi is also included to illustrate better resolution of homoeologous sequences as well as the benefit of mapping to complete genome sequences.

A coverage map was generated for each of the B. The average depth of coverage for non-target regions was Additionally, the set of filtered SNPs were analyzed to determine their origin, , SNPs were found in the target regions, representing The average depth of coverage in captured and non-captured regions across all 19 A and C Brassica pseudomolecules is illustrated. Captured regions are those from the original sequence capture selection combined with the orthologous sequence from the complementary genome.

The assay for SNP robustness among the set of 25 diverse B. For instance, polymorphisms between the spring type parents, DH and PSA12 ; polymorphisms between the winter type parents, V8 and Express ; and polymorphisms detected for both sets of parental lines at the same SNP locus. During array validation, tests using almost B. These preliminary results coupled with the KASPar assays serve as a confirmation of the filtering criteria used in the SNP discovery pipeline, suggesting sequence capture coupled with NGS is a promising tool for specific SNP marker development in polyploid genomes.

In order to confirm the chromosomal location for the discovered SNPs and associate them with the target QTL regions, we mapped a subset of the validated SNP markers in both mapping populations. We were able to map 45 out of 48 SNP markers SNP discovery and molecular marker development in B.

Molecular Marker Systems in Plant Breeding and Crop Improvement

Such an increment has been mainly the result of the implementation of NGS technologies, microarrays and bioinformatics [ 2 , 57 , 58 ]. Sequence capture has addressed this limitation allowing for the isolation of user-defined genomic sequences in one simple step [ 36 - 38 ]. In this context, given the vast history of QTL mapping for various agronomical and nutritional traits in B.

We utilized sequence capture to target and re-sequence meta-QTL regions [ 2 ] for key complex traits including yield, yield components, seedling vigor, seed quality and disease Table S2 in ten founder B. In total, 47 genomic regions were targeted comprising However, since the capture process is unlikely to be able to differentiate the closely related sequences of the A and C genomes, a more accurate estimate of the genome surveyed would be Captured DNAs for the ten founder genotypes were sequenced using sequencing technology.

Two different reference sets were used to map the sequenced reads obtained, the capture design reference and the A and C genome pseudomolecules. This proved to be crucial allowing us to discern more efficiently between A and C homoeologues and resulted in a higher percentage RMp of sequence reads mapped Table 2.

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The use of two different reference sets illustrated the benefit of a more complete genome reference as well as differences in sequencing platforms. Mapping of the sequenced reads obtained using Illumina HiSeq technology 2 genotypes showed a higher RMp for the capture design reference set compared to However, due to the lower specificity of the shorter reads a more subtle increase was observed on the RMp values when the Illumina sequenced reads were uniquely mapped to the A and C genome pseudomolecules, since the short reads have more opportunity for multiple equivalent matches Table 2 , Table 4.

Nonetheless, the number of putative SNP calls increased for both sequencing platforms when the larger reference genome set was utilized Table 2. In addition, the use of more complete reference sequences in the SNP identification process should increase the likelihood of identifying single loci in the amphidiploid genome.

In Trick et al [ 56 ], SNP discovery among the ten contrasting B. The polymorphism rate was compared across all 10 genotypes using the equivalent data sets. On average, one filtered SNP was detected every Outside of the capture regions, the rate dropped to an average of one SNP every Previously any bias in observed polymorphism in B. However, in the current study the use of one resynthesized line PSA12 and two B.

Recently, Li and coworkers [ 61 ] explored the relative contributions of genic and nongenic SNPs to phenotypic variation for five quantitative traits in maize.


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This highlights the importance of discovering SNPs not only in genic but also in non-genic regions for genome wide association studies GWAS in crop species with complex genomes, like maize or rapeseed canola. This study provides a large and unique source of nongenic , and intronic 88, SNPs Figure 3 that could not be discovered by EST-based or RNA-seq methods previously utilized [ 62 - 64 ]. This resource will provide a further tool to help enhance our understanding of the genetic architecture of quantitative traits in this species. As seen in plants and other organisms the percentage of transitions The number of the four different types of transitions was found to be balanced, whereas the number of the eight different types of transversions varied Figure 2.

A similar result was recently reported for B. The same trend between exonic and intronic regions was also observed in B. The efficiency of target sequence enrichment was evaluated by comparing the mapping of the obtained sequenced reads to multiple reference sets Table 4. Beside those regions which are still unrepresented in the reference, the requirement for unique matches in a complex polyploid genome will necessarily limit the ability to map all sequenced reads. Comparing the reads mapped to the target regions to those mapped to the A and C genome pseudomolecules indicated that on average In addition, the depth of coverage which effectively dictates the ability to accurately call SNPs was surveyed for both target and non-target regions across the A and C Brassica pseudomolecules Figure 4.

Validation of discovered SNPs is a crucial step to estimate the percentage that could be converted into robust and informative molecular markers [ 69 ]. Filtered SNPs were classified based on their available flanking sequence, and thus, the possibility of being utilized in different downstream assays as KASPar , , Illumina Infinium 2, , or both , Validation rates varied depending on whether the KASPar oligonucleotides were specifically designed to test for SNPs identified between the parental lines of the two mapping populations.

Importantly, 71 of the SNPs were also informative when tested using a B. Since the singleplex KASPar SNP validation assay relies on consistent template sequences, amplification failure could result from non-specific oligonucleotide annealing and not be related to the nature of the tested SNP. Therefore, corrected validation rates, not considering the 12 evaluated SNPs showing no amplification, were as follows: These rates demonstrate the robustness of our SNP discovery pipeline protocol. In addition, an independent assay evaluated 4, SNPs in 96 B.

This was probably due to the small mapping population size used to perform the linkage analysis. Significantly, this approach utilized an improved reference sequence set, by combining a 95K Brassica unigene dataset [70] with the B. Our work expands on previous studies by:

Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry) Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)
Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry) Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)
Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry) Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)
Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry) Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)
Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry) Brassicas and Legumes From Genome Structure to Breeding (Biotechnology in Agriculture and Forestry)

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