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Assessing the Barley Genome Zipper and

Genomic Resources for Breeding Purposes

Cristina Silvar, Mihaela-Maria Martis, Thomas Nussbaumer, Nicolai Haag, Ruben Rauser, Jens Keilwagen, Viktor Korzun, Klaus F. X. Mayer, Frank Ordon and Dragan Perovic

Linköping University Post Print

N.B.: When citing this work, cite the original article.

Original Publication:

Cristina Silvar, Mihaela-Maria Martis, Thomas Nussbaumer, Nicolai Haag, Ruben Rauser, Jens Keilwagen, Viktor Korzun, Klaus F. X. Mayer, Frank Ordon and Dragan Perovic, Assessing the Barley Genome Zipper and Genomic Resources for Breeding Purposes, 2015, PLANT GENOME, (8), 3.

http://dx.doi.org/10.3835/plantgenome2015.06.0045 Copyright: Crop Science Society of America

https://www.crops.org/

Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-124523

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Assessing the barley genome zipper and genomic resources for breeding

purposes

Cristina Silvar1,2, Mihaela M. Martis3,4, Thomas Nussbaumer3,8, Nicolai Haag1,5, Ruben Rauser1, Jens Keilwagen6, Viktor Korzun7, Klaus F.X. Mayer3, Frank Ordon1, Dragan Perovic1*

Running Head: Assessment of barley genomic resources

1Julius Kühn-Institute (JKI), Federal Research Institute for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, 06484-Quedlinburg, Germany

2Grupo de Investigación en Bioloxía Evolutiva, Departamento de Bioloxía Animal, Bioloxía Vexetal e Ecoloxía, Universidade da Coruna, 15071-A Coruña, Spain

3Plant Genome and System Biology (PGSB), Helmholtz Center Munich, 85764-Neuherberg, Germany

4BILS (Bioinformatics Infrastructure for Life Sciences), Division of Cell Biology, IKE, Faculty of Health Sciences, Linköping University, SE-581 85 Linköping, Sweden

5Julius Kühn-Institute (JKI), Federal Research Institute for Cultivated Plants, Institute for Plant Protection in Fruit Crops and Viticulture, 76833 Siebeldingen, Germany

6Julius Kühn-Institute (JKI), Federal Research Institute for Cultivated Plants, Institute for Biosafety in Plant Biotechnology, 06484Quedlinburg, Germany

7KWS LOCHOW GmbH, 37574 Einbeck, Germany

8Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, 1090, Vienna, Austria

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2 Abstract

The aim of this study was to estimate the accuracy and convergence of newly developed barley

genomic resources, primarily GenomeZipper (GZ) and POPulation SEQuencing (POPSEQ), at the genome-wide level and to assess their usefulness in applied barley breeding by analysing seven

known loci. Comparison of barley GenomeZipper and POPSEQ maps to a newly developed

consensus genetic map constructed with data from thirteen individual linkage maps yielded an

accuracy of 97.8% (GenomeZipper) and 99.3% (POPSEQ), respectively, regarding the chromosome

assignment. The percentage of agreement in marker position indicates that on average only 3.7%

GenomeZipper and 0.7% POPSEQ positions are not in accordance with their cM coordinates in the

consensus map. The fine scale comparison involved seven genetic regions on chromosomes 1H, 2H,

4H, 6H and 7H, harboring major genes and quantitative trait loci (QTL) for disease resistance. In

total, 179 GZ loci were analyzed and 64 polymorphic markers were developed. Entirely, 89.1% of

these were allocated within the targeted intervals and 84.2% followed the predicted order.

Forty-four markers showed a match to a POPSEQ-anchored contig, the percentage of collinearity being

93.2% on average. Forty-four markers allowed the identification of twenty-five fingerprinted

contigs (FPC) and a more clear delimitation of the physical regions containing the traits of interest.

Our results demonstrate that an increase in marker density of barley maps by using new genomic

data significantly improves the accuracy of GenomeZipper. In addition, the combination of different

barley genomic resources can be considered as a powerful tool to accelerate barley breeding.

Abbreviations: Genome Zipper, virtually ordered barley genes; POPSEQ, Population sequencing,

Consensus map, Integration of individual linkage maps; PCR, polymerase chain reaction; QTL, quantitative trait loci.

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Barley (Hordeum vulgare L.) was domesticated in the Fertile Crescent about 10,000 years ago

(Badr et al., 2000) and independently in Tibet, as the adaptation to the extreme environmental

conditions, about 3,500–4,000 years ago (Dai et al., 2012). Today, barley is one of the most

important cereal crops worldwide, ranking fourth in terms of total production (FAOSTAT, 2012).

Such relevance arises from its versatility to adapt to different stress conditions and from its essential

use in malting and brewing industries as well as for animal feed (Baik and Ullrich, 2008; Ceccarelli

et al., 2010; Verstegen et al., 2014). Recent reports on barley’s health benefits have also promoted a

renewed interest for this ancient food grain (Brockman et al., 2013; Sullivan et al., 2013). Apart

from this key role in agriculture, the diploid and inbreeding nature of barley makes it also a very

attractive model species for genetic studies within the Triticeae tribe (Bockelman and Valkoun,

2011). The major impediment for its full exploitation comes from the presence of a large and

complex repeat-rich genome of 5.1 Gbp (Dolezel et al., 1998). Nevertheless, progress in barley

genetics and genomics research has been continuously moving forward (Graner et al., 2011;

Kumlehn and Stein, 2014).

From the pioneering work of Sturtevant (1913), who constructed the first genetic map of barley, the

mapping of genes, morphological traits, and now molecular markers and sequences was one of the

most challenging tasks of many generations of geneticists. In the meantime, many tools and

strategies for the ordering of markers and sequences were developed, but all of them had some

advantageous and disadvantageous features, in introducing certain level of errors (Romero et al.,

2009; He et al., 2001). Nowadays, with the decline in the costs of next-generation sequencing

(NGS) technologies and high throughput genotyping platforms, permitting the generation of

thousands of data points in a very short time, there is a genuine need for new methods for ordering

of genetic data and strategies that assess the accuracy of the order. Among the ordering approaches,

the barley GenomeZipper (GZ) (Mayer et al., 2011) and POPulation SEQuencing (POPSEQ)

(Mascher et al., 2013a) are the most advanced ones in barley genetics. Furthermore, single maps in

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many thousands of markers and NGS data. However, up to now little is known about the error rate

and precision of constructed maps. In last years, high-throughput techniques i.e. Illumina iSelect

platform, Genotyping by Sequencing (GBS) along with the flow-sorting of chromosomes have

revolutionized barley genotyping (Simková et al., 2008; Muñoz-Amatriaín et al., 2011, 2014a). For

example, an Illumina 9K SNP chip based on sequence polymorphisms in ten diverse cultivated

barley genotypes and a Genotyping-by-Sequencing (GBS) approach for barley have been recently

developed (Comadran et al., 2012; Poland et al., 2012). In spite of the enormous progress in barley

genomics, these are of limited use without the availability of a draft genome sequence. In 2012, the

International Barley Sequencing Consortium (IBSC) generated a densely anchored physical map of

the barley genome comprising 9,265 fingerprinted BAC contigs spanning 4.98 Gb (IBSC, 2012).

Furthermore, Mayer et al. (2009; 2011) developed another genomic resource, which provided clues

on the barley genome composition. They constructed linear ordered virtual gene maps of barley by

using the so-called GenomeZipper approach. The barley GZ assembles 86% of the barley genes in a

putative linear order along the individual barley chromosomes by exploiting the high synteny

among three reference grass genomes, namely Brachypodium distachyon, Oryza sativa, and

Sorghum bicolor (Goff et al., 2002; Yu et al., 2002; Paterson et al., 2009; International Brachypodium Initiative, 2010). More recently, POPSEQ facilitated the development of genetically

ordered contigs from a whole genome shotgun (WGS) assembly of barley cv. Morex by genotyping

a mapping population with shallow genome coverage (Mascher et al., 2013a). Subsequently, the

new information provided by POPSEQ was employed to order and genetically anchor the barley

physical map, establishing a minimum tilling path (MTP) that comprises more than 65,000 BAC

clones (Ariyadasa et al., 2014). Recently, 15,622 BACs representing the minimum tiling path of

72,052 physical-mapped gene-bearing BACs, were identified and sequenced (Muñoz-Amatriaín et

al. 2015).

Undoubtedly, all these advancements will be extremely beneficial in a wide range of studies in both

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context, the use of this information remains mostly unexploited. Only few reports in the last years

have been partly focused on the application of the barley GenomeZipper for marker saturation of

genetic intervals containing interesting traits, such as spike density, resistance to powdery mildew,

barley yellow/mild mosaic virus or barley yellow dwarf disease (Shahinnia et al., 2012; Silvar et al.,

2013; Ordon and Perovic, 2013; Yang et al., 2013; Lüpken et al., 2013, 2014). In barley breeding

there is an urgent need for tools mostly directed to the quick and efficient identification of sets of

molecular markers which are closely linked to the traits of interest. Such markers may be readily

applied to marker-assisted selection (MAS), marker-assisted backcrossing strategies or so called

precision breeding (McCouch, 2004), which enable to select traits with greater accuracy and

deploying them cost-effectively into new varieties (Collard and Mackill, 2008). Similarly, those

markers would be advantageous in accelerating map-based cloning approaches (Bolger et al., 2014;

Yang et al., 2014) followed by allele mining and exploration of natural genetic variation

(Muñoz-Amatriaín et al., 2014b). The novel genomic resources of barley are valuable tools in this respect.

Nevertheless, to take full advantage of these, it is essential to firstly evaluate and validate these

tools in a breeding context.

In this present work, thirteen linkage maps (Muñoz-Amatriaín et al., 2014a; Perovic et al., in

preparation) were used to construct a consensus map, which was compared to the barley GenomeZipper, POPSEQ and the barley physical map. Furthermore, seven well-defined loci were

employed to assess the same resources at a microsyntenic level to get information on the accuracy

and usefulness to accomplish fine mapping schemes and physical delimitation of genomic regions

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MATERIALS AND METHODS

Construction of a consensus marker map

A set of 13 genetic linkage maps was used for the construction of a consensus map. Twelve of them were taken from Muñoz-Amatriaín et al. (2014a), while the thirteenth map was developed on the cross MBR1012×Scarlett (Perovic et al., in preparation). This latter population of 86 doubled haploid (DH) lines was genotyped with the barley iSelect 9K SNP chip (Comadran et al., 2012).The

consensus map was constructed using the R package LPmerge according to Endelman and Plomion

(2014). In short, a consensus map was computed independently for each chromosome using a range

of possible interval values from 1 to 5. These interval values specify the number of neighboring

markers that were used to compute the consensus map. Subsequently, the root mean square error

(RMSE) values between the corresponding consensus map and the individual maps were computed

for each possible interval and chromosome. Based on these RMSE values, the final consensus map

was selected for a specific chromosome with the smallest value. All chromosomal maps were

manually verified and additional SNP markers, originating from different Illumina platforms, i.e. the 9K iSelect chip (Comadran et al., 2012), and a set of 459 BOPA markers (Close et al., 2009),

were included.

Comparative analysis of the consensus map to the Barley GenomeZipper and the POPSEQ map

On the basis of common BOPA markers, the genetic positions between the consensus marker map of this study and the consensus map of Close et al. (2009) were identified and compared. The results of this comparison were visualized for each barley chromosome individually by generating dot plots and statistically evaluated by calculating Spearman correlations using the python packages Matplotlib (Hunter, 2007) and NumPy, respectively.

The consensus map created in this work was used in assessment of the robustness of the

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iSelect markers were aligned against the POPSEQ-anchored cv. Morex contigs (Mascher et al.,

2013a) and against the gene indices of the GenomeZipper for all seven barley chromosomes(Mayer

et al., 2011) by using BLASTN (Altschul et al., 1997). The barley 'zipper' data set consists of anchored barley fl-cDNAs, barley markers, and genes from the reference genomes of

Brachypodium, rice, and Sorghum. Only the first best hits with an alignment length of at least 100

bp and location on the same chromosomes were considered. The quality of the observed overlap between the three maps (consensus map, virtual ordered gene map (GZ), and POPSEQ map) was assessed by dividing each position by the total map length and allowing a 10% difference. The recombination frequency was computed in non-overlapping bins of 50 GenomeZipper loci. All consensus markers in a given bin were considered and the genetic distance between the marker with the highest and lowest position was computed. In order to filter for wrongly assigned marker, all markers with >10 cM compared to the median position per bin were removed. The results were statistically evaluated through a non-parametric measure of correlation (Spearman's rank correlation coefficient, SRC) and visualized by using CIRCOS (Krzywinski et al., 2009).

Microcollinearity - Comparative analysis of seven genetically mapped loci to GenomeZipper, POPSEQ and barley physical map

Seven loci or Quantitative Trait Loci (QTL)(subsequently termed from L1 to L7) located on five

different barley chromosomes and genetically mapped in the context of other studies were used for

comparative purposes at the microsynteny level: L1 (chromosome 1HS) (RphMBR1012)(König et

al.,2012), L2 (rym7) (1H centromere) (Yang et al., 2013), L3 (2HL) (RydHb_2HL)(Perovic et al.,

2013), L4 (rym11) (4H centromere) (Lüpken et al., 2013), L5 (Bg_QTL_6HL) (6HL) (Silvar et al.,

2011a; 2013), L6 (Bg_QTL_7HS) (7HS) (Silvar et al., 2010; 2012; 2013) and L7 (Bg_QTL_7HL)

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some of them (L1, L5, L6 and L7) are under assumption of being less conserved (Leister et al.,

1998).

Firstly, the synteny-ordered virtual gene map of barley (GZ) was validated by using this set of

seven genetically mapped loci and developing markers from corresponding barley ‘zippers’

followed by mapping as described in the original publications. For the short arm of chromosome 1H

and the long arm of chromosome 2H (unpublished data) comparative analysis to barley ‘zippers’,

marker design and genotyping was essentially done according to Perovic et al. (2004) and Silvar et

al. (2013). Briefly, markers genetically flanking the regions of interest were used to select the target

intervals in the virtual linear gene map. Zipper-based markers were used for amplification in both

parental lines and amplicons were sequenced on an ABI377XL instrument using BigDye terminator

sequencing chemistry (ABI Perkin Elmer, Weiterstadt, Germany). Markers, for which

polymorphisms were based on presence/absence of PCR fragments between parental lines, were

directly mapped. In turn, SNPs were transformed to CAPS markers (Perovic et al., 2013) or

pyrosequencing markers using a biotin-labelled M13 primer (Silvar et al., 2011b). Linkage analyses

were performed with JoinMap 4.0 (van Ooijen, 2006). Secondly, 57 markers derived from the target

intervals of the seven loci were compared to the POPSEQ-anchored contigs of cultivar Morex

(Mascher et al., 2013a) by using BLAT (Kent, 2002)requiring an identity of 99% and a minimum

match length of 50 nucleotides. The whole-genome shotgun (WGS) contigs and the physical

fingerprinted contigs (FPC) were linked to each other by BLAST using stringent criteria and only

matches with at least 99% sequence identity and at least 90% sequence coverage of the smallest

sequence (either WGS contig or a physical contig) were considered. The genetic markers from the

seven loci were also compared to 15,622 recently sequenced barley clones (Muñoz-Amatriaín et al.,

2015). Marker sequences were assigned to a clone with help of the Harvest BLAST

(http://www.harvest-blast.org/) when clones matched a marker with an e-value of<=10e-5 and with a match length of at least 100 nucleotides. Clones were further linked to physical contigs on the

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(http://harvest-9

web.org/hweb/utilmenu.wc?job=RTRVFORM&db=MOREX_HV3_10.4.1) where a mapping of

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RESULTS

Construction of a consensus genetic map

The consensus map of barley in this study consists of SNP markers with an average length of 125

nucleotides and it was constructed by using 13 mapping populations and different Illumina

platforms (9K Infinium iSelect high density custom genotyping bead chip and Illumina BeadXpress

Array)(Supplemental Table S1).The resulting consensus map holds a total of 6,405 markers in

1,978 unique positions (bins) (Table 1). The total length of the genetic map is 1,120.27cM,

providing a density of theoretically one marker per 0.17 cM and one marker bin every 0.57 cM.

Chromosomes 2H and 5H had the highest number of markers and bins and their genetic maps were

also the largest in size, whereas chromosomes 1H, 4H and 6H were the smallest, containing the

lowest number of markers and bins (Table 1). Ordering conflicts among the set of linkage maps

ranged from zero for chromosome 6H to 18 for chromosome 2H (Table 1). The map displays two

gaps of 7.46 and 6 cM in the long arms of chromosomes 2H and 6H, respectively, with the

remaining gaps being smaller than 5 cM (Supplemental Table S1and Supplemental Fig. S1).

Comparative analysis to the barley GenomeZipper and POPSEQ at the genome-wide scale

A map comprising 2,785 BOPA markers constituted the genome-wide framework along which the

barley genes were ordered and positioned for each individual barley chromosome in the

GenomeZipper (Mayer et al., 2011). A proportion of these BOPA markers is also represented in the

iSelect 9K chip and is segregating in the consensus map. First comparison, which also served as a

positive control, consisted in corroborating the positions of commonly positioned BOPA markers

between both datasets. Entirely, 2,447 markers were in common between the consensus map and

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chromosome 5H (Supplemental Fig. S2). The agreement in the order of shared BOPA markers

between the two maps at the individual chromosome level varied from 0.993 (4H) to 0.999 (2H,

7H), as measured by Spearman´s rank correlation coefficient. On average, the agreement in the

intra-chromosomal location for the BOPA markers was 99.6% (Supplemental Fig. S2).

The marker order in the consensus map was employed to evaluate the collinearity of predicted

genes in the barley ‘zippers’ and in their sequence counterparts in the POPSEQ-anchored data.

Based on the sequence homology, 689 markers (10.76%) from the consensus map did not find a

counterpart neither in the virtual map nor in the WGS contigs based map (Table 2). Additionally,

1,337 (20.9%) markers did not match to any loci inferred by the GenomeZipper. Out of 6,405

markers, 4,379 (68.4%) were represented in the GenomeZipper (Table 2). A high percentage

(97.8%) of the marker loci is located on the same chromosome in both datasets and only 2.2% of

markers showed a hit to an erroneous chromosome (Table 2). Detailed analysis considering the

number of markers per chromosome revealed that chromosome 2H showed the highest amount of

disagreements regarding chromosome assignments (3.2%), while chromosome 5H displayed the

lowest level of misaligned markers (1.6%). The genetic coordinates of markers in the consensus

map and the inferred loci in ‘zippers’ was consistent at the genome-wide scale, although the

resolution of comparison was lower in genetic pericentromeric regions, were a large amount of

genetic markers were co-segregating (Fig. 1). The percentage of agreement in marker position was

high (96.24% on average), varying from 94.19% (7H) to 97.63% (4H) (Table 2).

The comparison of the consensus map to POPSEQ revealed that 941 (14.7%) loci did not show any

hit to a WGS contig. The remaining markers displayed a match to a POPSEQ contig, 99.30%

having congruent chromosome positions (Table 2). Screening of a 5 cM window within both maps

indicated that only 0.7% of positions on average disagreed in cM coordinates between the

consensus and the POPSEQ, varying from chromosome 5H (0.43%) to chromosome 2H

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The number of misaligned markers on each chromosome was plotted against their genetic

coordinates to evaluate the putative existence of specific regions holding higher frequency of

misallocated markers (Supplemental Fig. S3). Misaligned markers between the consensus map and

GZ or POPSEQ were evenly distributed along each barley chromosome and a specific pattern of

misallocation associated to initial positions could not be clearly appreciated (Supplemental Fig. S3).

On the contrary, recombination frequencies were not uniformly distributed along the

centromere-telomere axis, but tended to be higher in the distal ends of each chromosome (Fig. 1, track f).

Comparative analysis of seven genetically mapped loci to GenomeZipper, POPSEQ and Barley physical map

Seven loci (L1-L7) located on barley chromosomes 1H, 2H, 4H, 6H and 7H were employed to

experimentally test the accuracy in virtually ordered genes of the barley GenomeZipper at a fine

scale (Fig. 1). L1 and L4 were genetically positioned previously in a high-resolution mapping

population, while the others were mapped at a lower resolution. The sequences of flanking markers

from all loci were used to survey the data on the barley GenomeZipper. The fourteen markers

matched the corresponding barley unigenes, spanning intervals in the GZ that varied in size from

0.90 (L5) to 7.49 cM (L3). The locus L2, at the centromeric region of chromosome 1H, did not

display any interval, since the flanking markers match barley unigenes which are co-segregating

(Table 3). The combination of intervals contained a total of 486 loci included in the GenomeZipper

ranging from 30 (L6) to 198 (L2). Among these, only 62 loci (12.76%) corresponded to originally

used BOPA markers, the other 424 were postulated according to the sequence homology to

Brachypodium, rice and Sorghum genes. In total, 39.7% of targeted GenomeZipper loci possessed an orthologous gene in all three reference genomes, while the positions of 16.2%, 10.1% and7.3%

of selected gene models was based on their homology to Brachypodium, Sorghum or rice,

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support from the above mentioned model grass species (Supplemental Table S2). A set of 179

barley predicted genes, varying from 10 on chromosome 7HL (L7) to 66 on chromosome 1H (L2),

putatively located within the target intervals were selected for further work (Table 3). Twenty-five

of them (13.9%) did not generate any PCR amplicon, even when different primers pairs were tested

at different positions in the gene. The majority of these were identified for the loci L1 (5.02%) and

L3 (5.02%) on chromosomes 1HS and 2HL (Table 4). The remaining 154 loci were amplified and

sequenced in the corresponding parental lines of the mapping population. Eighty-five markers

(55.2%) turned out to be monomorphic. The lowest level of polymorphism was observed in the

centromeric region of chromosome 1H, which showed 83.3% of monomorphic loci. On the

contrary, those regions on the distal part of chromosomes 6HL and 7HS turned out to be highly

polymorphic, the rate being 100% (Table 4). Among the remaining 69 polymorphic markers, 7 were

genotyped based on the presence/absence of the PCR amplicon in one of the parental lines, whereas

10 markers were mapped according to a size polymorphism. The other 47 loci containing SNPs

were converted to CAPS markers (Table 4). Eleven polymorphic markers were identified in the L2

interval, but five of them were not mapped in the original work (Yang et al., 2013), therefore they

were not considered in subsequent analyses.

In total, 8, 6, 13, 6, 11, 12 and 8 markers could be genetically mapped to chromosomes 1HS, 1H,

2HL, 4H, 6HL, 7HS and 7HL, respectively, in the corresponding mapping populations yielding 64

new markers. Seven markers (10.9%) were located outside of the target intervals, mainly on

chromosome 7HS (6.3%). Forty-eight (84.2%) markers out of 57 mapped in good collinearity with

their estimated positions in the barley ‘zippers’ (Table 5, Fig. 2), especially on chromosomes 4H

and 6HL, where 100% of zipper-based markers are located in the same position as those predicted

in the putative barley gene indexes (Table 5, Fig. 2).

The sequences from 57 consensus markers within the selected intervals from all seven target loci

were used to survey the POPSEQ (Mascher et al., 2013a), the IBSC (2012) and the

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barley. In total, 44 (77.2%) newly developed markers showed a match to POPSEQ-anchored WGS

contigs. The highest number of hits was observed for locus L5 (6HL), where all newly developed

markers found a counterpart in POPSEQ. The lowest number of matches (3 out of 6) was detected

for the centromeric region of the chromosome 4H (Table 5, Supplemental Table S3). On average,

the percentage of collinearity between new zipper markers and contigs in POPSEQ compared to our

consensus map was of 93.2% in the combination of chromosomes (Table 5). The comparison to

POPSEQ allowed verifying the correct ordering for the majority of genome-zipper based markers in

their corresponding genetic map, but also the identification of markers with inconsistent location.

Thus, 5 out of 9 inaccurately predicted loci on chromosomes 1H and 7H were mapped at a right

position according to POPSEQ (data not shown).

The comparison of the evaluated intervals against the genetically anchored physical map of IBSC

(2012) and the recently 15,622 sequenced barley clones from Muñoz-Amatriaín et al. (2015)

allowed identifying the fingerprinted contigs (FPC) associated to the genome zipper-based markers

and therefore to delimit the physical regions underlying the seven target loci (Table 5, Fig. 2). In

total, forty-four markers (77.2%) showed a hit to a FPC allowing the identification of twenty-five

FPCs; two for L6 (7HS) and L7 (7HL), three for L2 (1H), four for L1 (1HS), L4 (4H), L5 (6HL)

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DISCUSSION

Despite the difficulties due to the large size and complexity of the barley genome, relevant

achievements in barley genomics have been accomplished during the last years. GenomeZipper,

POPSEQ and the resources established in the framework of the IBSC, including various high

density marker maps, have accelerated the exploitation of this Triticeae crop (Graner et al., 2011;

Feuillet et al., 2012). However, these resources need further validation to become beneficial for

plant breeders, who demand tools that allow not only the expeditious improvement of molecular

markers tightly linked to genes or QTL of interest but also the usefulness of these markers across

different germplasm resources (Varshney et al., 2006; Kilian and Graner, 2012; Keilwagen et al.,

2014).

The construction of robust and highly resolved consensus linkage maps derived from experimental

mapping data has been a long-standing challenge in barley genetics. Integrated genetic maps

represent a more reliable resource for genetic anchoring of contig-based local or genome-wide

physical maps and allow the orientation of scaffolds in genome assemblies (Paux et al., 2008; Alsop

et al., 2011). Additionally, the accuracy and density of markers in a consensus map serve as

valuable features towards the assessment of newly developed barley genomic resources. Within the

barley research community, two integrated consensus maps have been recently published

(Muñoz-Amatriaín et al., 2011; 2014a). The consensus map constructed in the present study was intended to

be an improved version of those reported above, by incorporating additional informative

recombination events derived from the mapping population MBR1012×Scarlett. The inclusion of

this population had a relevant impact on the consensus map resolution. Thus, the newly integrated

linkage map consists of 6,405 markers, which represents an increase of 740 markers over the map

developed by Muñoz-Amatriaín et al. (2014a). If only iSelect SNPs are considered, the

MBR1012×Scarlett linkage map contributed 1,205 markers to the previous consensus map

representing an improvement of 2,438 SNPs with respect to the Morex×Barke map developed by

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the map by Muñoz-Amatriaín et al. (2014a) revealed a good consistence in the locus order, except

for chromosome 2H, where a higher number of ordering conflicts were observed. Seventeen out of

eighteen markers in conflict derived from the MBR1012×Scarlett population, although they affected intervals equal or smaller than 2 cM. Considering the numbers of marker bins, the resolution of the current consensus linkage map was particularly improved for chromosomes 2H,

3H and 5H, each one showing an increase of 5, 18 and 86 unique positions over the consensus map

from Muñoz-Amatriaín et al. (2014a). Dense genetic maps will be also valuable for applied barley

breeding, to perform precise introgression of improved traits in elite cultivars as well as for accurate

association mapping studies and genomic selection approaches (Heffner et al., 2010; Lorenz et al.,

2012).

The newly developed consensus map was employed to investigate in silico the accuracy and

robustness of the barley ‘zippers’ at the genome-wide scale. A similar approach was performed

previously by Poursarebani et al. (2013), but in that report only an individual map containing 1,596

transcript derived markers was used for comparative purposes. On the contrary, the utilization of a

high-density consensus map holding 6,405 markers is a more appropriate framework for such

comparison and validation. Indeed, a higher percentage (68.4%) of shared markers was observed in

our work compared to Poursarebani et al. (2013), who found that only 37.8% of their genetic

markers were represented among the GenomeZipper gene panels. Additionally, the percentage of

markers and gene models which possess the same chromosomal location was also higher in the

present study (97.8% versus 95%). The average percentage of collinearity between both datasets, as

measured by the Spearman's coefficient, was similar to that reported by Poursarebani et al. (2013) (96.2% and 96%, respectively). Such results support the greater suitability of consensus maps in

order to validate grass-based comparative genome organization models in barley. Notwithstanding,

as proposed by Poursarebani et al. (2013), the predicted chromosomal positions and virtually gene

order postulated by genome ‘zippers’ resulted to be highly precise (~96% accuracy) at the

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number of matches than those obtained with the GenomeZipper, the increase being of 6.2% of the

total markers. POPSEQ provides a linear order of WGS contigs genetically positioned along the

seven individual barley chromosomes (Mascher et al., 2013a). The power of such methodology has

been sufficiently demonstrated in previous reports by using different genotyping platforms and

mapping populations (Mascher et al., 2013a; Ariyadasa et al., 2014; Chapman et al., 2015). Our

results corroborated the robustness of POPSEQ, the genetic coordinates of contigs being coincident

with markers positions in the integrated map at a 99.30% on average.

Although the performance of barley ‘zippers’ at the genome-wide level appeared to be highly

reliable, the development of any genomics-based breeding strategy requires the examination of the

virtual gene order at a finer scale, when exploited syntenic relationships to B. distachyon, rice and

Sorghum might be more influenced by misinterpretations (Li et al., 2002; Caldwell et al., 2004; Pourkheirandish et al., 2007). With these drawbacks in mind, the predicted linear gene index was

investigated with experimental evidences at a low and high genetic resolution level, so called

microcollinearity (Keller and Feuilet, 2000). Original data were compiled from earlier studies

covering a set of seven loci conferring resistance to various fungal and viral diseases and traced to

distinct barley chromosomes (Lüpken et al., 2013; Perovic et al., 2013;Yang et al., 2013; Silvar et

al., 2010, 2012, 2013). A similar procedure was performed by Poursarebani et al. (2013), but in that

report a section in the long arm of chromosome 2H was randomly selected for comparative

purposes. On the contrary, the screening of several chromosomal regions spanning resistance genes

or QTLs, carried out in the present study, will guarantee a more reliable representation of the

GenomeZipper power at the one-to-one relationship among orthologous genes. Disease resistance

loci are particularly unstable, tend to be located in less conserved regions and are commonly

affected by structural variation (Leister et al., 1998; Meyers et al., 2003; Wicker et al., 2009). In

total, 179 out of 486 GZ genes were considered for further development of tightly linked markers.

Entirely, 13.9% of the loci could not be amplified on parental lines from different mapping

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support previous data by Silvar et al. (2013), who suggested apparent modifications in the

gene-space of landrace-derived lines compared to the genome sequences of modern barley cultivars.

Additionally, sequencing errors in the reads generated by 454-pyrosequencing could not be

discarded as the source of absence of PCR products. Out of 154 primer pairs tested for

polymorphism on the five examined chromosomes, 85 (55.2%) generated a monomorphic PCR

amplicon. This was due to the presence of two centromeric loci on chromosomes 1H (L2) and 4H

(L4), which contributed 85.8% of non-polymorphic markers. Loci in centromeric and

peri-centromeric regions are on average less polymorphic than loci located on the rest of the barley

chromosomes (Dvorák et al., 1998) as they are commonly organized in haplotype blocks locked

into recombination-“inert” genomic regions (Thiel et al., 2009; Comadran et al., 2010). If these loci

were removed, the rate of polymorphisms increases up to 81.3%, which is similar to that found in

other reports based on barley ESTs or unigenes (Liu et al., 2010; Silvar et al., 2012). All 64 newly

developed markers were accurately assigned to the corresponding chromosome. However, the

genetic maps obtained with those markers were not in complete accordance with the putative linear

gene order described in the GenomeZipper. Thus, 10.9% were located outside of the initial target

intervals defined by the virtual gene index and 15.8% were not genetically mapped according to the

gene order expected from the barley ‘zippers’. Such absence of collinearity might be attributed to

insertions or inversions hypothesized in the virtually ordered gene inventory but not confirmed in

our results, as suggested by Silvar et al. (2013). This outcome supports the well documented

existence of multiple inter- or intra-chromosomal rearrangements throughout the evolution among

grass genomes (Bossolini et al., 2007; Salse et al., 2008; Bolot et al., 2009; Wicker et al., 2011).

Variations in collinearity can be also explained by some gene models that are supported only by

their counterpart in one or two reference genomes (Mayer et al., 2011). However, as suggested by

Poursarebani et al. (2013), a general rule asserting that more than one model genome will increase

the accuracy of the genome zipper, should not be established, since a few markers based on single

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model genomes might serve to overcome limitations imposed by species-specific regional

variations (Mayer et al., 2011). Various reports also demonstrated that collinearity is commonly less

conserved at the most distal telomeric regions of chromosomes (Li et al., 2002; Caldwell et al.,

2004). This was actually the case of target loci on chromosomes 1HS, 2HL and 7HS (see Fig. 2),

but not for that on chromosome 6HL, which showed a 100% of agreement of the order for the

zipper-derived markers mapped to the interval of interest. Beyond this, a good performance was

observed for the barley ‘zippers’, allowing the development of 64 new markers and their mapping

with an accuracy of almost 85%. From a breeding point of view, the quality of the order prediction

permitted a more precise dissection of the regions containing interesting traits located on five

different barley chromosomes.

These new zipper-based markers were employed further for comparisons to the POPSEQ (Mascher

et al., 2013a) and barley physical map (IBSC, 2012; Muñoz-Amatriaín et al., 2015) in order to

circumscribe the regions in the barley genome conferring resistances. Based on sequence homology,

61.40% of the total markers found a hit to a Morex contig in POPSEQ. The genetic order of these

loci in their respective linkage maps coincide with the position of the POPSEQ contigs to a 100%.

This output served to verify that 55.5% of the target gene models previously cataloged with

erroneous positions according to the GenomeZipper, were correctly mapped and allowed to

partially resolve in silico blocks of co-segregating markers, which were assigned to distinct

positions in the linearly ordered index of WGS contigs. These aspects pave the way towards the

valuable utilization of POPSEQ in breeding. This tool should be more amenable than the barley

‘zippers’ for fine-mapping and cloning of agronomically important genes, provided that genetic

markers sufficiently close to the loci of interest and adequate resolution in the mapping population

are available. The outcome arising from POPSEQ could be anchored in a straightforward manner to

the barley physical map, accelerating the identification of BAC contigs and subsequent isolation of

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than ordinary anchoring strategies relying on sequence comparison of flanking markers derived

from sequence tags (Mascher and Stein, 2014).

The new markers developed from the barley ‘zippers’ were also employed to demarcate, based on

sequence homology, the physical regions in the barley genome responsible for the traits of interest.

Between two and six contigs were identified by sequence comparison for the seven barley loci.

Those loci derived from mapping populations with higher resolution permitted the definition of

tiling paths holding a lower number of FPCs (data not shown) as long as the target loci are not

allocated to centromeric regions (Lüpken et al., 2014).. Identified contigs were employed to make a

shallow approximation to the lately anchored physical map of barley and the established minimum

tilling path (MTP) containing 66,772 overlapping clones (Ariyadasa et al., 2014). Discordant contig

placements were only observed on chromosome 7HS and might be explained by the technical and

biological inaccuracy inherent to the construction of any genetic map (Wenzl et al., 2006; Wu et al.,

2008). In addition, the short arm of chromosome 7H has been described as a “hot spot” of

recombination, which might also contribute to the order controversy (Drader et al., 2009). The

precise exploration of those disagreements should be carefully considered on the way to positional

isolation of resistance genes (Liu et al., 2014). Even though the rough identification of the physical

contigs provided extended information about the genomic context and local neighborhood

underlying the traits of interest, the low genetic resolution of the majority of assayed mapping

populations did not encourage us to speculate about the number or nature of putative candidate

genes lying within the delimited genomic areas. That should be the principal task of further projects

aimed to the map-based cloning of genes, as it was the case of rym11 (Yang et al., 2014).

The present work clearly demonstrated that recently established barley genomic resources can be

efficiently exploited for breeding purposes. In spite of the appearance of few discrepancies, such as

zipper-based markers outside the target intervals or erroneously positioned, our data elucidates that

GenomeZipper and POPSEQ are very powerful tools for marker saturation, chromosome dissection

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meet some limitations depending on the target chromosomal region. This could be the case for those

loci allocated in the proximity of centromeres, which usually show low recombination (IBSC, 2012;

Muñoz-Amatriaín et al. 2015). Those strategies might be also employed with success in other

complex genomic contexts, such as wheat(http://wheat-urgi.versailles.inra.fr), or unsequenced

orphan crops of economic importance, like rye (Secale cereale) or perennial ryegrass (Lolium

perenne) (Pfeifer et al., 2013; Martis et al., 2013). As demonstrated in the present work, the combined use of various genomic tools will help plant breeders and geneticists in different manners.

Firstly, it will permit the rapid development of markers tightly associated with the gene of interest,

which might be further exploited or optimized for molecular marker-assisted selection (MAS) or

even cataloged as functional markers (Andersen and Lübberstedt, 2003; Palloix and Ordon, 2011).

Secondly, it will facilitate the disclosure of blocks of co-segregating markers, typically associated to

low-resolution mapping populations, in a more efficient manner (Silvar et al., 2013). Finally, the

combination of those different genomic resources should lead to a more straightforward and faster

physical delimitation of promising regions in the barley genome, which constitute the starting point

towards map-based cloning strategies (Lüpken et al., 2013; Yang et al., 2014). Some recently

published bioinformatics tools, such as Ensembl Plants (http://www.ensemblgenomes.org),

chromoWIZ (http://mips.helmholtz-muenchen.de/plant/chromowiz/indez.jsp) or BarleyMap (http://floresta.eead.csic.es/barleymap) should ease the integration of different available genomics resources, allowing plant geneticist and breeders to manage these information in a time-saving

manner (Kersey et al., 2014; Nussbaumer et al., 2014; Cantalapiedra et al., 2015). To our

knowledge, this study is among the first efforts oriented towards the unification of various genomic

resources with breeding purposes. Altogether the fast enrichment of barley genome sequence

information and novel techniques such as exome capture (Mascher et al., 2013b) should help to

move barley breeding to an unprecedented level of precision and productivity, as foresee by Bevan

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22 Acknowledgements

This work was supported by the German Federal Ministry of Education and Research under the grant number AZ 0315702 and by the Spanish Ministry for Science under the grant number EUI2009-04075. The authors like to thank Nils Stein and Ping Yang for fruitful discussion about the rym7 locus. CS was supported by a mobility fellowship from Universidade da Coruña.

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29 Figure captions

Figure 1. Comparison of the Consensus–map against the barley GenomeZipper and POPSEQ based anchoring. The figure illustrates the comparison of the Consensus–map of this study against

the barley GenomeZipper (Track B) on the basis of common marker sequences. Track A illustrates the agreement between both maps with black showing perfect agreement and white showing an agreement of less than 80%. Markers were classified as correct when the respective genetic position was within 5 cM compared to the average genetic position of all markers that matched to a GenomeZipper Bin where a Bin comprises always 50 non–overlapping loci. The regions spanned by the seven resistance loci or QTL are shown in Track C. Track D gives the comparison of POPSEQ anchoring against the GenomeZipper–based anchoring. Connections were drawn between the POPSEQ based genetic positions of the WGS contigs of cultivar Morex and anchored resources from the GenomeZipper. Track E illustrates the agreement between POPSEQ and the GenomeZipper. Track F illustrates the recombination frequency within a GenomeZipper bin of 50 loci.

Figure 2.Comparison of collinearities among GenomeZipper–based markers, genome–zipper gene models and WGS contigs derived from POPSEQ at the seven target barley loci. The

different colors in the physical map (IBSC et al. (2012)) represent the different barley chromosomes. The different shades of grey indicate the genetic position according to the consensus map of this study.

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30

Tables

Table 1. Statistics of the consensus map

Chrom MapLength

(cM) # markers # bins # conflicts

1H 146.30 655 226 4 2H 183.54 1116 357 18 3H 168.25 1036 355 2 4H 131.11 671 163 3 5H 191.15 1245 400 5 6H 137.68 811 235 0 7H 162.24 871 242 4 Total 1120.27 6405 1978 36

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Table 2. Statistics of the comparison between consensus map, GenomeZipper and POPSEQ

Chrom.† No Hit‡ GenomeZipper POPSEQ # hit§ # hits to identical chrom. Collinearity (%) # hits to different chrom. No hit¶ # hits to identical chrom. Collinearity (%) # hits to different chrom. 1H 67 111 464 97.16 13 114 471 99.36 3 2H 114 244 734 95.90 24 156 836 98.81 10 3H 112 199 711 96.16 14 165 754 99.34 5 4H 74 124 465 97.63 8 113 481 99.38 3 5H 152 276 804 96.88 13 162 927 99.57 4 6H 94 189 519 95.74 9 111 603 99.50 3 7H 76 194 587 94.19 14 120 670 99.25 5 Total 689 1337 4284 96.24 95 941 4742 99.30 33

Chromosome in the consensus map

Number of markers in the consensus map that did not show any hit to GenomeZipper or POPSEQ §Number of markers in the consensus map that did not show any hit to GenomeZipper

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Table 3. Description of the seven loci employed for the microsyntenic comparisons and number of GenomeZipper loci present in the target intervals

Locus name Chrom. Population type† Interval in GZ (cM)

# GZ loci # targeted GZ loci # BOPA markers‡ # inferred loci§ # BOPA markers‡ # inferred loci§ L1 1HS HR 1.51 10 23 5 19 L2 1H LR 0.00 10 188 3 63 L3 2HL LR 7.49 20 85 4 22 L4 4H HR 1.38 9 30 2 24 L5 6HL LR 0.90 4 43 1 11 L6 7HS LR 0.64 3 27 1 14 L7 7HL LR 4.30 6 28 2 8 Total 62 424 18 161

HR (High Resolution) or LR (Low Resolution) mapping population

Number of GZ loci whose position coincides with a BOPA marker

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33

Table 4. Description of the genome zipper–based markers developed for the seven target intervals

Locus name L1 L2 L3 L4 L5 L6 L7 Total

Chromosome 1HS 1H 2HL 4H 6HL 7HS 7HL –

# targeted gene models 24 66 26 26 12 15 10 179

None amplification 9 0 9 2 1 3 1 25 Monomorphic markers 7 55 4 18 0 0 1 85 # developed markers 8 11† 13 6 11 12 8 69 Presence/Absence 0 0 2 0 3 2 0 7 Size polymorphism 1 2 4 0 1 2 0 10 CAPS 7 4 7 6 7 8 8 47

Five of these markers were not mapped previously (Yang et al., 2013) and accordingly they were not

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Table 5. Statistics of the comparisons of zipper–based markers to POPSEQ and IBSC

Locus name L1 L2 L3 L4 L5 L6 L7 Total

Chromosome 1HS 1H 2HL 4H 6HL 7HS 7HL - Markers outside of target region 0 (0%) 1 (17%) 0 (0%) 0 (0%) 0 (0%) 4 (33%) 2 (25%) 7 (10.9%) Markers within the

target region 8 (100%) 5 (83%) 13 (100%) 6 (100%) 11 (100%) 8 (67%) 6 (75%) 57 (89.1%) Markers in collinearity with Genome Zipper 5 (62.5%) 4 (80%) 11 (84.6%) 6 (100%) 11 (100%) 6 (75%) 5 (83.3%) 48 (84.2%) Hits to POPSEQ 4 (50%) 4 (80%) 8 (61.5%) 3 (50%) 11 (100%) 8 (100%) 6 (100%) 44 (77.2%) Markers in correct collinearity with POPSEQ 4 (100%) 4 (100%) 7 (87.5%) 3 (100%) 11 (100%) 6 (75%) 6 (100%) 41 (93.2%) Hits to FPcontigs 8 (100%) 3 (60%) 8 (61.5%) 4 (66.6%) 10 (90.9%) 8 (100%) 3 (50%) 44(77.2%) N° identified FPcontigs 4 3 6 4 4 2 2 25

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Supplementary material

Figure S1. Root mean square error (RMSE) values between the corresponding consensus map

derived by LPmerge and the individual maps for different interval parameters of LPmerge. Among the different potential consensus maps for different interval values, we selected the one yielding the smallest median RMSE and highlighted the corresponding box in blue.

Figure S2. Dot plot comparison of shared BOPA markers between the consensus map and the map

developed by Close et al., (2009) and employed as the framework for barley ‘zippers’ anchoring. Numbers at the top of the graphics show the Spearman's rank correlation coefficient. Numbers at the bottom right hand corner indicate the amount of common markers between both datasets.

Figure S3. Misaligned markers were plotted along the consensus map when the genetic positions

were discordant between Consensus/GenomeZipper and Consensus/POPSEQ map.

Table S1. Consensus genetic map

Table S2. Number of GenomeZipper loci comprised in the seven target intervals and their

orthology to any of three different reference genomes

Table S3. Markers mapping to the physical contigs of the 15,622 sequenced clones ( Muñoz-Amatriaín et al. 2015) and information of the respective physical contigs of the IBSC (2012).

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1H

2H

3H

4H

5H

6H

7H

a) Zipper - Consensus map [80, 100%]

b) Connector Zipper - Consensus map

c) Location resistance QTLs

d) Connector Zipper - POPSEQ

e) Zipper - POPSEQ agreement [80, 100%]

f) Cumulative recombination frequency [0, 10 cM]

a

b

c

d

e

f

L1

L2

L3

L4

L5

L6

L7

References

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