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  • Research article
  • Open Access

Simple SNP-based minimal marker genotyping for Humulus lupulus L. identification and variety validation

BMC Research Notes20158:542

https://doi.org/10.1186/s13104-015-1492-2

  • Received: 26 September 2014
  • Accepted: 21 September 2015
  • Published:

Abstract

Background

Hop is an economically important crop for the Pacific Northwest USA as well as other regions of the world. It is a perennial crop with rhizomatous or clonal propagation system for varietal distribution. A big concern for growers as well as brewers is variety purity and questions are regularly posed to public agencies concerning the availability of genotype testing. Current means for genotyping are based upon 25 microsatellites that provides relatively accurate genotyping but cannot always differentiate sister-lines. In addition, numerous PCR runs (25) are required to complete this process and only a few laboratories exist that perform this service. A genotyping protocol based upon SNPs would enable rapid accurate genotyping that can be assayed at any laboratory facility set up for SNP-based genotyping. The results of this study arose from a larger project designed for whole genome association studies upon the USDA-ARS hop germplasm collection consisting of approximately 116 distinct hop varieties and germplasm (female lines) from around the world.

Results

The original dataset that arose from partial sequencing of 121 genotypes resulted in the identification of 374,829 SNPs using TASSEL-UNEAK pipeline. After filtering out genotypes with more than 50 % missing data (5 genotypes) and SNP markers with more than 20 % missing data, 32,206 highly filtered SNP markers across 116 genotypes were identified and considered for this study. Minor allele frequency (MAF) was calculated for each SNP and ranked according to the most informative to least informative. Only those markers without missing data across genotypes as well as 60 % or less heterozygous gamete calls were considered for further analysis. Genetic distances among individuals in the study were calculated using the marker with the highest MAF value, then by using a combination of the two markers with highest MAF values and so on. This process was reiterated until a set of markers was identified that allowed for all genotypes in the study to be genetically differentiated from each other. Next, we compared genetic matrices calculated from the minimal marker sets [(Table 2; 6-, 7-, 8-, 10- and 12-marker set matrices] and that of a matrix calculated from a set of markers with no missing data across all 116 samples (1006 SNP markers). The minimum number of markers required to meet both specifications was a set of 7-markers (Table 3). These seven SNPs were then aligned with a genome assembly, and DNA sequence both upstream and downstream were used to identify primer sequences that can be used to develop seven amplicons for high resolution melting curve PCR detection or other SNP-based PCR detection methods.

Conclusions

This study identifies a set of 7 SNP markers that may prove useful for the identification and validation of hop varieties and accessions. Variety validation of unknown samples assumes that the variety under question has been included a priori in a discovery panel. These results are based upon in silica studies and markers need to be validated using different SNP marker technology upon a differential set of hop genotypes. The marker sequence data and suggested primer sets provide potential means to fingerprint hop varieties in most genetic laboratories utilizing SNP-marker technology.

Keywords

  • Genotyping
  • DNA fingerprint
  • Hop
  • Humulus
  • Minimal marker
  • Minor allele frequency
  • SNP
  • TASSEL
  • Variety identification

Background

Hop is an important cash crop for the Pacific Northwest USA as well as several European countries, China, Australia, South Africa and other minor production regions. It is primarily used as a flavoring and bittering additive in beer brewing but alternative uses have become increasingly important [1, 2]. Hop is a dioecious perennial plant species propagated via rhizomatous cuttings. The female inflorescence (or hop “cone”) is the harvested product. While male hop plants are required for breeding purposes female hop plants will produce cones without pollination [3]. Male hop plants disperse pollen via air and if present near production hop yards, can pollinate and produce seed on female hop varieties. Seedlings from these crosses can supersede the previous genotype if they possess superior fitness. Furthermore, when new varieties are produced on yards previously producing a different variety, it is possible for escapes to continue production. Both of these scenario’s can be compounded over the life of a new hop variety planting with the result that a yard becomes contaminated. If rhizome cuttings are subsequently sold from this yard, the recipient grower could end up with either a partially or fully contaminated yard. Hop sales from this field are then rejected due to unexpected flavors or bittering capacity. In addition, farms located across the USA with historical importance have requested help in identifying feral hops growing on their property ([4]; Personal Observation). In these cases, the goal would be to eliminate the possibility that the unknown line is a currently available hop variety.

Regardless of the scenario, the hop industry does not currently have an efficient, accurate and widely available method for marker-based genotyping of hop accessions. Current means used by the National Clean Plant Network for genotyping hop are based upon 25 microsatellites that provides relatively accurate genotyping but cannot always differentiate sister-lines (Dr. Ken Eastwell, Personal Communication 2015). Patzak and Matoušek [5] reported on the use of expressed sequence tagged, simple sequence repeat (EST-SSR) markers as a means of differentiating hop varieties. The reported PCR-based method utilized 30 EST-SSR markers to differentiate 11 different hop genotypes representing a wide genetic pool. Unfortunately, no broad-based evaluation of related and unrelated genotypes was reported. In addition, a significant number of PCR steps (30) are required to utilize this method. Koelling et al. [6] reported on the identification of a 952 new SSR markers identified from expressed sequence tagged data sets deposited with National Center for Biotechnology Information (NCBI: http://www.ncbi.nlm.nih.gov/). These 952 markers were tested across 8 different cultivars to determine differentiation power of the markers. The combination of all 952 markers was successful in differentiating among the 8 cultivars. Again, no minimal number of SSR markers was identified in this study. Howard et al. [7] reported on the genotyping capabilities of diversity array technology markers (DArT) in hop. While Howard et al. [7] demonstrated DArT markers as having sufficient capability to resolve closely related hop genotypes, its cost and dependence upon a single service provider (Diversity Array Technology Inc.; http://www.diversityarrays.com/) limit availability. What is needed is a simple, widely available methodology that utilizes a minimal number of markers to differentiate between both related and unrelated hop genotypes.

Single nucleotide polymorphic (SNP) markers represent the most abundant source of variation that can be utilized to differentiate among genotypes especially as they are found in both coding [8] and non-coding regions [9]. Recent genome sequencing work (data not published) shows the presence of a SNP every 346 bp on average in hop. Matthews et al. [10] was the first group to identify and report on next generation sequencing derived SNP markers having identified 17,128 SNPs. This group utilized SNP markers to genotype hop varieties and concluded that a highly filtered group of 3068 SNP markers resulted in a dendrogram that did not significantly differ from dendrograms obtained using the lower stringency filtered set of 16,106 SNP markers. However, no minimum number of markers required to differentiate among all genotypes were identified and reported.

The minimal number of markers chosen for DNA fingerprinting cultivars has been examined in numerous crops (see [11] for review) and computer programs have been written to address this application [12] across any plant species. In essence, the primary means of identifying the minimal number of markers consists of some means of ranking markers upon their effectiveness at describing population variation and reiteratively including more and more markers until all genotypes in the population can be genetically differentiated. This process was utilized to identify a small set of SNP markers that could, upon validation, be utilized to differentiate among genetically diverse hop accessions and be widely adaptable and available to genetic laboratories worldwide.

Results and discussion

A total of 374,829 SNP markers were identified using the TASSEL-UNEAK Ver 3.0 pipeline [13] across a population of 121 individual varieties and germplasm accessions. Filtering of SNP sites, as well as filtering out individuals with poor sequencing results, was accomplished using TASSEL ver 4.3.4 [14] resulting in a set of 32,206 high quality SNP markers across 116 genotypes (Table 1). SNP marker filtration settings were set to require presence in 80 % of all genotypes for acceptance into the data set. Presence of greater than 50 % of all 32,206 SNP markers was set as cut-off for inclusion of a variety into the final data set. Some genetic lines with higher than 50 % missing gamete calls were kept in the study due to their importance in hop production (Hallertau Mittelfrueh, Wye Zenith, etc., Table 1). Cut-off specifications did not differ significantly from those utilized by Matthews et al. [10].
Table 1

Summary genotypic information of the results of partial sequencing for 116 varieties and experimental lines

Taxa name

Number of sites

Proportion missing gametes

Proportion heterozygous gametes

19105x19058M

32,206

0.28507

0.18871

21397X19058M

32,206

0.18466

0.18729

21397x21381M

32,206

0.15677

0.17053

21521x64035M

32,206

0.18596

0.13964

21534x21088M

32,206

0.4392

0.10476

21534x64037M

32,206

0.52431

0.09608

61021x21618M

32,206

0.21002

0.21445

Ahil

32,206

0.03049

0.36273

Alliance

32,206

0.03732

0.21691

Alpha Aroma (AlphAroma)

32,206

0.39319

0.17024

Apolon

32,206

0.05154

0.28246

Aquila

32,206

0.1772

0.27239

Atlas

32,206

0.11662

0.28633

Aurora

32,206

0.14354

0.18298

Backa

32,206

0.02468

0.29732

Banner

32,206

0.1142

0.32253

Bianca

32,206

0.14423

0.22318

Blisk

32,206

0.08713

0.36031

Bobek

32,206

0.17009

0.16915

Brewers Gold (BrewGold)

32,206

0.28507

0.26866

Buket

32,206

0.05192

0.21582

Bullion10A

32,206

0.40048

0.22913

Canadian Red Vine (CanadRV)

32,206

0.27271

0.23507

Canterbury Golding (CantGold)

32,206

0.14643

0.15526

Cascade

32,206

0.3255

0.22064

Cekin

32,206

0.18571

0.17781

Celeia

32,206

0.05766

0.26788

Centennial

32,206

0.17133

0.24194

Cerera

32,206

0.13302

0.22355

Chinook

32,206

0.1383

0.26647

Columbia

32,206

0.33441

0.15395

Comet

32,206

0.18922

0.25911

Crystal

32,206

0.10868

0.30513

Dunav

32,206

0.23157

0.13835

Early Prolific (E_Prolific)

32,206

0.06421

0.21249

Early Promise (E_Promise)

32,206

0.03006

0.21634

East Kent Golding (EKentGold)

32,206

0.06483

0.21535

Eastern Gold (EastGold)

32,206

0.22095

0.18928

Eastern Green (E_Green)

32,206

0.16379

0.17467

Eastwell Golding (EastGolding)

32,206

0.29116

0.11792

English Inter. 30 (EnglishInt30)

32,206

0.3341

0.0996

Eroica

32,206

0.11917

0.26562

FirstChoice

32,206

0.14323

0.22448

FuggleH

32,206

0.12516

0.18949

FuggleN

32,206

0.0857

0.2075

FuranoAce

32,206

0.12808

0.21036

Galena

32,206

0.38692

0.26569

Hallertau Gold (Hgold)

32,206

0.33096

0.11496

Hallertau Magnum (Hmagnum)

32,206

0.25899

0.17465

Hallertau Mittelfrueh (Mittelfrueh)

32,206

0.67742

0.0463

Hallertau Tradition (Htradition)

32,206

0.17407

0.14282

Hersbrucker Alpha (HersbrAlpha)

32,206

0.48696

0.07656

Hersbrucker Pure (HersbPure)

32,206

0.29786

0.12347

Hersbrucker Red Stem(HersbrRedSt)

32,206

0.06629

0.2267

Hersbrucker6

32,206

0.12982

0.17527

Hersbrucker8

32,206

0.04707

0.24249

Horizon

32,206

0.29004

0.1965

Hueller Bitter (HuelBitter)

32,206

0.03521

0.37111

Hybrid_2

32,206

0.39747

0.14934

Keyworths Early (KeywEarly)

32,206

0.17602

0.18427

Keyworths Midseason (KeyMidseas)

32,206

0.50528

0.15967

KirinC-601

32,206

0.25194

0.21036

KirinII

32,206

0.22378

0.26565

Kitamidori

32,206

0.18096

0.21097

Liberty

32,206

0.16286

0.16932

Lublin

32,206

0.60681

0.0597

Magnumx21267M

32,206

0.20984

0.21381

Mt.Rainier

32,206

0.12457

0.24576

Nadwislanka

32,206

0.08095

0.22092

Neoplanta

32,206

0.21996

0.15608

New Zealand Hallertau (NZHaller)

32,206

0.15227

0.23691

Newport

32,206

0.09275

0.24251

Northern Brewer (N_Brewer)

32,206

0.21956

0.15174

Nugget

32,206

0.27486

0.15633

Olympic

32,206

0.13122

0.29321

Omega

32,206

0.03704

0.22652

Orion

32,206

0.10194

0.18165

Perle

32,206

0.07421

0.20046

Pride of Kent (Pride_Kent)

32,206

0.26327

0.19063

Pride of Ringwood (PrideRing)

32,206

0.37487

0.15845

Saazer clone (Osvald72Y)

32,206

0.05564

0.26113

Saazer38

32,206

0.13563

0.18299

Santiam

32,206

0.06334

0.35109

Savinja Golding (SavGolding)

32,206

0.2352

0.13686

Saxon

32,206

0.07893

0.21433

Scarlet

32,206

0.19711

0.21966

Shinshuwase

32,206

0.19844

0.30134

SorachiAce

32,206

0.1492

0.20448

Southern Brewer (S_Brewer)

32,206

0.07319

0.25495

Spalter Select (SpaltSelect)

32,206

0.19931

0.1367

Sterling

32,206

0.14547

0.17772

Stricklebract (Strickle)

32,206

0.17292

0.2563

Styrian

32,206

0.26334

0.13467

Sunbeam

32,206

0.23052

0.20951

Sunshine

32,206

0.03322

0.23503

SuperAlpha

32,206

0.0866

0.31166

Talisman

32,206

0.28066

0.23918

Tardif de Bourgogne (Tardif)

32,206

0.11808

0.18833

Teamaker

32,206

0.31466

0.20682

Teamakerx19046M (Teax19046M)

32,206

0.34202

0.17163

Teamakerx21119M (Teax21119M)

32,206

0.25222

0.22697

Tettnanger

32,206

0.29926

0.12606

Tolhurst

32,206

0.07222

0.20669

Toyomidori

32,206

0.16177

0.22822

Ultra

32,206

0.17248

0.1974

USDA21734

32,206

0.17264

0.25824

Vojvodina (Vojvod)

32,206

0.02742

0.28998

Whitsbred Golding (WhitGold)

32,206

0.05415

0.22438

Willamette

32,206

0.43871

0.18969

Wuerttenburger (Wuertt)

32,206

0.29178

0.12613

Wye Challenger

32,206

0.27296

0.12466

Wye Target

32,206

0.23701

0.19757

Wye Viking

32,206

0.09039

0.19696

Wye Yeoman

32,206

0.25638

0.14648

Wye Zenith

32,206

0.411

0.06146

Yugoslavia Golding (YugoGold)

32,206

0.15783

0.17273

Labels for lines present in dendrogram (Figs. 1, 2) are defined in the first column (Taxa Name)

Genotype summaries using all 32,206 SNP markers were obtained using TASSEL. Included in TASSEL’s genotype summary were estimations of the minor allele frequency (MAF). MAF-values are important statistics utilized to filter out markers with high error potential (MAF <0.05) or provide the best discrimination power between genotypes [15]. Ranking of MAF-values from highest to lowest identified numerous markers with MAF <0.5. SNP markers that were heterozygous across all genotypes were discarded from consideration. Using a reiterative process of additive inclusion of a single marker with highest MAF values we identified a set of six (6) SNPs that were capable of differentiating among all 116 genotypes in the study.

The dendrogram resulting from the use of these six SNP markers did not match up well with dendrograms developed from the use of a complete set of SNP markers (data not shown). As a result, we continued to include additional markers with high-MAF values to the minimal set of markers and then compared the resulting genetic diversity matrices to a matrix calculated from a complete set of 1006 markers (no missing markers from data set) (Table 2). It was determined that the seven SNP markers (Table 3; Fig. 1) with highest MAF-values were required to both differentiate all 116 genotypes and define statistically similar dendrograms (approximate Mantel T test; t = −15.7471, p = 0.00001) as compared to a complete set of 1006 SNP markers (Fig. 2).
Table 2

3-way Mantel’s t test [23] for cophenetic comparisons among genetic distance matrices comparing genetic distances calculated via 6-, 7-, 8-, 10-, and 12-markers (X-matrix, no missing markers) to that of a matrix calculated with 1006 SNP markers (Y-matrix, no missing markers) using a Z-matrix calculated from 32,217 markers (20 % missing marker data allowed)

#Markers

Mantel’s

r

t test

p

6

−0.0010

−0.1125

0.4552

7

−0.2190

−15.7471

0.0000

8

−0.2217

−15.9771

0.0000

10

−0.2105

−13.0887

0.0000

12

−0.2199

−13.5649

0.0000

This test uses residuals of regression of X on Z and of Y on Z

Table 3

List of seven SNP sequences (SNP shown in parentheses) differentiating all 116 hop accessions

>TP137094

agaaaattcatatttgggaatgtatatgaatgattacatargagggaacccacatttggattttaacatgttgtctccac

aattttgtgggcatgatcagcagccttactcgactgctacttcaatattggaaatggatggtgcaattgtaactact(a/g)ct

taaatgccacaattcccatcatcgtcatcatcatgtgctgctatgaaaagtaatggtgcaatgggacatatcgattatca

taacacataatgcatacatgaaat

>TP15403

tcaggacaagtgcttatagatggtgttgatttgaagaatttgcagctcaaatggataagggagaagattggactagttag

ccaagaacctgttctgtttgcagc(a/c)actttaagagaaaatatagcttatgggaaggaaaatgcaacagatgaggagatta

aaacagccattgagcttgctaatgctgctaaattcattaacaaacttcctcaggtaaacacagaaaaaacccatctcttt

gtttcaagttatgtacttttcttc

>TP245055

agagttctgtggttgcacacgtagaggattcccttcttgctgttttgaggatcatttgttttccaatgggtgcctccttt

gaccgttaagtcaacgccagctgcaatgcc(c/t)agaggggttcctatctggaaaggaaggaaccccactgagattcgaaata

tggctagaatgactcccaagggaagccataggaacatgacaagagtcgcagccggtgtgggcaagaaggctagtctccca

tcatggaaaataagtggtttgggg

>TP295074

aaacgacccctaaactttaagcacccgtgcaccatcgagtaccctccactgtcacggcccaaactaagctcttgaatcac

tttagacggggttgggtcggctgccac(a/g)tgagcttgcaagttcggaatagaaaggagtgttggagtggatatggctgcca

agggcatcagctgctttattttcaaggccaggtcggtacacaatgtaaaaatcgtagcccaataacttggtgagccattt

ttgatgtttt

>TP400349

ccaaaatcatcaagcaactcgactcacccgccgccagagaacaagccatgcgcaccatcatattccagtccgacgcacgc

gccgcccaccctgttggtggctgctacca(c/t)atcatccaagaactcctgcgcaagattgaagctaccaaagctgaactcga

cctcgttaatcaagatctcgccgtctaccgtgctgccgccgcggccgctgcagtgccaccacaacctcagggtgtctctt

ctcatcatcatgtggatgatcatc

>TP411590

agcgacaaatttctgaacatcatccctcattcctgaccaatgcaaatctcttgcaagtcgctgaaatgttttgaaaaccc

ctgaatgaccctcaatgttgttgctatggtattcttggagtaacagtggaataaa(c/t)ggtgaagaagagggaataaccaaa

cgaaccctaaactttaggcacccatgcaccatcgagtaccctccaccgtcaagacccaaactaagmttttgaatcacttt

agacagggttggatcrgtggccac

>TP437202

gctctagaaggaacaagatgccatttcccttcaccatacttgtctatgcaatccctaagaagatcgtcttcttctctggt

ccatgcgcctttcctcaccgctgct(g/c)tcccagacgaaccgccctcagtttgttccgttactagtactgtcaccatattaa

tattgatattgctgcgcataatgtatttatatgaattatgtaaaatacgatatataatataatatgngaatactganaag

ntaattaactagctttccagtcct

Fig. 1
Fig. 1

Dendrogram of the 116 hop varieties and germplasm resources as determined using the seven SNPs proposed as the minimal number of markers to genetically differentiate hop accessions

Fig. 2
Fig. 2

Dendrogram of hop 116 hop varieties and germplasm resources as determined by use of 1006 SNP markers with no missing data out of the pool of 32,206 SNPs utilized for this study

PCR-based methodology to screen SNP markers varies from simple (single strand conformational polymorphism, SSCP; [4] to resequencing using next generation sequencing. This study identified a set of SNP markers that could potentially be used to differentiate hop genotypes. We propose the use of high-resolution melting (HRM) curve analyses as a simple and rapid means to perform genetic fingerprinting on hop genotypes. Utilizing a draft hop genome, we aligned the raw reads for informative SNP markers to extend reads to a total length of 264-bp. Primer3 software identified optimum primer sequences that can be used to develop Amplicons for HRM analysis (Table 4).
Table 4

Suggested primers pair sets and amplicon specifications for high resolution melting curve analysis of seven SNP markers differentiating among 116 hop genotypes

SNP marker

Direction

Length

Primer sequence

Pair Tm

Product Tm

Diff

Size

TP137094

Forward primer

24

GGGCATGATCAGCAGCCTTACTCG

2.39

99

59.71

TP137094

Reverse primer

25

TGACGATGATGGGAATTGTGGCATT

2.39

99

57.32

TP15403

Forward primer

24

TGCAGCTCAAATGGATAAGGGAGA

0.54

114

55.84

TP15403

Reverse primer

25

CCTCATCTGTTGCATTTTCCTTCCC

0.54

114

56.38

TP245055

Forward primer

20

TGGGTGCCTCCTTTGACCGT

0.64

83

57.89

TP245055

Reverse primer

23

TCAGTGGGGTTCCTTCCTTTCCA

0.64

83

57.26

TP295074

Forward primer

19

AGACGGGGTTGGGTCGGCT

0.1

92

59.81

TP295074

Reverse primer

21

AGCAGCTGATGCCCTTGGCAG

0.1

92

59.71

TP400349

Forward primer

19

GCCGCCCACCCTGTTGGTG

2.26

86

60.55

TP400349

Reverse primer

23

ACGAGGTCGAGTTCAGCTTTGGT

2.26

86

58.28

TP411590

Forward primer

27

AATGACCCTCAATGTTGTTGCTATGGT

0.15

110

57.18

TP411590

Reverse primer

23

GATGGTGCATGGGTGCCTAAAGT

0.15

110

57.33

TP437202

Forward primer

24

CTTCTTCTCTGGTCCATGCGCCTT

0.37

70

59

TP437202

Reverse primer

21

ACGGAACAAACTGAGGGCGGT

0.37

70

58.63

Several of the accessions used in this study are thought to be clonal selections from other lines contained in this study. As an example, Savinja Golding is thought to be a clonal selection from Fuggle (see: “Slovenian Styrian Goldings: https://bsgcraftbrewing.com/slovenian-styrian-goldings) as are Fuggle H and Fuggle N (A. Haunold, Personal Communication, 2014). In addition, Hersbrucker 6 and 8 are thought to be clonal selections from the original German ‘Hersbrucker’ landrace (see: USDA ACCESSION No. 21514; http://www.ars.usda.gov/SP2UserFiles/person/2450/hopcultivars/21514.html). All these “clonal selections” show sufficient phenotypic differences from the related lines as well as parent lines to suggest genetic differences between them, although differences are expected to be minor. The inclusion of clonal selections was to determine if a sufficiently robust method could be devised to differentiate among such lines.

Previous work in hop have focused upon the identification of male plants from a population of offspring [16] or genetic diversity and DNA fingerprinting using older marker technology such as STS, SSR, AFLP, RAPD and DArT [7, 1719]. In all publications, differentiation of accessions required the full compliment of markers used for defining genetic diversity in hop populations. In several reports, a few hop varieties were not differentiated from one another and complete validation was not possible given the marker technology used. Furthermore, none of the published reports identified a subset of markers that could be used independently to fingerprint hop varieties.

In this study, use of the full compliment of 1006 SNP markers found in all cultivars (Fig. 2) and use of the minimum number of markers (7 SNPs—Fig. 1) completely differentiated all female lines contained in this study. In this report, 7 SNPs were identified that effectively differentiated all varieties and accessions present in the study. The hop lines chosen for this study represent a broad spectrum of hop lines from around the world. Some of the varieties evaluated in this study were not adequately differentiated using older marker technology such as AFLP or SSR’s. Thus, these older technologies have sufficient limitations in their usefulness for variety validation or identification. Partial sequencing through next generation sequencing technology allows for the identification of thousands of SNP markers from across the genome. These markers are not limited to clustered regions such as SSRs and DArT markers [16, 20] and are therefore more representative of the genome. Because of their distribution throughout the genome, SNP markers offer a greater likelihood of differentiating among accessions.

The 7 SNPs identified in this study were the minimum number of markers required to differentiate all the hop accessions in this study. They have not yet been tested using high resolution melting (HRM) or other SNP detection methods. Furthermore, the use of these 7 SNPs as a discriminating tool for samples consisting of mixtures of different cultivars has not been tested but may have limited applicability given the small number of markers used. The primers for use in HRM are reported for implementation by other projects (Table 3). If one or two of these SNPs prove to be insufficient for use in HRM or other PCR techniques, there are additional SNP markers that can be utilized (Supplementary Data).

Conclusions

This note reports on the identification of a minimal number of markers (7 SNPs) required to differentiate among 116 widely divergent hop accessions including clonal selections and sister hop lines. As such, it is the first publication outlining a simple widely available protocol for the identification of, and discrimination among, hop varieties. The SNPs and associated primer sequences for HRM analysis are provided and supplementary data provided to aid genetic.

Laboratories ensure their own set of markers that can be used for differentiation among hop lines.

Methods

Plant material consisted of 121 genotypes (varieties and experimental germplasm) contained in the USDA-ARS hop genetics and breeding program located at Corvallis, OR. Due to poor DNA quality of a few of the lines, the final sample number used was 116. DNA was extracted using DNAeasy Kits (Qiagen Inc) with the exception that the amount of RNase A was doubled and the QIAshredder spin column was not used. Library preparation, sequencing and were as reported by Elshire [21]. Because hop does not currently have a reference genome, SNP identification and production of hapmap files were accomplished using the TASSEL-UNEAK pipeline (http://www.maizegenetics.net/tassel/docs/TasselPipelineUNEAK.pdf). Resulting hapmap was analyzed by TASSEL 5.2.1 [14]. Marker and genotype summaries were exported as csv-format files which were imported into Microsoft ® Excel ® for Mac 2011. Minor allele frequency (MAF) were calculated in TASSEL and subsequently sorted from highest to lowest values. Initially, the top two markers with the highest MAF values were chosen for data analysis. These two markers with the highest MAF values were filtered into a separate data file in TASSEL v 5.0 using the “filter sites” option and genetic diversity values estimated from this filtered data. The resulting genetic diversity matrix was scanned for presence of genetic diversity estimates equal to zero. If present, the process was repeated adding the next marker with highest MAF value. These steps were reiterated until all genetic diversity estimates were greater than zero (matrix with six SNP markers having the highest MAF values). Additional high-MAF, SNP markers were added to this set of six SNPs to form additional genetic distance matrices (genetic distance matrices formed from 7-, 8-, 10- and 12-markers) for comparison to a complete set of polymorphic markers with no missing data (1006 SNP markers). NTSYSpc V2.21c [22] was used to estimate correlations between genetic matrices for minimal marker sets (6-, 7-, 8-, 10-, 12-markers) and the complete data set using 3-way Mantel’s t test [23] and a matrix calculated (constant or “Z-matrix”) from the original set of 32,206 SNP markers.

The 64-bp reads representing minimal marker data sets were aligned with a USDA-ARS/OSU draft hop genome (http://hopbase.cgrb.oregonstate.edu/app_dev.php/) to extend reads by 100-bp on either side of the 64-bp read using Geneious Pro ver 5.5.9 (http://www.geneious.com, [24] (Table 3). As an aid to interested parties, we developed primer pairs (Table 4) that are appropriate for high-resolution melting curve analyses [25] using Primer3 [26]. Default settings were used and product size was limited to a range of 70- to 115-bp length. Other PCR-based SNP assays are available and can be designed using the information in Table 3.

Availability of supporting data

The data set supporting the results of this article is included within the article while the hapmap file from which this study derives is included as supplementary files (Additional file 1).

Abbreviations

AFLP: 

amplified fragment length polymorphism

DArT: 

diversity array technology

HRM: 

high resolution melting

MAF: 

minor allele frequency

RAPD: 

restriction amplified polymorphism DNA

SNP: 

single nucleotide polymorphism

SSR: 

single sequence repeat

STS: 

sequence tagged site

Declarations

Authors’ contributions

The author laboratory was responsible for DNA preparation, data analyses and manuscript preparation. Library preparation and sequencing were performed by Oregon State University—Center for Genomic Research and Bioinformatics. All authors read and approved the final manuscript.

Acknowledgements

The author acknowledges the USDA-ARS, Hop Research Council (http://hopresearchcouncil.org/) and the Hop Breeding Company (http://www.hopbreeding.com/) for funding this research.

Compliance with ethical guidelines

Competing interests The authors has no competing interests with the data or results of this study.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
USDA-ARS, 3450 SW Campus Way, Corvallis, OR 97331, USA
(2)
ROY FARMS, INC., 401 Walters Road, Moxee, WA 98936, USA
(3)
CGRB, ALS Building, Oregon State University, Corvallis, OR 97331, USA

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