GENETIC MAPPING OF HOP (Humulus lupulus L.) APPLIED TO THE

DETECTION OF QTLs FOR ALPHA-ACID CONTENT

Andreja Čerenak1, Zlatko Satovic

3 and Branka Javornik

1,2,4

1Slovenian Institute for Hop Research and Brewing, Cesta Žalskega tabora 2, 3310 Žalec,

Slovenia 2University of Ljubljana, Biotechnical Faculty, Jamnikarjeva 101, 1000 Ljubljana,

Slovenia 3University of Zagreb, Faculty of Agriculture, Svetosimunska 25, 10000 Zagreb, Croatia

4Corresponding author (tel.: +386 1 423 11 61; fax: +386 1 423 10 88;

e-mail: branka.javornik@bf.uni-lj.si)

Abstract

The map locations and effects of quantitative trait loci (QTLs) were estimated for alpha-

acid content in hop (Humulus lupulus L.) using amplified fragment length polymorphism

(AFLP) and microsatellite markers (SSRs) genetic linkage maps constructed from a

double pseudo-testcross. The mapping population consisted of 111 progeny from the

cross between the German variety ‘Magnum’ showing high levels of alpha-acids and a

wild Slovene male hop ‘2/1’. The progeny segregated quantitatively for alpha-acid

content determined in the years 2002, 2003 and 2004. The maternal map consisted of 96

markers mapped on 14 linkage groups defining 661.90 cM of total map distance. The

paternal map included 70 markers assigned to 12 linkage groups covering 445.90 cM of

hop genome. QTL analysis indicated 4 putative QTLs (alpha1, alpha2, alpha3 and

alpha4) on linkage groups LG03, LG01, LG09, and LG03 of the female map,

respectively. QTLs explained 11.9 to 24.8% of the phenotypic variance. The most

promising QTL to be used in marker-assisted selection is alpha2 with the peak co-located

exactly with the AFLP marker. Three chalcone synthase-like genes (chs2, chs3, and chs4)

involved in hop bitter acid synthesis mapped together on the LG04 on female map. The

saturation of the maps will be carried out, particularly the putative QTL regions, using

SSR markers and stability of the QTLs will be tested in next years.

Introduction

Hop (Humulus lupulus L.) is an important ingredient in beer-brewing process. Female

hop plants are cultivated for their secondary metabolites, resins and essential oils, which

give flavour, bitterness and aroma to beer and are also of pharmaceutical interest. For hop

industry the content of alpha-acids in hop cones is of prime importance according to

which hop cultivars are divided into two main classes, aroma and high-alpha varieties.

Aroma hop varieties with alpha content up to 8% are typical of European hop production

and high alpha varieties, mainly grown in USA gave up to 18% of alpha-acids. In

Slovenia traditional European aroma hops is produced by the domestic hop varieties.

Overall orientation in hop production is towards high alpha varieties following the

demands of brewing industry.

The typical bitterness of beer comes from the isomerisation of hop bitter acids, highly

prenylated phloroglucinols (humulone and lupulone). The valerophenone synthase (VPS)

was the first chalcone synthases (CHS) homologues cloned which forms intermediates in

the biosynthesis of the hop bitter acids (Paniego et al. 1999; Okada and Ito 2001). Four

other chs homologues were later isolated from hop: chs_H1 with characterized enzymatic

activity (Matoušek et al. 2002) and chs2, chs3 and chs4 (Novak et al. 2003). Recent

enzymatic activity study of all cloned homologues confirmed that VPS and CHS_H1

enzymes were involved in biosynthesis of bitter acids, CHS_H1 alone in biosynthesis of

prenyflavonoids while CHS3 showed no enzymatic activity and CHS2 and CHS4

produced only some intermediate compounds (Okada et al. 2004). However, more genes

and regulatory factors are involved in the production of bitter acids as well as other hop

secondary metabolites and efforts are being made to elucidate their biosynthesis. This

knowledge could greatly contribute to more efficient breeding of hops on high alpha acid

content. The direct selection for an alpha-acid content, a quantitatively inherited trait,

cannot be carried out until the female plants flower and develop cones after a minimum

of three years of growth. If prior knowledge of linkage relationships between marker loci

or gene and an important resin characteristics were available, undesirable individuals

could be eliminated from progeny populations with marker-assisted selection as early as

when the seedlings develop the first few leaves. Such efficiency of breeding by using

molecular markers linked to agronomically important traits has been shown in many

important crops. Moreover, in perennial species, QTL mapping is a useful tool for

studying gene expression at different developmental stages (Wu et al. 2000; Lercetau et

al. 2000; Yin et al. 2003; Atienza et al. 2004), which is highly desirable for carrying out

early stage selection. However, identifying genetic basis of alpha-acid content in hop is a

demanding task particulary in the view of hop genetic complexity. Cultivated European

hop is diploid (2n = 2x = 20), with nine autosomal bivalents and two sex chromosomes

(Karlov et al. 2003). It is dioecious plant producing highly heterozygous offsprings, while

male plants enter a breeding process as nearly unknown pollinators. Backcrossing is an

unrealistic option as for other outcrossing perennial species (Maliepaard 1998), so genetic

analysis in hop is typically performed on the full-sib progeny of a single cross, which is

also the base population for selection. Both parents of a cross are expected to display a

high level of heterozygosity, thereby allowing markers to be found that are heterozygous

in one or both parents. This type of analysis has been called double pseudo-testcross or

two-way pseudo-testcross (Grattapaglia and Sederoff 1994). Such strategy was already

used in construction of first published hop male and female linkage map (Seefelder et al.

2000a) and as reported later by Seefelder et al. (2005) in construction of hop maps on

progeny segregating for resistance to powdery mildew where they identified two tightly

linked AFLP markers to resistance locus R2. Similarly, Koie et al. (2005) reported on the

use of pseudo-testcross strategy in development of hop genetic maps for detection of

QTLs of chemical components of hop resin.

We have also chosen this mapping approach for the development of hop genetic maps

using AFLP and microsatellite markers on the family segregating quantitatively for

alpha-acid content. Maternal and paternal linkage groups were constructed and data of

repeated evaluations of alpha-acid content over three vegetation periods were used for

mapping of QTLs. Quantitative analysis indicated four putative QTLs on three linkage

groups of the female map of which one overlapped with the AFLP marker.

Materials and methods

Plant material

The F1 full-sib family used in this study is part of the breeding program carried out by the

Slovenian Institute of Hop Research and Brewing, Žalec, Slovenia. Our mapping

population was obtained by crossing the German variety ‘Magnum’ with the Slovene

breeding line ‘2/1’. The analysis included 111 full-sib genotypes, 97 female and 14 male

plants and the two parents. The progenies display clear morphological variations in many

traits, such as growth rate, vigour and alpha-acid content. The mapping population was

selected on the basis of observations made in the breeding programme.

DNA isolation

Total genomic DNA was extracted from young leaves using a modified CTAB method

according to Kump and Javornik (1996). The ground leaf suspensions were treated with

CTAB buffer [2% (w/v) CTAB, 1.4 M NaCl, 20 mM EDTA, 100 mM Tris-HCl (pH 8.0),

0.2% (v/v) -mercaptoethanol; Sigma-Aldrich Chemie GmbH], and extracted by

repetitions with a phenol : chlorophorm : isoamylalcohol mixture (25:24:1). The DNA

was precipitated by the addition of sodium acetate (3M, pH 5.2) and ice cooled

isopropanol. The pellet was washed with cold ethanol (70%), dried and re-suspended in

TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). The DNA concentration was

estimated by mini TKO 100 DNA fluorimeter (Hoefer Scientific) and samples were

stored at 4°C.

AFLP analysis

The AFLP protocol was carried out according to Vos et al. (1995). Restriction digests

were performed by using 2.5 U EcoRI and 2.5 U MseI enzymes in 1x NE EcoRI buffer

(50 mM NaCl, 100 mM Tris-HCl, 10 mM MgCl2, 0.025% Triton X-100 pH 7.5) or 2.5 U

PstI and 2.5 U MseI enzymes in 1x NE buffer 2 [50 mM NaCl, 10 mM Tris-HCl, 10 mM

MgCl2, 1 mM dithiothreitol (pH 7.9), 1x BSA (100 g/mL)] in a final volume of 40 L at

37°C incubated overnight. Ligation was performed at the same temperature adding

EcoRI/PstI (5 pmol) and MseI (50 pmol) adapters in 1x T4 DNA ligase buffer (50 mM

Tris-HCl, 10 mM MgCl2, 10 mM ditiotreitol, 1 mM ATP, 25 g/L BSA pH 7.8) adding

1 U Weiss T4 – DNA ligase.

Restricted and ligated DNA (50 ng DNA) was pre-amplified in standard 1x PCR buffer

(10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3) with 50 ng EcoRI, PstI and MseI

primers with one additional nucleotide, 1.25 U Taq DNA polymerase (Promega), 0.8 mM

mixture of four dNTPs (Roche Molecular Biochemicals) in a final volume of 50 L. The

pre-amplification mixture was amplified in a MWG Primus 96 using 20 cycles of 94°C

for 30 s, 56°C for 60 s, and 72°C for 60 s.

The following touchdown protocol was used for selective amplification of 2 L

preamplified DNA in 1x PCR buffer (10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH

8.3), 15 ng EcoRI/PstI and MseI primers with two or three selective nucleotides, 0.3 U

Taq DNA polymerase (Promega), 0.8 mM mixture of four dNTPs (Roche Molecular

Biochemicals) in a final volume of 10 L: 13 cycles of 94°C for 30 s, 65°C for 30 s with

a decrease of –0.7°C per cycle, and 72°C for 1 min; followed by 23 cycles at the

annealing temperature of 56°C.

For amplification of parental DNA, 108 primer pairs were screened in total. The

progenies of the family were selectively amplified with 18 EcoRI/MseI and 18 PstI/MseI

primer pair combinations selected after screening parental DNA samples. EcoRI or PstI

primers in selective amplification were labelled with Cy5 to allow detection of separated

fragments by ALFexpress II automated sequencer (Amersham Biosciences). Results were

analysed by the software package ALFwinTM

Fragment Analyser 1.03 (Amersham

Biosciences) and visually.

The AFLP markers were named after the selective primer set used and the number of

detected bases.

SSR analysis

Twenty-two SSR markers, expressing polymorphism in parents, were screened on the F1

crossing family. Amplification of SSR markers was carried out according to Štajner et al.

(2005 ali 2004??) for 15 markers (HlGA24, HlAGA4, HlAGA7, HlAGA6, HlGA23,

HlGA35, HlGA31, HlGT24, HlGT20, HlGT17, HlGT16, HlGT14, HlAGA8, HlGA36,

HlACA3), according to Brady et al. (1996) for two SSR markers (Hl3a88, Hl7a82) and

according to Araki et al. (1998) for STS marker HlBrapd. 4 SSR markers were found in

chalcone synthase genes (chs2, chs3, chs4) and in endochitinase precursor gene hch1.

Markers CHS2 (AB061020, SSR repeat motif (TC)11CATATATAC(AT)6, position 232-

274 bp), CHS3 (AB061022, SSR repeat motif (AT)6, position 230-241 bp) and CHS4

(AJ430353, SSR repeat motif (AT)5(AC)4(AT)4N236(TA)7, position 1372-1648 bp) and

HCH1B (AF147497, SSR motif (TAGTTA)5, position 1935-1964 bp) were amplified in

PCR reactions in a volume of 20 L containing 1x PCR buffer (10 mM Tris-HCl, 50 mM

KCl, pH 8.3), 1.5 mM MgCl2, 0.2 mM each of dNTPs, 10 μM of each primer (forward

Cy5 labelled), 0.5 U of Taq polymerase (Biotools) and 40 ng of hop DNA. Amplification

was performed in a PE9700 Thermal Cycler (Applied Biosystems) using the following

thermal cycling protocol: 95°C for 5 min, followed by the 26 cycles of 45 sec at 94°C, 30

sec at 52 – 64°C and 1 min 30 sec at 72°C. The reactions were completed by incubating

at 72°C for 8 min and held at 4°C. Microsatellites were separated and detected on an

ALFexpress II automated sequencer (Amersham Biosciences).

Alpha-acid content

The content of alpha-acids was measured in 2002, 2003 and 2004 with the lead

conductance value (LCV) method (Analytica EBC 1998). With this standardised method

the dried hop cones (5 g) of each female hop plant were grinded up and extracted with 50

mL toluene. After shaking (30 min) and filtration the LCV of hop was determined with

conductometric titration with lead acetate in the methanolic solution (20 g/L, acidified

with acetic acid). Determination of the equivalent point gave the LCV, which is

proportional to the percentage content of alpha-acids.

The distribution of alpha-acid content in all 3 years was tested with the Kolmogorov-

Smirnov test. Phenotypic data from each year were used in the QTL analysis.

Data analysis

Dominant AFLP loci were separated into three segregation patterns: 1:1 for loci showing

segregation for the female parent (‘Magnum’ x ‘2/1’; type ab×aa); 1:1 for loci showing

segregation for the male parent (type aa×ab), and loci heterozygous in both of them (type

a0×a0). The SSR markers revealed three patterns of segregation: 1:1 for markers

segregating only in the male or in the female (types ab×aa and aa×ab), 1:2:1 for markers

segregating in male and female (type ab×ab) and more informative 1:1:1:1 segregation in

markers segregating in both parents with three alleles (type ab×ac).

Markers segregation was tested by chi-square test although all markers were used for the

mapping including those demonstrating significant departure from the expected

Mendelian ratios.

Monoparental AFLP markers and all SSR markers were used to construct separate

genetic linkage maps for the female (‘Magnum’) and male (‘2/1’) parents, using the two-

way pseudo-testcross strategy (Grattapaglia and Sederoff 1994). The hypothetical M

marker linked absolutely to the male plants was added to mapping data.

Map construction was carried out using the JoinMap 3.0 programme (Van Ooijen and

Voorrips 2001). The phase was determined internally by software based on the

recombination frequency. The map was constructed using a LOD of 5.0 for the grouping

of markers. In each linkage group, the order of markers was inferred using pairwise data

of only those loci that had a recombination frequency lower than 0.35 and a LOD value

higher than 2.0. The optimal marker order was determined by using the ripple value of

1.0 and the jump threshold of 5.0. The Kosambi mapping function was used to convert

recombination data to map distances.

MapQTL version 4.0 (Van Ooijen and Maliepaard 1996) was used to identify and locate

QTLs associated with alpha-acid content, by performing the non-parametric test of

Kruskal-Wallis as well as both interval mapping (Lander and Botstein 1989) and

multiple-QTL mapping (MQM; Jansen and Stam 1994). Each linkage group was scanned

at 1-cM intervals. In the regions of the putative QTLs, the markers with the highest LOD

values were chosen as co-factors. A backward elimination procedure was used to select

cofactors significantly associated with each trait at p < 0.02 to be used in MQM. A LOD

score of 3.0 was used to declare the presence of a QTL linked to alpha-acid content. The

results are reported in terms of the individual parental effects (i.e., difference in effect of

the alleles inherited from each parent) and the interaction effect (i.e., deviation from

additivity, where a value of zero indicates complete additivity) (Table 1). For each LOD

peak, the 2-LOD support intervals were determined (Conneally et al. 1985).

Maps were drawn using the MapChart version 2.1 software (Voorrips 2002).

Result and discussion

The molecular mapping of the cultivar ‘Magnum’, a representative of high alpha-acid

varieties, and ‘2/1’, a traditional Slovene male hop, is described. Constructed maps were

used to initiate a dissection of genetic factors responsible for alpha-acid content by QTL

mapping.

Mapping population and genotyping

Our population consisted of 97 female and 14 male plants derived from the cross

‘Magnum’ x ‘2/1’. Sex segregation ratio was highly distorted (6.9:1) in our mapping

population as has been frequently observed in hop 2:1 (Neve 1991), 3:1 (Nesvadba et al.

1999), 4:1 (Patzak et al. 2002) and even 7.5:1 (Seefelder et al. 2000a). However, the

genetic basis of sex determination in hops remains still largely unknown. The population

was selected from our breeding programme on the basis of the variability of alpha-acid

content ranging in values from 2% to 17%. Such range covers almost all known values of

alpha-acid content since alpha content more than 18% is very rarely reported in breeding

lines. This variability was obtained only in female plants since lupulin, which contains

most of hop resins and essential oils, originates in the cones. Thus, only female plants

were included in the evaluation of alpha acids and together with sex segregation

distortion presumably affected the statistical potential of the test.

The parents were analysed with 108 AFLP primer combinations using EcoRI, PstI and

MseI restriction enzymes. Thirty-six primer combinations were selected out of 108

combinations and amplified on the entire mapping population. AFLP analysis with 18

PstI/MseI primer combinations gave a total of 807 amplified fragments in comparison to

1145 bands obtained with 18 EcoRI/MseI primer combinations but much higher (50.0%)

polymorphism was obtained in first set than in the second one (30.8%). The percentage of

revealed polymorphism with our AFLP analysis on the entire population was 40.5%,

which is comparable with other reports (Seefelder et al. 2000b; Jakše et al. 2001; Čerenak

and Javornik 2002).

From 36 primer combinations, 1955 AFLP markers were scored, ranging from 28 to 625

bp, of which 762 (~40%) were polymorphic. Of these markers, 137 were inherited from

the female parent cv. ‘Magnum’ (1:1), 104 from male parent ‘2/1’ (1:1), and 521 were

heterozygous in both parents (3:1). Segregation ratios were tested using the 2 test and a

total of 479 AFLP markers (62.86%) did not fit the expected Mendelian ratios (p<0.01)

(Table 2).

Two restriction enzyme combinations, MseI/PstI and MseI/EcoRI, differing in sensitivity

to cytosine methylation, were used in an attempt to achieve a wider distribution of

markers across the genome (Young et al. 1999, Hanley et al. 2002). On average, 20%

higher polymorphism was obtained using MseI/PstI comparing to MseI/EcoRI primer

combinations. In the developed maps, pronounced clustering of any marker type was not

evident similar to the results in grapevine (Fisher et al. 2004). In other mapping

approaches, evidence for the clustering of AFLP markers has been reported (Doligez et

al. 2002) and AFLP marker distribution in general has been discussed as being potentially

biased by methylation patterns influencing the frequency and distribution of restriction

enzyme target sequences in a given genome.

Segregation analysis of all three types of AFLP markers revealed a high percentage of

distorted markers. Segregation distortion has often been observed in perennial species,

such as forest and fruit trees (Grattapaglia and Sederoff 1994; Marques et al. 1999;

Cervera et al. 2001). Departures from expected Mendelian ratios often indicate a linkage

between molecular markers and distorting factors and by excluding such markers from

the map part of the linkage group would be missing (Hackett and Broadfoot 2003).

Therefore it is suggested that all markers should be used in the mapping process. For

example, the use of distorted markers in the construction of chestnut map did not affect

the result of marker grouping (Casasoli et al. 2001). However in pine, eucalyptus and

oak, general trend was observed for distorted markers to cluster in particular linkage

groups (Verhaegen and Plomion 1996; Barreneche et al. 1998). We included all markers

in mapping regardless of the segregation ratio since majority of them were sex linked and

no linkage groups containing clusters of distorted markers were found.

The AFLP technique considerably decreases the cost and time required to construct

genetic linkage maps, relative to other PCR-based marker systems (Vos et al. 1995). A

growing number of genetic maps based on different kinds of molecular markers now

include AFLP, indicating their efficiency for mapping purposes. However, microsatellites

or SSR are co-dominant and highly informative markers in the linkage analysis of

outbreeding populations but they are considerable more costly than AFLP markers.

We used 17 hop genomic SSRs (ali se piše množina?), one STS marker and four SSRs

found in hop cloned genes. From the set of 22 microsatellite markers tested, five

segregated in highly informative 1:1:1:1 ratio, one in 1:2:1 ratio and 16 segregate in 1:1

ratio (Table 2). 18 SSR markers were heterozygous in female parent and 15 of them were

mapped while 10 SSR markers were inherited from male parent of which 8 were mapped.

Altogether, out of 22 SSR markers 19 markers were placed on either map of which 3

markers were linked on both maps (HlAGA6, HlAGA7, HlGT14).

Within sequences of the cloned chs-like genes we found microsatellite like structures

which we used to develop SSR markers. These genes are of importance since they are

key players (?) in the biosynthesis of hop bitter acids. Only SSRs developed from

enzymatically not yet well defined genes (chs2, chs3 and chs4) were polymorphic in our

mapping population and they cluster together on the same linkage group. The

microsatellite HCH1B obtained from sequences of endochitinase precursor gene mapped

on LG14 which is assumed to be a part of one major linkage group.

Map construction

A total of 96 markers (81 AFLPs, 15 SSRs) were placed on the ‘Magnum’ map forming 9

linkage groups, 2 triplets and 3 doublets which are assumed to be part of the other groups,

defining 661.90 cM of total map distance. The average distance between adjacent

markers was 11.51 cM; the largest interval was 26.47 cM in LG 12 (doublet), but there

were also significantly higher intervals in three other minor groups. In the remaining 9

‘major’ linkage groups, the average interval ranged between 4.43 and 11.92 cM. The

‘Magnum’ map has 37.1% more markers than the ‘2/1’ male map. A total of 70 markers

(61 AFLPs, 1 hypothetical marker, 8 SSRs) were placed on the ‘2/1’ map, defining

445.90 cM of total map distance, with an average interval of 12.26 cM between adjacent

markers. The largest interval 24.41 cM was obtained in LG 10 (doublet). Seven ‘major’

linkage groups containing from 4 to 23 markers were evidently in ‘2/1’ and ranged from

26.07 to 81.90 cM. The other 5 linkage groups are only doublets, and ranged from 1.87 to

24.41 cM.

Marker distribution in the resulting maps seemed fairly even with only slight clustering

of SSRs on LG04 on female map. Large gaps of around 20 cM distance between two

markers remain on 6 ‘Magnum’ linkage groups and 4 linkage groups identified from

‘2/1’ if doublets and triplets are excluded. These gaps seem to occur predominantly in

terminal regions (Fig. 3, 4).

For both the paternal and maternal maps, the number of resolved linkage groups

exceeded the haploid number of hop (n=10) suggesting that additional markers will be

required to bridge gaps between linkage groups and to improve resolution. Small linkage

groups (doublets and triplets) are assumed to be the part of other major groups. The

presence of large gaps may also be explained by hot spots of recombination (Cervera et

al. 2001). It is to be expected that the number of linkage groups will be reduced as more

molecular markers are mapped, especially SSRs with a co-dominant nature.

The ‘Magnum’ and ‘2/1’ maps were deliberately constructed with high statistical

stringency (linkage threshold of LOD 5.0), which is two orders of magnitude more

stringent than the significance threshold of LOD 3.0 first suggested by Lander and

Botstein (1989) in order to construct a very robust and reliable skeleton for QTL analysis.

The size of female linkage map was larger than male map. The smaller total map length

of the paternal map in comparison to the maternal map suggests that some regions are

still under-represented by markers available in the paternal data set. However, our maps

coverage is larger than in the hop maps published by Seefelder et al. (2000a), using 217

AFLPs, 3 RAPDs, 1 STS and 3 SSR markers segregating in an 68 F1 population

obtaining coverage of 346.7 cM in the maternal and 227.4 cM in the paternal map or on

their other maps Seefelder et al. (2005) using large mapping family and higher number of

AFLP markers. Koie et al. (2005) also covered only 272 cM and 102 cM on female and

male maps respectively, but using smaller number of AFLP markers.

QTL analysis

QTL analyses were carried out using data of alpha-acid contents obtained in the years

2002, 2003 and 2004. In 3 years, during which the plants were grown in experimental

field, the 76, 80 or 79 female plants of the segregating population showed variation of

alpha-acid content. In addition to variation of this trait, the individuals varied also in

other agronomic traits, such as growth habit, the beginning of ripening and cone

characteristics. The frequencies of observed alpha-acid contents showed normal

distribution as seen in Fig. 1. Good correlations with average correlation coefficients of

0.66 for 2002/2003, 0.53 for 2002/2004 and 0.58 for 2003/2004, were obtained at p<0.01.

The distribution of alpha-acid content in all 3 years was tested with the Kolmogorov-

Smirnov test and none of the 3 distributions deviated significantly from the expected

normal distribution (p>0.05) (Table 3).

QTL analysis showed no significant QTLs for alpha-acid content in 2002 but in 2003 one

putative QTL, alpha1, was identified on linkage group LG03 of the female map. Using

multiple-QTL mapping (MQM) its peak LOD value was estimated as 3.13. This QTL

accounted for 24.8% of the observed phenotypic variation (Table 4). In 2004 three QTLs

(alpha2, alpha3, and alpha4) associated with alpha-acid content were localized on

linkage groups LG01, LG09, and LG03 of the female map, respectively. Its LOD values

were 4.60, 3.60, and 3.10 explaining 18.6, 20.6, and 11.9% of the phenotypic variance.

The location of alpha1 was not significant using data of alpha-acids content obtained in

2004. The most interesting for eventual marker-assisted selection is QTL alfa2, having

the 2-LOD confidence interval of only 4 cM. The peak of putative QTL is found exactly

on the location of AFLP marker EACGMCTT440. The paternal effect of alfa2 was null

suggesting that the male parent ‘2/1’ was homozygous. The alfa3 QTL exhibit a strong

interaction effect, which suggests some degree of non-additive (i.e., dominant or

epistatic) expression for alleles at this QTL.

The genetic effects related to the alpha-acid content were thus identified in three different

chromosomal regions on LG01, LG03 and LG09 of ‘Magnum’ although all of these

QTLs were not repeatedly obtained by analyzing field data from different years. But Koie

et al. (2005) provided preliminary information that their three QTLs for alpha acids on

three linkage groups were detected repeatedly in the same region by independent analysis

of phenotypic data from three different years. Therefore, we will continue with our work

by including additional data of alpha-acid content and saturating the map with more

markers, particularly the putative QTL regions.

Our QTLs were localized on the different linkage groups than three chalcone synthase -

like genes (chs2, chs3, and chs4) which mapped together on the same linkage group. We

were not able to map vps and chs_h1 which encode enzymes directly involved in

biosynthesis of bitter acids but it has been suggested that vps gene is part of the gene

cluster together with chs3, and chs4 (Novak et al. 2003) and therefore we can assume that

at least vps gene could be linked to mapped genes but not to our QTLs. Considering the

lack of co_localization of chs_like genes and detected QTLs we can speculate that some

of our alpha acid QTL may represent a regulatory factors since it has been indicated that

regulation rather than number of chs-genes determine varying level of bitter acids in hops

(Matoušek et al. 2002).

Conclusion

We constructed hop genetic maps by using mapping family displaying variations in

alpha-acid content in order to detect QTLs for this trait. We examined linkages between

molecular markers and quantitative traits related to alpha-acid content. This trait explains

a continuous phenotypic distribution and marker loci were used to estimate the number

and effect of QTLs associated with this character. The identification of such QTLs will

aid hop breeding by increasing our understanding of the inheritance of the character and

provide the basis for marker-assisted selection. Although these maps are not complete

and constitute the skeleton on which more co-dominant microsatellite markers can be

mapped progressively to construct a saturated map, we were able to detect some QTLs.

The most interesting for marker-assisted selection is QTL alfa2, explaining 18.6% of

phenotypic variability of alpha-acid content, which is co-located with a marker. Mapping

of chs-like genes which encode enzymes involved in biosynthesis of bitter acids to

different linkage group than detected QTLs support the complexity of the production of

hop secondary metabolites. Addition of more functional genes and regulatory factors

involved in the secondary metabolite biosynthesis to our framework maps will allow us

to search for QTLs co-localize with the genes and to establish their putative role in the

determination of alpha acid content.

Acknowledgment: This work was supported by research grants PO-0513-0481 and S4-

486-019/18828/2001 from the Slovene Ministry of Education, Science and Sport. We

thank B. Juvančič for skilful technical assistance in the laboratory.

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Table 1: Model used to test the effects of QTL alleles (Knott et al. 1997).

Parental cross Q1Q2 x Q3Q4 → Q1Q3, Q1Q4, Q2Q3, Q2Q4

Maternal effect (M) = (Q1Q3 + Q1Q4) - (Q2Q3 + Q2Q4)

Paternal effect (P) = (Q1Q3 + Q2Q3) - (Q1Q4 + Q2Q4)

Interaction effect (I) = (Q1Q3 + Q2Q4) - (Q1Q4 + Q2Q3)

Table 2: Number and segregation types of markers.

Marker type Segregation type No. polymorphic

markers

No. of loci at

p < 0.01

AFLP

<a0×a0> 521 341

<ab×aa> 137 72

<aa×ab> 104 66

SSR

<ab×ab> 1 1

<ab×aa> 8 1

<aa×ab> 4 0

<ab×ac> 5 4

CHS + HCH * <ab×aa> 4 0

Total 784 485

* - Designation in Material and methods

Table 3: Number of progenies of crossing family (N) included in analysis of alpha-acid

content, mean of alpha-acid content, variance (Var.), minimum (Min.) and

maximum (Max.) value, coefficient of skewness and kurtosis and P value

from Kolmogorov-Smirnov test for normality.

Trait - alpha

acid N Mean Var. Min. Max.

Coeficient

of skewness

Coeficient of

kurtosis P

2002 76 10.42 7.51 4.50 16.40 -0.25 -0.61 0.74

2003 80 7.09 5.67 3.00 15.10 0.64 0.45 0.51

2004 79 8.7 7.90 2.00 17.0 0.62 0.60 0.48

Table 4: Location, significance and genetic effects of individual QTLs detected related

to alpha-acid content using multiple-QTL mapping approach.

QTL Year LGa cM

b Marker LOD

c %

d M

e P

f I

g

alfa1 2003 LG03 4.7 EACCMCAA103F* 3.13 24.8 -4.60 -1.53 0.16

alfa2 2004 LG01 35.5 EACGMCTT440F 4.60 18.6 5.23 0.00 0.00

alfa3 2004 LG09 20.5 EAGGMCAC135F* 3.60 20.6 0.17 3.58 -3.58

alfa4 2004 LG03 24.7 EAGCMCAG100F 3.10 11.9 -3.62 -2.02 -0.22

a LG – linkage group;

b QTL position as estimated by the location of the local LOD maximum in cM from

the 'top' of the linkage group; c LOD value at QTL position;

d percentage of phenotypic variance explained

by QTL; marker – marker nearest to the QTL position and genetic effects (e maternal,

f paternal and

g

interaction effect) (Knott et al. 1997)

Figure 1: Distribution of alpha-acid content in the mapping population in relation to the

sample size in 2002, 2003 and 2004.

0

2

4

6

8

10

12

14

16

18

20

22

24

[1;2] [2;3] [3;4] [4;5] [5;6] [6;7] [7;8] [8;9] [9;10] [10;11] [11;12] [12;13] [13;14] [14;15] [15;16] [16;17]

% of the alpha acid content in the year 2002

sa

mp

le s

ize

0

2

4

6

8

10

12

14

16

18

20

22

24

[1;2] [2;3] [3;4] [4;5] [5;6] [6;7] [7;8] [8;9] [9;10] [10;11] [11;12] [12;13] [13;14] [14;15] [15;16] [16;17]

% of alpha acid content in the year 2003

sam

ple

siz

e

0

2

4

6

8

10

12

14

16

18

20

22

24

[1;2] [2;3] [3;4] [4;5] [5;6] [6;7] [7;8] [8;9] [9;10] [10;11] [11;12] [12;13] [13;14] [14;15] [15;16] [16;17]

% of the alpha acid content in the year 2004

sa

mp

le s

ize

Figure 3: Schematic presentation of female genetic linkage groups for the mapping

population of ‘Magnum’ × ‘2/1’ containing AFLP and microsatellite (marked

bold) markers. Numbers on the left side of the linkage groups indicate genetic

distances in centi-Morgan (cM). Bars indicate QTL location.

PACAMCTA1420,0

PAACMCTT2786,9EACCMCAA33513,3EACCMCAA5518,0EACCMCAA15521,2EAGGMCAC26323,5EAGGMCTC43525,9PACAMCAT16528,6EACGMCTT44035,5PAGAMCTC19735,9PAGAMCTC38042,6EACGMCTA16847,8PAGAMCTC22051,5PAGAMCAG11853,2

PAAMCAC16064,1

PAGAMCTC22475,6

PAGAMCTC7184,3

PAGAMCTC17599,8

PAGAMCTC126117,3

alp

ha

2LG01

EAGGMCAG1290,0PAGAMCAC2652,5PAAMCAC2615,0

PAGAMCT13919,8PAGAMCT25528,0PAGAMCTT25433,0EAGGMCTC27233,7HlGA3534,1PAGAMCAA38834,7EAGCMCTG17036,8

EACGMCTA8147,2

EAGCMCTA19760,5

LG02

PACAMCAC4120,0

EAGCMCTA2848,1

EAGCMCAC42621,1EAGGMCAC39222,5HlGT1423,9EAGCMCAG10024,7EACGMCTA17725,6EACCMCTT26226,5EAGGMCTC17728,2EAGCMCTT8233,1EAGCMCTC8639,8

EACCMCAA10362,5

alp

ha

1

alp

ha

4

LG03

PAGAMCAT2350,0

CHS326,1

CHS4B30,7

CHS242,2

PACAMCAT30363,1Hl3a8866,6HlGT1767,9HlGA2468,0PAGAMCT16272,0EACCMCTT34176,6

LG04

EAGCMCAC3800,0EACCMCAT937,1PAGAMCAA22210,0EAGCMCAG14612,1HlAGA613,3EAGCMCTC6814,1PAGAMCAA26817,4EACCMCAT21322,0

EACCMCAA26334,7

LG05

PAAMCAC1360,0

PAACMCT10915,8

PAAMCAC45932,0HlGA3134,1

EACGMCTA26954,6

PAGAMCTT15962,9

PAGAMCTT4471,5

LG06

EACCMCTT810,0

EAGCMCT5058,1

PAACMCTC7717,8

HlBrapd30,7

HlAGA738,2

LG07

PAGAMCTC1480,0PAGAMCTC503,5

EAGCMCAT19011,4

PACAMCTA16519,4

PAACMCAC20145,7

LG08

PAGAMCTT1060,0

EAGGMCAC13520,5HlGT1624,7HlAGA425,4

PAGAMCAG18032,4

alp

ha

3

LG09

EAGCMCTC3440,0

EAGCMCT34317,2

PAAMCAC46235,9

LG10

PAACMCAC2470,0

PAACMCT20718,3

PAACMCT34930,0

LG11

EAGGMCAG1140,0

PAGAMCTC36826,5

LG12

EACGMCAT1540,0

PAGAMCAC47720,9

LG13

HCH1B0,0

EAGCMCAT1419,2

LG14

Figure 4: Schematic presentation of male genetic linkage groups for the mapping

population of ‘Magnum’ × ‘2/1’ containing AFLP and microsatellite (marked

bold) markers. Numbers on the left side of the linkage groups indicate genetic

distances in centi-Morgan (cM).

PAACMCT2290,0PAGAMCTC3225,0PAACMCAC767,7PAAMCAC2098,7PAACMCT34410,2EACGMCAT15712,2PAGAMCT22813,7PACAMCTA13915,1PACAMCTA15315,9PAACMCT20416,1PAAMCAC10918,2MHlAGA7

19,6

PAGAMCAA41825,0PAGAMCAT13826,7PAGAMCAG21033,5PACAMCAC10035,5PACAMCAT10040,4PACAMCAT7343,6PACAMCAT11148,0PACAMCAT9850,9

PACAMCAT12258,6

PAGAMCAT18073,8

LG01

PACAMCAT880,0

PAACMCTT11120,1

PAGAMCAT13537,2

PACAMCTA21442,9

PACAMCTA8346,7PACAMCAC38450,9PAACMCTC8651,3PACAMCTA8054,1PAAMCAC5058,3

PACAMCAC28862,1

PACAMCTA9466,8

PAGAMCAC9981,9

LG02

PAAMCAC3270,0

EACCMCAA23619,6Hl7a82EACCMCTT265HlAGA8

22,4

HlGA3122,9HlACA323,3

PAACMCAC36747,4

LG03

EAGGMCTG740,0

EAGGMCTG11614,2EAGGMCTG25416,0EAGGMCTG21616,9

EAGCMCAC10926,1

LG04

PAGAMCAT3150,0

PAACMCT17823,0

PACAMCTA11445,3

PAACMCT32851,0

LG05

PAACMCT490,0

EACCMCTT19027,5HlGT1429,5

PAGAMCTG8235,1

LG06

EACGMCAT1900,0

PACAMCAT13817,8

PAGAMCAT15634,6

PAGAMCAT7354,3

LG07

HlAGA60,0

EAGCMCTA30018,6

LG08

EAGGMCTG2580,0

EAGGMCTG458,5

LG09

EACGMCTA660,0

PAACMCAA21524,4

LG10

HlGA230,0EACCMCAT2791,9

LG11

PAGAMCAC1770,0

PAGAMCAC18022,8

LG12