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PubMed2005

QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid.

Tolerance to low nitrogen conditions is a highly desired characteristic for sustainable crop production. In this study, we analyzed the genetic components associated with low N tolerance in rice at seedling stage, including main effects, epistatic effects of the quantitative trait locus (QTLs), and QTL by environment interactions (QEs), using a population of 239 recombinant inbred lines (RILs) from a cross between Zhenshan 97 and Minghui 63, the parents of an elite hybrid. A genetic linkage map with 253 DNA maker loci was constructed. Seedlings of RILs were cultivated in low N and normal N solutions. Root, shoot and plant weight in the two N treatments were measured and the relative weight of the two treatments for each trait was considered as measurements for low N tolerance. Four to eight QTLs with main effects were detected for each of the nine traits. Very few QTLs were detected in both low and normal N conditions, and most QTLs for the relative measurements were different from those for traits under the two N treatments, indicating very little commonality in the genetic basis of the traits and their relative performance under low and normal N conditions. A total of 103 digenic interactions were detected for the nine traits. While the epistatic effects collectively accounted for large proportions of the variation for several traits, the effects of QEs appeared to be trivial. It was concluded that low N tolerance of rice seedling had complex genetic basis that requires extensive studies for full characterization.

Chimera↗
PubMed2006

Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods.

The study of QTL x environment interaction (QEI) is important for understanding genotype x environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F2 derived F3 families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs.

Chromosome Mapping↗
PubMed2005

Identification and validation of QTLs conferring resistance to sorghum downy mildew (Peronosclerospora sorghi) and Rajasthan downy mildew (P. heteropogoni) in maize.

We have mapped the quantitative trait loci (QTLs) conferring resistance to sorghum downy mildew (Peronosclerospora sorghi; SDM) and Rajasthan downy mildew (P. heteropogoni; RDM), two species of DM prevalent throughout India. QTL mapping was carried out on a backcross population of 151 individuals derived from a cross between CM139 (susceptible parent) and NAI116 (highly resistant to both SDM and RDM). Heritability estimates were 0.74 for SDM and 0.67 for RDM. Composite interval mapping combined with a linkage map constructed with 80 simple sequence repeat (SSR) markers resulted in the identification of three QTLs (one each on chromosomes 2, 3 and 6) for SDM resistance and two QTLs (one each on chromosomes 3 and 6) for RDM resistance, all of which were contributed by NAI116. The significance of the major QTL on chromosome 6 (bin 6.05) that confers resistance to diverse DMs in tropical Asia, including SDM and RDM in India, was also verified. The results confirmed that some common QTLs contribute to both SDM and RDM resistance, while additional loci might specifically govern resistance to SDM. The QTL information generated in this study provide information that will aid in undertaking an integrated breeding strategy for the transfer of resistance to SDM and RDM in maize lines using marker-assisted selection.

Breeding↗
PubMed2005

Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments.

Rice appearance quality, including traits specifying grain dimension and endosperm chalkiness, represents a major problem in many rice-producing areas of the world. In this study, the genetic basis of six appearance quality traits of milled rice was dissected into quantitative trait loci (QTL) main effects, and the stability of these QTLs was assessed in a population of 66 chromosome segment substitution lines (CSSLs) across eight environments. The CSSLs showed transgressive segregation for many of the traits, and significant correlations were detected among most of the traits. Twenty-two QTLs were identified on eight chromosomes, and numerous QTLs affecting related traits were mapped in the same regions, probably reflecting pleiotropic effects. Nine QTLs, namely qGL-1,qGL-3, qGW-5,qLWR-3, qLWR-5,qPGWC-8, qPGWC-9, qACE-8, and qDEC-8, were consistently detected across the eight environments. The additive main effect and multiplicative interaction (AMMI) analysis showed that genotype (G) x environment (E) interaction was significant for all six traits, with the first three iPCA terms accounting for over 80% of the G x E variance. Both D(I) values and the iPCA1-iPCA2 biplots showed that the CSSLs harboring the nine QTL alleles were more stable than those carrying any of the additional 13 QTL alleles, thereby confirming their environmental stability and pointing to their appropriateness as targets for marker-assisted selection for high-quality rice varieties.

Analysis of Variance↗
PubMed2005

Strain-specific and recessive QTLs involved in the control of partial resistance to Fusarium oxysporum f. sp. melonis race 1.2 in a recombinant inbred line population of melon.

Fusarium oxysporum f. sp. melonis (FOM) causes serious economic losses in melon (Cucumis melo L.). Two dominant resistance genes have been identified, Fom-1 and Fom-2, which provide resistance to races 0 and 2 and races 0 and 1, respectively, however FOM race 1.2 overcomes these resistance genes. A partial resistance to FOM race 1.2 that has been found in some Far East accessions is under polygenic control. A genetic map of melon was constructed to tag FOM race 1.2 resistance with DNA markers on a recombinant inbred line population derived from a cross between resistant (Isabelle) and susceptible (cv. Védrantais) lines. Artificial root inoculations on plantlets of this population using two strains, one that causes wilting (FOM 1.2w) and one that causes yellowing (FOM 1.2y), resulted in phenotypic and genotypic data that enabled the identification of nine quantitative trait loci (QTLs). These QTLs were detected on five linkage groups by composite interval mapping and explained between 41.9% and 66.4% of the total variation. Four digenic epistatic interactions involving seven loci were detected and increased the total phenotypic variation that was explained. Co-localizations between QTLs and resistance gene homologs or resistance genes, such as Fom-2 and Vat, were observed. A strain-specific QTL was detected, and some QTLs appeared to be recessive.

Chromosome Mapping↗
PubMed2005

Identification of a major QTL together with several minor additive or epistatic QTLs for resistance to fire blight in apple in two related progenies.

Although fire blight, caused by the bacterium Erwinia amylovora, is one of the most destructive diseases of apple (Malus x domestica) worldwide, no major, qualitative gene for resistance to this disease has been identified to date in apple. We conducted a quantitative trait locus (QTL) analysis in two F(1) progenies derived from crosses between the cultivars Fiesta and either Discovery or Prima. Both progenies were inoculated in the greenhouse with the same strain of E. amylovora, and the length of necrosis was scored 7 days and 14 days after inoculation. Additive QTLs were identified using the MAPQTL: software, and digenic epistatic interactions, which are an indication of putative epistatic QTLs, were detected by two-way analyses of variance. A major QTL explaining 34.3--46.6% of the phenotypic variation was identified on linkage group (LG) 7 of Fiesta in both progenies at the same genetic position. Four minor QTLs were also identified on LGs 3, 12 and 13. In addition, several significant digenic interactions were identified in both progenies. These results confirm the complex polygenic nature of resistance to fire blight in the progenies studied and also reveal the existence of a major QTL on LG7 that is stable in two distinct genetic backgrounds. This QTL could be a valuable target in marker-assisted selection to obtain new, fire blight-resistant apple cultivars and forms a starting point for discovering the function of the genes underlying such QTLs involved in fire blight control.

Analysis of Variance↗
PubMed2005

Mapping of QTLs conferring extremely early heading in rice (Oryza sativa L.).

Two genes related to extremely early heading were identified in populations derived from crosses between Hoshinoyume, a variety adapted to the northernmost limit of rice cultivation (Hokkaido), and Nipponbare, a variety adapted to the temperate region of Japan. The segregations for heading date clearly revealed that a two-gene model determined the extremely early heading in the F(2) and BC(1)F(1) populations under natural field conditions in Hokkaido. Using molecular markers corresponding to ten known quantitative trait loci (QTLs) for heading date, we carried out QTL analysis in the BC(1)F(1) population and detected two QTLs, qDTH-7-1 and qDTH-7-2, both on chromosome 7, and observed epistatic interaction between them. We conclude that the recessive alleles of these two genes contribute to extremely early heading for the adaptation to Hokkaido environment and to stable rice production in Hokkaido. The relationships between the two QTLs identified in this study and known QTLs are discussed.

Crosses, Genetic↗
PubMed2005

Genetic mapping of QTLs controlling horticultural traits in diploid roses.

A segregating progeny set of 96 F1 diploid hybrids (2n = 2x = 14) between "Blush Noisette" (D10), one of the first seedlings from the original "Champneys' Pink Cluster", and Rosa wichurana (E15), was used to construct a genetic linkage map of the rose genome following a "pseudo-testcross" mapping strategy. A total of 133 markers (130 RAPD, one morphological and two microsatellites) were located on the 14 linkage groups (LGs) of the D10 and E15 maps, covering total map lengths of 388 and 260 cM, respectively. Due to the presence of common biparental markers the homology of four LGs between parental maps (D10-1/E15-1 to D10-4/E15-4) could be inferred. Four horticulturally interesting quantitative traits, flower size (FS), days to flowering (DF), leaf size (LS), and resistance to powdery mildew (PM) were analysed in the progeny in order to map quantitative trait loci (QTLs) controlling these traits. A total of 13 putative QTLs (LOD > 3.0) were identified, four for FS, two for flowering time, five for LS, and two for resistance to PM. Possible homologies between QTLs detected in the D10 and E15 maps could be established between Fs1 and Fs3, Fs2 and Fs4, and Ls1 and Ls3. Screening for pairwise epistatic interactions between loci revealed additional, epistatic QTLs (EQTLs) for DF and LS that were not detected in the original QTL analysis. The genetic maps developed in this study will be useful to add new markers and locate genes for important traits in the genus providing a practical resource for marker-assisted selection programs in roses.

Chromosome Mapping↗
PubMed2026

Discovering common and population-specific QTLs for leaf rust resistance in different Barley populations.

Multi-population GWAS lead to identification of common and population-specific QTLs for leaf rust resistance in barley. Genome-wide association studies (GWAS) are a powerful tool for detecting genetic markers associated with traits of interest. However, these studies are typically restricted to a single population, and transferability of identified marker effects across populations is challenged by population differences in linkage, allele frequencies, epistatic effects, and environmental context. When comparing GWAS results between populations, a lack of overlapping signals is often interpreted as a lack of common quantitative trait loci (QTLs), although such discrepancies may result from differences in statistical power to detect signals. In barley (Hordeum vulgare L.), where genetic leaf rust resistance is rapidly overcome by evolving pathogens, identification of cross-population robust and potentially transferable resistance loci is a key task. Here, we present a mixed model approach for multi-population GWAS that estimates correlated marker effects in multiple populations and use this to test for significant effects across and within populations. Applying this model to four barley breeding populations revealed both common and population-specific QTL effects for leaf rust resistance, including loci colocalizing with known Rph genes and novel regions with plausible candidate genes. Multi-population GWAS increased power, revealing signals not detected by GWAS within populations. We categorized the reported QTLs into three groups based on marker-associated allele effects: (1) consistent effect direction across populations, (2) differing effect direction across populations, and (3) present in a single population. The study highlights the transferability and limitations of leaf rust resistance QTLs across different barley populations and provides a general statistical framework to support robust marker-assisted selection across populations.

Quantitative Trait Loci↗
PubMed2001

The replicability of QTLs for murine alcohol preference drinking behavior across eight independent studies.

On the basis of eight independent quantitative trait loci (QTL) studies of ethanol (alcohol) preference drinking in mice, a meta-analysis was carried out to examine the replicability of QTLs across studies and to enhance the power of QTL detection and parameter estimation. To avoid genetic heterogeneity, we analyzed only studies of mapping populations derived from the C57BL/6 (B6) and DBA/2 (D2) inbred progenitor strains. Because these studies were carried out in five different laboratories, there were substantial differences in testing procedure, data analysis, and especially in the choice of mapping population (BXD recombinant inbred strains, F2, backcross, selected lines, or congenic strains). Despite this, we found several QTLs that were sufficiently robust as to appear consistently across studies given the strengths and weaknesses of the mapping populations employed. These were on Chromosomes (Chrs) 2 (proximal to mid), 3 (mid to distal), 4 (distal), and 9 (proximal to mid). The P value for each of these QTLs, combined across all applicable studies, ranged from 10(-7) to 10(-15), with the additive effect of each QTL accounting for 3-5% of the trait variance extrapolated to an F2 population. Two other QTLs on Chrs 1 (distal) and 11 (mid) were less consistent, but still reached overall significance (P <.0001).

Alcohol Drinking↗
PubMed2006

Diabetic modifier QTLs identified in F2 intercrosses between Akita and A/J mice.

To identify novel genetic modifiers of type 2 diabetes (T2D), we performed quantitative trait loci (QTL) analysis on F(2) progeny of hypoinsulinemic diabetic Akita mice, heterozygous for the Ins2 gene Cys96Tyr mutation, and nondiabetic A/J mice. We generated 625 heterozygous (F(2)-Hetero) and 338 wild-type (F(2)-Wild) mice with regard to the Ins2 mutation in F(2) intercross progeny. We measured quantitative traits, including plasma glucose and insulin concentrations during the intraperitoneal glucose tolerance test (IPGTT), and body weight (BW). We observed three significant QTLs in hypoinsulinemic hyperglycemic male F(2)-Hetero mice, designated Dbm1, Dbm3, and Dbm4 on Chromosomes 6, 14, and 15, respectively. They showed linkage to plasma glucose concentrations, with significant maximum logarithm of odds (LOD) scores of 4.12, 4.17, and 6.17, respectively, all exceeding threshold values by permutation tests. In normoinsulinemic normoglycemic male F(2)-Wild mice, Dbm1 on Chromosome 6 showed linkage to both plasma insulin concentrations and BW, and Dbm2 on Chromosome 11 showed linkage to plasma glucose concentrations only, with LOD scores of 4.52 and 6.32, and 5.78, respectively. Based on these results, we concluded that Dbm1, Dbm2, Dbm3, and Dbm4 represent four major modifier QTLs specifically affecting T2D-related traits and that these diabetic modifier QTLs are conditional on the heterozygous Ins2 gene mutation and sex to exert their modifier functions. Identification of the genes responsible for these QTLs would provide new drug development targets for human T2D.

Animals↗
PubMed2000

Effect of genetic cross on the detection of quantitative trait loci and a novel approach to mapping QTLs.

A genome-wide scan was conducted in two F(2) intercrosses, C57BL/6J (B6)xDBA/2J (D2) and BALB/cJ (C)xLP/J (LP), for three different phenotypes: basal locomotor activity, ethanol-induced locomotor activity, and haloperidol-induced catalepsy. For basal activity, significant quantitative trait loci (QTLs, LOD> or =4.3) were detected on chromosomes 9 and 19 for the CxLP intercross and chromosome 1 for the B6xD2 intercross. Significant QTLs for ethanol-induced activation were detected on chromosome 6 for the CxLP intercross, and on chromosomes 1 and 2 for the B6xD2 intercross. For haloperidol-induced catalepsy, significant QTLs were detected on chromosome 14 (two different QTLs) in the CxLP intercross, and chromosomes 1 and 9 in the B6xD2 intercross. These data illustrate the importance of the genetic cross for QTL detection. Finally, the data reported here, and elsewhere, are also used to demonstrate a novel approach to QTL detection and localization.

Animals↗
PubMed1999

Aging and salt-loading modulate blood pressure QTLs in rats.

To evaluate the effects of nongenetic factors, aging, and salt-loading on the quantitative trait loci (QTLs) for blood pressure (BP), we conducted a genome-wide linkage analysis using multiple sets of BP measurements in 125 male F2 generation cross derived from stroke-prone spontaneously hypertensive rats and normotensive Wistar-Kyoto rats. The experiment was arranged in two stages. In the first stage, corresponding to the developing period of the rats, BP was measured repeatedly without loading of salt; this continued until the rats were 5 months of age. In the second stage, after the baseline BP leveled off, 1% salt water was given to the rats and BP was monitored for the subsequent 7 months. Genome scanning was performed using 201 markers. In the developing period, three QTLs were identified on chromosomes 1, 3, and 4 (logarithmic odds [LOD] scores of 5.6, 3.1, and 3.2, respectively), which had peaks at 8 or 10 weeks of age. In the latter salt-loading stage, QTLs for BP were detected on chromosomes 1 and 10 (LOD scores 4.6 and 4.5, respectively). When the BP increase during salt-loading was analyzed as a phenotype, however, only the region on chromosome 10 showed linkage at a suggestive level (LOD score 3.2). The present study provides experimental evidence that QTLs for BP could be modulated by nongenetic factors, such as aging and salt-loading.

Aging↗
PubMed2002

Two QTLs located on chromosomes 1 and 5 modulate different aspects of the performance of mice of the B x D Ty RI strain series in the Morris navigation task.

The Morris navigation task is widely used to study spatial abilities in rodents; namely, to analyze the effects of mutations in genetically engineered mice. Although quantitative and Mendelian genetic studies have shown that the variation of these abilities is partly under genetic control, little is known about these genetic factors. In order to analyze the genetic architecture of spatial navigation in mice, a wide genome scan was performed to map the QTLs that control various aspects of the performance, using the RI strain methodology. Latencies to locate the submerged platform across learning sessions and performance to the spatial probe test were analyzed in the 26 strains of the B x D RI series. Both cluster analysis of behavioral measurements and QTL mapping confirmed previous data showing that the escape latencies and the spatial bias rely on two distinct components of the task, controlled by different loci. A QTL on chromosome 1 influenced escape latencies during the four training sessions, whereas another QTL, located on chromosome 5, was shown to control spatial performance at the probe trial and also exhibited epistatic interactions with two other QTLs on chromosomes 2 and 13. The function of these QTLs is examined in the broader context of hippocampal-dependent learning processes and in relation to QTLs already found in similar positions in other behavioral traits.

Animals↗
PubMed2000

The relationship between loci for mating system and fitness-related traits in Mimulus (Scrophulariaceae): a test for deleterious pleiotropy of QTLs with large effects.

Loci with large phenotypic effects are generally not thought to be important in the evolution of quantitative traits because of their deleterious pleiotropic effects, yet empirical studies of such pleiotropic effects are lacking. Here I use molecular markers to test the extent of deleterious pleiotropy of quantitative trait loci (QTLs) that have large effects on mating system differences between the wild plants Mimulus guttatus and M. platycalyx (Scrophulariaceae). Six fitness-related traits, namely germination rate (GR), number of nodes (NN), number of flowers (NF), plant height (HT), above-ground biomass (WT), and flowering time (FT) were examined in a growth chamber for a backcross population between M. guttatus and M. platycalyx (with M. platycalyx as recurrent parent). Interval mapping based upon a linkage map consisting of isozyme and random amplified polymorphic DNA (RAPD) markers detected no QTL for fitness-related traits near the mating system QTLs. Single-marker analysis based upon 13 markers flanking the mating system QTLs detected three significant marker-fitness trait associations, and these associations indicate beneficial effects of mating system loci. This suggests that QTLs with large effects on mating system traits do not have significant deleterious pleiotropic effects, and that they could be important factors in adaptive evolution of Mimulus.

Alleles↗
PubMed2005

Molecular mapping of QTLs for resistance to Gibberella ear rot, in corn, caused by Fusarium graminearum.

Gibberella ear rot, caused by the fungus Fusarium graminearum Schwabe, is a serious disease of corn (Zea mays) grown in northern climates. Infected corn is lower yielding and contains toxins that are dangerous to livestock and humans. Resistance to ear rot in corn is quantitative, specific to the mode of fungal entry (silk channels or kernel wounds), and highly influenced by the environment. Evaluations of ear rot resistance are complex and subjective; and they need to be repeated over several years. All of these factors have hampered attempts to develop F. graminearum resistant corn varieties. The aim of this study was to identify molecular markers linked to the genes for resistance to Gibberella ear rot. A recombinant inbred (RI) population, produced from a cross between a Gibberella ear rot resistant line (CO387) and a susceptible line (CG62), was field-inoculated and scored for Gibberella ear rot symptoms in the F4, F6, and F7 generations. The distributions of disease scores were continuous, indicating that resistance is probably conditioned by multiple loci. A molecular linkage map, based on segregation in the F5 RI population, contained 162 markers distributed over 10 linkage groups and had a total length of 2237 cM with an average distance between markers of 13.8 cM. Composite interval mapping identified 11 quantitative trait loci (QTLs) for Gibberella ear rot resistance following silk inoculation and 18 QTLs following kernel inoculation in 4 environments that accounted for 6.7%-35% of the total phenotypic variation. Only 2 QTLs (on linkage group 7) were detected in more than 1 test for silk resistance, and only 1 QTL (on linkage group 5) was detected in more than 1 test for kernel resistance, confirming the strong influence of the environment on these traits. The majority of the favorable alleles were derived from the resistant parent (CO387). The germplasm and markers for QTLs with significant phenotypic effects may be useful for marker-assisted selection to incorporate Gibberella ear rot resistance into commercial corn cultivars.

Chromosome Mapping↗
PubMed2005

Construction of 2 intraspecific linkage maps and identification of resistance QTLs for Phytophthora capsici root-rot and foliar-blight diseases of pepper (Capsicum annuum L.).

Two linkage maps of pepper were constructed and used to identify quantitative trait loci (QTLs) conferring resistance to Phytophthora capsici. Inoculations were done with 7 isolates: 3 from Taiwan, 3 from California, and 1 from New Mexico. The first map was constructed from a set of recombinant inbred lines (RILs) of the PSP-11 (susceptible) x PI201234 (resistant) cross; and the second map was from a set of F(2) lines of the Joe E. Parker' (susceptible) x 'Criollo de Morelos 334' (resistant) cross. The RIL map covered 1466.1 cM of the pepper genome, and it consisted of 144 markers -- 91 amplified fragment length polymorphisms (AFLPs), 34 random amplified polymorphic DNA (RAPDs), 15 simple sequence repeats (SSRs), 1 sequence characterized amplified region (SCAR), and 3 morphological markers -- distributed over 17 linkage groups. The morphological markers mapped on this population were erect fruit habit (up), elongated fruit shape (fs(e)), and fasciculate fruit clusters (fa). The F(2) map consisted of 113 markers (51 AFLPs, 45 RAPDs, 14 SSRs, and 3 SCARs) distributed in 16 linkage groups, covering a total of 1089.2 cM of the pepper genome. Resistance to both root rot and foliar blight were evaluated in the RIL population using the 3 Taiwan isolates; the remaining isolates were used for the root-rot test only. Sixteen chromosomal regions of the RIL map contained single QTLs or clusters of resistance QTLs that had an effect on root rot and (or) foliar blight, revealing a complex set of genetics involved in resistance to P. capsici. Five QTLs were detected in the F(2) map that had an effect on resistance to root rot.

Analysis of Variance↗
PubMed2006

Genetic mapping of hop (Humulus lupulus L.) applied to the detection of QTLs for alpha-acid content.

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 marker (simple sequence repeat (SSR)) genetic linkage maps constructed from a double pseudotestcross. The mapping population consisted of 111 progeny from a cross between the German hop cultivar 'Magnum', which exhibits high levels of alpha-acids, and a wild Slovene male hop, 2/1. The progeny segregated quantitatively for alpha-acid content determined in 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 (LGs) 03, 01, 09, and 03 of the female map, respectively. QTLs explained 11.9%-24.8% of the phenotypic variance. The most promising QTL to be used in marker-assisted selection is alpha2, the peak of which colocated exactly with the AFLP marker. Three chalcone synthase-like genes (chs2, chs3, and chs4) involved in hop bitter acid synthesis mapped together on LG04 of the female map. Saturation of the maps, particularly the putative QTL regions, will be carried out using SSR markers, and the stability of the QTLs will be tested in the coming years.

Chromosome Mapping↗