Quantitative trait loci in hop (Humulus lupulus L.) reveal complex genetic architecture underlying variation in sex, yield and cone chemistry

Abstract

BackgroundHop (Humulus lupulus L.) is cultivated for its cones, the secondary metabolites of which contribute bitterness, flavour and aroma to beer. Molecular breeding methods, such as marker assisted selection (MAS), have great potential for improving the efficiency of hop breeding. The success of MAS is reliant on the identification of reliable marker-trait associations. This study used quantitative trait loci (QTL) analysis to identify marker-trait associations for hop, focusing on traits related to expediting plant sex identification, increasing yield capacity and improving bittering, flavour and aroma chemistry.ResultsQTL analysis was performed on two new linkage maps incorporating transferable Diversity Arrays Technology (DArT) markers. Sixty-three QTL were identified, influencing 36 of the 50 traits examined. A putative sex-linked marker was validated in a different pedigree, confirming the potential of this marker as a screening tool in hop breeding programs. An ontogenetically stable QTL was identified for the yield trait dry cone weight; and a QTL was identified for essential oil content, which verified the genetic basis for variation in secondary metabolite accumulation in hop cones. A total of 60 QTL were identified for 33 secondary metabolite traits. Of these, 51 were pleiotropic/linked, affecting a substantial number of secondary metabolites; nine were specific to individual secondary metabolites.ConclusionsPleiotropy and linkage, found for the first time to influence multiple hop secondary metabolites, have important implications for molecular selection methods. The selection of particular secondary metabolite profiles using pleiotropic/linked QTL will be challenging because of the difficulty of selecting for specific traits without adversely changing others. QTL specific to individual secondary metabolites, however, offer unequalled value to selection programs. In addition to their potential for selection, the QTL identified in this study advance our understanding of the genetic control of traits of current economic and breeding significance in hop and demonstrate the complex genetic architecture underlying variation in these traits. The linkage information obtained in this study, based on transferable markers, can be used to facilitate the validation of QTL, crucial to the success of MAS.

Quantitative trait locus analysis of leaf morphological characters, yield-related traits, and secondary metabolite contents in Eucommia ulmoides

“…Similarly, we were able to detect QTLs with a moderate-to-large effect for plant height and diameter growth in the same mapping population (Li et al, 2014c). A few QTLs with relatively large effect have also been observed for growth, phenology, quality, and yield traits in other plant species (Bradshaw and Stettler, 1995;Wang et al, 2000;Ronnberg-Wastljung et al, 2005;McAdam et al, 2013). This indicates that these quantitative traits may be controlled by a few genes that have a large effect.…”

“…Co-location of QTLs for multiple traits may result from the pleiotropic effect of major genes in these genomic regions (McAdam et al, 2013;Zhang et al, 2013). It is possible that a single gene could independently control either two or more different traits.…”

Identification and Mapping of Eastern Filbert Blight Resistance Quantitative Trait Loci in European Hazelnut Using Double Digestion Restriction Site Associated DNA Sequencing

“…QTL analysis of single year (2016 and 2017) and mean EFB response was conducted using MapQTL 6.0 software (van Ooijen, 2009). Because of memory constraints with the CP mapping population, the two-way pseudo-testcross approach with markers recoded as DH population type as described by van Ooijen (2009) was used to construct two separate parental maps that were then used for all QTL mapping procedures (Herrmann et al, 2006;McAdam et al, 2013;Studer et al, 2006;van Heerden et al, 2014;Yun et al, 2014;Zyprian et al, 2016). Initial analysis was conducted using interval mapping (IM), followed by multiple rounds of multiple QTL mapping (MQM) to refine the location and magnitude of QTL.…”

Genomic approaches to selection in outcrossing perennials: focus on essential oil crops

“…QTL analysis also identified 13 widely spread QTL regions associated with the foliar concentration of terpenes in E. globulus explaining up to 71 % of trait variance (O'Reilly-Wapstra et al 2011). In Humulus lupulus (Hop), linkage mapping and QTL analyses (Cerenak et al 2009;McAdam et al 2013) have revealed several large genomic regions of significance for total oil content, terpene concentrations (e.g. humulene) and biomass (e.g.…”