Dataset Information

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

ABSTRACT:

Background

Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging.

Results

With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis.

Conclusions

Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.

SUBMITTER: Neumann K

PROVIDER: S-EPMC5554006 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

Neumann Kerstin K Zhao Yusheng Y Chu Jianting J Keilwagen Jens J Reif Jochen C JC Kilian Benjamin B Graner Andreas A

BMC plant biology 20170810 1

<h4>Background</h4>Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging.<h4>Results</h4>With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High ...[more]

PMID: 28797222

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Project description:BackgroundIn woody plants from temperate regions, adaptation to the local climate results in annual cycles of growth and dormancy, and optimal regulation of these cycles are critical for growth, long-term survival, and competitive success. In this study we have investigated the genetic background to growth phenology in a Salix pedigree by assessing genetic and phenotypic variation in growth cessation, leaf senescence and bud burst in different years and environments. A previously constructed linkage map using the same pedigree and anchored to the annotated genome of P. trichocarpa was improved in target regions and used for QTL analysis of the traits. The major aims in this study were to map QTLs for phenology traits in Salix, and to identify candidate genes in QTL hot spots through comparative mapping with the closely related Populus trichocarpa.ResultsAll traits varied significantly among genotypes and the broad-sense heritabilities ranged between 0.5 and 0.9, with the highest for leaf senescence. In total across experiment and years, 80 QTLs were detected. For individual traits, the QTLs explained together from 21.5 to 56.5% of the variation. Generally each individual QTL explained a low amount of the variation but three QTLs explained above 15% of the variation with one QTL for leaf senescence explaining 34% of the variation. The majority of the QTLs were recurrently identified across traits, years and environments. Two hotspots were identified on linkage group (LG) II and X where narrow QTLs for all traits co-localized.ConclusionsThis study provides the most detailed analysis of QTL detection for phenology in Salix conducted so far. Several hotspot regions were found where QTLs for different traits and QTLs for the same trait but identified during different years co-localised. Many QTLs co-localised with QTLs found in poplar for similar traits that could indicate common pathways for these traits in Salicaceae. This study is an important first step in identifying QTLs and candidate genes for phenology traits in Salix.

Dataset Information

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

Background

Results

Conclusions

Publications

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

Similar Datasets

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