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      Additive QTLs on three chromosomes control flowering time in woodland strawberry ( Fragaria vesca L.)

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          Abstract

          Flowering time is an important trait that affects survival, reproduction and yield in both wild and cultivated plants. Therefore, many studies have focused on the identification of flowering time quantitative trait locus (QTLs) in different crops, and molecular control of this trait has been extensively investigated in model species. Here we report the mapping of QTLs for flowering time and vegetative traits in a large woodland strawberry mapping population that was phenotyped both under field conditions and in a greenhouse after flower induction in the field. The greenhouse experiment revealed additive QTLs in three linkage groups (LG), two on both LG4 and LG7, and one on LG6 that explain about half of the flowering time variance in the population. Three of the QTLs were newly identified in this study, and one co-localized with the previously characterized FvTFL1 gene. An additional strong QTL corresponding to previously mapped PFRU was detected in both field and greenhouse experiments indicating that gene(s) in this locus can control the timing of flowering in different environments in addition to the duration of flowering and axillary bud differentiation to runners and branch crowns. Several putative flowering time genes were identified in these QTL regions that await functional validation. Our results indicate that a few major QTLs may control flowering time and axillary bud differentiation in strawberries. We suggest that the identification of causal genes in the diploid strawberry may enable fine tuning of flowering time and vegetative growth in the closely related octoploid cultivated strawberry.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

            (2013)
            Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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              FKF1 conveys timing information for CONSTANS stabilization in photoperiodic flowering.

              Plants use day-length information to coordinate flowering time with the appropriate season to maximize reproduction. In Arabidopsis, the long day-specific expression of CONSTANS (CO) protein is crucial for flowering induction. Although light signaling regulates CO protein stability, the mechanism by which CO is stabilized in the long-day afternoon has remained elusive. Here, we demonstrate that FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1) protein stabilizes CO protein in the afternoon in long days. FKF1 interacts with CO through its LOV domain, and blue light enhances this interaction. In addition, FKF1 simultaneously removes CYCLING DOF FACTOR 1 (CDF1), which represses CO and FLOWERING LOCUS T (FT) transcription. Together with CO transcriptional regulation, FKF1 protein controls robust FT mRNA induction through multiple feedforward mechanisms that accurately control flowering timing.
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                Author and article information

                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group
                2052-7276
                24 May 2017
                2017
                : 4
                : 17020
                Affiliations
                [1 ]Department of Agricultural Sciences, Viikki Plant Science Centre , University of Helsinki , 00014 Helsinki, Finland
                [2 ]Fondazione Edmund Mach, Research and Innovation Centre , San Michele All'adige, 38010 TN, Italy
                [3 ]Faculty of Agriculture, Utsunomiya University , Tochigi, 321-8505, Japan
                [4 ]Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research , 50829 Cologne, Germany
                [5 ]Driscoll’s Genetics Limited, East Malling Enterprise Centre, East Malling , Kent ME19 6BJ, UK
                [6 ]Department of Biosciences, Viikki Plant Science Centre , University of Helsinki , 00014 Helsinki, Finland
                Author notes
                [7]

                These authors contributed equally to this study.

                Author information
                http://orcid.org/0000-0003-3023-4978
                Article
                hortres201720
                10.1038/hortres.2017.20
                5442962
                54dc53e3-46a9-4517-b445-bd318e6b2854
                Copyright © 2017 The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 07 September 2016
                : 21 April 2017
                : 21 April 2017
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