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      Genome-wide identification of quantitative trait loci for important plant and flower traits in petunia using a high-density linkage map and an interspecific recombinant inbred population derived from Petunia integrifolia and P. axillaris

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          Abstract

          Petunia is a very important flower in the global floriculture industry and has played a critical role as a model in plant genetic studies. Owing to limited genetic variability in commercial germplasm, development of novel petunia phenotypes and new varieties has become increasingly difficult. To enrich petunia germplasm and facilitate genetic improvement, it is important to explore genetic variation in progenitor species that may contain highly valuable genes/alleles. In this study, an interspecific recombinant inbred population (168 recombinant inbreds) derived from Petunia integrifolia ×  P. axillaris were phenotyped for days to anthesis (DTA), flower count (Flower_C), flower diameter (Flower_D), flower length (Flower_L), plant height (Plant_H), plant spread (Plant_S), and plant size (Plant_Z) in 2014 and 2015. Transgressive segregation was observed for all traits in both years. The broad-sense heritability on a 2-year basis varied from 0.38 (Flower_C) to 0.82 (Flower_L). Ten QTL were consistently identified in both years and by two mapping strategies [multiple QTL mapping (MQM) in MapQTL and inclusive composite interval mapping (ICIM) in IciMapping]. Major QTL explained up to 30.2, 35.5, and 47.1% of the total phenotypic variation for Plant_S, Flower_L, and Flower_D, respectively. These findings should be of significant values for introgression of desirable genes from wild petunias into commercial varieties and future genetic improvement of this important flower.

          Petunia: Enhancing commercial cultivars

          Uncovering the genetics behind desirable traits in petunia plants could help improve commercial cultivars. While petunia plants are a key feature of the global horticultural industry, the limited genetic variability in commercial plants makes it difficult to improve desired plant traits. Zhanao Deng at the University of Florida, US, and co-workers conducted a genome-wide QTL identification study using a crossbred population combining two petunia  species, P. integrifolia and P. axillaris. The team phenotyped the petunias in an open-air sub-tropical field rather than in an artificial environment like previous studies. They identified and characterized 17 genetic loci for seven important aesthetic traits, ranging from flower count to plant size. The petunia species had very different genetic backgrounds, likely stemming from their different geographic origins. The two species each can contribute novel genes for enhancing cultivated petunia cultivars.

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          Cytokinin oxidase regulates rice grain production.

          Most agriculturally important traits are regulated by genes known as quantitative trait loci (QTLs) derived from natural allelic variations. We here show that a QTL that increases grain productivity in rice, Gn1a, is a gene for cytokinin oxidase/dehydrogenase (OsCKX2), an enzyme that degrades the phytohormone cytokinin. Reduced expression of OsCKX2 causes cytokinin accumulation in inflorescence meristems and increases the number of reproductive organs, resulting in enhanced grain yield. QTL pyramiding to combine loci for grain number and plant height in the same genetic background generated lines exhibiting both beneficial traits. These results provide a strategy for tailormade crop improvement.
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            A modified algorithm for the improvement of composite interval mapping.

            Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.
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              Past and Future Use of Wild Relatives in Crop Breeding

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                Author and article information

                Contributors
                zdeng@ufl.edu
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2052-7276
                1 February 2019
                1 February 2019
                2019
                : 6
                : 27
                Affiliations
                [1 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Environmental Horticulture, Gulf Coast Research and Education Center, IFAS, , University of Florida, ; 14625 County Road 672, Wimauma, FL 33598 USA
                [2 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Horticulture, , Michigan State University, ; East Lansing, MI 48824 USA
                [3 ]ISNI 0000 0004 1790 4137, GRID grid.35155.37, Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture and Forestry Sciences, , Huazhong Agricultural University, ; 430070 Wuhan, Hubei China
                [4 ]ISNI 0000 0000 9477 7793, GRID grid.412258.8, Department of Horticulture, Faculty of Agriculture, , Tanta University, ; Tanta, 31527 Egypt
                Author information
                http://orcid.org/0000-0002-7338-3298
                Article
                91
                10.1038/s41438-018-0091-5
                6355904
                6f279b02-49b5-4783-960a-faafa4796f5d
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 May 2018
                : 19 September 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000199, U.S. Department of Agriculture (USDA);
                Award ID: 2011-51181-30666
                Award Recipient :
                Funded by: USDA-SCRI grant 2011-51181-30666 and USDA-NIFA hatch projects FLA-GCR-005065 and FLA-GCC-005507
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                © The Author(s) 2019

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