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      RosBREED: bridging the chasm between discovery and application to enable DNA-informed breeding in rosaceous crops

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          Abstract

          The Rosaceae crop family (including almond, apple, apricot, blackberry, peach, pear, plum, raspberry, rose, strawberry, sweet cherry, and sour cherry) provides vital contributions to human well-being and is economically significant across the U.S. In 2003, industry stakeholder initiatives prioritized the utilization of genomics, genetics, and breeding to develop new cultivars exhibiting both disease resistance and superior horticultural quality. However, rosaceous crop breeders lacked certain knowledge and tools to fully implement DNA-informed breeding—a “chasm” existed between existing genomics and genetic information and the application of this knowledge in breeding. The RosBREED project (“Ros” signifying a Rosaceae genomics, genetics, and breeding community initiative, and “BREED”, indicating the core focus on breeding programs), addressed this challenge through a comprehensive and coordinated 10-year effort funded by the USDA-NIFA Specialty Crop Research Initiative. RosBREED was designed to enable the routine application of modern genomics and genetics technologies in U.S. rosaceous crop breeding programs, thereby enhancing their efficiency and effectiveness in delivering cultivars with producer-required disease resistances and market-essential horticultural quality. This review presents a synopsis of the approach, deliverables, and impacts of RosBREED, highlighting synergistic global collaborations and future needs. Enabling technologies and tools developed are described, including genome-wide scanning platforms and DNA diagnostic tests. Examples of DNA-informed breeding use by project participants are presented for all breeding stages, including pre-breeding for disease resistance, parental and seedling selection, and elite selection advancement. The chasm is now bridged, accelerating rosaceous crop genetic improvement.

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          Most cited references193

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          Efficient methods to compute genomic predictions.

          P VanRaden (2008)
          Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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            JBrowse: a dynamic web platform for genome visualization and analysis

            Background JBrowse is a fast and full-featured genome browser built with JavaScript and HTML5. It is easily embedded into websites or apps but can also be served as a standalone web page. Results Overall improvements to speed and scalability are accompanied by specific enhancements that support complex interactive queries on large track sets. Analysis functions can readily be added using the plugin framework; most visual aspects of tracks can also be customized, along with clicks, mouseovers, menus, and popup boxes. JBrowse can also be used to browse local annotation files offline and to generate high-resolution figures for publication. Conclusions JBrowse is a mature web application suitable for genome visualization and analysis.
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              Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

              False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.
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                Author and article information

                Contributors
                iezzoni@msu.edu
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 November 2020
                1 November 2020
                2020
                : 7
                : 177
                Affiliations
                [1 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Michigan State University, ; East Lansing, MI 48824 USA
                [2 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State University, ; Wenatchee, WA 98801 USA
                [3 ]GRID grid.17635.36, ISNI 0000000419368657, University of Minnesota, ; St. Paul, MN 55108 USA
                [4 ]GRID grid.26090.3d, ISNI 0000 0001 0665 0280, Clemson University, ; Clemson, SC 29634 USA
                [5 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, University of Florida, ; Wimauma, FL 33598 USA
                [6 ]GRID grid.507310.0, USDA-ARS, ; Corvallis, OR 97333 USA
                [7 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State University, ; Puyallup, WA 98371 USA
                [8 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State University, ; Pullman, WA 99164 USA
                [9 ]Cedar Lake Research Group, Portland, OR 97215 USA
                [10 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, University Queensland, ; Brisbane, QLD Australia
                [11 ]GRID grid.17635.36, ISNI 0000000419368657, University of Minnesota, ; St. Paul, MN 55108 USA
                [12 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Wageningen University and Research, ; 6700 AA Wageningen, The Netherlands
                [13 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State University, ; Pullman, WA 99164 USA
                [14 ]GRID grid.167436.1, ISNI 0000 0001 2192 7145, University of New Hampshire, ; Durham, NH 03824 USA
                Author information
                http://orcid.org/0000-0003-4391-5262
                http://orcid.org/0000-0001-8625-2740
                http://orcid.org/0000-0001-8360-486X
                http://orcid.org/0000-0002-9443-5974
                http://orcid.org/0000-0001-5455-0524
                Article
                398
                10.1038/s41438-020-00398-7
                7603521
                6135ad68-edc6-44b5-9f39-504b56dcf5e8
                © The Author(s) 2020

                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
                : 28 June 2020
                : 16 July 2020
                : 30 August 2020
                Funding
                Funded by: USDA’s National Institute of Food and Agriculture-Specialty Crop Research Initiative (2009-51181-05858; 2014-51181-22378)
                Funded by: USDA’s National Institute of Food and Agriculture-Specialty Crop Research Initiative (2014-51181-22378)
                Categories
                Review Article
                Custom metadata
                © The Author(s) 2020

                plant breeding
                plant breeding

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