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      1k-RiCA ( 1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice

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          Abstract

          Background

          While a multitude of genotyping platforms have been developed for rice, the majority of them have not been optimized for breeding where cost, turnaround time, throughput and ease of use, relative to density and informativeness are critical parameters of their utility. With that in mind we report the development of the 1K-Rice Custom Amplicon, or 1k-RiCA, a robust custom sequencing-based amplicon panel of ~ 1000-SNPs that are uniformly distributed across the rice genome, designed to be highly informative within indica rice breeding pools, and tailored for genomic prediction in elite indica rice breeding programs.

          Results

          Empirical validation tests performed on the 1k-RiCA showed average marker call rates of 95% with marker repeatability and concordance rates of 99%. These technical properties were not affected when two common DNA extraction protocols were used. The average distance between SNPs in the 1k-RiCA was 1.5 cM, similar to the theoretical distance which would be expected between 1,000 uniformly distributed markers across the rice genome. The average minor allele frequencies on a panel of indica lines was 0.36 and polymorphic SNPs estimated on pairwise comparisons between indica by indica accessions and indica by japonica accessions were on average 430 and 450 respectively. The specific design parameters of the 1k-RiCA allow for a detailed view of genetic relationships and unambiguous molecular IDs within indica accessions and good cost vs. marker-density balance for genomic prediction applications in elite indica germplasm. Predictive abilities of Genomic Selection models for flowering time, grain yield, and plant height were on average 0.71, 0.36, and 0.65 respectively based on cross-validation analysis. Furthermore the inclusion of important trait markers associated with 11 different genes and QTL adds value to parental selection in crossing schemes and marker-assisted selection in forward breeding applications.

          Conclusions

          This study validated the marker quality and robustness of the 1k-RiCA genotypic platform for genotyping populations derived from indica rice subpopulation for genetic and breeding purposes including MAS and genomic selection. The 1k-RiCA has proven to be an alternative cost-effective genotyping system for breeding applications.

          Electronic supplementary material

          The online version of this article (10.1186/s12284-019-0311-0) contains supplementary material, which is available to authorized users.

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          Yield Trends Are Insufficient to Double Global Crop Production by 2050

          Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.
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            The Bayesian Lasso

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              Best linear unbiased estimation and prediction under a selection model.

              Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
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                Author and article information

                Contributors
                j.arbelaezvelez@irri.org
                dwiyanti@abs.agr.hokudai.ac.jp
                tandayu@irri.org
                k.llantada@irri.org
                a.jarana@irri.org
                j.ignacio@irri.org
                J.Platten@irri.org
                j.cobb@irri.org
                j.rutkoski@irri.org
                m.thomson@tamu.edu
                Tobias.Kretzschmar@scu.edu.au
                Journal
                Rice (N Y)
                Rice (N Y)
                Rice
                Springer US (New York )
                1939-8425
                1939-8433
                26 July 2019
                26 July 2019
                2019
                : 12
                : 55
                Affiliations
                [1 ]ISNI 0000 0001 0729 330X, GRID grid.419387.0, International Rice Research Institute, ; DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
                [2 ]ISNI 0000 0001 2173 7691, GRID grid.39158.36, Research Faculty of Agriculture, , Hokkaido University, ; Sapporo, Hokkaido 060-8589 Japan
                [3 ]ISNI 0000 0004 4687 2082, GRID grid.264756.4, Department of Soil and Crop Sciences, , Texas A&M University, ; College Station, Houston, TX 77843 USA
                [4 ]ISNI 0000000121532610, GRID grid.1031.3, Southern Cross Plant Sciences, , Southern Cross University, ; PO Box 157, Lismore, NSW 2480 Australia
                Author information
                http://orcid.org/0000-0002-8227-0746
                Article
                311
                10.1186/s12284-019-0311-0
                6660535
                31350673
                c9506c2f-fbcf-4f96-89da-3722485e6ccf
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

                History
                : 26 April 2019
                : 2 July 2019
                Funding
                Funded by: Bill & Melinda Gates Foundation
                Award ID: OPP1076488
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2019

                Agriculture
                single nucleotide polymorphism (snp),oryza sativa,indica,snp fingerprinting,genomic selection,marker-assisted selection (mas),amplicon-based next generation sequencing,breeding and genotyping

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