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      Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references

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          Abstract

          Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat ( Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

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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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            A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

            We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.
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              Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 January 2019
                2019
                : 14
                : 1
                : e0208614
                Affiliations
                [1 ] Department of Agronomy, Kansas State University, Manhattan, Kansas, United States of America
                [2 ] Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Urmia University, Urmia, Iran
                [3 ] USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, United States of America
                [4 ] Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Tehran, Karaj, Iran
                Ben-Gurion University, ISRAEL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0086-002X
                http://orcid.org/0000-0003-1342-8578
                Article
                PONE-D-18-12652
                10.1371/journal.pone.0208614
                6322752
                30615624
                f131fd8b-f5b6-4831-9713-393dc18cfde1

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 26 April 2018
                : 20 November 2018
                Page count
                Figures: 4, Tables: 6, Pages: 20
                Funding
                Funded by: USDA NIFA
                Award ID: 2017-67007-25939
                Award Recipient :
                Funded by: USDA NIFA
                Award ID: 2017-67007-25929
                Award Recipient :
                This project is partly funded by US Wheat and Barley Scab Initiative and the National Research Initiative Competitive Grants 2017-67007-25939 to GB and 2017-67007-25929 to GB from the National Institute of Food and Agriculture, U.S. Department of Agriculture. Funders had no role in the study design, data collection and analysis, decision to publication, or manuscript preparation.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Wheat
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genomic Libraries
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genomic Libraries
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                DNA construction
                DNA library construction
                Genomic Library Construction
                Research and analysis methods
                Molecular biology techniques
                DNA construction
                DNA library construction
                Genomic Library Construction
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Barley
                Biology and Life Sciences
                Genetics
                Genomics
                Plant Genomics
                Biology and Life Sciences
                Bioengineering
                Biotechnology
                Plant Biotechnology
                Plant Genomics
                Engineering and Technology
                Bioengineering
                Biotechnology
                Plant Biotechnology
                Plant Genomics
                Biology and Life Sciences
                Plant Science
                Plant Biotechnology
                Plant Genomics
                Biology and Life Sciences
                Genetics
                Plant Genetics
                Plant Genomics
                Biology and Life Sciences
                Plant Science
                Plant Genetics
                Plant Genomics
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Haplotypes
                Custom metadata
                All relevant data are within the paper and SNP data files are deposited in Figshare. SNP data files for 5, 6, 7, and 8 runs can be each accessed at https://figshare.com/s/92cd9f23d9dddb224304; https://figshare.com/s/b402031d86d969a7cb0f; https://figshare.com/s/83b4abe205a8a5409017; https://figshare.com/s/253e39735d226b78b6bd. All the original GBS reads will be available upon request. Please send request to Guihua.Bai@ 123456ARS.USDA.GOV , Hard Winter Wheat Genetics Research, ARS-USDA, Manhattan, KS 66506, USA.

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