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      Optimizing quantitative trait loci introgression in elite rice germplasms: Comparing methods and population sizes to develop new recipients via stochastic simulations

      1 , 2
      Plant Breeding
      Wiley

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          Abstract

          This study compared three strategies to develop new recipients for quantitative trait loci (QTL) introgression (background recovery [BG], selective sweep [SS] and breeding value [BV]) in a short‐term rice breeding programme (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used the International Rice Research Institute (IRRI) rice breeding framework as the model to perform the stochastic simulations. Each strategy was simulated and replicated 100 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation outperforming other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated BV) displayed the best trade‐off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles.

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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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            Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

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              Genomic scans for selective sweeps using SNP data.

              Detecting selective sweeps from genomic SNP data is complicated by the intricate ascertainment schemes used to discover SNPs, and by the confounding influence of the underlying complex demographics and varying mutation and recombination rates. Current methods for detecting selective sweeps have little or no robustness to the demographic assumptions and varying recombination rates, and provide no method for correcting for ascertainment biases. Here, we present several new tests aimed at detecting selective sweeps from genomic SNP data. Using extensive simulations, we show that a new parametric test, based on composite likelihood, has a high power to detect selective sweeps and is surprisingly robust to assumptions regarding recombination rates and demography (i.e., has low Type I error). Our new test also provides estimates of the location of the selective sweep(s) and the magnitude of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence for selective sweeps is also found in many other regions, including genes known to be associated with disease risk such as DPP10 and COL4A3.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Plant Breeding
                Plant Breeding
                Wiley
                0179-9541
                1439-0523
                August 2023
                May 30 2023
                August 2023
                : 142
                : 4
                : 439-448
                Affiliations
                [1 ] International Rice Research Institute (IRRI) Los Baños Philippines
                [2 ] H. Rouse Caffey Rice Research Station LSU AgCenter Rayne Louisiana USA
                Article
                10.1111/pbr.13118
                9668a740-c42b-473c-b4dd-6dc8e6ee25f8
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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