金融不安定性の分析

研究科: 千葉大学大学院社会文化科学研究科修了年:2000.09学位:経済

学生との連携による市民社会に開かれた「学び」の創造 : 「在宅での看取り」から持続可能な福祉社会を捉える

千葉大学公共研究センター21世紀COEプログラム「持続可能な福祉社会に向けた公共研究拠点

Meeting the challenges facing wheat production: The strategic research agenda of the Global Wheat Initiative

Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses

Ascertainment bias from imputation methods evaluation in wheat

Background: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. Results: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. Conclusions: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.</p

Additional file 26: of Ascertainment bias from imputation methods evaluation in wheat

Best linear unbiased predictors (BLUPs) for each genotype for plant height. (txt 9 kb

Additional file 6: Figure S6. of Ascertainment bias from imputation methods evaluation in wheat

Power (PO) and false positives rate (FPR) with 25 QTL and 35 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a Bonferroni threshold corrected by the effective number of independent markers. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM, Ysim-Mean and Ysim-RF ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM, G Mean and G RF ). (PDF 156 KB

Additional file 14: Figure S14. of Ascertainment bias from imputation methods evaluation in wheat

Boxplots of false positives rate (FPR) with 25 QTL, for major and minor QTL for ascertainment bias in imputation performance comparison in barley, with a Bonferroni threshold corrected by the effective number of independent markers. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM, Ysim-Mean and Ysim-RF ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM, G Mean and G RF ). (PDF 139 KB

Additional file 8: Figure S8. of Ascertainment bias from imputation methods evaluation in wheat

Power (PO) and false positives rate (FPR) with 25 QTL and 50 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a α = 0.01 threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM and Ysim-Mean ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 36 KB