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

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

Additional file 17: Figure S17. of Ascertainment bias from imputation methods evaluation in wheat

Boxplots of 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 143 KB

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 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 2: Figure S2. of Ascertainment bias from imputation methods evaluation in wheat

Power (PO) and false positives rate (FPR) for major and minor QTL with 25 QTL, for the golden standard from barley, with α = 0.01 threshold. Each parameter was calculated for the combinations of: number of QTL (q), heritabilties (h 2 ), a marker score matrix to simulate the QTL (i.e. Ysim-NoNA ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 28 KB

Additional file 1: Figure S1. of Ascertainment bias from imputation methods evaluation in wheat

Power (PO) and false positives rate (FPR) for major and minor QTL with 25 QTL, for the golden standard form barley, with a Bonferroni threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), a marker score matrix to simulate the QTL (i.e. Ysim-NoNA ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 28 KB