14 research outputs found
éèäžćźćźæ§ăźćæ
ç 究ç§: ćè性ćŠć€§ćŠéąç€ŸäŒæćç§ćŠç 究ç§äżźäșćčŽ:2000.09ćŠäœ:ç”æž
ćŠçăšăźéŁæșă«ăăćžæ°ç€ŸäŒă«éăăăăćŠăłăăźć”é : ăćšćź ă§ăźçćăăăăæç¶ćŻèœăȘçŠç„瀟äŒăæăă
ćè性ćŠć
Źć
±ç 究ă»ăłăżăŒ21äžçŽCOEăăă°ă©ă ăæç¶ćŻèœăȘçŠç„瀟äŒă«ćăăć
Źć
±ç 究æ çč
Additional file 26: of Ascertainment bias from imputation methods evaluation in wheat
Best linear unbiased predictors (BLUPs) for each genotype for plant height. (txt 9Â 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 6: Figure S6. of Ascertainment bias from imputation methods evaluation in wheat
Power (PO) and false positives rate (FPR) with 25 QTL and 35Â % 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 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 αâ=â0.01 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 35Â % 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 9Â 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 αâ=â0.01 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