Commit 2c45ff93 authored by Helene Rimbert's avatar Helene Rimbert
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Update README.md

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...@@ -24,7 +24,8 @@ Description: Package for Breed Wheat Genomic Selection pipeline ...@@ -24,7 +24,8 @@ Description: Package for Breed Wheat Genomic Selection pipeline
**Date of Publication:** **Date of Publication:**
##R topics documented: ## R topics documented:
1. [AM - Additive relationship matrix](#am) 1. [AM - Additive relationship matrix](#am)
2. [ANO - Selectio of N markers with the lowest Pvalues in GWAS](#ano) 2. [ANO - Selectio of N markers with the lowest Pvalues in GWAS](#ano)
3. [bwgs.cv - BreedWheat Genomic Selection Cross Validation](#bwgscv) 3. [bwgs.cv - BreedWheat Genomic Selection Cross Validation](#bwgscv)
...@@ -38,7 +39,7 @@ Description: Package for Breed Wheat Genomic Selection pipeline ...@@ -38,7 +39,7 @@ Description: Package for Breed Wheat Genomic Selection pipeline
11. [RMR - Random Marker Recontruction](#rmr) 11. [RMR - Random Marker Recontruction](#rmr)
12. [RPS - Random Pop Size](#rps) 12. [RPS - Random Pop Size](#rps)
##<a name="am"></a> AM - Additive relationship matrix ## <a name="am"></a> AM - Additive relationship matrix
**Description** **Description**
...@@ -79,7 +80,7 @@ realized relationship matrix. ...@@ -79,7 +80,7 @@ realized relationship matrix.
G3:Genes, Genomes, Genetics. 2:1405-1413. doi: 10.1534/g3.112.004259 G3:Genes, Genomes, Genetics. 2:1405-1413. doi: 10.1534/g3.112.004259
##<a name="ano"></a> ANO - selection of N markers with the lowest Pvalue in GWAS ## <a name="ano"></a> ANO - selection of N markers with the lowest Pvalue in GWAS
**Description** **Description**
...@@ -120,7 +121,7 @@ geno_impote <- MNI (geno47K) ...@@ -120,7 +121,7 @@ geno_impote <- MNI (geno47K)
geno_shrink001 <- ANO(pheno, geno_impute, pval=0.001) geno_shrink001 <- ANO(pheno, geno_impute, pval=0.001)
``` ```
##<a name="bwgscv"></a> bwgs.cv - Breed Wheat Genomic Selection Cross Validation ## <a name="bwgscv"></a> bwgs.cv - Breed Wheat Genomic Selection Cross Validation
**Description** **Description**
...@@ -273,7 +274,7 @@ testRKHSANO<bwgs.cv(geno47K,pheno,geno.reduct.method="ANO",pval=0.001,geno.imput ...@@ -273,7 +274,7 @@ testRKHSANO<bwgs.cv(geno47K,pheno,geno.reduct.method="ANO",pval=0.001,geno.imput
**References** **References**
Breiman, L. 2001. \"Random Forests\". Machine Learning 45 (1): 5--32. Breiman, L. 2001. "Random Forests". Machine Learning 45 (1): 5--32.
Breiman L. and Cutler A. (2013) Breiman and Cutler's random forests for Breiman L. and Cutler A. (2013) Breiman and Cutler's random forests for
classification and regression. classification and regression.
...@@ -323,7 +324,7 @@ Zou H and Hastie T (2005) Regularization and variable selection via the ...@@ -323,7 +324,7 @@ Zou H and Hastie T (2005) Regularization and variable selection via the
elastic net. J. R. Statist. Soc. 67: 301--320 elastic net. J. R. Statist. Soc. 67: 301--320
##<a name="bwgspredict"></a> bwgs.predict Breed Wheat Genomic Selection Prediction ## <a name="bwgspredict"></a> bwgs.predict Breed Wheat Genomic Selection Prediction
**Description** **Description**
...@@ -448,7 +449,7 @@ cor ...@@ -448,7 +449,7 @@ cor
cbind((testPREDICT_GBLUP[,1],testPREDICT_EGBLUP[,1],testPREDICT_BA[,1])) cbind((testPREDICT_GBLUP[,1],testPREDICT_EGBLUP[,1],testPREDICT_BA[,1]))
``` ```
##<a name="chromld"></a> CHROMLD - Marker selection by LD pruning within each chromosome ## <a name="chromld"></a> CHROMLD - Marker selection by LD pruning within each chromosome
**Description** **Description**
...@@ -496,7 +497,7 @@ data(inra) ...@@ -496,7 +497,7 @@ data(inra)
genoLD95 <- CHROMLD(geno47K, R2seuil=0.95, MAP) genoLD95 <- CHROMLD(geno47K, R2seuil=0.95, MAP)
``` ```
##<a name="emi"></a> EMI - Expectation-Maximization Imputation ## <a name="emi"></a> EMI - Expectation-Maximization Imputation
**Description** **Description**
...@@ -553,7 +554,7 @@ Y., Dreisigacker, S., Crossa, J., Sanchez-Villeda, H., Sorrells, M.E., & ...@@ -553,7 +554,7 @@ Y., Dreisigacker, S., Crossa, J., Sanchez-Villeda, H., Sorrells, M.E., &
Jannink, J. (2012). Genomic Selection in Wheat Breeding using Jannink, J. (2012). Genomic Selection in Wheat Breeding using
Genotyping-by-Sequencing. Genotyping-by-Sequencing.
##<a name="inra"></a> inra - data from INRA breeding data, genotype and phenotype ## <a name="inra"></a> inra - data from INRA breeding data, genotype and phenotype
**Description** **Description**
...@@ -566,7 +567,7 @@ inra data contains a set of geno47K(760 x 47839), pheno (760 x 1). The phenotype ...@@ -566,7 +567,7 @@ inra data contains a set of geno47K(760 x 47839), pheno (760 x 1). The phenotype
data(inra) data(inra)
``` ```
##<a name="mni"></a> MNI - MeaN Impute ## <a name="mni"></a> MNI - MeaN Impute
**Description** **Description**
...@@ -603,7 +604,7 @@ data(inra) ...@@ -603,7 +604,7 @@ data(inra)
geno_MNI <- MNI(geno47K) geno_MNI <- MNI(geno47K)
``` ```
##<a name="optitrain"></a> optiTRAIN - Optimization of TRAINING set by CDmean ## <a name="optitrain"></a> optiTRAIN - Optimization of TRAINING set by CDmean
**Description** **Description**
...@@ -652,7 +653,7 @@ Train_opti300 <- optiTRAIN (geno47K, 300, 1000) ...@@ -652,7 +653,7 @@ Train_opti300 <- optiTRAIN (geno47K, 300, 1000)
Rincent, R., D. Laloe, S. Nicolas, T. Altmann, D. Brunel, P. Revilla, V., M Rodriguez J. Moreno-Gonzalez, A. Melchinger, E. Bauer, C.C. Schoen, N. Meyer, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, H. Monod, P. Flament, A. Charcosset, and L. Moreau. 2012. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea may L.) Genetics 192:715--728 doi:10.1534/genetics.112.141473 Rincent, R., D. Laloe, S. Nicolas, T. Altmann, D. Brunel, P. Revilla, V., M Rodriguez J. Moreno-Gonzalez, A. Melchinger, E. Bauer, C.C. Schoen, N. Meyer, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, H. Monod, P. Flament, A. Charcosset, and L. Moreau. 2012. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea may L.) Genetics 192:715--728 doi:10.1534/genetics.112.141473
##<a name="qtlsim"></a> qtlSIM - Simulation of QTL ## <a name="qtlsim"></a> qtlSIM - Simulation of QTL
**Description** **Description**
...@@ -702,7 +703,7 @@ data(inra) ...@@ -702,7 +703,7 @@ data(inra)
TestQTL = qtlSIM (geno47K, NQLT=100, h2=0.3) TestQTL = qtlSIM (geno47K, NQLT=100, h2=0.3)
``` ```
##<a name="rmr"></a> RMR - Random Marker Reconstruction ## <a name="rmr"></a> RMR - Random Marker Reconstruction
**Description** **Description**
...@@ -741,7 +742,7 @@ data(inra) ...@@ -741,7 +742,7 @@ data(inra)
Geno5K <- RMR(geno47K, 5000) Geno5K <- RMR(geno47K, 5000)
``` ```
##<a name="rps"></a> RPS - Random Pop Size ## <a name="rps"></a> RPS - Random Pop Size
**Description** **Description**
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