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

parent 7f680093
......@@ -24,7 +24,8 @@ Description: Package for Breed Wheat Genomic Selection pipeline
**Date of Publication:**
##R topics documented:
## R topics documented:
1. [AM - Additive relationship matrix](#am)
2. [ANO - Selectio of N markers with the lowest Pvalues in GWAS](#ano)
3. [bwgs.cv - BreedWheat Genomic Selection Cross Validation](#bwgscv)
......@@ -38,7 +39,7 @@ Description: Package for Breed Wheat Genomic Selection pipeline
11. [RMR - Random Marker Recontruction](#rmr)
12. [RPS - Random Pop Size](#rps)
##<a name="am"></a> AM - Additive relationship matrix
## <a name="am"></a> AM - Additive relationship matrix
**Description**
......@@ -79,7 +80,7 @@ realized relationship matrix.
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**
......@@ -120,7 +121,7 @@ geno_impote <- MNI (geno47K)
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**
......@@ -273,7 +274,7 @@ testRKHSANO<bwgs.cv(geno47K,pheno,geno.reduct.method="ANO",pval=0.001,geno.imput
**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
classification and regression.
......@@ -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
##<a name="bwgspredict"></a> bwgs.predict Breed Wheat Genomic Selection Prediction
## <a name="bwgspredict"></a> bwgs.predict Breed Wheat Genomic Selection Prediction
**Description**
......@@ -448,7 +449,7 @@ cor
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**
......@@ -496,7 +497,7 @@ data(inra)
genoLD95 <- CHROMLD(geno47K, R2seuil=0.95, MAP)
```
##<a name="emi"></a> EMI - Expectation-Maximization Imputation
## <a name="emi"></a> EMI - Expectation-Maximization Imputation
**Description**
......@@ -553,7 +554,7 @@ Y., Dreisigacker, S., Crossa, J., Sanchez-Villeda, H., Sorrells, M.E., &
Jannink, J. (2012). Genomic Selection in Wheat Breeding using
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**
......@@ -566,7 +567,7 @@ inra data contains a set of geno47K(760 x 47839), pheno (760 x 1). The phenotype
data(inra)
```
##<a name="mni"></a> MNI - MeaN Impute
## <a name="mni"></a> MNI - MeaN Impute
**Description**
......@@ -603,7 +604,7 @@ data(inra)
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**
......@@ -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
##<a name="qtlsim"></a> qtlSIM - Simulation of QTL
## <a name="qtlsim"></a> qtlSIM - Simulation of QTL
**Description**
......@@ -702,7 +703,7 @@ data(inra)
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**
......@@ -741,7 +742,7 @@ data(inra)
Geno5K <- RMR(geno47K, 5000)
```
##<a name="rps"></a> RPS - Random Pop Size
## <a name="rps"></a> RPS - Random Pop Size
**Description**
......
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