| Type: | Package |
| Title: | Genomic Prediction of Cross Performance |
| Version: | 0.1.0 |
| Maintainer: | Christine Nyaga <cmn92@cornell.edu> |
| Description: | This function performs genomic prediction of cross performance using genotype and phenotype data. It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data, fitting mixed models, and predicting cross performance based on weighted marker effects. For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| LinkingTo: | Rcpp, RcppArmadillo |
| Imports: | BiocManager, Rcpp, dplyr, sommer, AGHmatrix, snpStats, VariantAnnotation, tools, magrittr, methods |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | yes |
| Packaged: | 2024-11-04 13:31:44 UTC; cmn92 |
| Author: | Marlee Labroo [aut], Christine Nyaga [cre, aut], Lukas Mueller [aut] |
| Repository: | CRAN |
| Date/Publication: | 2024-11-06 15:50:02 UTC |
Example Phenotype Data
Description
This is a sample phenotype dataset used for genomic prediction.
Usage
phenotypeFile
Format
A data frame with 24 columns:
- ATW
Description of ATW
- AUDPC_YAD
Area Under Disease Progress Curve for YAD
- AUDPC_YMV
Area Under Disease Progress Curve for YMV
- Accession
Genotype IDs for each individual
- Block
Block information
- DMC
Dry Matter Content values
- Design
Experimental design
- LOC
Location of the trials
- NPH
Number of Plants Harvested
- OXBI
Oxidation Index
- Oxint180Minutes
Oxidation intensity after 180 minutes
- PLOT
Plot number
- REP
Replication number
- Settweight
Weight of the planting setts
- TTNPL
Total Tuber Number per Plant
- TTWPL
Total Tuber Weight per Plant
- Trial
Trial name or ID
- Vigor
Plant vigor score
- YIELD
Yield values
- Year
Year of the experiment
- Yield.per.plot..kg.
Yield per plot in kilograms
- Yield_udj
Unadjusted Yield
- rAUDPC_YAD
Relative AUDPC for YAD
- rAUDPC_YMV
Relative AUDPC for YMV
Source
Generated for the gpcp package example
Examples
data(phenotypeFile)
head(phenotypeFile)
Genomic Prediction of Cross Performance This function performs genomic prediction of cross performance using genotype and phenotype data.
Description
Genomic Prediction of Cross Performance This function performs genomic prediction of cross performance using genotype and phenotype data.
Usage
runGPCP(
phenotypeFile,
genotypeFile,
genotypes,
traits,
weights = NA,
userSexes = "",
userFixed = NA,
userRandom = NA,
Ploidy = NA,
NCrosses = NA
)
Arguments
phenotypeFile |
A data frame containing phenotypic data, typically read from a CSV file. |
genotypeFile |
Path to the genotypic data, either in VCF or HapMap format. |
genotypes |
A character string representing the column name in the phenotype file for the genotype IDs. |
traits |
A string of comma-separated trait names from the phenotype file. |
weights |
A numeric vector specifying weights for the traits. |
userSexes |
A string representing the column name corresponding to the individuals' sexes. |
userFixed |
A string of comma-separated fixed effect variables. |
userRandom |
A string of comma-separated random effect variables. |
Ploidy |
An integer representing the ploidy level of the organism. |
NCrosses |
An integer specifying the number of top crosses to output. |
Value
A data frame containing predicted cross performance.
Examples
# Load phenotype data from CSV
phenotypeFile <- read.csv(system.file("extdata", "phenotypeFile.csv", package = "gpcp"))
genotypeFile <- system.file("extdata", "genotypeFile_Chr9and11.vcf", package = "gpcp")
finalcrosses <- runGPCP(
phenotypeFile = phenotypeFile,
genotypeFile = genotypeFile,
genotypes = "Accession",
traits = "YIELD,DMC",
weights = c(3, 1),
userFixed = "LOC,REP",
Ploidy = 2,
NCrosses = 150
)
print(finalcrosses)