locus_pred {HaploCatcher}

Haplotype Prediction: Using Trained Models to Make Predictions

Description

Haplotype Prediction: Using Trained Models to Make Predictions

Usage

locus_pred(locus_train_results, geno_mat, genotypes_to_predict)

Arguments

locus_train_results

This object is a the results object from the "locus_train" function.

geno_mat

This is a genotypic matrix with genotypes of individuals you have genotyped and characterized for the locus/QTL/gene of interest and individuals that have only been genotyped with a genome wide marker panel. It is important to note that the markers in the genome wide panel must be shared between training population and the population you wish to forward predict. In the case of genotyping-by-sequencing markers, the training and test populations should be discovered and produced together into a genotype file. All marker platforms, however, are compatable, as long as the training and forward prediction population share the same markers genome-wide.

genotypes_to_predict

This is a character vector of genotypes in the geno_mat which the user wishes to predict. If this object contains names in the training population, they will be omitted in the results to avoid bias.

Value

a data frame with two columns: genotype names and predictions.

Examples

#set seed for reproducible sampling
set.seed(022294)

#read in the genotypic data matrix
data("geno_mat")

#read in the marker information
data("marker_info")

#read in the gene compendium file
data("gene_comp")

#Note: in practice you would have something like a gene file
#that does not contain any lines you are trying to predict.
#However, this is for illustrative purposes on how to run the function

#sample data in the gene_comp file to make a traning population
train<-gene_comp[gene_comp$FullSampleName %in%
                   sample(gene_comp$FullSampleName,
                          round(length(gene_comp$FullSampleName)*0.8),0),]

#pull vector of names, not in the train, for forward prediction
test<-gene_comp[!gene_comp$FullSampleName
                %in% train$FullSampleName,
                "FullSampleName"]

#run the function with hets
fit<-locus_train(geno_mat=geno_mat, #the genotypic matrix
                 gene_file=train, #the gene compendium file
                 gene_name="sst1_solid_stem", #the name of the gene
                 marker_info=marker_info, #the marker information file
                 chromosome="3B", #name of the chromosome
                 ncor_markers=2, #number of markers to retain
                 n_neighbors=3, #number of neighbors
                 include_hets=FALSE, #include hets in the model
                 verbose = FALSE, #allows for text and graph output
                 set_seed = 022294, #sets a seed for reproduction of results
                 models = "knn") #sets what models are requested

#predict the lines in the test population
pred<-locus_pred(locus_train_results=fit,
                 geno_mat=geno_mat,
                 genotypes_to_predict=test)

#see predictions
head(pred)