PreHap: Pre-process the data before fitting it with hapassoc

In hapassoc: Inference of Trait Associations with SNP Haplotypes and Other Attributes using the EM Algorithm

Pre-process the data before fitting it with hapassoc

Description

This function takes as an argument a dataframe with non-SNP and SNP data and converts the genotype data at single SNPs (the single-locus genotypes) into haplotype data. The rows of the input data frame should correspond to subjects. Single-locus SNP genotypes may be specified in one of two ways: (i) as pairs of columns, with one column for each allele of the single-locus genotypes (“allelic format”), or (ii) as columns of two-character genotypes (“genotypic format”). The SNP data should comprise the last 2*numSNPs columns (allelic format) or the last numSNPs columns (genotypic format) of the data frame.

If the haplotypes for a subject cannot be inferred from his or her genotype data, “pseudo-individuals” representing all possible haplotype combinations consistent with the single-locus genotypes are considered. Missing single-locus genotypes, up to a maximum of maxMissingGenos (see below), are allowed, but subjects with missing data in more than maxMissingGenos, or with missing non-SNP data, are removed. Initial estimates of haplotype frequencies are then obtained using the EM algorithm applied to the genotype data pooled over all subjects. Haplotypes with frequencies below a user-specified tolerance (zero.tol) are assumed not to exist and are removed from further consideration. (Pseudo-individuals having haplotypes of negligible frequency are deleted and the column in the design matrix corresponding to that haplotype is deleted.) For the remaining haplotypes, those with non-negligible frequency below a user-defined pooling tolerance (pooling.tol) are pooled into a single category called “pooled” in the design matrix for the risk model. However, the frequencies of each of these pooled haplotypes are still calculated separately.

Usage

pre.hapassoc(dat,numSNPs,maxMissingGenos=1,pooling.tol = 0.05, 
       zero.tol = 1/(2 * nrow(dat) * 10), allelic=TRUE, verbose=TRUE)

Arguments

dat	the non-SNP and SNP data as a data frame. The SNP data should comprise the last 2*numSNPs columns (allelic format) or last numSNPs columns (genotypic format). Missing allelic data should be coded as NA or "" and missing genotypic data should be coded as, e.g., "A" if one allele is missing and "" if both alleles are missing.
numSNPs	number of SNPs per haplotype
maxMissingGenos	maximum number of single-locus genotypes with missing data to allow for each subject. (Subjects with more missing data, or with missing non-SNP data are removed.) The default is 1.
pooling.tol	pooling tolerance – by default set to 0.05
zero.tol	tolerance for haplotype frequencies below which haplotypes are assumed not to exist – by default set to 1/(2*N*10) where N is the number of subjects
allelic	TRUE if single-locus SNP genotypes are in allelic format and FALSE if in genotypic format; default is TRUE.
verbose	indicates whether or not a list of the genotype variables used to form haplotypes and a list of other non-genetic variables should be printed; default is TRUE.

Details

See the hapassoc vignette, of the same name as the package, for details.

Value

haplotest	logical, TRUE if some haplotypes had frequency less than zero.tol and are assumed not to exist
initFreq	initial estimates of haplotype frequencies
zeroFreqHaplos	list of haplotypes assumed not to exist
pooledHaplos	list of haplotypes pooled into a single category in the design matrix
haploDM	Haplotype portion of the data frame augmented with pseudo-individuals. Has 2^numSNPs columns scoring number of copies of each haplotype for each pseudo-individual
nonHaploDM	non-haplotype portion of the data frame augmented with pseudo-individuals
haploMat	matrix with 2 columns listing haplotype labels for each pseudo-individual
wt	vector giving initial weights for each pseudo-individual for the EM algorithm
ID	index for each individual in the original data frame. Note that all pseudo-individuals have the same ID value

References

Burkett K, McNeney B, Graham J (2004). A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models. Human Heredity, 57:200-206

Burkett K, Graham J and McNeney B (2006). hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes. Journal of Statistical Software, 16(2):1-19

See Also

hapassoc,summary.hapassoc.

Examples

#First example data set has single-locus genotypes in "allelic format"
data(hypoDat)
example.pre.hapassoc<-pre.hapassoc(hypoDat, numSNPs=3)

# To get the initial haplotype frequencies:
example.pre.hapassoc$initFreq
#      h000       h001       h010       h011       h100       h101       h110 
#0.25179111 0.26050418 0.23606001 0.09164470 0.10133627 0.02636844 0.01081260 
#      h111 
#0.02148268 
# The '001' haplotype is estimated to be the most frequent

example.pre.hapassoc$pooledHaplos
# "h101" "h110" "h111"
# These haplotypes are to be pooled in the design matrix for the risk model

names(example.pre.hapassoc$haploDM)
# "h000"   "h001"   "h010"   "h011"   "h100"   "pooled"

####
#Second example data set has single-locus genotypes in "genotypic format"
data(hypoDatGeno)
example2.pre.hapassoc<-pre.hapassoc(hypoDatGeno, numSNPs=3, allelic=FALSE)

# To get the initial haplotype frequencies:
example2.pre.hapassoc$initFreq
#      hAAA       hAAC       hACA       hACC       hCAA       hCAC
#0.25179111 0.26050418 0.23606001 0.09164470 0.10133627 0.02636844
#      hCCA           hCCC 
#0.01081260	0.02148268 
# The 'hAAC' haplotype is estimated to be the most frequent

example2.pre.hapassoc$pooledHaplos
#  "hCAC" "hCCA" "hCCC"
# These haplotypes are to be pooled in the design matrix for the risk model

names(example2.pre.hapassoc$haploDM)
#  "hAAA"   "hAAC"   "hACA"   "hACC"   "hCAA"   "pooled"

hapassoc documentation built on May 28, 2022, 1:13 a.m.