duplicateDiscordanceAcrossDatasets: Functions to check discordance and allelic dosage correlation...

In smgogarten/GWASTools: Tools for Genome Wide Association Studies

Functions to check discordance and allelic dosage correlation across datasets

Description

These functions compare genotypes in pairs of duplicate scans of the same sample across multiple datasets. 'duplicateDiscordanceAcrossDatasets' finds the number of discordant genotypes both by scan and by SNP. 'dupDosageCorAcrossDatasets' calculates correlations between allelic dosages both by scan and by SNP, allowing for comparision between imputed datasets or between imputed and observed - i.e., where one or more of the datasets contains continuous dosage [0,2] rather than discrete allele counts {0,1,2}.

Usage

duplicateDiscordanceAcrossDatasets(genoData1, genoData2,
  match.snps.on=c("position", "alleles"),
  subjName.cols, snpName.cols=NULL,
  one.pair.per.subj=TRUE, minor.allele.only=FALSE,
  missing.fail=c(FALSE, FALSE),
  scan.exclude1=NULL, scan.exclude2=NULL,
  snp.exclude1=NULL, snp.exclude2=NULL,
  snp.include=NULL,
  verbose=TRUE)

minorAlleleDetectionAccuracy(genoData1, genoData2,
  match.snps.on=c("position", "alleles"),
  subjName.cols, snpName.cols=NULL,
  missing.fail=TRUE,
  scan.exclude1=NULL, scan.exclude2=NULL,
  snp.exclude1=NULL, snp.exclude2=NULL,
  snp.include=NULL,
  verbose=TRUE)

dupDosageCorAcrossDatasets(genoData1, genoData2,
  match.snps.on=c("position", "alleles"),
  subjName.cols="subjectID", snpName.cols=NULL,
  scan.exclude1=NULL, scan.exclude2=NULL,
  snp.exclude1=NULL, snp.exclude2=NULL,
  snp.include=NULL,
  snp.block.size=5000, scan.block.size=100,
  verbose=TRUE)

Arguments

genoData1	GenotypeData object containing the first dataset.
genoData2	GenotypeData object containing the second dataset.
match.snps.on	One or more of ("position", "alleles", "name") indicating how to match SNPs. "position" will match SNPs on chromosome and position, "alleles" will also require the same alleles (but A/B designations need not be the same), and "name" will match on the columns give in snpName.cols.
subjName.cols	2-element character vector indicating the names of the annotation variables that will be identical for duplicate scans in the two datasets. (Alternatively, one character value that will be recycled).
snpName.cols	2-element character vector indicating the names of the annotation variables that will be identical for the same SNPs in the two datasets. (Alternatively, one character value that will be recycled).
one.pair.per.subj	A logical indicating whether a single pair of scans should be randomly selected for each subject with more than 2 scans.
minor.allele.only	A logical indicating whether discordance should be calculated only between pairs of scans in which at least one scan has a genotype with the minor allele (i.e., exclude major allele homozygotes).
missing.fail	For duplicateDiscordanceAcrossDatasets, a 2-element logical vector indicating whether missing values in datasets 1 and 2, respectively, will be considered failures (discordances with called genotypes in the other dataset). For minorAlleleDetectionAccuracy, a single logical indicating whether missing values in dataset 2 will be considered false negatives (missing.fail=TRUE) or ignored (missing.fail=FALSE).
scan.exclude1	An integer vector containing the ids of scans to be excluded from the first dataset.
scan.exclude2	An integer vector containing the ids of scans to be excluded from the second dataset.
snp.exclude1	An integer vector containing the ids of SNPs to be excluded from the first dataset.
snp.exclude2	An integer vector containing the ids of SNPs to be excluded from the second dataset.
snp.include	List of SNPs to include in the comparison. Should match the contents of the columns referred to by snpName.cols. Only valid if match.snps.on includes "name".
snp.block.size	Block size for SNPs
scan.block.size	Block size for scans
verbose	Logical value specifying whether to show progress information.

Details

duplicateDiscordanceAcrossDatasets calculates discordance metrics both by scan and by SNP. If one.pair.per.subj=TRUE (the default), each subject with more than two duplicate genotyping instances will have one scan from each dataset randomly selected for computing discordance. If one.pair.per.subj=FALSE, discordances will be calculated pair-wise for all possible cross-dataset pairs for each subject.

dupDosageCorAcrossDatasets calculates dosage correlation (Pearson correlation coefficient) both by scan and by SNP. Note it only allows for one pair of duplicate scans per sample. For this function only, genoData1 and genoData2 must have been created with GdsGenotypeReader objects.

By default, overlapping variants are identified based on position and alleles. Alleles are determined via 'getAlleleA' and 'getAlleleB' accessors, so users should ensure these variables are referring to the same strand orientation in both datests (e.g., both plus strand alleles). It is not necessary for the A/B ordering to be consistent across datasets. For example, two variants at the same position with alleleA="C" and alleleB="T" in genoData1 and alleleA="T" and alleleB="C" in genoData2 will stil be identified as overlapping.

If minor.allele.only=TRUE, the allele frequency will be calculated in genoData1, using only samples common to both datasets.

If snp.include=NULL (the default), discordances will be found for all SNPs common to both datasets.

genoData1 and genoData2 should each have "alleleA" and "alleleB" defined in their SNP annotation. If allele coding cannot be found, the two datasets are assumed to have identical coding. Note that 'dupDosageCorAcrossDatasets' can NOT detect where strand-ambiguous (A/T or C/G) SNPs are annotated on different strands, although the r2 in these instances would be unaffected: r may be negative but r2 will be positive.

minorAlleleDetectionAccuracy summarizes the accuracy of minor allele detection in genoData2 with respect to genoData1 (the "gold standard"). TP=number of true positives, TN=number of true negatives, FP=number of false positives, and FN=number of false negatives. Accuracy is represented by four metrics:

• sensitivity for each SNP as TP/(TP+FN)

• specificity for each SNP as TN/(TN+FP)

• positive predictive value for each SNP as TP/(TP+FP)

• negative predictive value for each SNP as TN/(TN+FN).

TP, TN, FP, and FN are calculated as follows:

			genoData1
		mm	Mm	MM
	mm	2TP	1TP + 1FP	2FP
genoData2	Mm	1TP + 1FN	1TN + 1TP	1TN + 1FP
	MM	2FN	1FN + 1TN	2TN
	--	2FN	1FN

"M" is the major allele and "m" is the minor allele (as calculated in genoData1). "-" is a missing call in genoData2. Missing calls in genoData1 are ignored. If missing.fail=FALSE, missing calls in genoData2 (the last row of the table) are also ignored.

Value

SNP annotation columns returned by all functions are:

chromosome	chromosome
position	base pair position
snpID1	snpID from genoData1
snpID2	snpID from genoData2

If matching on "alleles":

alleles	alleles sorted alphabetically
alleleA1	allele A from genoData1
alleleB1	allele B from genoData2
alleleA2	allele A from genoData2
alleleB2	allele B from genoData2

If matching on "name":

name	the common SNP name given in snpName.cols

duplicateDiscordanceAcrossDatasets returns a list with two data frames: The data.frame "discordance.by.snp" contains the SNP annotation columns listed above as well as:

discordant	number of discordant pairs
npair	number of pairs examined
n.disc.subj	number of subjects with at least one discordance
discord.rate	discordance rate i.e. discordant/npair

The data.frame "discordance.by.subject" contains a list of matrices (one for each subject) with the pair-wise discordance between the different genotyping instances of the subject.

minorAlleleDetectionAccuracy returns a data.frame with the SNP annotation columns listed above as well as:

npair	number of sample pairs compared (non-missing in genoData1)
sensitivity	sensitivity
specificity	specificity
positivePredictiveValue	Positive predictive value
negativePredictiveValue	Negative predictive value

dupDosageCorAcrossDatasets returns a list with two data frames:

The data.frame "snps" contains the by-SNP correlation (r) values with the SNP annotation columns listed above as well as:

nsamp.dosageR	number of samples in r calculation (i.e., non missing data in both genoData1 and genoData2)
dosageR	dosage correlation

The data.frame "samps" contains the by-sample r values with the following columns:

subjectID	subject-level identifier for duplicate sample pair
scanID1	scanID from genoData1
scanID2	scanID from genoData2
nsnp.dosageR	number of SNPs in r calculation (i.e., non missing data in both genoData1 and genoData2)
dosageR	dosage correlation

If no duplicate scans or no common SNPs are found, these functions issue a warning message and return NULL.

Author(s)

Stephanie Gogarten, Jess Shen, Sarah Nelson

See Also

GenotypeData, duplicateDiscordance, duplicateDiscordanceProbability

Examples

# first set
snp1 <- data.frame(snpID=1:10, chromosome=1L, position=101:110,
                   rsID=paste("rs", 101:110, sep=""),
                   alleleA="A", alleleB="G", stringsAsFactors=FALSE)
scan1 <- data.frame(scanID=1:3, subjectID=c("A","B","C"), sex="F", stringsAsFactors=FALSE)
mgr <- MatrixGenotypeReader(genotype=matrix(c(0,1,2), ncol=3, nrow=10), snpID=snp1$snpID,
                            chromosome=snp1$chromosome, position=snp1$position, scanID=1:3)
genoData1 <- GenotypeData(mgr, snpAnnot=SnpAnnotationDataFrame(snp1),
                          scanAnnot=ScanAnnotationDataFrame(scan1))

# second set
snp2 <- data.frame(snpID=1:5, chromosome=1L,
                   position=as.integer(c(101,103,105,107,107)),
                   rsID=c("rs101", "rs103", "rs105", "rs107", "rsXXX"),
                   alleleA= c("A","C","G","A","A"),
                   alleleB=c("G","T","A","G","G"),
                   stringsAsFactors=FALSE)
scan2 <- data.frame(scanID=1:3, subjectID=c("A","C","C"), sex="F", stringsAsFactors=FALSE)
mgr <- MatrixGenotypeReader(genotype=matrix(c(1,2,0), ncol=3, nrow=5), snpID=snp2$snpID,
                            chromosome=snp2$chromosome, position=snp2$position, scanID=1:3)
genoData2 <- GenotypeData(mgr, snpAnnot=SnpAnnotationDataFrame(snp2),
                          scanAnnot=ScanAnnotationDataFrame(scan2))

duplicateDiscordanceAcrossDatasets(genoData1, genoData2,
  match.snps.on="position",
  subjName.cols="subjectID")

duplicateDiscordanceAcrossDatasets(genoData1, genoData2,
  match.snps.on=c("position", "alleles"),
  subjName.cols="subjectID")

duplicateDiscordanceAcrossDatasets(genoData1, genoData2,
  match.snps.on=c("position", "alleles", "name"),
  subjName.cols="subjectID",
  snpName.cols="rsID")

duplicateDiscordanceAcrossDatasets(genoData1, genoData2,
  subjName.cols="subjectID",
  one.pair.per.subj=FALSE)

minorAlleleDetectionAccuracy(genoData1, genoData2,
  subjName.cols="subjectID")

dupDosageCorAcrossDatasets(genoData1, genoData2,
  scan.exclude2=scan2$scanID[duplicated(scan2$subjectID)])

smgogarten/GWASTools documentation built on May 18, 2024, 1:19 a.m.