duplicateDiscordanceAcrossDatasets {GWASTools} | R Documentation

## 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)])

[Package GWASTools version 1.36.0 Index]