GWASTools 1.48.0
This vignette describes how to prepare Affymetrix data for use in GWASTools. After following the examples here for preparing the data, the analysis procedure is the same as presented in the GWAS Data Cleaning vignette. For a description of the file formats (NetCDF and GDS), see the Data Formats vignette.
All of the functions in GWASTools require a minimum set of variables in the SNP annotation data object. The minimum required variables are
snpID, a unique integer identifier for each SNP
chromosome, an integer mapping for each chromosome, with values
1-27, mapped in order from 1-22, 23=X, 24=XY (the pseudoautosomal
region), 25=Y, 26=M (the mitochondrial probes), and 27=U (probes
with unknown positions)
position, the base position of each SNP on the chromosome.
We create the integer chromosome mapping for a few reasons. The chromosome is
stored as an integer in the NetCDF or GDS files, so in order to link the SNP
annotation with the NetCDF/GDS file, we use the integer values in the annotation
as well. For convenience when using GWASTools functions, the chromosome
variable is most times assumed to be an integer value. Thus, for the sex
chromosomes, we can simply use the chromosome values. For presentation of
results, it is important to have the mapping of the integer values back to the
standard designations for the chromosome names, thus the getChromosome()
functions in the
GWASTools objects have a char=TRUE option to return the characters
1-22, X, XY, Y, M, U. The position variable should hold all numeric values of
the physical position of a probe. The SNP annotation file is assumed to list
the probes in order of chromosome and position within chromosome.
Note that for Affymetrix data, the rs ID is often not unique, as there may be multiple probes for a given SNP. The probe ID is usually the unique SNP identifier. Also, Affymetrix annotation generally has a separate column or columns to indicate pseudoautosomal regions; some manipulation is usually required to include this information within the chromosome column itself.
library(GWASTools)
library(GWASdata)
# Load the SNP annotation (simple data frame)
data(affy_snp_annot)
# Create a SnpAnnotationDataFrame
snpAnnot <- SnpAnnotationDataFrame(affy_snp_annot)
# names of columns
varLabels(snpAnnot)## [1] "snpID" "chromosome" "position" "rsID" "probeID"# data
head(pData(snpAnnot))## snpID chromosome position rsID probeID
## 1 869828 21 13733610 rs3132407 SNP_A-8340403
## 2 869844 21 13852569 rs2775671 SNP_A-8340413
## 3 869864 21 14038583 rs2775018 SNP_A-8340427
## 4 869889 21 14136579 rs3115511 SNP_A-8340440
## 5 869922 21 14396024 rs2822404 SNP_A-8340775
## 6 869925 21 14404476 rs1556276 SNP_A-1968967# Add metadata to describe the columns
meta <- varMetadata(snpAnnot)
meta[c("snpID", "chromosome", "position", "rsID", "probeID"),
"labelDescription"] <- c("unique integer ID for SNPs",
paste("integer code for chromosome: 1:22=autosomes,",
"23=X, 24=pseudoautosomal, 25=Y, 26=Mitochondrial, 27=Unknown"),
"base pair position on chromosome (build 36)",
"RS identifier",
"unique ID from Affymetrix")
varMetadata(snpAnnot) <- metaThe scan annotation file holds attributes for each genotyping scan that are
relevant to genotypic data cleaning. These data include processing variables
such as tissue type, DNA extraction method, and genotype processing batch. They
also include individual characteristics such as gender and race. The initial
sample annotation file is created from the raw data supplied by the genotyping
center and/or study investigator, providing a mapping from the raw data file(s)
for each sample scan to other sample information such as sex, coded as M and
F, ethnicity, unique scan identifier, called scanID, and unique subject
identifier. Since a single subject may have been genotyped multiple times as a
quality control measure, it is important to distinguish between the scanID
(unique genotyping instance) and subjectID (person providing a DNA sample).
# Load the scan annotation (simple data frame)
data(affy_scan_annot)
# Create a ScanAnnotationDataFrame
scanAnnot <- ScanAnnotationDataFrame(affy_scan_annot)
# names of columns
varLabels(scanAnnot)## [1] "scanID" "subjectID" "family" "father" "mother"
## [6] "CoriellID" "race" "sex" "status" "genoRunID"
## [11] "plate" "alleleFile" "chpFile"# data
head(pData(scanAnnot))## scanID subjectID family father mother CoriellID race sex status
## 1 3 200150062 28 0 0 NA18912 YRI F 0
## 2 5 200122600 1341 0 0 NA07034 CEU M 1
## 3 14 200122151 58 0 0 NA19222 YRI F 0
## 4 15 200033736 9 0 0 NA18508 YRI F 1
## 5 17 200116780 1344 0 0 NA12056 CEU M 1
## 6 28 200003216 28 0 0 NA18913 YRI M 0
## genoRunID plate
## 1 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A03_31250 GAINmixHapMapAffy2
## 2 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A05_31282 GAINmixHapMapAffy2
## 3 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B02_31236 GAINmixHapMapAffy2
## 4 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B03_31252 GAINmixHapMapAffy2
## 5 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B05_31284 GAINmixHapMapAffy2
## 6 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_C04_31270 GAINmixHapMapAffy2
## alleleFile
## 1 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A03_31250.BIRDSEED.ALLELE_SUMMARY.TXT
## 2 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A05_31282.BIRDSEED.ALLELE_SUMMARY.TXT
## 3 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B02_31236.BIRDSEED.ALLELE_SUMMARY.TXT
## 4 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B03_31252.BIRDSEED.ALLELE_SUMMARY.TXT
## 5 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B05_31284.BIRDSEED.ALLELE_SUMMARY.TXT
## 6 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_C04_31270.BIRDSEED.ALLELE_SUMMARY.TXT
## chpFile
## 1 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A03_31250.BIRDSEED.CHP.TXT
## 2 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_A05_31282.BIRDSEED.CHP.TXT
## 3 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B02_31236.BIRDSEED.CHP.TXT
## 4 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B03_31252.BIRDSEED.CHP.TXT
## 5 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_B05_31284.BIRDSEED.CHP.TXT
## 6 GIGAS_g_GAINmixHapMapAffy2_GenomeWideEx_6_C04_31270.BIRDSEED.CHP.TXT# Add metadata to describe the columns
meta <- varMetadata(scanAnnot)
meta[c("scanID", "subjectID", "family", "father", "mother",
"CoriellID", "race", "sex", "status", "genoRunID", "plate",
"alleleFile", "chpFile"), "labelDescription"] <-
c("unique integer ID for scans",
"subject identifier (may have multiple scans)",
"family identifier",
"father identifier as subjectID",
"mother identifier as subjectID",
"Coriell subject identifier",
"HapMap population group",
"sex coded as M=male and F=female",
"simulated case/control status" ,
"genotyping instance identifier",
"plate containing samples processed together for genotyping chemistry",
"data file with intensities",
"data file with genotypes and quality scores")
varMetadata(scanAnnot) <- metaThe data for genotype calls, allelic intensities and other variables such as BAlleleFrequency are stored as NetCDF or GDS files.
The genotype files store genotypic data in 0, 1, 2 format indicating the number of “A” alleles in the genotype (i.e. AA=2, AB=1, BB=0 and missing=-1). The conversion from AB format and forward strand (or other) allele formats can be stored in the SNP annotation file.
geno.file <- "tmp.geno.gds"
# first 3 samples only
scan.annotation <- affy_scan_annot[1:3, c("scanID", "genoRunID", "chpFile")]
names(scan.annotation) <- c("scanID", "scanName", "file")
# indicate which column of SNP annotation is referenced in data files
snp.annotation <- affy_snp_annot[,c("snpID", "probeID", "chromosome", "position")]
names(snp.annotation)[2] <- "snpName"
# col.nums is an integer vector indicating which columns of the raw text file
# contain variables for input
col.nums <- as.integer(c(2,3))
names(col.nums) <- c("snp", "geno")
# Define a path to the raw data CHP text files
path <- system.file("extdata", "affy_raw_data", package="GWASdata")
diag.geno.file <- "diag.geno.RData"
diag.geno <- createDataFile(path = path,
filename = geno.file,
file.type = "gds",
variables = "genotype",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
sep.type = "\t",
skip.num = 1,
col.total = 6,
col.nums = col.nums,
scan.name.in.file = -1,
diagnostics.filename = diag.geno.file,
verbose = FALSE)## adding variables: genotype# Look at the values included in the "diag.geno" object which holds
# all output from the function call
names(diag.geno)## [1] "read.file" "row.num" "samples" "sample.match" "missg"
## [6] "snp.chk" "chk"# `read.file' is a vector indicating whether (1) or not (0) each file
# specified in the `files' argument was read successfully
table(diag.geno$read.file)##
## 1
## 3# `row.num' is a vector of the number of rows read from each file
table(diag.geno$row.num) ##
## 3300
## 3# `sample.match' is a vector indicating whether (1) or not (0)
# the sample name inside the raw text file matches that in the
# sample annotation data.frame
table(diag.geno$sample.match)##
## 1
## 3# `snp.chk' is a vector indicating whether (1) or not (0)
# the raw text file has the expected set of SNP names
table(diag.geno$snp.chk)##
## 1
## 3# `chk' is a vector indicating whether (1) or not (0) all previous
# checks were successful and the data were written to the file
table(diag.geno$chk)##
## 1
## 3# open the file we just created
(genofile <- GdsGenotypeReader(geno.file))## File: /tmp/Rtmpuc7MJV/Rbuild2951794d8b089d/GWASTools/vignettes/tmp.geno.gds (25.1K)
## + [ ]
## |--+ sample.id { Int32 3 LZMA_ra(683.3%), 89B }
## |--+ snp.id { Int32 3300 LZMA_ra(25.0%), 3.2K }
## |--+ snp.chromosome { UInt8 3300 LZMA_ra(3.45%), 121B }
## |--+ snp.position { Int32 3300 LZMA_ra(68.0%), 8.8K }
## |--+ snp.rs.id { Str8 3300 LZMA_ra(20.5%), 9.3K }
## \--+ genotype { Bit2 3300x3, 2.4K } *# Take out genotype data for the first 3 samples and
# the first 5 SNPs
(genos <- getGenotype(genofile, snp=c(1,5), scan=c(1,3)))## [,1] [,2] [,3]
## [1,] 0 0 0
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 1 0 2
## [5,] 0 2 1close(genofile)
# check that the data file matches the raw text files
check.geno.file <- "check.geno.RData"
check.geno <- checkGenotypeFile(path = path,
filename = geno.file,
file.type = "gds",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
sep.type = "\t",
skip.num = 1,
col.total = 6,
col.nums = col.nums,
scan.name.in.file = -1,
check.scan.index = 1:3,
n.scans.loaded = 3,
diagnostics.filename = check.geno.file,
verbose = FALSE)
# Look at the values included in the "check.geno" object which holds
# all output from the function call
names(check.geno)## [1] "read.file" "row.num" "sample.names" "sample.match" "missg"
## [6] "snp.chk" "chk" "geno.chk"# 'geno.chk' is a vector indicating whether (1) or not (0) the genotypes
# match the text file
table(check.geno$geno.chk)##
## 1
## 3The intensity files store quality scores and allelic intensity data for each
SNP. Affymetrix data are provided in two files per genotyping scan. The CHP file
holds the genotype calls, used to create the genotype file, as well as the
confidence score, which is written to the quality file. The normalized X and Y
intensity data are stored in the allele_summary files in the format of two
rows per SNP, one for each allelic probe. These are written to the intensity
file.
qual.file <- "tmp.qual.gds"
scan.annotation <- affy_scan_annot[1:3, c("scanID", "genoRunID", "chpFile")]
names(scan.annotation) <- c("scanID", "scanName", "file")
snp.annotation <- affy_snp_annot[,c("snpID", "probeID", "chromosome", "position")]
names(snp.annotation)[2] <- "snpName"
col.nums <- as.integer(c(2,4))
names(col.nums) <- c("snp", "quality")
path <- system.file("extdata", "affy_raw_data", package="GWASdata")
diag.qual.file <- "diag.qual.RData"
diag.qual <- createDataFile(path = path,
filename = qual.file,
file.type = "gds",
variables = "quality",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
sep.type = "\t",
skip.num = 1,
col.total = 6,
col.nums = col.nums,
scan.name.in.file = -1,
diagnostics.filename = diag.qual.file,
verbose = FALSE)## adding variables: quality# Open the GDS file we just created
(qualfile <- GdsIntensityReader(qual.file))## File: /tmp/Rtmpuc7MJV/Rbuild2951794d8b089d/GWASTools/vignettes/tmp.qual.gds (56.4K)
## + [ ]
## |--+ sample.id { Int32 3 LZMA_ra(683.3%), 89B }
## |--+ snp.id { Int32 3300 LZMA_ra(25.0%), 3.2K }
## |--+ snp.chromosome { UInt8 3300 LZMA_ra(3.45%), 121B }
## |--+ snp.position { Int32 3300 LZMA_ra(68.0%), 8.8K }
## |--+ snp.rs.id { Str8 3300 LZMA_ra(20.5%), 9.3K }
## \--+ quality { Float32 3300x3 LZMA_ra(87.0%), 33.7K }# Take out the quality scores for the first
# 5 SNPs for the first 3 samples
(qual <- getQuality(qualfile, snp=c(1,5), scan=c(1,3)))## [,1] [,2] [,3]
## [1,] 0.014382409 0.002228203 0.004184095
## [2,] 0.005246114 0.003796505 0.005314134
## [3,] 0.002767287 0.017275169 0.005179304
## [4,] 0.002980622 0.005857171 0.005547870
## [5,] 0.006078269 0.005080181 0.002624712close(qualfile)
# check
check.qual.file <- "check.qual.RData"
check.qual <- checkIntensityFile(path = path,
filename = qual.file,
file.type = "gds",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
sep.type = "\t",
skip.num = 1,
col.total = 6,
col.nums = col.nums,
scan.name.in.file = -1,
check.scan.index = 1:3,
n.scans.loaded = 3,
affy.inten = FALSE,
diagnostics.filename = check.qual.file,
verbose = FALSE)xy.file <- "tmp.xy.gds"
# we need the allele files instead of CHP files
scan.annotation <- affy_scan_annot[1:3, c("scanID", "genoRunID", "alleleFile")]
names(scan.annotation) <- c("scanID", "scanName", "file")
snp.annotation <- affy_snp_annot[,c("snpID", "probeID", "chromosome", "position")]
names(snp.annotation)[2] <- "snpName"
diag.xy.file <- "diag.xy.RData"
diag.xy <- createAffyIntensityFile(path = path,
filename = xy.file,
file.type = "gds",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
diagnostics.filename = diag.xy.file,
verbose = FALSE)
# Open the file we just created
(intenfile <- GdsIntensityReader(xy.file))## File: /tmp/Rtmpuc7MJV/Rbuild2951794d8b089d/GWASTools/vignettes/tmp.xy.gds (88.3K)
## + [ ]
## |--+ sample.id { Int32 3 LZMA_ra(683.3%), 89B }
## |--+ snp.id { Int32 3300 LZMA_ra(25.0%), 3.2K }
## |--+ snp.chromosome { UInt8 3300 LZMA_ra(3.45%), 121B }
## |--+ snp.position { Int32 3300 LZMA_ra(68.0%), 8.8K }
## |--+ snp.rs.id { Str8 3300 LZMA_ra(20.5%), 9.3K }
## |--+ X { Float32 3300x3 LZMA_ra(85.2%), 32.9K }
## \--+ Y { Float32 3300x3 LZMA_ra(83.8%), 32.4K }# Take out the normalized X intensity values for the first
# 5 SNPs for the first 3 samples
(xinten <- getX(intenfile, snp=c(1,5), scan=c(1,3)))## [,1] [,2] [,3]
## [1,] 501.2622 385.5622 356.8760
## [2,] 614.1541 651.9782 710.6095
## [3,] 1968.4933 2550.7141 2265.3674
## [4,] 1607.2856 293.1671 2906.5942
## [5,] 398.2835 1902.5592 1355.5342close(intenfile)
# check the intensity files - note affy.inten=TRUE
check.xy.file <- "check.xy.RData"
check.xy <- checkIntensityFile(path = path,
filename = xy.file,
file.type = "gds",
snp.annotation = snp.annotation,
scan.annotation = scan.annotation,
sep.type = "\t",
skip.num = 1,
col.total = 2,
col.nums = setNames(as.integer(c(1,2,2)), c("snp", "X", "Y")),
scan.name.in.file = -1,
check.scan.index = 1:3,
n.scans.loaded = 3,
affy.inten = TRUE,
diagnostics.filename = check.xy.file,
verbose = FALSE)The BAlleleFrequency and LogRRatio file stores these values for every sample by SNP. For Affymetrix data, these values must be calculated by the user. For a thorough explanation and presentation of an application of these values, please refer to Peiffer, Daniel A., et al. (2006).1 Peiffer, Daniel A., et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Research 16, 1136-1148 (September 2006).
For a given sample and SNP, R and \(\theta\) are calculated using the X and Y intensities, where \[\label{rthe} R=X+Y\] \[\theta = \frac{2\arctan(Y/X)}{\pi}\] \(\theta\) corresponds to the polar coordinate angle and R is the sum of the normalized X and Y intensities (not, as one might assume, the magnitude of the polar coordinate vector). It is from these values that we calculate the LogRRatio and BAlleleFrequency.
The LogRRatio is given below. The expected value of R is derived from a plot of \(\theta\) versus R for a given SNP. It is the predicted value of R derived from a line connecting the centers of the two nearest genotype clusters. \[{\rm LogRRatio} =\log \left( \frac{R_{\text{observed values}}}{R_{\text{expected values}}}\right)\] Variation in the LogRRatio across a single chromosome indicates possible duplication or deletion, and is an indication of overall sample quality.
The BAlleleFrequency is the frequency of the B allele in the population of cells from which the DNA is extracted. Each sample and SNP combination has a BAlleleFrequency value. Note the BAlleleFrequency values vary for a subject with each DNA extraction and tissue used. After all SNPs have been read and all samples have been clustered for a probe, the mean \(\theta\) “cluster” value is calculated for each probe, for each of the three genotype clusters, resulting in \(\theta_{AA}, \theta_{AB} \text{ and }\theta_{BB}\) for every probe. Then the \(\theta\) value for each sample, call it \(\theta_{n}\), is compared to \(\theta_{AA}, \theta_{AB}\text{ and }\theta_{BB}\). The BAlleleFrequency is calculated \[\text{BAlleleFrequency}= \begin{cases} 0 & \text{if } \theta_{n} < \theta_{AA}\\ \mbox{}\\ \frac{{\textstyle (1/2)(\theta_{n}-\theta_{AA})}}{{\textstyle \theta_{AB}-\theta_{AA}}} & \text{if } \theta_{AA} \leq \theta_{n} < \theta_{AB}\\ \mbox{}\\ \frac{{\textstyle 1}}{{\textstyle 2}}+\frac{{\textstyle (1/2)(\theta_{n}-\theta_{AB})}} {{\textstyle \theta_{BB}-\theta_{AB}}} & \text{if } \theta_{AB} \leq \theta_{n} < \theta_{BB}\\ \mbox{}\\ 1 & \text{if } \theta_{n} \geq \theta_{BB} \end{cases}\] A \(\theta_{n}\) value of 0 or 1 corresponds to a homozygote genotype for sample \(n\) at that particular probe, and a \(\theta_{n}\) value of 1/2 indicates a heterozygote genotype. Thus, \(BAlleleFrequency \in[0,1]\) for each probe. Across a chromosome, three bands are expected, one hovering around 0, one around 1 and one around 0.5, and any deviation from this is considered aberrant.
We use the BAlleleFrequency and LogRRatio values to detect mixed samples or samples of low quality, as well as chromosomal duplications and deletions. Samples that have a significantly large (partial or full chromosome) aberration for a particular chromosome as detected from the BAlleleFrequency values are recommended to be filtered out, for the genotype data are not reliable in these situations. Because of these applications, the BAlleleFrequency and LogRRatio values are a salient part of the data cleaning steps.
Because we have already completed the creation of both the genotype and intensity files, we simply use those files to access the data. The BAlleleFrequency and LogRRatio values are calculated in subsets for efficiency and written to the corresponding subset indices in the file.
We calculate the BAlleleFrequency and LogRRatio values for each sample by SNP
and write these values to a data file by calling the function
BAFfromGenotypes. We will also select “by.study” as the call method, so all 3
samples have their genotype clusters called together. In normal usage, we
recommend calling Affymetrix genotypes “by.plate” (in which case the plate.name
argument is passed to the function). For more detail regarding the
BAFfromGenotypes function, please see the function documentation. After the
function is complete, we will look at a few values to ensure the file was
created successfully.
bl.file <- "tmp.bl.gds"
xyData <- IntensityData(GdsIntensityReader(xy.file),
snpAnnot=snpAnnot)
genoData <- GenotypeData(GdsGenotypeReader(geno.file),
snpAnnot=snpAnnot)
BAFfromGenotypes(xyData, genoData,
filename = bl.file,
file.type = "gds",
min.n.genotypes = 0,
call.method ="by.study",
verbose = FALSE)
close(xyData)
close(genoData)
# Open the file we just created
(blfile <- GdsIntensityReader(bl.file))## File: /tmp/Rtmpuc7MJV/Rbuild2951794d8b089d/GWASTools/vignettes/tmp.bl.gds (90.7K)
## + [ ]
## |--+ sample.id { Int32 3 LZMA_ra(683.3%), 89B }
## |--+ snp.id { Int32 3300 LZMA_ra(25.0%), 3.2K }
## |--+ snp.chromosome { UInt8 3300 LZMA_ra(3.45%), 121B }
## |--+ snp.position { Int32 3300 LZMA_ra(68.0%), 8.8K }
## |--+ BAlleleFreq { Float32 3300x3, 38.7K }
## \--+ LogRRatio { Float32 3300x3, 38.7K }# Look at the BAlleleFrequency values for the first 5 SNPs
(baf <- getBAlleleFreq(blfile, snp=c(1,5), scan=c(1,3)))## [,1] [,2] [,3]
## [1,] NaN 1 NaN
## [2,] 0.0 NaN 0.0
## [3,] 0.0 NaN NaN
## [4,] 0.5 1 0.0
## [5,] 1.0 0 0.5close(blfile)