Genotype 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.

Creating the Genotype 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/RtmpoLJL6r/Rbuild1ced58fbc09f/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    1

close(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 
## 3

Intensity Files

The 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.

Creating the quality 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/RtmpoLJL6r/Rbuild1ced58fbc09f/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 0.002624712

close(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)

Creating the Intensity file

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/RtmpoLJL6r/Rbuild1ced58fbc09f/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 1355.5342

close(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)

BAlleleFrequency and LogRRatio Files

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

For a given sample and SNP, R and θ are calculated using the X and Y intensities, where R = X + Y $$\theta = \frac{2\arctan(Y/X)}{\pi}$$ θ 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 θ 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 θ “cluster” value is calculated for each probe, for each of the three genotype clusters, resulting in θ_AA, θ_AB and θ_BB for every probe. Then the θ value for each sample, call it θ_n, is compared to θ_AA, θ_AB and θ_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 θ_n value of 0 or 1 corresponds to a homozygote genotype for sample n at that particular probe, and a θ_n value of 1/2 indicates a heterozygote genotype. Thus, BAlleleFrequency ∈ [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.

Creating the BAlleleFrequency and LogRRatio 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/RtmpoLJL6r/Rbuild1ced58fbc09f/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  0.5

close(blfile)