Test for batch effect

Description

This function test for the allele frequency difference between the study cohort and the external datasets.

Usage

Batcheffect.Test(n0, n1, n.ext, maf0, maf1, maf.ext, pop.prev, var.ratio = 1)

Arguments

Value

A numeric of batch effect p-value

An analytical p-value combination method using the Cauchy distribution

Description

The CCT function takes in a numeric vector of p-values, a numeric vector of non-negative weights, and return the aggregated p-value using Cauchy method.

Usage

CCT(pvals, weights = NULL)

Arguments

Value

the aggregated p-value combining p-values from the vector pvals.

References

Liu, Y., & Xie, J. (2020). Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. Journal of the American Statistical Association 115(529), 393-402. (pub)

Examples

pvalues <- c(2e-02, 4e-04, 0.2, 0.1, 0.8)
CCT(pvals = pvalues)

Conduct marker-level genetic association testing

Description

Performs GWAS between a trait and individual genetic markers.

Usage

GRAB.Marker(
  objNull,
  GenoFile,
  GenoFileIndex = NULL,
  OutputFile,
  OutputFileIndex = NULL,
  control = NULL
)

Arguments

Details

The GRAB package supports multiple statistical methods: POLMM, SPACox, SPAGRM, SPAmix, and WtCoxG. Detailed information about these analysis methods is provided in the Details section of GRAB.NullModel. Users do not need to specify the method explicitly since GRAB.Marker and GRAB.Region automatically detect it from class(objNull).

Control Parameters

The following parameters allow users to customize which markers to include in the analysis. If these parameters are not specified, GRAB will analyze all markers in the file. For PLINK files, the default is control$AlleleOrder = "alt-first"; for BGEN files, the default is control$AlleleOrder = "ref-first".

• IDsToIncludeFile: See the Details section of GRAB.ReadGeno.

• IDsToExcludeFile: See the Details section of GRAB.ReadGeno.

• RangesToIncludeFile: See the Details section of GRAB.ReadGeno.

• RangesToExcludeFile: See the Details section of GRAB.ReadGeno.

• AlleleOrder: See the Details section of GRAB.ReadGeno.

The following parameters customize the quality control (QC) process:

• ImputeMethod: A character string specifying imputation method: "mean" (default), "bestguess", or "drop". See the Details section of GRAB.ReadGeno.

• MissingRateCutoff: A numeric value (default=0.15). Markers with missing rate exceeding this value will be excluded from analysis.

• MinMAFCutoff: A numeric value (default=0.001). Markers with minor allele frequency (MAF) below this value will be excluded from analysis.

• MinMACCutoff: A numeric value (default=20). Markers with minor allele count (MAC) below this value will be excluded from analysis.

• nMarkersEachChunk: Number of markers (default=10000) processed in each output chunk.

The following parameters customize the columns in the OutputFile. The columns Marker, Info, AltFreq, AltCounts, MissingRate, and Pvalue are included for all methods.

• outputColumns: Specifies additional columns to include in the output. For example, for the POLMM method, users can set control$outputColumns = c("beta", "seBeta", "AltFreqInGroup"):

  • POLMM: Default: beta, seBeta; Optional: zScore, AltFreqInGroup, nSamplesInGroup, AltCountsInGroup

  • SPACox: Optional: zScore

Value

The analysis results are written to OutputFile, which includes the following columns:

Marker

Marker IDs extracted from GenoFile and GenoFileIndex.

Info

Marker information in format "CHR:POS:REF:ALT". The order of REF/ALT depends on control$AlleleOrder: "ref-first" or "alt-first".

AltFreq

Alternative allele frequency (before genotype imputation, might be > 0.5). If most markers have AltFreq > 0.5, consider resetting control$AlleleOrder.

AltCounts

Alternative allele counts (before genotype imputation).

MissingRate

Missing rate for each marker.

Pvalue

Association test p-value.

The following columns can be customized using control$outputColumns. See makeGroup for details about phenotype grouping, which is used for nSamplesInGroup, AltCountsInGroup, and AltFreqInGroup.

beta

Estimated effect size of the ALT allele.

seBeta

Estimated standard error of the effect size.

zScore

Standardized score statistic, usually follows a standard normal distribution.

nSamplesInGroup

Number of subjects in different phenotype groups. This may differ slightly from the original distribution due to missing genotypes.

AltCountsInGroup

Alternative allele counts (before genotype imputation) in different phenotype groups.

AltFreqInGroup

Alternative allele frequency (before genotype imputation) in different phenotype groups.

Examples

# Load a precomputed POLMM_NULL_Model object to perform step 2 without repeating step 1
objNullFile <- system.file("extdata", "objPOLMMnull.RData", package = "GRAB")
load(objNullFile)
class(obj.POLMM)

GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputFile <- file.path(tempdir(), "simuOUTPUT.txt")
outputColumns <- c(
  "beta", "seBeta", "zScore", 
  "nSamplesInGroup", "AltCountsInGroup", "AltFreqInGroup"
)

GRAB.Marker(
  obj.POLMM,
  GenoFile = GenoFile,
  OutputFile = OutputFile,
  control = list(outputColumns = outputColumns)
)

data.table::fread(OutputFile)

Fit a null model to estimate parameters and residuals

Description

Fits a null model that includes response variables, covariates, and optionally a Genetic Relationship Matrix (GRM) to estimate model parameters and residuals for subsequent association testing.

Usage

GRAB.NullModel(
  formula,
  data,
  subset = NULL,
  subjData,
  method,
  traitType,
  GenoFile = NULL,
  GenoFileIndex = NULL,
  SparseGRMFile = NULL,
  control = NULL,
  ...
)

Arguments

Details

The GRAB package uses score testing which consists of two steps:

1. GRAB.NullModel fits a null model including response variable, covariates, and Genetic Relationship Matrix (GRM) if needed

2. GRAB.Marker and GRAB.Region perform genome-wide marker-level analysis and region-level analysis, respectively

Step 1 fits a null model to get an R object, which is passed to Step 2 for association testing. Functions save and load can save and load this object.

GRAB package includes multiple methods which support a wide variety of phenotypes as follows.

• POLMM: Support traitType = "ordinal". Check GRAB.POLMM for more details.

• SPACox: Support traitType = "time-to-event" or "Residual". Check GRAB.SPACox for more details.

• SPAmix: Support traitType = "time-to-event" or "Residual". Check GRAB.SPAmix for more details.

• WtCoxG: Support traitType = "time-to-event". Check GRAB.WtCoxG for more details.

The GRAB package supports both Dense and Sparse GRM to adjust for sample relatedness. If Dense GRM is used, then GenoFile is required to construct GRM. If Sparse GRM is used, then SparseGRMFile is required. See getTempFilesFullGRM and getSparseGRM for details.

Control Parameters

The control argument includes a list of parameters for controlling the null model fitting process:

Basic Parameters:

maxiter

Maximum number of iterations used to fit the null model (default: 100)

seed

Random seed for reproducible results (default: 12345678)

tolBeta

Tolerance for fixed effects convergence: |beta - beta_old| / (|beta| + |beta_old| + tolBeta) < tolBeta (default: 0.001)

showInfo

Whether to show detailed information for troubleshooting (default: FALSE)

Variance Component Parameters:

tau

Initial value of the variance component (default: 0.2)

tolTau

Tolerance for variance component convergence: |tau - tau_old| / (|tau| + |tau_old| + tolTau) < tolTau (default: 0.002)

Dense GRM Parameters (when using PLINK files):

maxiterPCG

Maximum iterations for Preconditioned Conjugate Gradient (default: 100)

tolEps

Tolerance for PCG convergence (default: 1e-6)

minMafVarRatio

Minimum MAF for markers used in variance ratio estimation (default: 0.1)

maxMissingVarRatio

Maximum missing rate for markers used in variance ratio estimation (default: 0.1)

nSNPsVarRatio

Initial number of markers for variance ratio estimation (default: 20)

CVcutoff

Maximum coefficient of variation for variance ratio estimation (default: 0.0025)

LOCO

Whether to apply leave-one-chromosome-out approach (default: TRUE)

stackSize

Stack size (bytes) for worker threads (default: "auto")

grainSize

Minimum chunk size for parallelization (default: 1)

minMafGRM

Minimum MAF for markers used in dense GRM construction (default: 0.01)

memoryChunk

Memory chunk size (GB) when reading PLINK files (default: 2)

tracenrun

Number of runs for trace estimator (default: 30)

maxMissingGRM

Maximum missing rate for markers used in dense GRM construction (default: 0.1)

onlyCheckTime

Only check computation time without fitting model (default: FALSE)

Value

A list object with class "XXXXX_NULL_Model" where XXXXX is the specified method. The returned object contains the following components:

N

Sample size in analysis

yVec

Phenotype data vector

beta

Coefficient parameters corresponding to covariates

subjData

Subject IDs included in analysis

sessionInfo

Version information about R, OS, and attached packages

Call

Function call with all specified arguments by their full names

time

Timestamp when analysis was completed

control

Control parameters used for null model fitting

tau

Estimated variance components (if using mixed models)

SparseGRM

Sparse genetic relationship matrix (if specified)

This object serves as input for downstream association testing with GRAB.Marker and GRAB.Region.

Examples

PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- read.table(PhenoFile, header = TRUE)
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")

# Fit a null model using POLMM with a dense GRM constructed from PLINK files.
Sys.setenv(RCPP_PARALLEL_NUM_THREADS = 2) # Limit threads for CRAN checks (optional for users).

obj.POLMM <- GRAB.NullModel(
  formula = factor(OrdinalPheno) ~ AGE + GENDER,
  data = PhenoData,
  subjData = IID,
  method = "POLMM",
  traitType = "ordinal",
  GenoFile = GenoFile,
  control = list(showInfo = FALSE, LOCO = FALSE, tolTau = 0.2, tolBeta = 0.1)
)

names(obj.POLMM)

# Fit a null model using POLMM with a sparse GRM pre-calculated by getSparseGRM()
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")

obj.POLMM <- GRAB.NullModel(
  formula = factor(OrdinalPheno) ~ AGE + GENDER,
  data = PhenoData,
  subjData = IID,
  method = "POLMM",
  traitType = "ordinal",
  GenoFile = GenoFile,
  SparseGRMFile = SparseGRMFile,
  control = list(showInfo = FALSE, LOCO = FALSE, tolTau = 0.2, tolBeta = 0.1)
)

# Save the null model object for downstream analysis
OutputFile <- file.path(tempdir(), "objPOLMMnull.RData")
save(obj.POLMM, file = OutputFile)

# For SPACox method, check ?GRAB.SPACox
# For SPAmix method, check ?GRAB.SPAmix
# For SPAGRM method, check ?GRAB.SPAGRM
# For WtCoxG method, check ?GRAB.WtCoxG

POLMM method in GRAB package

Description

POLMM method is to analyze ordinal categorical data for related samples in a large-scale biobank.

Usage

GRAB.POLMM()

Details

Please check ?GRAB.control for the generic list of control in GRAB.NullModel() and GRAB.Marker().

Additional list of control in GRAB.NullModel() function Additional list of control in GRAB.Marker() function Additional list of control in GRAB.Region() function

Value

No return value, called for side effects (prints information about the POLMM method to the console).

Examples

### First, read phenotype data and convert to a factor
PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- data.table::fread(PhenoFile, header = TRUE)
PhenoData$OrdinalPheno <- factor(PhenoData$OrdinalPheno, levels = c(0, 1, 2))

### Step 1: Fit a null model
# If SparseGRMFile is provided, the sparse GRM will be used in model fitting.
# If SparseGRMFile isn't provided, GRAB.NullModel() will calculate dense GRM from GenoFile.
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")

obj.POLMM <- GRAB.NullModel(
  formula = OrdinalPheno ~ AGE + GENDER,
  data = PhenoData,
  subjData = PhenoData$IID,
  method = "POLMM",
  traitType = "ordinal",
  GenoFile = GenoFile,
  SparseGRMFile = SparseGRMFile,
  control = list(
    showInfo = FALSE,
    LOCO = FALSE,
    tolTau = 0.2,
    tolBeta = 0.1
  )
)

### Step 2(a): Single-variant tests using POLMM
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputFile <- file.path(tempdir(), "simuMarkerOutput.txt")

GRAB.Marker(
  objNull = obj.POLMM,
  GenoFile = GenoFile,
  OutputFile = OutputFile
)

data.table::fread(OutputFile)

### Step 2(b): Set-based tests using POLMM-GENE
GenoFile <- system.file("extdata", "simuPLINK_RV.bed", package = "GRAB")
OutputFile <- file.path(tempdir(), "simuRegionOutput.txt")
GroupFile <- system.file("extdata", "simuPLINK_RV.group", package = "GRAB")
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")

GRAB.Region(
  objNull = obj.POLMM,
  GenoFile = GenoFile,
  GenoFileIndex = NULL,
  OutputFile = OutputFile,
  OutputFileIndex = NULL,
  GroupFile = GroupFile,
  SparseGRMFile = SparseGRMFile,
  MaxMAFVec = "0.01,0.005"
)

data.table::fread(OutputFile)

Read in genotype data

Description

GRAB package provides functions to read in genotype data. Currently, we support genotype formats of PLINK and BGEN. Other formats such as VCF will be added later.

Usage

GRAB.ReadGeno(
  GenoFile,
  GenoFileIndex = NULL,
  SampleIDs = NULL,
  control = NULL,
  sparse = FALSE
)

Arguments

Details

Details about GenoFile and GenoFileIndex

Currently, we support two formats of genotype input including PLINK and BGEN. Other formats such as VCF will be added later. Users do not need to specify the genotype format, GRAB package will check the extension of the file name for that purpose. If GenoFileIndex is not specified, GRAB package assumes the prefix is the same as GenoFile.

PLINK format

Check link for more details about this format

• GenoFile: "prefix.bed". The full file name (including the extension ".bed") of the PLINK binary bed file.

• GenoFileIndex: c("prefix.bim", "prefix.fam"). If not specified, GRAB package assumes that bim and fam files have the same prefix as the bed file.

BGEN format

Check link for more details about this format. Currently, only version 1.2 with 8 bits suppression is supported

• GenoFile: "prefix.bgen". The full file name (including the extension ".bgen") of the BGEN binary bgen file.

• GenoFileIndex: "prefix.bgen.bgi" or c("prefix.bgen.bgi", "prefix.sample"). If not specified, GRAB package assumes that bgi and sample files have the same prefix as the bgen file. If only one element is given for GenoFileIndex, then it should be a bgi file. Check link for more details about bgi file.

• If the bgen file does not include sample identifiers, then sample file is required, whose detailed description can ben seen in link. If you are not sure if sample identifiers are in BGEN file, please refer to checkIfSampleIDsExist.

VCF format

will be supported later. GenoFile: "prefix.vcf"; GenoFileIndex: "prefix.vcf.tbi"

Details about argument control

Argument control is used to include and exclude markers for function GRAB.ReadGeno. The function supports two include files of (IDsToIncludeFile, RangesToIncludeFile) and two exclude files of (IDsToExcludeFile, RangesToExcludeFile), but does not support both include and exclude files at the same time.

• IDsToIncludeFile: a file of marker IDs to include, one column (no header). Check system.file("extdata", "IDsToInclude.txt", package = "GRAB") for an example.

• IDsToExcludeFile: a file of marker IDs to exclude, one column (no header).

• RangesToIncludeFile: a file of ranges to include, three columns (no headers): chromosome, start position, end position. Check system.file("extdata", "RangesToInclude.txt", package = "GRAB") for an example.

• RangesToExcludeFile: a file of ranges to exclude, three columns (no headers): chromosome, start position, end position.

• AlleleOrder: a character, "ref-first" or "alt-first", to determine whether the REF/major allele should appear first or second. Default is "alt-first" for PLINK and "ref-first" for BGEN. If the ALT allele frequencies of most markers are > 0.5, you should consider resetting this option. NOTE, if you use plink2 to convert PLINK file to BGEN file, then 'ref-first' modifier is to reset the order.

• AllMarkers: a logical value (default: FALSE) to indicate if all markers are extracted. It might take too much memory to put genotype of all markers in R. This parameter is to remind users.

• ImputeMethod: a character, "none" (default), "bestguess", or "mean". By default, missing genotype is NA. Suppose alternative allele frequency is p, then missing genotype is imputed as 2p (ImputeMethod = "mean") or round(2p) (ImputeMethod = "bestguess").

Value

An R list including a genotype matrix and an information matrix.

• GenoMat: Genotype matrix, each row is for one sample and each column is for one marker.

• markerInfo: Information matrix including 5 columns of CHROM, POS, ID, REF, and ALT.

Examples

## Raw genotype data
RawFile <- system.file("extdata", "simuRAW.raw.gz", package = "GRAB")
GenoMat <- data.table::fread(RawFile)
GenoMat[1:10, 1:10]

## PLINK files
PLINKFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
# If include/exclude files are not specified, then control$AllMarker should be TRUE
GenoList <- GRAB.ReadGeno(PLINKFile, control = list(AllMarkers = TRUE))
GenoMat <- GenoList$GenoMat
markerInfo <- GenoList$markerInfo
head(GenoMat[, 1:6])
head(markerInfo)

## BGEN files (Note the different REF/ALT order for BGEN and PLINK formats)
BGENFile <- system.file("extdata", "simuBGEN.bgen", package = "GRAB")
GenoList <- GRAB.ReadGeno(BGENFile, control = list(AllMarkers = TRUE))
GenoMat <- GenoList$GenoMat
markerInfo <- GenoList$markerInfo
head(GenoMat[, 1:6])
head(markerInfo)

## The below is to demonstrate parameters in control
PLINKFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
IDsToIncludeFile <- system.file("extdata", "simuGENO.IDsToInclude", package = "GRAB")
RangesToIncludeFile <- system.file("extdata", "RangesToInclude.txt", package = "GRAB")
GenoList <- GRAB.ReadGeno(PLINKFile,
  control = list(
    IDsToIncludeFile = IDsToIncludeFile,
    RangesToIncludeFile = RangesToIncludeFile,
    AlleleOrder = "ref-first"
  )
)
GenoMat <- GenoList$GenoMat
head(GenoMat)
markerInfo <- GenoList$markerInfo
head(markerInfo)

## The below is for PLINK/BGEN files with missing data
PLINKFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
GenoList <- GRAB.ReadGeno(PLINKFile, control = list(AllMarkers = TRUE))
head(GenoList$GenoMat)

GenoList <- GRAB.ReadGeno(PLINKFile, control = list(AllMarkers = TRUE, ImputeMethod = "mean"))
head(GenoList$GenoMat)

BGENFile <- system.file("extdata", "simuBGEN.bgen", package = "GRAB")
GenoList <- GRAB.ReadGeno(BGENFile, control = list(AllMarkers = TRUE))
head(GenoList$GenoMat)

Conduct region-level genetic association testing

Description

Test for association between phenotype of interest and regions including multiple genetic marker (mostly low-frequency or rare variants).

Usage

GRAB.Region(
  objNull,
  GenoFile,
  GenoFileIndex = NULL,
  OutputFile,
  OutputFileIndex = NULL,
  GroupFile,
  SparseGRMFile = NULL,
  SampleFile = NULL,
  MaxMAFVec = "0.01,0.001,0.0005",
  annoVec = "lof,lof:missense,lof:missense:synonymous",
  chrom = "LOCO=F",
  control = NULL
)

Arguments

Details

GRAB package supports POLMM, SPACox, SPAGRM, SPAmix, and WtCoxG methods. Detailed information about the analysis methods is given in the Details section of GRAB.NullModel. Users do not need to specify them since functions GRAB.Marker and GRAB.Region will check the class(objNull).

The following details are about argument control

For PLINK files, the default control$AlleleOrder = "alt-first"; for BGEN files, the default control$AlleleOrder = "ref-first".

• AlleleOrder: please refer to the Details section of GRAB.ReadGeno.

The below is to customize the quality-control (QC) process.

• omp_num_threads: (To be added later) a numeric value (default: value from data.table::getDTthreads()) to specify the number of threads in OpenMP for parallel computation.

• ImputeMethod: a character, "mean", "bestguess" (default), or "drop" (to be added later). Please refer to the Details section of GRAB.ReadGeno.

• MissingRateCutoff: a numeric value (default=0.15). Markers with missing rate > this value will be excluded from analysis.

• MinMACCutoff: a numeric value (default=5). Markers with MAC < this value will be treated as Ultra-Rare Variants (URV) and collapsed as one value.

• nRegionsEachChunk: number of regions (default=1) in one chunk to output.

The below is for kernel-based approaches including SKAT and SKAT-O. For more details, please refer to the SKAT package.

• kernel: a type of kernel (default="linear.weighted").

• weights_beta: a numeric vector of parameters for the beta weights for the weighted kernels (default=c(1, 25)). If you want to use your own weights, please use the control$weights parameter. It will be ignored if control$weights parameter is not NULL.

• weights: a numeric vector of weights for the weighted kernels. If it is NULL (default), the beta weight with the control$weights.beta parameter is used.

• r.corr: the rho parameter for the compound symmetric correlation structure kernels. If you give a vector value, SKAT will conduct the optimal test. It will be ignored if method="optimal" or method="optimal.adj" (default=c(0, 0.1^2, 0.2^2, 0.3^2, 0.4^2, 0.5^2, 0.5, 1)).

The below is to customize the columns in the OutputMarkerFile. Columns of Marker, Info, AltFreq, AltCounts, MissingRate, Pvalue are included for all methods.

• outputColumns: For example, for POLMM method, users can set control$outputColumns = c("beta", "seBeta", "AltFreqInGroup"):

  • POLMM: Default: beta, seBeta; Optional: zScore, AltFreqInGroup, nSamplesInGroup, AltCountsInGroup

  • SPACox: Optional: zScore

Value

Region-based analysis results are saved into two files: OutputFile and OutputMarkerFile = paste0(OutputFile, ".markerInfo").

The file of OutputMarkerFile is the same as the results of GRAB.Marker. The file of OutputFile includes columns as below.

Region

Region IDs from RegionFile

Anno.Type

Annotation type from RegionFile

maxMAF

the maximal cutoff of the MAF to select low-frequency/rare variants into analysis.

nSamples

Number of samples in analysis.

nMarkers

Number of markers whose MAF < control$MaxMAFCutoff and MAC > control$MinMACCutoff. Markers with annotation value <= 0 will be excluded from analysis.

nMarkersURV

Number of Ultra-Rare Variants (URV) whose MAC < control$MinMACCutoff. Markers with annotation value <= 0 will be excluded from analysis.

pval.SKATO

p-values based on SKAT-O method

pval.SKAT

p-values based on SKAT method

pval.Burden

p-values based on Burden test

Examples

# Load a precomputed example object to perform step 2 without repeating step 1
objNullFile <- system.file("extdata", "objPOLMMnull.RData", package = "GRAB")
load(objNullFile)
class(obj.POLMM) # "POLMM_NULL_Model" is an object from POLMM method.

OutputDir <- tempdir()
OutputFile <- file.path(OutputDir, "simuRegionOutput.txt")
GenoFile <- system.file("extdata", "simuPLINK_RV.bed", package = "GRAB")
GroupFile <- system.file("extdata", "simuPLINK_RV.group", package = "GRAB")
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")

GRAB.Region(
  objNull = obj.POLMM,
  GenoFile = GenoFile,
  OutputFile = OutputFile,
  GroupFile = GroupFile,
  SparseGRMFile = SparseGRMFile,
  MaxMAFVec = "0.01,0.005"
)

data.table::fread(OutputFile)
data.table::fread(paste0(OutputFile, ".markerInfo"))
data.table::fread(paste0(OutputFile, ".otherMarkerInfo"))
data.table::fread(paste0(OutputFile, ".index"), sep = "\t", header = FALSE)

SAGELD method in GRAB package

Description

SAGELD method is Scalable and Accurate algorithm for Gene-Environment interaction analysis using Longitudinal Data for related samples in a large-scale biobank. SAGELD extended SPA_GRM to support gene-environment interaction analysis.

Usage

GRAB.SAGELD()

Details

Additional list of control in SAGELD.NullModel() function.

Additional list of control in GRAB.Marker() function.

Value

No return value, called for side effects (prints information about the SAGELD method to the console).

SPACox method in GRAB package

Description

SPACox method is an empirical approach to analyzing complex traits (including but not limited to time-to-event trait) for unrelated samples in a large-scale biobank.

Usage

GRAB.SPACox()

Details

Additional list of control in GRAB.NullModel() function.

Additional list of control in GRAB.Marker() function.

Value

No return value, called for side effects (prints information about the SPACox method to the console).

Examples

# Step 1: fit a null model
PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- data.table::fread(PhenoFile, header = TRUE)
obj.SPACox <- GRAB.NullModel(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER,
  data = PhenoData,
  subjData = IID,
  method = "SPACox",
  traitType = "time-to-event"
)

# Using model residuals performs exactly the same as the above. Note that
# confounding factors are still required in the right of the formula.
obj.coxph <- survival::coxph(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER,
  data = PhenoData,
  x = TRUE
)

obj.SPACox <- GRAB.NullModel(
  obj.coxph$residuals ~ AGE + GENDER,
  data = PhenoData,
  subjData = IID,
  method = "SPACox",
  traitType = "Residual"
)

# Step 2: conduct score test
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputDir <- tempdir()
OutputFile <- file.path(OutputDir, "Results_SPACox.txt")
GRAB.Marker(
  objNull = obj.SPACox,
  GenoFile = GenoFile,
  OutputFile = OutputFile,
  control = list(outputColumns = "zScore")
)
data.table::fread(OutputFile)

SPA_GRM method in GRAB package

Description

SPA_GRM method is an empirical approach to analyzing complex traits (including but not limited to longitudinal trait) for related samples in a large-scale biobank. SPA_GRM extend SPACox to support an related populations.

Usage

GRAB.SPAGRM()

Details

Additional list of control in SPAGRM.NullModel() function.

Additional list of control in GRAB.Marker() function.

Value

No return value, called for side effects (prints information about the SPAGRM method to the console).

Examples

# Step 2a: process model residuals
ResidMatFile <- system.file("extdata", "ResidMat.txt", package = "GRAB")
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")
PairwiseIBDFile <- system.file("extdata", "PairwiseIBD.txt", package = "GRAB")
obj.SPAGRM <- SPAGRM.NullModel(
  ResidMatFile = ResidMatFile,
  SparseGRMFile = SparseGRMFile,
  PairwiseIBDFile = PairwiseIBDFile,
  control = list(ControlOutlier = FALSE)
)

# Step 2b: perform score test
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputDir <- tempdir()
OutputFile <- file.path(OutputDir, "SPAGRMMarkers.txt")
GRAB.Marker(
  objNull = obj.SPAGRM,
  GenoFile = GenoFile,
  OutputFile = OutputFile
)
head(read.table(OutputFile, header = TRUE))

SPAmix method in GRAB package

Description

SPAmix method is an empirical approach to analyzing complex traits (including but not limited to time-to-event trait) for unrelated samples in a large-scale biobank. SPAmix extend SPACox to support an admixture population or multiple populations.

Usage

GRAB.SPAmix()

Details

For SPAmix, the confounding factors of SNP-derived PCs are required and should be specified in control.

Value

No return value, called for side effects (prints information about the SPAmix method to the console).

Examples

# Step 1: fit a null model
PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- data.table::fread(PhenoFile, header = TRUE)

# Users can directly specify a time-to-event trait to analyze
obj.SPAmix <- GRAB.NullModel(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER + PC1 + PC2,
  data = PhenoData,
  subjData = IID,
  method = "SPAmix",
  traitType = "time-to-event",
  control = list(PC_columns = "PC1,PC2")
)

# Using model residuals performs exactly the same as the above. Note that
# confounding factors are still required in the right of the formula.
obj.coxph <- survival::coxph(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER + PC1 + PC2,
  data = PhenoData
)

obj.SPAmix <- GRAB.NullModel(
  obj.coxph$residuals ~ AGE + GENDER + PC1 + PC2,
  data = PhenoData,
  subjData = IID,
  method = "SPAmix",
  traitType = "Residual",
  control = list(PC_columns = "PC1,PC2")
)

# SPAmix also supports multiple residuals as below
obj.coxph <- survival::coxph(
  survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER + PC1 + PC2, 
  data = PhenoData
)
obj.lm <- lm(QuantPheno ~ AGE + GENDER + PC1 + PC2, data = PhenoData)

obj.SPAmix <- GRAB.NullModel(
  obj.coxph$residuals + obj.lm$residuals ~ AGE + GENDER + PC1 + PC2,
  data = PhenoData,
  subjData = IID,
  method = "SPAmix",
  traitType = "Residual",
  control = list(PC_columns = "PC1,PC2")
)

# Step 2: conduct score test
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputDir <- tempdir()
OutputFile <- file.path(OutputDir, "Results_SPAmix.txt")
GRAB.Marker(
  objNull = obj.SPAmix,
  GenoFile = GenoFile,
  OutputFile = OutputFile,
  control = list(outputColumns = "zScore")
)
data.table::fread(OutputFile)

Simulate an R matrix of genotype data

Description

GRAB package provides functions to simulate genotype data. We support simulations based on unrelated subjects and related subjects.

Usage

GRAB.SimuGMat(
  nSub,
  nFam,
  FamMode,
  nSNP,
  MaxMAF = 0.5,
  MinMAF = 0.05,
  MAF = NULL
)

Arguments

Details

Currently, function GRAB.SimuGMat supports both unrelated and related subjects. Genotype data is simulated following Hardy-Weinberg Equilibrium with allele frequency ~ runif(MinMAF, MaxMAF).

If FamMode = "4-members"

Total number of subjects is nSub + 4 * nFam. Each family includes 4 members with the family structure as below: 1+2->3+4.

If FamMode = "10-members"

Total number of subjects is nSub + 10 * nFam. Each family includes 10 members with the family structure as below: 1+2->5+6, 3+5->7+8, 4+6->9+10.

If FamMode = "20-members"

Total number of subjects is nSub + 20 * nFam. Each family includes 20 members with the family structure as below: 1+2->9+10, 3+9->11+12, 4+10->13+14, 5+11->15+16, 6+12->17, 7+13->18, 8+14->19+20.

Value

an R list including genotype matrix and marker information

• GenoMat a numeric matrix of genotype: each row is for one subject and each column is for one SNP

• markerInfo a data frame with the following 2 columns: SNP ID and minor allele frequency

See Also

GRAB.makePlink can make PLINK files using the genotype matrix.

Examples

nSub <- 100
nFam <- 10
FamMode <- "10-members"
nSNP <- 10000
OutList <- GRAB.SimuGMat(nSub, nFam, FamMode, nSNP)
GenoMat <- OutList$GenoMat
markerInfo <- OutList$markerInfo
GenoMat[1:10, 1:10]
head(markerInfo)

## The following is to calculate GRM
MAF <- apply(GenoMat, 2, mean) / 2
GenoMatSD <- t((t(GenoMat) - 2 * MAF) / sqrt(2 * MAF * (1 - MAF)))
GRM <- GenoMatSD %*% t(GenoMatSD) / ncol(GenoMat)
GRM1 <- GRM[1:10, 1:10]
GRM2 <- GRM[100 + 1:10, 100 + 1:10]
GRM1
GRM2

GRAB: simulate genotype matrix based on family structure

Description

Simulate genotype matrix based on family structure using haplotype information from genotype files. This function is mainly to simulate genotype data for rare variants analysis. NOTE: if simulating related subjects, the genotype of two allele will be assigned to two haplotypes of one allele randomly.

Usage

GRAB.SimuGMatFromGenoFile(
  nFam,
  nSub,
  FamMode,
  GenoFile,
  GenoFileIndex = NULL,
  SampleIDs = NULL,
  control = NULL
)

Arguments

Details

Currently, function GRAB.SimuGMatFromGenoFile supports both unrelated and related subjects. Genotype data of founders is from GenoFile and GenoFileIndex.

If FamMode = "4-members"

Total number of subjects is nSub + 4 * nFam. Each family includes 4 members with the family structure as below: 1+2->3+4.

If FamMode = "10-members"

Total number of subjects is nSub + 10 * nFam. Each family includes 10 members with the family structure as below: 1+2->5+6, 3+5->7+8, 4+6->9+10.

If FamMode = "20-members"

Total number of subjects is nSub + 20 * nFam. Each family includes 20 members with the family structure as below: 1+2->9+10, 3+9->11+12, 4+10->13+14, 5+11->15+16, 6+12->17, 7+13->18, 8+14->19+20.

Value

a genotype matrix of genotype data

Examples

nFam <- 50
nSub <- 500
FamMode <- "10-members"

# PLINK data format. Currently, this function does not support BGEN data format.
PLINKFile <- system.file("extdata", "example_n1000_m236.bed", package = "GRAB")
IDsToIncludeFile <- system.file("extdata", "example_n1000_m236.IDsToInclude", package = "GRAB")

GenoList <- GRAB.SimuGMatFromGenoFile(nFam, nSub, FamMode, PLINKFile,
  control = list(IDsToIncludeFile = IDsToIncludeFile)
)

Simulate phenotype using linear predictor eta

Description

GRAB package can help simulate a wide variaty of phenotypes

Usage

GRAB.SimuPheno(
  eta,
  traitType = "binary",
  control = list(pCase = 0.1, sdError = 1, pEachGroup = c(1, 1, 1), eventRate = 0.1),
  seed = NULL
)

Arguments

Details

Check https://wenjianbi.github.io//grab.github.io/docs/simulation_phenotype.html for more details.

Value

a numeric vector of phenotype

GRAB: simulate random effect (i.e. bVec) based on family structure

Description

Simulate random effect (i.e. bVec) based on family structure

Usage

GRAB.SimubVec(nSub, nFam, FamMode, tau)

Arguments

Value

a data frame including two columns: ID and random effect following a multivariate normal distribution

Examples

nSub <- 10
nFam <- 1
FamMode <- "10-members"
tau <- 2
bVec <- GRAB.SimubVec(nSub, nFam, FamMode, tau)

WtCoxG method in GRAB package

Description

WtCoxG is an accurate, powerful, and computationally efficient Cox-based approach to perform genome-wide time-to-event data analyses in study cohorts with case ascertainment.

Usage

GRAB.WtCoxG()

Details

Additional arguments in GRAB.NullModel():

• RefAfFile: A character string specifying a reference allele frequency file, which is a csv file (with a header) and includes columns of CHROM, POS, ID, REF, ALT, AF_ref, and AN_ref.

• OutputFile: A character string specifying the output file name.

• SampleIDColumn: A character string specifying the column name in the input data that contains sample IDs.

• SurvTimeColumn: A character string specifying the column name in the input data that contains survival time information.

• IndicatorColumn: A character string specifying the column name in the input data that indicates case-control status (should be 0 for controls and 1 for cases).

Additional arguments in list control in GRAB.NullModel():

• RefPrevalence: A numeric value specifying the population-level disease prevalence used for weighting in the analysis.

• SNPnum: Minimum number of SNPs. Default is 1e4.

Additional arguments in list control in GRAB.Marker():

• cutoff: A numeric value specifying the batch effect p-value cutoff for method selection of an association test. Default is 0.1.

Value

No return value, called for side effects (prints information about the WtCoxG method to the console).

Examples

# Step0&1: fit a null model and estimate parameters according to batch effect p-values
PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData <- data.table::fread(PhenoFile, header = TRUE)
SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB")

GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
RefAfFile <- system.file("extdata", "simuRefAf.txt", package = "GRAB")
RefPrevalence <- 0.1 # population-level disease prevalence

OutputDir <- tempdir()
OutputStep1 <- file.path(OutputDir, "WtCoxG_step1_out.txt")
OutputStep2 <- file.path(OutputDir, "WtCoxG_step2_out.txt")

obj.WtCoxG <- GRAB.NullModel(
  formula = survival::Surv(SurvTime, SurvEvent) ~ AGE + GENDER,
  data = PhenoData,
  subjData = PhenoData$IID,
  method = "WtCoxG",
  traitType = "time-to-event",
  GenoFile = GenoFile,
  SparseGRMFile = SparseGRMFile,
  control = list(
    AlleleOrder = "ref-first", 
    AllMarkers = TRUE, 
    RefPrevalence = RefPrevalence,
    SNPnum = 1000  # the minimum number of SNPs that TestforBatchEffect() needs
  ), 
  RefAfFile = RefAfFile,
  OutputFile = OutputStep1,
  SampleIDColumn = "IID",
  SurvTimeColumn = "SurvTime",
  IndicatorColumn = "SurvEvent"
)

resultStep1 <- data.table::fread(OutputStep1)
resultStep1[, c("CHROM", "POS", "pvalue_bat")]

# Step2: conduct association testing
GRAB.Marker(
  objNull = obj.WtCoxG,
  GenoFile = GenoFile,
  OutputFile = OutputStep2,
  control = list(
    AlleleOrder = "ref-first", 
    AllMarkers = TRUE,
    cutoff = 0.1, 
    nMarkersEachChunk = 5000
  )
)

resultStep2 <- data.table::fread(OutputStep2)
resultStep2[, c("CHROM", "POS", "WtCoxG.noext", "WtCoxG.ext")]

Get allele frequency and missing rate information from genotype data

Description

This function shares input as in function GRAB.ReadGeno, please check ?GRAB.ReadGeno for more details.

Usage

GRAB.getGenoInfo(
  GenoFile,
  GenoFileIndex = NULL,
  SampleIDs = NULL,
  control = NULL
)

Arguments

Value

A data frame containing marker information with allele frequencies and missing rates. The data frame includes columns from marker information (CHROM, POS, ID, REF, ALT, etc.) plus additional columns:

altFreq

Alternative allele frequency (before genotype imputation)

missingRate

Missing rate for each marker

Make PLINK files using a numeric R matrix

Description

Make PLINK files using a numeric matrix GenoMat (0,1,2,-9), rownames(GenoMat) are subject IDs and colnames(GenoMat) are marker IDs

Usage

GRAB.makePlink(
  GenoMat,
  OutputPrefix,
  A1 = "G",
  A2 = "A",
  CHR = NULL,
  BP = NULL,
  Pheno = NULL,
  Sex = NULL
)

Arguments

Details

Check link for detailed information of PLINK 2.00 alpha. Check link for detailed information of bgenix tool.

Convert PLINK text files to binary files

Run plink --file simuPLINK --make-bed --out simuPLINK to convert PLINK text files (MAP and PED) to binary files (BED, BIM, and FAM).

Convert PLINK binary files to raw files

Run plink --bfile simuPLINK --recode A --out simuRAW to convert PLINK binary files (BED, BIM, and FAM) to raw files (raw).

Convert PLINK binary files to bgen files

RUN plink2 --bfile simuPLINK --export bgen-1.2 bits=8 ref-first --out simuBGEN to convert PLINK binary files (BED, BIM, and FAM) to BGEN binary files (BGEN).

Make bgi file using bgenix tool

RUN bgenix -g simuBGEN.bgen --index

Value

PLINK text files (PED and MAP) are stored in 'OutputPrefix'. Suppose A1 is "G" and A2 is "A", then genotype of 0,1,2,-9 will be coded as "GG", "AG", "AA", "00". If PLINK binary files (BED, BIM, and FAM) are required, please download PLINK software and use option of "–make-bed". Please check Details section for the downstream process.

Examples

### Step 1: simulate a numeric genotype matrix
n <- 1000
m <- 20
MAF <- 0.3
set.seed(123)
GenoMat <- matrix(rbinom(n * m, 2, MAF), n, m)
rownames(GenoMat) <- paste0("Subj-", 1:n)
colnames(GenoMat) <- paste0("SNP-", 1:m)
OutputDir <- tempdir()
outputPrefix <- file.path(OutputDir, "simuPLINK")

### Step 2(a): make PLINK files without missing genotype
GRAB.makePlink(GenoMat, outputPrefix)

### Step 2(b): make PLINK files with genotype missing rate of 0.1
indexMissing <- sample(n * m, 0.1 * n * m)
GenoMat[indexMissing] <- -9
GRAB.makePlink(GenoMat, outputPrefix)

## The following are in shell environment
# plink --file simuPLINK --make-bed --out simuPLINK
# plink --bfile simuPLINK --recode A --out simuRAW
# plink2 --bfile simuPLINK --export bgen-1.2 bits=8 ref-first --out simuBGEN
# UK Biobank use 'ref-first'
# bgenix -g simuBGEN.bgen --index

Fit a SAGELD Null Model

Description

Fit a SAGELD Null Model

Usage

SAGELD.NullModel(
  NullModel,
  UsedMethod = "SAGELD",
  PlinkFile,
  SparseGRMFile,
  PairwiseIBDFile,
  PvalueCutoff = 0.001,
  control = list()
)

Arguments

Value

A SAGELD null model object

Fit a SPAGRM Null Model

Description

Fit a SPAGRM Null Model

Usage

SPAGRM.NullModel(
  ResidMatFile,
  SparseGRMFile,
  PairwiseIBDFile,
  control = list(MaxQuantile = 0.75, MinQuantile = 0.25, OutlierRatio = 1.5,
    ControlOutlier = TRUE, MaxNuminFam = 5, MAF_interval = c(1e-04, 5e-04, 0.001, 0.005,
    0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5))
)

Arguments

Value

A SPAGRM null model object

Quality control to check batch effect between study cohort and reference population.

Description

This function performs quality control to test for the batch effect between a study cohort and a reference population. And fit a weighted null model.

Usage

TestforBatchEffect(
  objNull,
  data,
  GenoFile = NULL,
  GenoFileIndex = NULL,
  Geno.mtx = NULL,
  SparseGRMFile = NULL,
  RefAfFile,
  OutputFile,
  IndicatorColumn,
  SurvTimeColumn,
  SampleIDColumn
)

Arguments

Value

A dataframe of marker info and reference MAF.

Check if sample identifiers are stored in a BGEN file

Description

Check if sample identifiers are stored in a BGEN file, only support BGEN v1.2. Check link for more details.

Usage

checkIfSampleIDsExist(bgenFile)

Arguments

Value

A logical value indicating whether sample identifiers are stored in the BGEN file. Returns TRUE if sample IDs are present, FALSE otherwise.

Examples

BGENFile <- system.file("extdata", "simuBGEN.bgen", package = "GRAB")
checkIfSampleIDsExist(BGENFile)

Suppose that a dense GRM is Phi and input is bVec, return Phi * bVec (only for developers)

Description

Suppose that a dense GRM is Phi and input is bVec, return Phi * bVec (only for developers), users can simply ignore this function

Usage

getDenseGRM(bVec)

Arguments

Value

a numeric vector of Phi * bVec

Examples

# set up the dense GRM in C++
GenoFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
famData <- read.table(gsub("bed", "fam", GenoFile))
subjData <- famData$V2
genoList <- setDenseGRM(GenoFile, subjData = subjData)

set.seed(1)
bVec <- rnorm(1000)
KinbVec <- getDenseGRM(bVec)

# The following is based on the definition of GRM to validate the DenseGRM object
PlinkFile <- system.file("extdata", "simuPLINK.bed", package = "GRAB")
IDsToIncludeFile <- system.file("extdata", "simuGENO.IDsToInclude", package = "GRAB")
GenoList <- GRAB.ReadGeno(PlinkFile, control = list(IDsToExcludeFile = IDsToIncludeFile))
GenoMat <- GenoList$GenoMat
markerInfo <- GenoList$markerInfo
pos <- which(markerInfo$CHROM != 1)
GenoMat <- GenoMat[, pos]
MAF <- apply(GenoMat, 2, mean) / 2
stdGenoMat <- (t(GenoMat) - 2 * MAF) / sqrt(2 * MAF * (1 - MAF)) / sqrt(ncol(GenoMat))
KinMat <- t(stdGenoMat) %*% stdGenoMat
KinbVec1 <- KinMat %*% bVec
# plot(KinbVec, KinbVec1)
head(cbind(KinbVec, KinbVec1))

Get sample identifiers from BGEN file

Description

Extract sample identifiers from BGEN file (only support BGEN v1.2, check link)

Usage

getSampleIDsFromBGEN(bgenFile)

Arguments

Value

A character vector of sample identifiers extracted from the BGEN file.

Examples

BGENFile <- system.file("extdata", "simuBGEN.bgen", package = "GRAB")
getSampleIDsFromBGEN(BGENFile)

Make a SparseGRMFile for GRAB.NullModel.

Description

If the sample size in analysis is greater than 100,000, we recommend using sparse GRM (instead of dense GRM) to adjust for sample relatedness. This function is to use GCTA (link) to make a SparseGRMFile to be passed to function GRAB.NullModel. This function can only support Linux and PLINK files as required by GCTA software. To make a SparseGRMFile, two steps are needed. Please check Details section for more details.

Usage

getSparseGRM(
  PlinkFile,
  nPartsGRM,
  SparseGRMFile,
  tempDir = NULL,
  relatednessCutoff = 0.05,
  minMafGRM = 0.01,
  maxMissingGRM = 0.1,
  rm.tempFiles = FALSE
)

Arguments

Details

• Step 1: Run getTempFilesFullGRM to save temporary files to tempDir.

• Step 2: Run getSparseGRM to combine the temporary files to make a SparseGRMFile to be passed to function GRAB.NullModel.

Users can customize parameters including (minMafGRM, maxMissingGRM, nPartsGRM), but functions getTempFilesFullGRM and getSparseGRM should use the same ones. Otherwise, package GRAB cannot accurately identify temporary files.

Value

A character string containing a message with the path to the output file where the sparse Genetic Relationship Matrix (SparseGRM) has been stored.

The following shows a typical workflow for creating a sparse GRM:

# Input data (We recommend setting nPartsGRM=250 for UKBB with N=500K):

GenoFile = system.file("extdata", "simuPLINK.bed", package = "GRAB")

PlinkFile = tools::file_path_sans_ext(GenoFile)

nPartsGRM = 2

Step 1: We strongly recommend parallel computing in high performance clusters (HPC).

# For Linux, get the file path of gcta64 by which command:

gcta64File <- system("which gcta64", intern = TRUE)

# For Windows, set the file path directly:

gcta64File <- "C:\\path\\to\\gcta64.exe"

# The temp outputs (may be large) will be in tempdir() by default:

for(partParallel in 1:nPartsGRM) getTempFilesFullGRM(PlinkFile, nPartsGRM, partParallel, gcta64File)

Step 2: Combine files in Step 1 to make a SparseGRMFile

tempDir = tempdir()

SparseGRMFile = file.path(tempDir, "SparseGRM.txt")

getSparseGRM(PlinkFile, nPartsGRM, SparseGRMFile)

Make temporary files to be passed to function getSparseGRM.

Description

Make temporary files to be passed to function getSparseGRM. We strongly suggest using parallel computing for different partParallel.

Usage

getTempFilesFullGRM(
  PlinkFile,
  nPartsGRM,
  partParallel,
  gcta64File,
  tempDir = NULL,
  subjData = NULL,
  minMafGRM = 0.01,
  maxMissingGRM = 0.1,
  threadNum = 8
)

Arguments

Details

• Step 1: Run getTempFilesFullGRM to get temporary files.

• Step 2: Run getSparseGRM to combine the temporary files to make a SparseGRMFile to be passed to GRAB.NullModel.

Value

A character string message indicating the completion status and location of the temporary files.

Examples

## Please check help(getSparseGRM) for an example.

Get version information from BGEN file

Description

Get version information from BGEN file (check link)

Usage

getVersionFromBGEN(bgenFile)

Arguments

Value

A character string indicating the BGEN file version. Possible values include:

v1.1

BGEN format version 1.1

v1.2

BGEN format version 1.2

Version Layout = 0, which is not supported...

Error message for unsupported version 0

Version Layout > 2, which is reserved for future use...

Warning message for future versions

Examples

BGENFile <- system.file("extdata", "simuBGEN.bgen", package = "GRAB")
getVersionFromBGEN(BGENFile)

handle a formula (used in GRAB.NullModel function)

Description

handle a formula (used in GRAB.NullModel function), this function can help users better understand the input of GRAB.NullModel() function

Usage

handleFormula(formula, data, subset, subjData)

Arguments

Value

an R list with elements of 'response', 'designMat', and 'subjData'.

Examples

n <- 20
subjData <- paste0("ID-", 1:n)
pheno <- rbinom(n, 1, 0.5)
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rbinom(n, 2, 0.5)
objFormula <- handleFormula(pheno ~ x1 + x2 * x3, subset = x2 > 0, subjData = subjData)
objFormula

A lower function to make groups based on phenotype

Description

In functions GRAB.Marker and GRAB.Region, users can get detailed information for each markers in different groups.

Usage

makeGroup(yVec)

Arguments

Details

If yVec is categorical with groups <= 10, then Group is the same as yVec. Otherwise, Group is calcualted based on the rank of yVec.

Value

a numeric vector (Group, starting from 0) for group information.

Set up a dense GRM (only for developers)

Description

Set up a dense GRM (only for developers), other users can ignore this function

Usage

setDenseGRM(GenoFile, GenoFileIndex = NULL, subjData = NULL)

Arguments

Value

no result is returned

Examples

# Check ?getDenseGRM() for an example.