SNP Genotype Association Testing with Mixed Models

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Description

assocTestMM performs SNP genotype association tests using the null model fit with fitNullMM.

Usage

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assocTestMM(genoData, nullMMobj, test = "Wald", snp.include = NULL,
            chromosome = NULL, impute.geno = TRUE, snp.block.size = 5000,
            ivars = NULL, ivar.return.betaCov = FALSE, verbose = TRUE)

Arguments

genoData

An object of class GenotypeData from the package GWASTools containing the genotype data for SNPs and samples to be used for the analysis. This object can easily be created from a matrix of SNP genotype data, PLINK files, or GDS files. Alternatively, this could be an object of class SeqVarData from the package SeqVarTools containing the genotype data for the sequencing variants and samples to be used for the analysis.

nullMMobj

A null model object returned by fitNullMM.

test

A character string specifying the type of test to be performed. The possibilities are "Wald" (default) or "Score"; only "Score" can be used when the family of the null model fit with fitNullMM is not gaussian.

snp.include

A vector of SNP IDs to include in the analysis. If NULL, see chromosome for further details.

chromosome

A vector of integers specifying which chromosomes to analyze. This parameter is only considred when snp.include is NULL; if chromosome is also NULL, then all SNPs are included.

impute.geno

A logical indicator of whether sporadic missing genotype values should be mean imputed. The default is TRUE. See 'Details' for further information.

snp.block.size

The number of SNPs to read-in/analyze at once. The default value is 5000. See 'Details' for further information regarding how this parameter works when impute.geno is FALSE.

ivars

A vector of character strings specifying the names of the variables for which a genotype interaction term should be included. If NULL (default) no genotype interactions are included. See 'Details' for further information.

ivar.return.betaCov

Logical indicator of whether the estimated covariance matrix of the effect size estimates (betas) for the genotype and genotype interaction terms should be returned; the default is FALSE.

verbose

Logical indicator of whether updates from the function should be printed to the console; the default is TRUE.

Details

When impute.geno is TRUE, sporadic missing genotype values are mean imputed using the minor allele frequency (MAF) calculated on all other samples at that SNP. When impute.geno is FALSE, samples with missing values for all of the SNP genotypes in the current SNP block are removed from the analysis for the block; this may significantly slow down computation time because many pre-computed matrices need to be re-computed each time the sample set changes. Also note: when impute.geno is FALSE, sporadic missingness for a sample inside of a SNP block will lead to an error.

The input ivars can be used to perform GxE tests. Multiple interaction variables may be specified, but all interaction variables specified must have been included as covariates in fitting the null model with fitNullMM. When performing GxE analyses, assocTestMM will report two tests: (1) the joint test of all genotype interaction terms in the model (this is the test for any genotype interaction effect), and (2) the joint test of the genotype term along with all of the genotype interaction terms (this is the test for any genetic effect). Individual genotype interaction terms can be tested by creating Wald test statistics from the reported effect size estimates and their standard errors (Note: when ivars contains a single continuous or binary covariate, this test is the same as the test for any genotype interaction effect mentioned above). In order to test more complex hypotheses regarding subsets of multiple genotype interaction terms, ivar.return.betaCov can be used to retrieve the estimated covariance matrix of the effect size estimates.

Value

A data.frame where each row refers to a different SNP with the columns:

snpID

The SNP ID

chr

The numeric chromosome value

n

The number of samples used to analyze the SNP

MAF

The estimated minor allele frequency

minor.allele

Either "A" or "B" indicating which allele is the minor allele

If test is "Score":

Score

The value of the score function

Var

The variance of the score function

Score.Stat

The score chi-squared test statistic

Score.pval

The score p-value

If test is "Wald" and ivars is NULL:

Est

The effect size estimate for each additional copy of the "A" allele

SE

The estimated standard error of the effect size estimate

Wald.Stat

The Wald chi-squared test statistic

Wald.pval

The Wald p-value

If test is "Wald" and ivars is not NULL:

Est.G

The effect size estimate for the genotype term

Est.G:ivar

The effect size estimate for the genotype*ivar interaction term. There will be as many of these terms as there are interaction variables, and "ivar" will be replaced with the variable name.

SE.G

The estimated standard error of the genotype term effect size estimate

SE.G:ivar

The estimated standard error of the genotype*ivar effect size estimate. There will be as many of these terms as there are interaction variables, and "ivar" will be replaced with the variable name.

GxE.Stat

The Wald chi-squared test statistic for the test of all genotype interaction terms. When there is only one genotype interaction term, this is the test statistic for that term.

GxE.pval

The Wald p-value for the test of all genotype interaction terms; i.e. the test of any genotype interaction effect

Joint.Stat

The Wald chi-squared test statistic for the joint test of the genotype term and all of the genotype interaction terms

Joint.pval

The Wald p-value for the joint test of the genotype term and all of the genotype interaction terms; i.e. the test of any genotype effect

When ivars is not NULL, if ivar.return.betaCov is TRUE, then the output is a list with two elements. The first, "results", is the data.frame described above. The second, "betaCov", is a list with length equal to the number of rows of "results", where each element of the list is the covariance matrix of the effect size estimates (betas) for the genotype and genotype interaction terms.

If genoData is a SeqVarData object, the effect size estimate is for each copy of the alternate allele.

Note

The GenotypeData function in the GWASTools package should be used to create the input genoData. Input to the GenotypeData function can easily be created from an R matrix or GDS file. PLINK .bed, .bim, and .fam files can easily be converted to a GDS file with the function snpgdsBED2GDS in the SNPRelate package. Alternatively, the SeqVarData function in the SeqVarTools package can be used to create the input genodata when working with sequencing data.

Author(s)

Matthew P. Conomos

See Also

fitNullMM for fitting the null mixed model needed as input to assocTestMM. qqPlot for a function to make QQ plots and manhattanPlot for a function to make Manhattan plots of p-values. GWASTools for a description of the package containing the following functions: GenotypeData for a description of creating a GenotypeData class object for storing sample and SNP genotype data, MatrixGenotypeReader for a description of reading in genotype data stored as a matrix, and GdsGenotypeReader for a description of reading in genotype data stored as a GDS file. Also see snpgdsBED2GDS in the SNPRelate package for a description of converting binary PLINK files to GDS.

Examples

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library(GWASTools)

# file path to GDS file
gdsfile <- system.file("extdata", "HapMap_ASW_MXL_geno.gds", package="GENESIS")
# read in GDS data
HapMap_geno <- GdsGenotypeReader(filename = gdsfile)
# create a GenotypeData class object
HapMap_genoData <- GenotypeData(HapMap_geno)
# load saved matrix of KING-robust estimates
data("HapMap_ASW_MXL_KINGmat")

# run PC-AiR
mypcair <- pcair(genoData = HapMap_genoData, kinMat = HapMap_ASW_MXL_KINGmat, 
                divMat = HapMap_ASW_MXL_KINGmat)
                
# run PC-Relate
mypcrel <- pcrelate(genoData = HapMap_genoData, pcMat = mypcair$vectors[,1],
        		training.set = mypcair$unrels)

# generate a phenotype
set.seed(4)
pheno <- 0.2*mypcair$vectors[,1] + rnorm(mypcair$nsamp, mean = 0, sd = 1)

# make ScanAnnotationDataFrame
scanAnnot <- ScanAnnotationDataFrame(data.frame(scanID = mypcrel$sample.id, 
              pc1 = mypcair$vectors[,1], pheno = pheno))

# make covMatList
covMatList <- list("Kin" = pcrelateMakeGRM(mypcrel))

# fit the null mixed model
nullmod <- fitNullMM(scanData = scanAnnot, outcome = "pheno", covars = "pc1", covMatList = covMatList)

# run the association test
myassoc <- assocTestMM(genoData = HapMap_genoData, nullMMobj = nullmod)
close(HapMap_genoData)

# make a QQ plot
qqPlot(myassoc$Wald.pval)