thgenetics (version 0.4-2)

rareGeneTest: Rare Variant Tests

Description

Runs tests for rare variants by running the "step-up" approach to choose the best set of variants (possibly signing them), and correcting by permutation. rareGeneTest tests a single gene, and also allows for other aggregation models described in Hoffmann et al.. rarePathwayTest provides an extension of this method, that does a step-up approach for inclusions of genes (for computional time). For larger genes, it may also be advantageous to run rarePathwayTest which will automatically chop large genes into subpieces and run step-up on those pieces.

Usage

rareGeneTest(genotype, phenotype,
  use_sign=TRUE, use_weight=TRUE, nperm=1000,
  binary=all(phenotype==0 | phenotype==1, na.rm=TRUE),
  strategy="step", thresh=1)
rarePathwayTest(genotype, genotype_gene, phenotype,
  use_sign=TRUE, use_weight=TRUE, nperm=1000,
  binary=all(phenotype==0 | phenotype==1, na.rm=TRUE),
  CUT=15)

Arguments

genotype

Matrix of individuals along the rows and genotypes along the columns. Each entry in the matrix should be coded NA for missing; and 0, 1, or 2 for the number of alleles a person has (must be bi-allelic).

phenotype

Vector of same length as the number of rows of the matrix. Either continuous, or dichotomous (case=1, control=0.

use_sign

Whether to use a sign in the model for w, or just to weight all variants the same. Uses the sign of the estimated covariance between each marker and the trait to determine the sign of the marker.

use_weight

Whether to use a weight in the model for w (inverse variance of allele frequency in everyone (continuous) or just the controls (dichotomous)).

nperm

Number of permutations to run.

binary

Is the phenotype dichotomous, or continuous? (TRUE/FALSE)

strategy

`step' uses the step-up routine (default, recommended), `afreq' tests all allele frequencies, and `thresh' uses a fixed threshold.

thresh

Allele frequency for inclusion, used only when strategy=`thresh'.

genotype_gene

For rarePathwayTest, this is a vector of length ncol(genotype). This can be strings to indicate what gene each variant is in; e.g. c("brca1", "brca1", "brca1", "brca2", "brca2") would indicate that the first three variants were in the first gene, and the last two variants are in the second gene. It can also be numeric.

CUT

For rarePathwayTest, genes bigger than CUT will be chopped into subpieces of size smaller than CUT (for computational speed).

Details

The methods here are as described in the Hoffmann et al. (PLoS ONE, to appear) paper. These methods are based on the idea of trying multiple models for rare variants, since prior information is generally not very accurate. The p-value is corrected for multiple comparisons by permutation.

References

Hoffmann, TJ, Marini, NJ, and Witte, JS. Comprehensive approach to analyzing rare variants. PLoS ONE, 5(11): e13584. https://doi.org/10.1371/journal/pone.0013584.

Examples

# NOT RUN {
## This inefficient (for clarity) code will simulate
##  a dataset in the correct format, and then run
##  the different approaches described here.

## Constants for data generation
SEED <- 2
NCASE <- NCONT <- 500
NGENE <- 6
FUNCTIONAL <- 1:(NGENE/2)  ## Half are functional
BETA0 <- -4
BETAG <- log(2)

set.seed(SEED) ## Reproducible results

nonfunctional <- setdiff(1:NGENE, FUNCTIONAL)
afreq <- runif(NGENE, 0.001, 0.01)

expit <- function(x)
  return(exp(x) / (1 + exp(x)))

gcase <- matrix(0, nrow=NCASE, ncol=NGENE)
for(indiv in 1:NCASE){
  affected <- FALSE
  while(!affected){ ## while not affected
    gcase[indiv, ] <- rbinom(NGENE, 2, afreq)  ## draw up genotype
    affected <- (expit(BETA0 + 
      BETAG*sum(gcase[indiv, FUNCTIONAL])) > runif(1))
  }
}
cat("\n")

gcont <- matrix(0, nrow=NCONT, ncol=NGENE)
for(indiv in 1:NCONT){
  unaffected <- FALSE
  while(!unaffected){ ## while not unaffected
    gcont[indiv, ] <- rbinom(NGENE, 2, afreq) ## draw up genotype
    unaffected <- (1-expit(BETA0 +
      BETAG*sum(gcont[indiv, FUNCTIONAL])) > runif(1))
  }
}
cat("\n")

cat("# Rare functional variants cases =", 
  sum(gcase[,FUNCTIONAL]), "\n")
cat("# Rare functional variants controls =",
  sum(gcont[,FUNCTIONAL]), "\n")
cat("# Rare non-functional variants cases =",
  sum(gcase[,nonfunctional]), "\n")
cat("# Rare non-functional variants controls =",
  sum(gcont[,nonfunctional]), "\n")

case <- c(rep(1,NCASE), rep(0,NCONT))
genotype <- rbind(gcase, gcont)

cat("P-value of the test:\n")
rareGeneTest(genotype, case)
# }