snp.rhs.tests: Score tests with SNP genotypes as independent variable

In chopsticks: The 'snp.matrix' and 'X.snp.matrix' Classes

Description

This function fits a generalized linear model with phenotype as dependent variable and, optionally, one or more potential confounders of a phenotype-genotype association as independent variable. A series of SNPs (or small groups of SNPs) are then tested for additional association with phenotype. In order to protect against misspecification of the variance function, "robust" tests may be selected.

Usage

snp.rhs.tests(formula, family = "binomial", link, weights, subset,
              data = parent.frame(), snp.data, tests=NULL, robust = FALSE,
              control = glm.test.control(maxit=20, epsilon=1e-4, R2Max=0.98),
              allow.missing = 0.01)

Arguments

formula	The base model formula, with phenotype as dependent variable
family	A string defining the generalized linear model family. This currently should (partially) match one of "binomial", "Poisson", "Gaussian" or "gamma" (case-insensitive)
link	A string defining the link function for the GLM. This currently should (partially) match one of "logit", "log", "identity" or "inverse". The default action is to use the "canonical" link for the family selected
data	The dataframe in which the base model is to be fitted
snp.data	An object of class "snp.matrix" or "X.snp.matrix" containing the SNP data
tests	Either a vector of column names or numbers for the SNPs to be tested, or a list of short vectors defining groups of SNPs to be tested (again by name or number). The default action is to carry out all single SNP tests, but single.snp.tests will often achieve the same result much faster
weights	"Prior" weights in the generalized linear model
subset	Array defining the subset of rows of data to use
robust	If TRUE, robust tests will be carried out
control	An object giving parameters for the IRLS algorithm fitting of the base model and for the acceptable aliasing amongst new terms to be tested. See\ codeglm.test.control
allow.missing	The maximum proportion of SNP genotype that can be missing before it becomes necessary to refit the base model

Details

The tests used are asymptotic chi-squared tests based on the vector of first and second derivatives of the log-likelihood with respect to the parameters of the additional model. The "robust" form is a generalized score test in the sense discussed by Boos(1992). The "base" model is first fitted, and a score test is performed for addition of one or more SNP genotypes to the model. Homozygous SNP genotypes are coded 0 or 2 and heterozygous genotypes are coded 1. For SNPs on the X chromosome, males are coded as homozygous females. For X SNPs, it will often be appropriate to include sex of subject in the base model (this is not done automatically).

If a data argument is supplied, the snp.data and data objects are aligned by rowname. Otherwise all variables in the model formulae are assumed to be stored in the same order as the columns of the snp.data object.

Value

A data frame containing, for each SNP,

Chi.squared	The value of the chi-squared test statistic
Df	The corresponding degrees of freedom
Df.residual	The residual degrees of freedom for the base model; i.e. the number of observations minus the number of parameters fitted

For the binomial family model, the base model can, in some circumstances, lead to perfect prediction of some observations (i.e. fitted probabilities of 0 or 1). These observations are ignored in subsequent calculations; in particular they are not counted in the residual degrees of freedom. Similarly for Poisson means fitted exactly to zero.

Note

A factor (or several factors) may be included as arguments to the function strata(...) in the formula. This fits all interactions of the factors so included, but leads to faster computation than fitting these in the normal way. Additionally, a cluster(...) call may be included in the base model formula. This identifies clusters of potentially correlated observations (e.g. for members of the same family); in this case, an appropriate robust estimate of the variance of the score test is used.

Author(s)

David Clayton [e-mail]

References

Boos, Dennis D. (1992) On generalized score tests. The American Statistician, 46:327-333.

See Also

single.snp.tests, snp.lhs.tests, snp.matrix-class, X.snp.matrix-class

Examples

data(testdata)
library(survival) # strata
slt3 <- snp.rhs.tests(cc~strata(region), family="binomial",
              data=subject.data, snp.data = Autosomes, tests=1:10)
print(slt3)

chopsticks documentation built on Nov. 8, 2020, 7:51 p.m.