snpStats (version 1.22.0)

tdt.snp: 1-df and 2-df tests for genetic associations with SNPs (or imputed SNPs) in family data

Description

Given large-scale SNP data for families comprising both parents and one or more affected offspring, this function computes 1 df tests (the TDT test) and a 2 df test based on observed and expected transmissions of genotypes. Tests based on imputation rules can also be carried out.

Usage

tdt.snp(ped, id, father, mother, affected, data = sys.parent(), snp.data,  rules = NULL, snp.subset, check.inheritance = TRUE, robust = FALSE, uncertain = FALSE, score = FALSE)

Arguments

ped

Pedigree identifiers

id

Subject identifiers

father

Identifiers for subjects' fathers

mother

Identifiers for subjects' mothers

affected

Disease status (TRUE if affected, FALSE otherwise)

data

A data frame in which to evaluate the previous five arguments

snp.data

An object of class "SnpMatrix" containing the SNP genotypes to be tested

rules

An object of class "ImputationRules". If supplied, the rules coded in this object are used, together with snp.data, to calculate tests for imputed SNPs

snp.subset

A vector describing the subset of SNPs to be considered. Default action is to test all SNPs in snp.data or, in imputation mode, as specified by rules

check.inheritance

If TRUE, each affected offspring/parent trio is tested for Mendelian inheritance and excluded if the test fails. If FALSE, misinheriting trios are used but the "robust" variance option is forced

robust

If TRUE, forces the robust (Huber-White) variance option (with ped determining independent "clusters")

uncertain

If TRUE, uncertain genotypes are handed by replacing score contributions by their posterior expectations. Otherwise these are treated as missing. Setting this option authomatically invokes use of robust variance estimates

score

If TRUE, the output object will contain, for each SNP, the score vector and its variance-covariance matrix

Value

An object of class "SingleSnpTests". If score=TRUE, the output object will be of the extended class "SingleSnpTestsScore" containing additional slots holding the score statistics and their variances (and covariances). This allows meta-analysis using the pool function.

Details

Formally, the test statistics are score tests for the "conditioning on parental genotype" (CPG) likelihood. Parametrization of associations is the same as for the population-based tests calculated by single.snp.tests so that results from family-based and population-based studies can be combined using pool.

When the function is used to calculate tests for imputed SNPs, the test is still an approximate score test. The current version does not use the family relationships in the imputation. With this option, the robust variance estimate is forced. The first five arguments are usually derived from a "pedfile". If a data frame is supplied for the data argument, the first five arguments will be evaluated in this frame. Otherwise they will be evaluated in the calling environment. If the arguments are missing, they will be assumed to be in their usual positions in the pedfile data frame i.e. in columns one to four for the identifiers and column six for disease status (with affected coded 2). If the pedfile data are obtained from a dataframe, the row names of the data and snp.data files will be used to align the pedfile and SNP data. Otherwise, these vectors will be assumed to be in the same order as the rows of snp.data.

The snp.subset argument can be a logical, integer, or character vector.

If imputed rather than observed SNPs are tested, or if check.inheritance is set to FALSE, the "robust" variance estimate is used regardless of the value supplied for the robust argument.

References

Clayton (2008) Testing for association on the X chromosome Biostatistics, 9:593-600.)

See Also

single.snp.tests, impute.snps, pool, ImputationRules-class, SingleSnpTests-class, SingleSnpTestsScore-class

Examples

data(families)
tdt.snp(data=pedData, snp.data=genotypes)