famBT: Family Burden Test
In FREGAT: Family REGional Association Tests

Description Usage Arguments Details Value References Examples

Burden test in related or population samples

famBT(formula, phenodata, genodata, kin = NULL, nullmod,
regions = NULL, sliding.window = c(20, 10), mode = "add",
ncores = 1, return.time = FALSE, beta.par = c(1, 25),
weights = NULL, flip.genotypes = FALSE, impute.method = 'mean',
write.file = FALSE, ...)

`formula`	referring to the column(s) in `phenodata` to be analyzed as outcome and, if needed, covariates.
`phenodata`	a data frame containing columns mentioned in `formula`: trait to analyze and, if needed, covariates. Individuals not measured for trait and/or covariates will be omitted.
`genodata`	an object with genotypes to analyze. Several formats are allowed: - a data frame or matrix (with individuals in the rows and genetic variants in the columns) containing genotypes coded as AA = 0, Aa = 1 and aa = 2, where a is a minor allele. - for PLINK binary data format, a character string indicating a .bed file name (.bim and .fam files should have the same prefix). This will make use of `read.plink()` function. - for VCF format, a character string indicating a vcf.gz file name. This will require `seqminer` R-package to be installed. Its `readVCFToMatrixByGene()` function will be used to read VCF file gene-wise. The function also requires a geneFile, a text file listing all genes in refFlat format (see Examples below). VCF file should be bgzipped and indexed by Tabix. - an object of `gwaa.data` or `snp.data` class (this will require `GenABEL` R-package to be installed).
`kin`	a square symmetric matrix giving the pairwise kinship coefficients between analyzed individuals. Under default `kin = NULL` all individuals will be considered as unrelated.
`nullmod`	an object containing parameter estimates under the null model. Setting `nullmod` allows to avoid re-estimation of the null model that does not depend on genotypes and can be calculated once for a trait. If not set, the null model parameters will be estimated within the function. The `nullmod` object in proper format can be obtained by `null.model()` function or any analysis function in `FREGAT`.
`regions`	an object assigning regions to be analyzed. This can be: - a vector of length equal to the number of genetic variants assigning the region for each variant (see Examples). - a data frame / matrix with names of genetic variants in the first column and names of regions in the second column (this format allows overlapping regions). - for VCF format, a character vector with names of genes to analyze. If NULL, `sliding.window` parameters will be used.
`sliding.window`	the sliding window size and step. Has no effect if `regions` is defined.
`mode`	the mode of inheritance: "add", "dom" or "rec" for additive, dominant or recessive mode, respectively. For dominant (recessive) mode genotypes will be recoded as AA = 0, Aa = 1 and aa = 1 (AA = 0, Aa = 0 and aa = 1), where a is a minor allele. Default mode is additive.
`ncores`	number of CPUs for parallel calculations. Default = 1.
`return.time`	a logical value indicating whether the running time should be returned.
`beta.par`	two positive numeric shape parameters in the beta distribution to assign weights for each SNP. Default = c(1, 25) is recommended for analysis of rare variants. For unweighted burden test, use `beta.par = c(1, 1)`. Has no effect if `weights` are defined.
`weights`	a numeric vector or a function of minor allele frequency (MAF) to assign weights for each SNP. If NULL, the weights will be calculated using beta distribution (see Details).
`flip.genotypes`	a logical value indicating whether the genotypes of some genetic variants should be flipped (relabeled) to ensure that all MAFs < 0.5. Default = FALSE, with warning of any MAF > 0.5.
`impute.method`	a method for imputation of missing genotypes. It can be either "mean" (default) or "blue". If "mean" the genotypes will be imputed by the simple mean values. If "blue" the best linear unbiased estimates (BLUEs) of mean genotypes will be calculated taking into account the relationships between individuals [McPeek, et al., 2004] and used for imputation.
`write.file`	output file name to write results as they come (sequential mode only).
`...`	other arguments that could be passed to `null()`, `read.plink()` and `readVCFToMatrixByGene()`.

Burden test (collapsing technique) suggests that the effects of causal genetic variants within a region have the same direction. If this is not the case, other regional tests (FFBSKAT, FLM) are shown to have higher power compared to famBT [Svishcheva, et al., 2015].
By default, famBT assigns weights calculated using the beta distribution. Given the shape parameters of the beta function, beta.par = c(a, b), the weights are defined using probability density function of the beta distribution:

W_{i}=(B(a,b))^{^{-1}}MAF_{i}^{a-1}(1-MAF_{i})^{b-1} ,

where MAF_{i} is a minor allelic frequency for the i^{th} genetic variant in region, which is estimated from genotypes, and B(a,b) is the beta function.
beta.par = c(1, 1) corresponds to the unweighted burden test.

A list with values:

`results`	a data frame containing P values, estimates of betas and their s.e., numbers of variants and polymorphic variants for each of analyzed regions.
`nullmod`	an object containing the estimates of the null model parameters: heritability (h2), total variance (total.var), estimates of fixed effects of covariates (alpha), the gradient (df), and the total log-likelihood (logLH).
`sample.size`	the sample size after omitting NAs.
`time`	If `return.time = TRUE` a list with running times for null model, regional analysis and total analysis is returned. See `proc.time()` for output format.

Svishcheva G.R., Belonogova N.M. and Axenovich T.I. (2015) Region-based association test for familial data under functional linear models. PLoS ONE 10(6): e0128999.
McPeek M.S., Wu X. and Ober C. (2004). Best linear unbiased allele-frequency estimation in complex pedigrees. Biometrics (60): 359-367.

data(example.data)  

## Run famBT with sliding window (default):
out <- famBT(trait ~ age + sex, phenodata, genodata, kin)

## Run famBT with regions defined in snpdata$gene and with
## null model parameters obtained in the first run:
out <- famBT(trait ~ age + sex, phenodata, genodata, kin,
out$nullmod, regions = snpdata$gene)

## Run famBT parallelized on two cores (this will require
## 'foreach' and 'doParallel' R-packages installed and
## cores available):
out <- famBT(trait ~ age + sex, phenodata, genodata, kin,
	out$nullmod, ncores = 2)

## Run famBT with genotypes in VCF format:
VCFfileName <- system.file(
	"testfiles/1000g.phase1.20110521.CFH.var.anno.vcf.gz",
	package = "FREGAT")
geneFile <- system.file("testfiles/refFlat_hg19_6col.txt.gz",
	package = "FREGAT")
phe <- data.frame(trait = rnorm(85))
out <- famBT(trait, phe, VCFfileName, geneFile = geneFile,
	reg = "CFH", annoType = "Nonsynonymous",
	flip.genotypes = TRUE)

## Run famBT with genotypes in PLINK binary data format:
bedFile <- system.file("testfiles/sample.bed",
	package = "FREGAT")
phe <- data.frame(trait = rnorm(120))
out <- famBT(trait, phe, bedFile)