R: Perform association tests between phenotype risk scores and...

getGeneticAssociations {phers}

R Documentation

Perform association tests between phenotype risk scores and genotypes

Description

The association test for each disease-variant pair is based on a linear model, with the phenotype risk score as the dependent variable.

Usage

getGeneticAssociations(
  scores,
  genotypes,
  demos,
  diseaseVariantMap,
  lmFormula,
  modelType = c("genotypic", "additive", "dominant", "recessive"),
  level = 0.95,
  dopar = FALSE
)

Arguments

`scores`	A data.table of phenotype risk scores. Must have columns `person_id`, `disease_id`, `score`.
`genotypes`	A matrix or 'BEDMatrix' object containing genetic data, with rownames corresponding to `person_id`s in `demos` and `scores`, and colnames corresponding to `variant_id`s in `diseaseVariantMap`.
`demos`	A data.table of characteristics for each person in the cohort. Must have column `person_id`.
`diseaseVariantMap`	A data.table indicating which genetic variants to test for association with phenotype risk scores for which diseases. Must have columns `disease_id` and `variant_id`.
`lmFormula`	A formula representing the linear model (excluding the term for genotype) to use for the association tests. All terms in the formula must correspond to columns in `demos`.
`modelType`	A string indicating how to encode genotype in the model.
`level`	A number indicating the level of the confidence interval. Default is 0.95.
`dopar`	Logical indicating whether to run calculations in parallel if a parallel backend is already set up, e.g., using `doParallel::registerDoParallel()`. Recommended to minimize runtime.

Value

A data.table of statistics for the association tests (if a model fails to converge, NAs will be reported):

disease_id: Disease identifier
variant_id: Variant identifier
n_total: Number of persons with non-missing genotype data for the given variant.
n_wt: Number of persons homozygous for the wild-type allele.
n_het: Number of persons having one copy of the alternate allele.
n_hom: Number of persons homozygous for the alternate allele.
beta: Coefficient for the association of genotype with score
se: Standard error for beta
pval: P-value for beta being non-zero
ci_lower: Lower bound of the confidence interval for beta
ci_upper: Upper bound of the confidence interval for beta

If modelType is "genotypic", the data.table will include separate statistics for heterozygous and homozygous genotypes.

Examples

library('data.table')
library('BEDMatrix')

# map ICD codes to phecodes
phecodeOccurrences = getPhecodeOccurrences(icdSample)

# calculate weights
weights = getWeights(demoSample, phecodeOccurrences)

# OMIM disease IDs for which to calculate phenotype risk scores
diseaseId = 154700

# map diseases to phecodes
diseasePhecodeMap = mapDiseaseToPhecode()

# calculate scores
scores = getScores(weights, diseasePhecodeMap[disease_id == diseaseId])

# map diseases to genetic variants
nvar = 10
diseaseVariantMap = data.table(disease_id = diseaseId, variant_id = paste0('snp', 1:nvar))

# load sample genetic data
npop = 50
genoSample = BEDMatrix(system.file('extdata', 'geno_sample.bed', package = 'phers'))
colnames(genoSample) = paste0('snp', 1:nvar)
rownames(genoSample) = 1:npop

# run genetic association tests
genoStats = getGeneticAssociations(
  scores, genoSample, demoSample, diseaseVariantMap, lmFormula = ~ sex,
  modelType = 'additive')