Perform association tests between phenotype risk scores and genotypes
Source:R/genetic_association.R
getGeneticAssociations.Rd
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 indemos
andscores
, and colnames corresponding tovariant_id
s indiseaseVariantMap
.- 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
andvariant_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 identifiervariant_id
: Variant identifiern_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 scorese
: Standard error forbeta
pval
: P-value forbeta
being non-zeroci_lower
: Lower bound of the confidence interval forbeta
ci_upper
: Upper bound of the confidence interval forbeta
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'))
#> Extracting number of samples and rownames from geno_sample.fam...
#> Extracting number of variants and colnames from geno_sample.bim...
colnames(genoSample) = paste0('snp', 1:nvar)
rownames(genoSample) = 1:npop
# run genetic association tests
genoStats = getGeneticAssociations(
scores, genoSample, demoSample, diseaseVariantMap, lmFormula = ~ sex,
modelType = 'additive')