Bayesian colocalisation analysis
coloc.abf(dataset1, dataset2, MAF = NULL, p1 = 1e-04, p2 = 1e-04,
p12 = 1e-05)a list with the following elements
P-values for each SNP in dataset 1
Number of samples in dataset 1
minor allele frequency of the variants
regression coefficient for each SNP from dataset 1
variance of beta
the type of data in dataset 1 - either "quant" or "cc" to denote quantitative or case-control
for a case control dataset, the proportion of samples in dataset 1 that are cases
for a quantitative trait, the population standard deviation of the trait. if not given, it can be estimated from the vectors of varbeta and MAF
a character vector of snp ids, optional. If present, it will be used to merge dataset1 and dataset2. Otherwise, the function assumes dataset1 and dataset2 contain results for the same SNPs in the same order.
Some of these items may be missing, but you must give
alwaystype
if type=="cc"s
if type=="quant" and sdY knownsdY
if type=="quant" and sdY unknownbeta, varbeta, N, MAF
and then either
pvalues, MAF
beta, varbeta
as above, for dataset 2
Common minor allele frequency vector to be used for both dataset1 and dataset2, a shorthand for supplying the same vector as parts of both datasets
prior probability a SNP is associated with trait 1, default 1e-4
prior probability a SNP is associated with trait 2, default 1e-4
prior probability a SNP is associated with both traits, default 1e-5
a list of two data.frames:
summary is a vector giving the number of SNPs analysed, and the posterior probabilities of H0 (no causal variant), H1 (causal variant for trait 1 only), H2 (causal variant for trait 2 only), H3 (two distinct causal variants) and H4 (one common causal variant)
results is an annotated version of the input data containing log Approximate Bayes Factors and intermediate calculations, and the posterior probability SNP.PP.H4 of the SNP being causal for the shared signal
This function calculates posterior probabilities of different causal variant configurations under the assumption of a single causal variant for each trait.
If regression coefficients and variances are available, it calculates Bayes factors for association at each SNP. If only p values are available, it uses an approximation that depends on the SNP's MAF and ignores any uncertainty in imputation. Regression coefficients should be used if available.