Description Usage Arguments Value See Also Examples
For a pair of SNPs (X1, X2) and a binary phenotype
Y, the BOOST
function computes the ratio of maximum
log-likelihoods for two models: the full model and the main effects model.
Mathematically speaking, the full model is a logistic regression model with
both main effects and interaction terms
(X1, X2, X1, X1 x X2).
The main effects model is a logistic regression model with only
(X1, X2) as covariates. Since we are
interested in the synergies with a single variant, we do not implement
the initial sure screening stage in BOOST which filters out non-significant
pairs.
1 | BOOST(A, X, Y, ncores = 1)
|
A |
target variant. The SNP A is encoded as 0, 1, 2. |
X |
genotype matrix (excluding A). The only accepted SNP values are also 0, 1 and 2. |
Y |
observed phenotype. Binary or two-level factor. |
ncores |
number of threads (default 1) |
The interaction statistic between each column in X
and A
The webpage http://bioinformatics.ust.hk/BOOST.html provides additional details about the BOOST software
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