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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