Description Usage Arguments Details Value Author(s) References See Also Examples

RARECOVER is an algorithm proposed by Bhatia et al (2010) that determines the set of variants in a manner of forward variable selection: starting from a null model without any genetic variants, genetic variants are selected one by one based on their statistical significance and then added into the model

1 | ```
RARECOVER(y, X, maf = 0.05, dif = 0.5, perm = 100)
``` |

`y` |
numeric vector with phenotype status: 0=controls, 1=cases. No missing data allowed |

`X` |
numeric matrix or data frame with genotype data coded as 0, 1, 2. Missing data is allowed |

`maf` |
numeric value indicating the minor allele frequency threshold for rare variants ( |

`dif` |
numeric value between 0 and 1 as a threshold for the decision criterion in the RARECOVER algorithm (default |

`perm` |
positive integer indicating the number of permutations (100 by default) |

The applied association test statistic (denoted as `XCORR`

in Bhatia et al, 2010) is based on the Pearsons chi-square statistic

The argument `maf`

is used to specify the threshold of the minor allele frequency for rare variants. By default, only variants below `maf=0.05`

are taken into account in the analysis. However, if all variants in `X`

are considered as rare variants, setting `maf=1`

will consider them all for the analysis

There is no imputation for the missing data. Missing values are simply ignored in the computations.

An object of class `"assoctest"`

, basically a list with the following elements:

`rc.stat` |
rarecover statistic |

`perm.pval` |
permuted p-value |

`set` |
set of selected variants |

`args` |
descriptive information with number of controls, cases, variants, rare variants, maf, number of selected variants, and permutations |

`name` |
name of the statistic |

Gaston Sanchez

Bhatia G, Bansal V, Harismendy O, Schork NJ, Topol EJ, Frazer K, Bafna V (2010) A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes. *PLoS Computational Biology*, **6(10)**: e1000954

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Not run:
# number of cases
cases = 500
# number of controls
controls = 500
# total (cases + controls)
total = cases + controls
# phenotype vector
phenotype = c(rep(1, cases), rep(0, controls))
# genotype matrix with 10 variants (random data)
set.seed(1234)
genotype = matrix(rbinom(total*10, 2, 0.051), nrow=total, ncol=10)
# apply RARECOVER with dif=0.05 and 500 permutations
myrc = RARECOVER(phenotype, genotype, maf=0.05, perm=500)
myrc
## End(Not run)
``` |

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