1. 1. Introduction
2. 2. Details
1. 2.1 Command interface
2. 2.2 Application

## 1.  Introduction

The RareCover test in Bhatia et al 20101 is an efficient heuristic greedy algorithm to find an optimized combination of variants in a loci with the strongest association signal. It uses the same collapsing strategy and test statistic as in Li and Leal, 20082 but scans over the loci, adding at each iteration the variants that contributes most to the statistic.

RareCover is related to the Variable Thresholds test yet differs in the sequence by which rare variants are incorporated into the test. Variable thresholds test assumes a fixed yet unknown MAF boundary of rare causal variants, while RareCover does not have the assumption. Still, it does not mean that RareCover would perform exhaustive search for all combinations of variants in a loci region. The "coverage" of RareCover method depends on the convergence cut-off {$Q$}.

RareCover method is implemented in this program as a two-sided test with the tuning parameter {$Q=0.5$}, as recommanded by the original paper.

## 2.  Details

### 2.1  Command interface

vtools show test RareCover

Name:          RareCover
Description:   A "covering" method for detecting rare variants association, Bhatia et
al 2010.
usage: vtools associate --method RareCover [-h] [--name NAME] [-q1 MAFUPPER]
[-q2 MAFLOWER] [-p N]

A "covering" method for detecting rare variants association, Bhatia et al
2010. The algorithm combines a disparate collection of rare variants and
maximize the association signal over the collection using a heuristic adaptive
approach, which can be computationally intensive. Different from VT method, it
does not require rare variants evaluated being adjacent in minor allele
frequency ranking. RareCover test is a two-tailed test.

optional arguments:
-h, --help            show this help message and exit
--name NAME           Name of the test that will be appended to names of
output fields, usually used to differentiate output of
different tests, or the same test with different
parameters.
-q1 MAFUPPER, --mafupper MAFUPPER
Minor allele frequency upper limit. All variants
having sample MAF<=m1 will be included in analysis.
Default set to 0.01
-q2 MAFLOWER, --maflower MAFLOWER
Minor allele frequency lower limit. All variants
having sample MAF>m2 will be included in analysis.
Default set to 0.0
-p N, --permutations N
Number of permutations
confidence interval for binomial distribution. The
program will compute a p-value every 1000 permutations
and compare the lower bound of the 95 percent CI of
p-value against "C", and quit permutations with the
p-value if it is larger than "C". It is recommended to
specify a "C" that is slightly larger than the
significance level for the study. To disable the
adaptive procedure, set C=1. Default is C=0.1
Mode of inheritance. Will code genotypes as 0/1/2/NA
for additive mode, 0/1/NA for dominant or recessive

Example using snapshot vt_ExomeAssociation
 vtools associate rare status -m "RareCover --name RareCover -p 5000" --group_by name2 --to_\ db rarecover -j8 > rarecover.txt  INFO: 3180 samples are found INFO: 2632 groups are found INFO: Starting 8 processes to load genotypes Loading genotypes: 100% [=============================] 3,180 32.8/s in 00:01:36 Testing for association: 100% [===============================] 2,632/591 6.0/s in 00:07:17 INFO: Association tests on 2632 groups have completed. 591 failed. INFO: Using annotation DB rarecover in project test. INFO: Annotation database used to record results of association tests. Created on Wed, 30 Jan 2013 05:40:44  vtools show fields | grep RareCover  rarecover.sample_size_RareCover sample size rarecover.num_variants_RareCover number of variants in each group (adjusted for specified MAF rarecover.total_mac_RareCover total minor allele counts in a group (adjusted for MOI) rarecover.statistic_RareCover test statistic. rarecover.pvalue_RareCover p-value rarecover.std_error_RareCover Empirical estimate of the standard deviation of statistic under the rarecover.num_permutations_RareCover number of permutations at which p-value is evaluated  head rarecover.txt  name2 sample_size_RareCover num_variants_RareCover total_mac_RareCover statistic_RareCover pvalue_RareCover std_error_RareCover num_permutations_RareCover ABCG5 3180 6 87 0.991364 0.911089 3.32099 1000 ABCB10 3180 6 122 5.54768 0.28971 3.25502 1000 ABHD1 3180 5 29 0.262705 0.901099 3.76918 1000 AAMP 3180 3 35 1.3233 0.667333 2.09356 1000 ABCD3 3180 3 42 0.394182 0.949051 2.33258 1000 AADACL4 3180 5 138 4.82996 0.200799 2.82611 1000 ABCB6 3180 7 151 1.26936 0.895105 3.0108 1000 ABL2 3180 4 41 0.344182 0.947053 3.3311 1000 ACAP3 3180 3 17 2.87639 0.277722 2.90011 1000  QQ-plot
1 Gaurav Bhatia, Vikas Bansal, Olivier Harismendy, Nicholas J. Schork, Eric J. Topol, Kelly Frazer and Vineet Bafna (2010) A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes. PLoS Computational Biology doi:10.1371/journal.pcbi.1000954. http://dx.plos.org/10.1371/journal.pcbi.1000954
2 Bingshan Li and Suzanne M. Leal (2008) Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data. The American Journal of Human Genetics doi:10.1016/j.ajhg.2008.06.024. http://linkinghub.elsevier.com/retrieve/pii/S0002929708004084