RVASSOC is a simple program for performing various tests for association between genotypes and disease status using biallelic sequence variants from unrelated cases and controls. It was designed to accommodate both rare variants and missing genotypes. More specifically, it implements the Cochran-Armitage (CA) max/sum tests, the Combined Multivariate and Collapsing (CMC) test, and the Weighted Sum Statistic (WSS) test for case-control designs with independent subjects, biallelic variants, and no covariates.

RVASSOC was written and is supported by:

Daniel D. Kinnamon
Division of Human Genetics
Department of Internal Medicine
The Ohio State University Wexner Medical Center
Biomedical Research Tower, Room 384
460 W. 12th Ave.
Columbus, OH 43210
E-mail: Daniel-DOT-Kinnamon-AT-osumc-DOT-edu

Version History

  • v1.12: Changed permutation p-value calculations to perform approximate floating-point comparisons between permutation and observed test statistics using a user-specified relative epsilon. This change increases the robustness of these comparisons to normal floating-point rounding and truncation errors and therefore yields more accurate permutation p-values when the permutation null distribution is highly discrete. To help identify such discrete permutation null distributions, the output now also includes an estimate of the permutation null distribution probability mass function at the observed test statistic value. We recommend that all users upgrade to this version to obtain the most accurate results with sparse data sets.
  • v1.11: Modified hotellingt2 function used in CMC to check whether F statistic numerator df (i.e., rank of S) are > 0 and return -99 for all results variables if not. This prevents premature program termination due to GSL domain errors when the rank of S is 0.
  • v1.10: Changes to improve robustness in small, highly selected samples with higher rates of missing genotypes. Specifically:
    1. The program now excludes variants with only a single genotype (0, 1, or 2 minor alleles) observed in the sample to avoid NaN CA statistics. Previously, only variants with all 0 genotypes in the sample were excluded.
    2. The program now issues warning messages to STDERR if any variants have observed genotypes only in cases or controls in the observed sample or in any permutation. This can only happen if there are as many or more individuals with missing genotypes in the entire sample than there are individuals in either the case or control groups.
  • v1.9: Initial publicly released version.


Copyright © 2010-12 University of Miami Miller School of Medicine

RVASSOC incorporates functions from the GNU Scientific Library (GSL), which is distributed under the GNU General Public License version 3. This statement hereby explicitly incorporates all copyrights pertaining to GSL functions used in RVASSOC, which can be found in the GSL header files included in the RVASSOC source.

RVASSOC is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

RVASSOC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License version 3 for more details.


RVASSOC requires two citations: one for the software and one for the statistical test used. Information that must be supplied by users in the software citation is in bold.

RVASSOC Software
Kinnamon DD. RVASSOC: Rare Variant ASSOCiation [Source code on Internet]. Version X. Miami (FL): University of Miami Miller School of Medicine; DD MMM YYYY version release date [cited DD MMM YYYY download date]. Available from: http://rvassoc.sourceforge.net.

CA Max/Sum Tests
Kinnamon DD, Hershberger RE, Martin ER. Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants. PLoS ONE. 2012;7(2):e30238. Epub 2012 Feb 17. PMID: 22363423

CMC Test
Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008 Sep 12;83(3):311-21. Epub 2008 Aug 7. PMID: 18691683.

WSS Test
Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 2009 Feb;5(2):e1000384. Epub 2009 Feb 13. PMID: 19214210.