There are more recently review papers (link1, link2) that cover even more methods for rare variant association studies. For example:

• single variants
  • χ2 test for contingency table
  • Fisher’s exact test
  • Cochran Armitage test for trends
  • others
• Multiple variants
  • Collapsing methods: such as CMC and CAST
  • Aggregation method: WSS and KBAC
  • Bi-directional Effect Methods: C-alpha , Logistic Kernel-Machine Test, etc

The authors of the first paper discussed that choosing a method will depends on many factors such as:

• Study design (if the trait is quantitative or dichotomous)
• The assumption of the underlying genetics (rare variants vs. both rare and common variants are expected to contribute to disease)
• Whether protective and risk variants are expected.

They didn't provide a recipe of how to choose a method rather they, rightly, raise the point that most of these methods depend on simulation data with a certain assumptions about genetic architecture of a specific trait. So, they may not be applied to different scenarios.

I've tried KBAC, Fisher's exact test, and few others on my samples (n < 100) which is underpowered for rare variant association. Each test outputs a different list of candidate genes. Without increasing the number of samples in my study, I have to go through the pain of looking for every candidate gene in the top of each list and check for biological clues that might connect it to my phenotype. A lot of false hopes and not fun at all.

IMO, until someone publishes a large scale project to test for rare variants association (number of sample >= 10,000) , I think there isn't currently a favorite method that everyone agrees on. You probably need to try many of the published methods on your data and get a sense of what they can provide.

I have not personally used this tool, but do know of the CCRaVAT (Case-Control Rare Variant Analysis Tool) available for download from Sanger. It implements a collapsing-based approach for rare variant analysis.

I believe you can do this in PLINK-SEQ:

http://atgu.mgh.harvard.edu/plinkseq/

BUT beware that the last time I checked it out it was [1] still under development and [2] DID NOT yet have any source code available. Probably won't until the full first release and paper to prevent getting scooped. I think it will let you do the C-alpha test on rare variants.

There is an R package called Assoteste (http://cran.r-project.org/web/packages/AssotesteR/AssotesteR.pdf) to perform most of the tests developed to detect rare variants

You could also try variant tools varianttools.sourceforge.net) which implements more than 20 association test methods for rare variants. It also provides a whole set of tools to perform quality control and other tasks needed for a complete analysis pipeline.

Are you working with population- or family-based data?