MsCAVIAR: Identifying Causal Variants by Fine Mapping Across Multiple Studies

MsCAVIAR is a method for fine-mapping (identifying causal variants among GWAS associated variants) by leveraging information from multiple studies. One important application area is trans-ethnic fine mapping. Details on installing and using MsCAVIAR are below. If you would like further details, see the paper below; if you use the software, please cite it.

Identifying Causal Variants by Fine Mapping Across Multiple Studies
Nathan LaPierre, Kodi Taraszka, Helen Huang, Rosemary He, Farhad Hormozdiari, Eleazar Eskin
bioRxiv 2020.01.15.908517; doi: https://doi.org/10.1101/2020.01.15.908517

Installation

Installation is very quick and simple:

git clone https://github.com/nlapier2/MsCAVIAR.git
cd MsCAVIAR/
make

Example & Basic Usage

We have included an example locus in the example/ folder. To run this example, assuming you have installed MsCAVIAR and are currently in the example/ directory, run the following line:

../MsCAVIAR -l ldfiles.txt -z zfiles.txt -n 191764,361194 -o mscaviar_results

The SNPs in the 95% confidence set will be in: mscaviar_results_set.txt. The -l, -z, -n, and -o arguments are required for every run of MsCAVIAR. We explain them in further detail below.

The -l and -z arguments, as can be seen in the example/ folder, list the files with the LD matrix and Z scores for the SNPs in the locus, respectively. The file paths should be given either as absolute file paths or relative paths to the current directory. The LD files for a locus with M SNPs should contain M lines, each with M space-separated numbers. The i th number in the j th line should be the LD between SNPs i and j.

The Z files should contain M lines, each with two tab-separated columns. The first column should be the SNP name and the second should be the Z-score for that SNP. See the files in the example/ folder for examples of these file types. Please make sure the exact same set of SNPs are present for all files and in the same order. Also, make sure the studies are in the same order for -l and -z.

The -n argument specifies the population size for each study, comma-separated. Again, this should be the same number of studies and in the same order as was given by the -l and -z arguments. The -o argument is simply the output name prefix for the MsCAVIAR output files.

Other command line options

The other command line options are listed below. We do not recommend changing -g or -s for most users. Users may consider changing the other options, but we recommend reading the paper to understand them before doing so. We explain them briefly here.

-c controls the maximum number of causal SNPs allowed at a locus; the default is 3. -r ("rho") determines the posterior probability threshold for MsCAVIAR; in other words, MsCAVIAR will return a set of SNPs that, with rho% probability, contains all causal SNPs. The default is 0.95. -t controls the heterogeneity between the studies; in other words, the variance in the effect sizes of causal SNPs not accounted for by sample size imbalance. The default is 0.52.

-f allows MsCAVIAR to output a hist.txt file that includes the likelihood of the true set containing 0, 1, 2, ... up to the maximum number of causal SNPs that the user sets using the -c option. We recommend using this option when the user is unsure about the number of causal snps in the study, and then adjust -c according to the likelihoods in the histogram file. For example, if the histogram file contains 5 numbers: "1.21973e-150" "2.54771e-09" "0.614328" "0.334465" "0.051207", it means that the causal set most likely contains 2 snps, with L(c=2) = 0.61. But since L(c=3)=0.33 indicates that c=3 is also relatively likely, we recommend the user to rerun the program using -c 2 or -c 3 as the parameters to get more accurate results.

-r RHO, --rho-prob=RHO     set $rho$ probability (default 0.95)
-g GAMMA, --gamma      set $gamma$ the prior of a SNP being causal (default 0.01)
-c causal          set the maximum number of causal SNPs (default 3)
-f 1               to out the probaility of different number of causal SNP
-t TAU_SQR, --tau_sqr=TAU_SQR  set the heterogeneity (t^2) across studies, default is 0.52
-s SIGMA_G_SQR, --sigma_g_squared=SIGMA_G_SQR    set the NCP variance for the smallest study, default is 5.2