AsaMap

Download

The program can be downloaded from github:

https://github.com/e-jorsboe/asaMap

git clone https://github.com/e-jorsboe/asaMap.git;
cd asaMap 
make

So far it has only been tested on Linux systems. Use curl if you are on a MAC.

Example

This an example!!

Input Files

Input files are called genotypes in the binary plink files (*.bed) format [1]. And estimated admixture proportions and population specific allele frequencies. For estimating admixture proportions and population specific allele frequencies ADMIXTURE, can be used, where .Q and .P files respectively can be given directly to asaMap.

A phenotype also has to be provided, this should just be text file with one line for each individual in the .fam file, sorted in the same way:

-0.712027291121767
-0.158413122435864
-1.77167888612947
-0.800940619551485
0.3016297021294
...

A covarite file can also be provided, where each column is a covariate and each row is an individual - should NOT have columns of 1s for intercept (intercept will be included automatically). This file has to have same number of rows as phenotype file and .fam file.

0.0127096117618385 -0.0181281029917176 -0.0616739439849275 -0.0304606694443973
0.0109944672768584 -0.0205785925514037 -0.0547523583405743 -0.0208813157640705
0.0128395346453956 -0.0142116856067135 -0.0471689997039534 -0.0266186436009881
0.00816783754598649 -0.0189271733933446 -0.0302259313905976 -0.0222247658768436
0.00695928218989132 -0.0089960963981644 -0.0384886176827146 -0.012649019770168
...

Example of a command of how to run asaMap with covariates included and first running ADMIXTURE:

#run admixture
admixture plinkFile.bed 2

#run asaMap with admix proportions
./asaMap -p plinkFile  -o out -c $COV -y pheno.files -Q plinkFile.2.Q -f plinkFile.2.P

This produces a out.log logfile and a out.res with results for each site (after filtering).

Running asaMap

Example of a command of how to run asaMap with covariates included and first running ADMIXTURE:

#run admixture
admixture plinkFile.bed 2

#run asaMap with admix proportions
./asaMap -p plinkFile  -o out -c $COV -y pheno.files -Q plinkFile.2.Q -f plinkFile.2.P

This produces a out.log logfile and a out.res with results for each site (after filtering).

A whole list of options can be explored by running asaMap without any input:

./asaMap

Must be specified:

-p <filename>

Plink prefix filename of binary plink files - so without .bed/.fam/.bim suffixes.

-o <filename>

Output filename - a .res file will be written with the results and a .log log file.

-y <filename>

Phenotypes file, has to be plain text file - with as many rows as .fam file.

-Q <filename> (either -a or -Q)

Admixture proportions, .Q file from ADMIXTURE. Either specify this or -a.

-a <filename> (either -a or -Q)

Admixture proportions (for source pop1) - so first column from .Q file from ADMIXTURE. Either specify this or -Q.

-f <filename>

Allele frequencies, .P file from ADMIXTURE.

Optional:

-c <filename>

Covariates, plain text file with one column for each covariates, same number of rows as .fam file. SHOULD NOT HAVE COLUMN OF 1s (for intercept) WILL BE ADDED AUTOMATICALLY!

-m <INT>

Model, whether an additive genotype model, or a recessive genotype model should be used (0: additive, 1: recessive - default: 0).

-l <INT>

Regression, whether a linear or logistic regression, should be used. Logistic regression is for binary phenotype data, linear regresion is fo quantative phenotype data. (0: linear regression, 1: logistic regression - default: 0)

-b <filename>

Text file containing a starting guess of the estimated coefficients.

-i <INT>

The maximum number of iterations to run for the EM algorithm (default: 80).

-t <FLOAT>

Tolerance for change in likelihood between EM iterations for finishing analysis (default: 0.0001).

-r <INT>

Give seed, for generation of starting values of coefficients.

-P <INT>

Number of threads to be used for analysis. Each thread will write to temporary file in path specified by "-o".

-e <INT>

Estimate standard error of coefficients (0: no, 1: yes - default: 0).

-w <INT>

Run M0/R0 model that models effect of other allele. Analyses are faster without having to run M0/R0. (0: no, 1: yes - default: 1)