asaMap

Article https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.22200

For more on this program see the popgen wiki: http://www.popgen.dk/software/index.php/AsaMap

More features might be added at some point: -- Poisson regression -- larger number of populations

Install

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

Options

Options:

   -p <filename>       plink prefix filename
   -o <filename>       output filename
   -c <filename>       covariates filename
   -y <filename>       phenotypes
   -a <filename>       admixproportions (for source pop1)
   -Q <filename>       .Q file from ADMIXTURE
   -f <filename>       allele frequencies (.P file)
   -m <INT>            model 0=add 1=rec (default: 0)
   -l <INT>            regression 0=linear regression, 1=logistic regression (default: 0)
   -b <filename>       file containing the start
   -i <INT>            maximum iterations (default: 40)
   -t <FLOAT>          tolerance for breaking EM (default: 0.0001)
   -r <INT>            seed for rand
   -P <INT>            number of threads
   -e <INT>            estimate standard error of coefficients (0: no (default), 1: yes)
   -w <INT>            run M0/R0 model that models effect of other allele (0: no, 1: yes (default))


All files must be specified: -p -c -y -a/-Q -f -o

All files must be specified: -p -c -y -a/-Q -f -o

Run example using ADMIXTURE

PH=pheno.files
COV=cov.file
PF=plinkFile #without postfix e.g. no .bim / .fam / .bed
#run admixture
admixture $PF.bed 2

#run asaMap with .Qfile
./asaMap -p $PF -o out -c $COV -y $PH -Q $PF.2.Q -f $PF.2.P

#get admixture proportions for population 1
cut -f1 -d" " $PF.2.Q > Q

#run asaMap with admix proportions
./asaMap -p $PF -o out -c $COV -y $PH -a Q -f $PF.2.P

p-values

Easy to optain in R

Using R/getPvalues.R (will create file: out.res.Pvalues)

Rscript R/getPvalues.R out.res

res <- read.table("out.res",head=T)
pval <- function(a,b,df)
1-pchisq(-2*(a-b),df=df)
#pvalues for M1 vs. M5
pval(res$llh.M1.,res$llh.M5.,df=2)
#pvalues for M1 vs. M4
pval(res$llh.M1.,res$llh.M4.,df=1)

models

model	parameters	notes	#effect Parameters
M0	(beta_1, beta_2, delta_1) in R^3	effect of non-assumed effect allele	1
M1	(beta_1, beta_2) in R^2	population specific effects	2
M2	beta_1=0, beta_2 in R	no effect in population 1	1
M3	beta_1 in R, beta_2=0	no effect in population 2	1
M4	beta_1=beta_2 in R	same effect in both populations	1
M5	beta_1=beta_2=0	no effect in any population	0

model	parameters	notes	#effect Parameters
R0	(beta_1, beta_m, beta_2, delta_1, delta_2) in R^5	recessive effect of non-assumed effect alleles	2
R1	(beta_1, beta_m, beta_2) in R^3	population specific effects	3
R2	beta_1 in R, beta_m=beta_2 in R	same effect when one or both variant alleles are from pop 2	2
R3	beta_1=beta_m in R, beta_2 in R	same effect when one or both variant alleles are from pop 1	2
R4	beta_1 in R, beta_m=beta_2=0	only an effect when both variant alleles are from pop 1	1
R5	beta_1=beta_m=0, beta_2 in R	only an effect when both variant alleles are from pop 2	1
R6	beta_1=beta_m=beta_2 in R	same effect regardless of ancestry	1
R7	beta_1=beta_m=beta_2=0	no effect in any population	0

Input files

Genotypes

plink binary (.bim .bam .fam)

Phenotypes (response)

A file with each individuals phenotypes on each line. e.g., has to have same number of rows as .fam file.

>head pheno
-0.712027291121767
-0.158413122435864
-1.77167888612947
-0.800940619551485
0.3016297021294
0.596892506547882
-0.661786423692485
-0.405728519330873
-1.04224674183241
0.0881848860116932

extra covariates (in addition to the intercept and genotypes)

A file 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.

>head cov
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 -0.0126490197701687
0.00908359304129912 -0.019562503526549 -0.0276058491506046 -0.0202388414332682
0.0193657006952317 -0.0219605099975189 -0.050537627417191 -0.0236411635865132
0.015862252334236 -0.0134969241244036 -0.0336244748700029 -0.0222294652006281
0.0194100156955457 -0.0371103372950621 0.00813012568415838 -0.015311879434991
0.0190516629849255 -0.0194012185542486 -0.0413589828106922 -0.0292318169458017

admixture proportions

Right now the implementation is just for two populations. Either using a .Qfile from ADMIXTURE - has to have same number of rows as .fam file.

>head $PF.2.Q
0.398834 0.601166
0.491314 0.508686
0.399352 0.600648
0.634947 0.365053
0.000010 0.999990
0.467470 0.532530
0.497885 0.502115
0.800272 0.199728
0.999990 0.000010
0.032108 0.967892

Or using a column of the admixture proportions of the first population - has to have same number of rows as .fam file.

>head Q
0.398834
0.491314
0.399352
0.634947
0.000010
0.467470
0.497885
0.800272
0.999990
0.032108

ancestral allele frequencies

A files where each column is a population and each row is a SNP. This file has to have same number of rows as .bim file.

>head P
0.907268 0.903723
0.498913 0.543486
0.865254 0.819332
0.737799 0.738876
0.812931 0.823395
0.783896 0.785741
0.711677 0.865518
0.592533 0.587921
0.834116 0.850966
0.837011 0.852711