Category Archives: Code

FTDNA FF to PED Conversion

Someone asked about how to convert a FTDNA Family Finder csv data file to the Plink format. I threw together a very simple Unix script to do that and I am sharing it here:

#!/bin/bash
if test -z "$1"
then
        echo "FTDNA raw data filename not supplied as argument."
        exit 0
fi
echo "Family ID: "
read fid
echo "Individual ID: "
read id
echo "Paternal ID: "
read pid
echo "Maternal ID: "
read mid
echo "Sex (m/f/u): "
read sexchr
if [[ $sexchr == m* ]]
then
        sex=1
elif [[ $sexchr == f* ]]
then
        sex=2
else
        sex=0
fi
pheno=0
 
echo "$fid $id $pid $mid $sex $pheno" > $id.tfam
 
dos2unix $1
sed '1d' $1 > $id.nocomment
awk -F, '{gsub(/"/,""); print $2,$1,"0",$3,substr($4,1,1),substr($4,2,1)}' $id.nocomment > $id.tped
rm $id.nocomment
 
plink --tfile $id --out $id --make-bed --missing-genotype - --output-missing-genotype 0

This script creates three files: *.bed, *.bim and *.fam, which are the binary format files for Plink. You can then use Plink to merge multiple files, filter SNPs or individuals and do other processing.

Related Reading:

Interactive Tree Generation

Anyone know of any software to generate a javascript (or something) tree/dendrogram for the web which is interactive, i.e. branches can be expanded and collapsed and one can search for different nodes.

I want to use it to generate dendrograms including all Harappa participants and individual reference samples. So we are looking at more than 4,000 nodes on the tree.

Related Reading:

Pan-Asian to PED Conversion

Even though the Pan-Asian dataset is not public, there was a request for my script to convert the data to Plink's PED format.

Here is how I convert the Pan-Asian data to Plink's transposed file format.

#!/usr/bin/perl -w
 
$file="Genotypes_All.txt";
 
open(INFILE,"<",$file);
open(TFAM,">","panasian.tfam");
open(TPED,">","panasian.tped");
 
$line = <INFILE>;
chomp $line;
@first = split('\t',$line);
foreach my $sample (5..$#first) {
        print TFAM "0 $first[$sample] 0 0 0 -9\n";
}
 
my $alleles;
 
while(<INFILE>) {
        chomp;
        @lines = split('\t',$_);
        my ($major,$minor) = split('/',$lines[4]);
        print TPED "$lines[2] $lines[1] 0 $lines[3]";
        foreach my $snp (5..$#lines) {
                if ($lines[$snp] == 0) {
                        $alleles = "$major $major";}
                elsif ($lines[$snp] == 1) {
                        $alleles = "$major $minor";}
                elsif ($lines[$snp] == 2) {
                        $alleles = "$minor $minor";}
                else {
                        $alleles = "0 0";}
                print TPED " $alleles";
        }
        print TPED "\n";
}
 
close(INFILE);
close(TFAM);
close(TPED);

Again, no guarantees! It's Perl though, so it should be more stable across various operating systems.

Related Reading:

Xing to PED Conversion

Following mallu's request, here is the code I used to convert Xing et al data to Plink's PED format.

#!/bin/bash
dos2unix *.csv
 
head --lines=1  JHS_Genotype.csv > header.txt
awk -F, '{for (i=2;i<=NF;i++) print "0",$i,"0","0","0", "0"}' header.txt > xing.tfam
sed '1d' JHS_Genotype.csv > genotype.csv
sort -t',' -k 1b,1 genotype.csv > genotype_sorted.csv
sort -t',' -k 1b,1 JHS_SNP.csv > snp_sorted.csv
join -t',' -j 1 snp_sorted.csv genotype_sorted.csv > xing_compound.csv
awk -F, '{printf("%s %s 0 %s ",substr($2,4),$1,$3); 
        for (i=6;i<=NF;i++)
                printf("%s %s ",substr($i,1,1),substr($i,2,1));
        printf("\n")}' xing_compound.csv > xing.tped
 
plink --tfile xing --out xing --make-bed --missing-genotype N --output-missing-genotype 0

I make no guarantees that it will work for you. I used it on my Ubuntu box, but I am sure it'll have trouble on Mac OS.

Related Reading:

HGDP to PED Conversion

For converting the HGDP data (from Stanford University) to Plink PED format, I used the following code.

#!/bin/bash
dos2unix HGDP_FinalReport_Forward.txt
dos2unix HGDP_Map.txt
dos2unix SampleInformation.txt
head --lines=1 HGDP_FinalReport_Forward.txt > header.txt
awk '{for (i=1;i<=NF;i++) print "0",$i,"0","0"}' header.txt > hgdp_nosex.tfam
sed '1d' HGDP_FinalReport_Forward.txt > HGDP_Data_NoHeader.txt
sort -k 1b,1 HGDP_Data_NoHeader.txt > HGDP_Data_Sorted.txt
sort -k 1b,1 HGDP_Map.txt > HGDP_Map_Sorted.txt
join -j 1 HGDP_Map_Sorted.txt HGDP_Data_Sorted.txt > HGDP_compound.txt
 
awk '{if ($2=="M") $2="MT";printf("%s %s 0 %s ",$2,$1,$3);
    for (i=4;i<=NF;i++)
        printf("%s %s ",substr($i,1,1),substr($i,2,1));
    printf("\n")}' HGDP_compound.txt > hgdp.tped
 
# Add sex info
sed '1d' SampleInformation.txt > temp.txt
sed '$d' temp.txt > SampleInfo_noheader.txt
awk '{printf("HGDP%05d ",$1);
    if ($6=="m") print "1";
    else if ($6=="f") print "2";
    else print "0";}' SampleInfo_noheader.txt > Sample_sex.txt
awk 'BEGIN {
	while ((getline < "Sample_sex.txt") > 0)
		f2array[$1] = $2}
	{if (f2array[$2])
		print $1, $2, $3, $4, f2array[$2], "0"
	else
		print $2 "not listed in file2" > "unmatched"
	}' hgdp_nosex.tfam > hgdp.tfam
 
# convert to ped
plink --tfile hgdp --out hgdp --make-bed --missing-genotype - --output-missing-genotype 0
 
# Filter to 952 (or 940) people using the SampleInformation.txt file
awk '{if ($16=="1") printf("0 HGDP%05d\n",$1);}' SampleInfo_noheader.txt > Sample_keep.txt
plink --bfile hgdp --keep Sample_keep.txt --make-bed --out hgdp940

This (and more) could easily be done in Perl. You can look at the SPSmart code for some help along these lines.

Related Reading:

23andme Conversion to PED

Someone asked about how to convert a 23andme raw data file to the Plink format. I threw together a very simple Unix script to do that and I am sharing it here:

#!/bin/bash
if test -z "$1"
then
	echo "23andme raw data filename not supplied as argument."
	exit 0
fi
echo "Family ID: "
read fid
echo "Individual ID: "
read id
echo "Paternal ID: "
read pid
echo "Maternal ID: "
read mid
echo "Sex (m/f/u): "
read sexchr
if [[ $sexchr == m* ]]
then
	sex=1
elif [[ $sexchr == f* ]]
then
	sex=2
else
	sex=0
fi
pheno=0
echo "$fid $id $pid $mid $sex $pheno" > $id.tfam
dos2unix $1
sed '/^\#/d' $1 > $id.nocomment
awk '{ if (length($4)==1) print $2,$1,"0",$3,substr($4,1,1),substr($4,1,1); else
    print $2,$1,"0",$3,substr($4,1,1),substr($4,2,1) }' $id.nocomment > $id.tped
plink --tfile $id --out $id --make-bed --missing-genotype - --output-missing-genotype 0

Well that's it! You can easily create a Perl script to do the same but this was faster for me.

This script creates three files: *.bed, *.bim and *.fam, which are the binary format files for Plink. You can then use Plink to merge multiple files, filter SNPs or individuals and do other processing.

UPDATE: A Perl script to do the same:

#!/usr/bin/perl -w
 
$numArgs = $#ARGV + 1;
if ($numArgs < 1) {
        print "23andme raw data filename not provided.\n";
        exit 0;
}
 
$file = $ARGV[0];
 
print "Family ID: ";
$fid = <STDIN>;
chomp $fid;
print "Individual ID: ";
$id = <STDIN>;
chomp $id;
print "Paternal ID: ";
$pid = <STDIN>;
chomp $pid;
print "Maternal ID: ";
$mid = <STDIN>;
chomp $mid;
print "Sex (m/f/u): ";
$sexchr = <STDIN>;
if (lc(substr($sexchr,0,1)) eq "m") {
        $sex = 1; }
elsif (lc(substr($sexchr,0,1)) eq "f") {
        $sex = 2; }
else {
        $sex = 0; }
$pheno = 0;
$tfamname = ">" . $id . ".tfam";
open(TFAM, $tfamname);
print TFAM "$fid $id $pid $mid $sex $pheno";
close TFAM;
 
open(INFILE,$file);
open(TPED,">" . $id . ".tped");
while (<INFILE>) {
        next if /#.*/;
        chomp;
        my($rsid,$chr,$pos,$geno) = split(/\s/);
        if (length($geno)==1) {
                $geno1 = $geno;
                $geno2 = $geno;
        }
        else {
                $geno1 = substr($geno,0,1);
                $geno2 = substr($geno,1,1);
        }
        print TPED "$chr $rsid 0 $pos $geno1 $geno2\n";
}
close TPED;
close INFILE;

That should work on Linux, Microsoft Windows and Mac OS X.

Related Reading:

Admixture: Reference Population

For regular admixture analysis, I am using HapMap, HGDP, SGVP and Behar datasets with some samples removed as I wrote earlier.

For each of these datasets,

  1. I first filtered to keep only the list of SNPs present in 23andme v2 chip.
    plink --bfile data --extract 23andmev2.snplist
  2. I also filtered for founders:
    plink --bfile data --filter-founders
  3. And excluded SNPs with missing rates greater than 1%:
    plink --bfile data --geno 0.01

Then, I merged the datasets one by one. The reason for doing it one by one was that there were conflicts of strand orientation (forward or reverse) between the different datasets. If the merge operation gave an error, I had to flip those strands in one dataset and try the merge again.

plink --bfile data1 --bmerge data2.bed data2.bim data2.fam --make-bed
plink --bfile data2 --flip plink.missnp --make-bed --out data2flip
plink --bfile data1 --bmerge data2flip.bed data2flip.bim data2flip.fam --make-bed

Once all the four datasets were merged, I processed the combined data file:

  1. Removed SNPs with a missing rate of more than 1% in the combined dataset
    plink --bfile data --geno 0.01
  2. Then i performed linkage disequilibrium based pruning using a window size of 50, a step of 5 and r^2 threshold of 0.3:
    plink --bfile data --indep-pairwise 50 5 0.3
    plink --bfile data --extract plink.prune.in --make-bed

This gave me a reference population of 2,693 2,654 individuals with each sample having about 186,000 SNPs. Out of these 2,693 2,654 individuals, we have a total of 398 South Asians belonging to 16 ethnic groups.

Finally, it's time to start having some fun!

UPDATE: I removed 39 Pygmy and San samples because they were causing some trouble with African ancestral components. Since we are not interested in detailed African ancestry and African admixture among South Asians is not likely to be pygmy or San, I decided it would be best to remove them.

Related Reading: