Abbababa2

The ABBABABA (multipop) compute the abbababa test (aka D-statistic), that means testing for an ancient admixture (or wrong tree topology). Differently from ABBABABA (D_stat) multiple individuals for each one of the groups are allowed. As all methods in ANGSD we require that the header of the BAM files are the same.

some of the options only works for the developmental version availeble from github

if you use -rf to specify regions. These MUST appear in the same ordering as your fai file.

<classdiagram type="dir:LR">

[*.bam and/or *.cram| NGS genome datasets{bg:orange}]->[Sequence data|All bases or Random bases]

[Sequence data]->[Elaborate multiple genomes per population] [Elaborate multiple genomes per population]->[*.abbababa2|weighted ABBA and BABA counts file {bg:blue}] </classdiagram>

<classdiagram type="dir:LR"> [*.abbababa2|weighted ABBA and BABA counts file {bg:blue}]->estAvgError.R[D stat and Z scores{bg:blue}] </classdiagram>

Method

Brief Overview

> ./angsd -doAbbababa2

--------------
abcDstat2.cpp:
	-doAbbababa2	                0	run the abbababa analysis
	-rmTrans		        0       remove transitions
	-blockSize		       5000000	size of each block in bases
	-anc			       (null)	fasta file with outgroup
	-sample			        0	sample a single base in each individual
	-maxDepth		        100	max depth of each site allowed
	-sizeFile		       (null)   file with sizes of the populations	
	-enhance			0	only analyze sites where outgroup H4 is non poly
	-Aanc			        0	set H4 outgroup allele as A in each site
        -useLast                        0       include fasta file defined by -anc in the analysis

This function will counts the number of ABBA and BABA sites.

Output

1)*.abbbababa2 (used for the 4-population test)

Each line represents a block with a chromsome name (Column 1), a start position (Column 2), an end postion (Column 3). The new columns are the counts of all 256 counted combination of alleles, starting from X0000=AAAA,X0001=AAAC,....,X3333=TTTT, with the correspondence 0=A,1=C,2=G,3=T. Every block is repeated a number of times corresponding to the combinations of 4 populations that are possible to do create from the data This file is used as input for the R script estAvgError.R.

Options

-doAbbababa2 1

take all bases at each position.

-rmTrans [int]

0; use all reads (default), 1 Remove ancient transitions (important for ancient DNA)

-blockSize [int]

Size of each block. Choose a number that is higher than the LD in the populations. For human 5Mb (5000000) is usually used.

-anc [fileName.fa]

Include an outgroup in fasta format.

-useLast [int]

1: include the file defined by -anc in the D-stat analysys. Default: 0

-doCounts 1

use -doCounts 1 in order to count the bases at each sites after filters.

-enhance [int]

1: use only sites where the reads for the outgroup has the same base for all reads.

-sample [int]

1: sample only one base at each position for every individual 0: all bases at each position are used for the ABBABABA test

-maxDepth [int]

allows for a maximum depth in each site to avoid overflow of the ABBA BABA counts. Default 100.

-sizeFile [fileName]

file that specifies number of individuals in each population (more than 4 populations can be defined)

-Aanc [int]

1: H4 allele is A in each site.

In order to do fancy filtering of bases based on quality scores see the Allele counts options.

Tutorial for the ABBABABA (Multipop) test

Prepare BAM and FASTA files

Download the latest version of angsd in your working folder from the github repository

https://github.com/ANGSD/angsd.git

Create symbolic links to angsd and the necessary R script

ln -s ./angsd/angsd ANGSD
ln -s ./angsd/R/estAvgError.R RSCRIPT

Get 10 example .bam datasets, position them in the folder ./bams/ and create a file bam.filelist listing the pathnames of those datasets

wget http://popgen.dk/software/download/angsd/bams.tar.gz
tar xf bams.tar.gz
for i in bams/*.bam;do samtools index $i;done #index bam files
ls bams/*.bam > bam.filelist
rm bams.tar.gz #remove zipped file

This is how the file bam.filelist looks like

cat bam.filelist

bams/smallNA06985.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA06994.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA07000.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA07056.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA07357.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA11829.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA11830.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA11831.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA11832.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam
bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam

Download a fasta file for the chimpanzee. This is going to be used as the outgroup for the 4-population test. One can use a bam file as well (see in one of the other examples after the tutorial how to do it).

wget http://popgen.dk/software/download/angsd/hg19ancNoChr.fa.gz
mv hg19ancNoChr.fa.gz chimpHg19.fa.gz
gunzip chimpHg19.fa.gz
samtools faidx chimpHg19.fa #indexing the fasta file
rm chimpHg19.fa.gz

Now, generate a fasta file for one of our 10 bam file. We assume such a genome has very high quality and we can use it as a reference for estimating error rates in others of our datasets.

./ANGSD -i bams/smallNA11840.mapped.ILLUMINA.bwa.CEU.low_coverage.20111114.bam -doFasta 1 -doCounts 1 -out perfectSampleCEU
gunzip perfectSampleCEU.fa.gz
samtools faidx perfectSampleCEU.fa

In this tutorial we perform the ABBABABA test on all the combinations of 4 populations amongst 6 populations of size 1,2,2,3,2,1 individuals (so that there are 360 possible combinations). The last population is represented by the fasta file defined with the option -anc, of which we enable the use by the option -useLast 1. Create a file named sizeFile.size and write the size of each population (skip the last referred to the -anc fasta file):

1
2
2
3
2

Generate files for the error correction

We will apply error correction to the group with 3 individuals, using "perfectSampleCEU" as high-quality reference genome. The population containing 3 individuals affected by transition error goes from line 6 to line 8 in the file bam.filelist. We select those individuals and write them in another file.

sed -n 6,8p bam.filelist > bamWithErrors.filelist

and then we use "doAncError" to generate the intermediate files that we will use later as input for the R script that calculates the D-statistic. "doAncError" applies the so called "perfect individual assumption", based on which error rates are estimate using a high quality genome (option -ref) and an outgroup (option -anc), both in fasta format. We have already prepared the two fasta files in our preparation phase.

./ANGSD -doAncError 1 -anc chimpHg19.fa -ref perfectSampleCEU.fa -out errorFile -bam bamWithErrors.filelist

4-population test

Now, we want to run the four population test using the 6 groups of 1,2,2,3,2,1 individuals (where the last one is the fasta file) After running ANGSD we will call the R script who applies error correction to the ABBA and BABA allele combinations and produces the final output files.

./ANGSD -doAbbababa2 1 -bam bam.filelist -sizeFile sizeFile.size -doCounts 1 -out bam.Angsd -anc chimpHg19.fa -useLast 1 -minQ 20 -minMapQ 30

The output file is bam.AllelePatterns.abbbababa2 (used for the 4-population test) Each line represents a block with a chromsome name (Column 1), a start position (Column 2), an end postion (Column 3). The new columns are the counts of all 256 counted combination of alleles, starting from X0000=AAAA,X0001=AAAC,....,X3333=TTTT, with the correspondence 0=A,1=C,2=G,3=T. This file is used as input for the R script estAvgError.R.

We run the R script specifying the intermediate error files for populations H1 and H2. We also want to study the effect of error correction if we add individually to each population an error rate between 0 and 0.005 with step 0.001 and involving transitions A->T. You can run more transitions separating the letters with a comma. It is also possible to specify the names of H1,H2,H3 to be seen on the plot (do not specify them to keep standard names). In this case we use the generic names CEU1,CEU2,CEU3. When at least an error file is given as input, the script will apply error correction.

Rscript RSCRIPT angsdFile="bam.AllelePatterns" out="result" file1="bamH1.ancError" file2="bamH2.ancError"  addErr="0,0.005,0.001;A;T;CEU1,CEU2,CEU3"

The script will show the calculated D statistic along with Z-score, Pvalues, Standard deviation and other quantities.

--- Table of Results ---
---------------------------------------------------------------------------------
  Mode		|Dstat		|sd(Dstat)	|Djack		|Zscore	|Pvalue
---------------------------------------------------------------------------------
Observed	|-6.323e-02	|6.985e-02	|-6.323e-02	|-0.905	|3.7e-01
---------------------------------------------------------------------------------
Err Corr	|-6.430e-02	|7.226e-02	|-6.431e-02	|-0.890	|3.7e-01
---------------------------------------------------------------------------------
No Trans	|-1.141e-02	|6.311e-02	|-1.141e-02	|-0.181	|8.6e-01
---------------------------------------------------------------------------------
Err Corr	|		|		|		|	|	
   and		|-1.494e-02	|6.615e-02	|-1.496e-02	|-0.226	|8.2e-01
No Trans	|		|		|		|	|	
---------------------------------------------------------------------------------
plots with effect of removed errors and D statistic files for all the removed errors are in folder result.errorDataFolder

Those results are also contained in four distinct files

1)result.Observed.txt

D-statistic calculated WITHOUT Error Correction and WITHOUT Ancient Transition removal

mean(D) JK-D    V(JK-D) Z       pvalue  nABBA   nBABA   nBBAA
-0.063233       -0.063233       0.004878        -0.905320       0.365296        246.033565      279.248560      292.834879

2) result.ErrorCorr.txt

D-statistic calculated WITH Error Correction and WITHOUT Ancient Transition removal

mean(D) JK-D    V(JK-D) Z       pvalue  nABBA   nBABA   nBBAA
-0.064295       -0.064309       0.005221        -0.889833       0.373555        238.242964      270.983960      293.326044

3) result.TransRemErrorCorr.txt

D-statistic calculated WITH Error Correction and WITH Ancient Transition removal

mean(D) JK-D    V(JK-D) Z       pvalue  nABBA   nBABA   nBBAA
-0.014939       -0.014959       0.004376        -0.225829       0.821335        81.636843       84.112983       293.326044

4) result.RemTrans.txt

D-statistic calculated WITHOUT Error Correction and WITH Ancient Transition removal

mean(D) JK-D    V(JK-D) Z       pvalue  nABBA   nBABA   nBBAA
-0.011406       -0.011406       0.003983        -0.180730       0.856580        85.730478       87.708709       292.834879

Specifically, the values contained in the four files are: mean(D)=average D-stat, JK-D=jackknife estimate of the D-stat, V(JK-D)=variance of the D-stat, Z=Z score, pvalue=pvalue from the Z score, nABBA=number of ABBA patterns observed, nBABA=number of BABA patterns observed, nBBAA=all the other observed patterns. Note that the number of patterns might not be integer because of how ANGSD treats multiple genomes per populations.

In case of error correction, the R script also creates the folder result.errorDataFolder containing:

-the file barPlotErrors.pdf showing a barplot of the error rates (plot shown below)

-the file plotAddErr.A2T.pdf showing the effect of error correction on transition A-->T (plot shown below)

-the file errorRates.txt showing in each line transition errors for each population, respectively

-all the files related to the addition of error correction to H1,H2,H3, necessary to plot the files plotAddErr.A2T.pdf.