Abbababa

Performs the abbababa test also called the D-statistic. This tests for ancient admixture (or wrong tree topology). 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 develupmental version availeble from github
• if you use -rf to specify regions. These MUST appear in the same ordering as your fai file.

flowchart LR
    n1["BAM files"]
    n2["Sequence data<br/>Random base"]
    n3["*.abbababa<br/>ABBA and BABA couts file"]
    n1 --> n2
    n2 --> n3
    style n1 fill:#ffe0b2,stroke:#e65100
    style n3 fill:#bbdefb,stroke:#0d47a1

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flowchart LR
    n1["*.abbababa<br/>ABBA and BABA couts file"]
    n2["D stat and Z scores"]
    n1 -->|jackKnife.R| n2
    style n1 fill:#bbdefb,stroke:#0d47a1
    style n2 fill:#bbdefb,stroke:#0d47a1

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Method

Brief Overview

> ./angsd -doAbbababa

--------------
abcDstat.cpp:
	-doAbbababa		0	run the abbababa analysis
	-rmTrans		0	remove transitions
	-blockSize		5000000	size of each block in bases
	-enhance		0	outgroup must have same base for all reads
	-ans			(null)	fasta file with outgroup
	-useLast		0	use the last individuals as outgroup instead of -anc

This function will counts the number of ABBA and BABA sites

Options

• -doAbbababa 1: sample a random base at each position.

• -rmTrans [int] 0; use all reads (default), 1 Remove 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.

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

• -useLast [int] 1: use the last individual in the bam file list as outgroup instead of a fasta file (-anc)

• -enhance [int] 1: use only sites where the reads for the outgroup has the same base for all reads. Only works with -useLast 1

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

Output

• .abbbababa Output: Each lines 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 ABBA and BABA sites. For each combination of 3 individuals (H1,H2,H3) two columns are printed. These number served as input to the R script called jackKnife.R. This script will skip combinations of individuals if there is less than 3 blocks with data. Type "Rscript R/jackKnife.R" to see additional options.

Jackknife

Rscript R/jackKnife.R
->  needed arguments:
	 file : the .abbababa filename
	 indNames : list of individual names (you can use the bam.filelist)
->  optional arguments (defaults):
	 outfile  ( out ) : name of output file
	 boot  ( 0 ) : print results for each bootstrap(jackknife), 0=NO

Example

Create a fasta file bases from a random samples of bases.

# select 5 individuals
head -n5 bam.filelist > smallBam.filelist

#run angsd
./angsd -out out -doAbbababa 1 -bam smallBam.filelist -doCounts 1 -anc chimpHG19.fa

#estimate Z score
Rscript R/jackKnife.R file=out.abbababa indNames=smallBam.filelist outfile=out

This results in a out.txt file with all the results.

Output

H1	H2	H3	 nABBA	nBABA	Dstat	jackEst	SE	Z
NA11830	NA12004	NA12763	269	322	-0.08967851	-0.08967851	0.09006086	-0.9957545
NA11830	NA06985	NA12763	267	298	-0.05486726	-0.05486726	0.122256	-0.4487898
NA12004	NA06985	NA12763	254	243	0.0221328	0.0221328	0.1386198	0.1596655
NA11830	NA11993	NA12763	225	336	-0.197861	-0.197861	0.08514797	-2.323731
NA12004	NA11993	NA12763	217	267	-0.1033058	-0.1033058	0.09471542	-1.090697
NA06985	NA11993	NA12763	242	302	-0.1102941	-0.1102941	0.1241554	-0.8883553
NA12763	NA12004	NA11830	237	322	-0.1520572	-0.1520572	0.1047361	-1.451813
NA12763	NA06985	NA11830	219	298	-0.1528046	-0.1528046	0.1115283	-1.370098

H1 H2 H3 are the 3 individuals in the tree that are not the outgroup. H1 and H2 are the ingroup (see figure of tree above)

nABBA the total counts of ABBA patterns

nBABA the total counts of BABA patterns

Dstat The test statistic: (nABBA-nBABA)/(nABBA+nBABA). A negative value means that H1 is closer to H3 than H2 is. A positive value means that H2 is closer to H3 than H1 is.

JackEst column is another estimate of the abbababa statistic that is bias corrected. This value is extremely similar to the value in the Dstat column

SE is the estimated m-delete blocked Jackknife Standard error of the estimate used to obtain the Z value

Z Z value that can be used to determine the significance of the test. As in Reich et al. an absolute value of the Z score above 3 is often used as a critical value. However, this note that this does not take into account the fact that we perform multiple tests.

bootstrap output

if you chose boot=1 then you will get an additional file out.boot.

A06994 NA11840 NA06985 -0.03640257     -0.02449889     -0.03913043     -0.02643172     -0.004694836    -0.05909091     -0.007125891
NA06994 NA07056 NA06985 -0.04844291     0.001808318     -0.04727273     -0.1287879      -0.005671078    -0.04270463     -0.06569343
NA11840 NA07056 NA06985 -0.01075269     0.02592593      -0.003831418    -0.1062992      0.005847953     0.00952381      -0.05794393
NA06994 NA07357 NA06985 0.05856833      0.04587156      0.07798165      -0.02403846     0.07476636      0.06190476      0.05217391
NA11840 NA07357 NA06985 0.07014028      0.05714286      0.09401709      -0.02222222     0.05240175      0.09051724      0.04175824
NA07056 NA07357 NA06985 0.1285988       0.06530612      0.1361868       0.1322957       0.09913793      0.1216495       0.1422764
NA06985 NA11840 NA06994 -0.1950617      -0.1302211      -0.1802469      -0.1708543      -0.1002571      -0.1620948      -0.07614213
NA06985 NA07056 NA06994 -0.1139706      -0.08661417     -0.06273063     -0.07775769     -0.03100775     -0.1204589      -0.06764168
NA11840 NA07056 NA06994 0.007380074     0.009784736     0.06114398      0.02495202      0.01581028      -0.007905138    -0.03875969
NA06985 NA07357 NA06994 -0.02358491     0.01886792      0.04285714      0.006993007     0.04578313      0.02955665      0.00456621
NA11840 NA07357 NA06994 0.1530398       0.1478261       0.2227273       0.1629956       0.1382488       0.1748879       0.06451613
NA07056 NA07357 NA06994 0.1127542       0.1181102       0.1127542       0.1011236       0.09278351      0.1583166       0.0754352
NA06985 NA06994 NA11840 -0.1597938      -0.1060606      -0.1421189      -0.1450777      -0.09560724     -0.104  -0.06905371
NA06985 NA07056 NA11840 -0.2287582      -0.1662971      -0.1491228      -0.190678       -0.1697248      -0.1685393      -0.1915789
NA06994 NA07056 NA11840 -0.08467742     -0.05857741     -0.01061571     -0.06498952     -0.07559395     -0.06694561     -0.1428571
NA06985 NA07357 NA11840 -0.1571072      -0.1105769      -0.06801008     -0.1192214      -0.09319899     -0.1076115      -0.07389163

The first 3 columns are the names of the individuals. The rest are the Dstat for each jacknife. Note that the number of bootstrapt can differ between tests since blocks without any data are discarded.