Questions regarding D-statistic calculation with qpDstat in ADMIXTURE

[Osteomeles]

Dear Professor,

Recently, I encountered some issues while calculating D-statistics using qpDstat from ADMIXTURE. Following the methods described in the paper "Genomic architecture and introgression shape a butterfly radiation", I used one individual per species for the analysis. My pipeline was as follows:

msa_view ./maf/scaffold_9.maf --in-format MAF --out-format FASTA > ./fasta/scaffold_9.fa

snp-sites -cv -o VCF/scaffold_9.vcf ./fasta/scaffold_9.fa

perl -I /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl /data/00/software/vcftools/src/perl/vcf-shuffle-cols -t template.vcf ./VCF/scaffold_9.vcf > VCF_2/scaffold_9.shuffle.vcf (the template.vcf was copied from a randomly selected VCF from the previous step as a reference)

perl -I /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl/vcf-concat VCF_2/*.vcf > allGenomes.vcf

python2 /data/00/software/gdc/vcfToEigenstrat.py -v allGenomes.vcf -o allGenomes

python3 scafNamesToNums_new.py allGenomes.snp allGenomes.geno allGenomes_chr chr_ptg_rename_chr.map (to map to chromosome-level genome)

Finally, use these files for the D-statistic analysis: qpDstat -p qpDstat/file.par > allGenomes_chr.D.out

However, two of my species are actually different geographic subspecies of the same species. In the VCF file, the genetic distance between these two subspecies is extremely small, with a Pearson correlation coefficient as high as 97.12%. Nevertheless, as shown in the figure (I converted the D-statistic results into average D values between different species pairs), the consistency of D-statistics between the first and second columns (i.e., these two subspecies) is very low. Upon further inspection, I found that for the same set of four species, the significance of D-statistics differs greatly between the two subspecies, and even when both are significant, the inferred direction of gene flow often points to different species pairs.

Why does this happen? How can I improve the analysis? Should I use only one of the two subspecies? Or should I merge them into a single group? If merging is recommended, how should I do that?

I would greatly appreciate your guidance. I look forward to your reply.

Best regards,
Liuyj

[bumblenick]

I can't (and won't) follow your pipeline. Is it possible to calculate F_st? (diploid samples or more than one haploid sample). That would give a clue. I'm guessing that you have some processing error. Nick

…

On Thu, May 14, 2026 at 9:55 AM Osteomeles ***@***.***> wrote: *Osteomeles* created an issue (DReichLab/AdmixTools#125) <#125> Dear Professor, Recently, I encountered some issues while calculating D-statistics using qpDstat from ADMIXTURE. Following the methods described in the paper "Genomic architecture and introgression shape a butterfly radiation", I used one individual per species for the analysis. My pipeline was as follows: msa_view ./maf/scaffold_9.maf --in-format MAF --out-format FASTA > ./fasta/scaffold_9.fa snp-sites -cv -o VCF/scaffold_9.vcf ./fasta/scaffold_9.fa perl -I /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl /data/00/software/vcftools/src/perl/vcf-shuffle-cols -t template.vcf ./VCF/scaffold_9.vcf > VCF_2/scaffold_9.shuffle.vcf (the template.vcf was copied from a randomly selected VCF from the previous step as a reference) perl -I /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl /data/00/software/vcftools/vcftools-vcftools-954e607/src/perl/vcf-concat VCF_2/*.vcf > allGenomes.vcf python2 /data/00/software/gdc/vcfToEigenstrat.py -v allGenomes.vcf -o allGenomes python3 scafNamesToNums_new.py allGenomes.snp allGenomes.geno allGenomes_chr chr_ptg_rename_chr.map (to map to chromosome-level genome) Finally, use these files for the D-statistic analysis: qpDstat -p qpDstat/file.par > allGenomes_chr.D.out However, two of my species are actually different geographic subspecies of the same species. In the VCF file, the genetic distance between these two subspecies is extremely small, with a Pearson correlation coefficient as high as 97.12%. Nevertheless, as shown in the figure (I converted the D-statistic results into average D values between different species pairs), the consistency of D-statistics between the first and second columns (i.e., these two subspecies) is very low. Upon further inspection, I found that for the same set of four species, the significance of D-statistics differs greatly between the two subspecies, and even when both are significant, the inferred direction of gene flow often points to different species pairs. Why does this happen? How can I improve the analysis? Should I use only one of the two subspecies? Or should I merge them into a single group? If merging is recommended, how should I do that? I would greatly appreciate your guidance. I look forward to your reply. Best regards, Liuyj image.png (view on web) <https://github.com/user-attachments/assets/d4a46bec-07c6-41cc-bd0c-81defda33546> — Reply to this email directly, view it on GitHub <#125>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEE77BYRZMXA2GSNZYHLMXT42XF6NAVCNFSM6AAAAACY6BFLOGVHI2DSMVQWIX3LMV43ASLTON2WKOZUGQ2DMNBXHAZDONA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[Osteomeles]

Dear Professor,

Thank you very much for your prompt reply.

Currently, for each species I have only a single whole-genome FASTA file and the corresponding MAF files generated from Cactus alignment. Two of these species are actually geographic subspecies (i.e., they belong to the same species but were sampled from geographically distant locations), so I may not be able to calculate Fst.

My pipeline follows the methods described in the paper "Genomic architecture and introgression shape a butterfly radiation", available at: https://zenodo.org/records/3401692 (specifically, the approach under nbedelman-DiscovarCompGenomics-f73a9aa/introgression/ADMIXTOOLS/allHeliconius_toHmel, as can be seen in their runAll.sh script). I have pasted their runAll.sh and the associated scripts below.

=== runAll.sh ===

#!/bin/bash

###################### Setup Environment #################

mkdir -p data
mkdir -p code
mkdir -p mafs
mkdir -p fastas
mkdir -p VCFs

ln -s /n/mallet_lab/edelman/18Genomes/results/DiscovarCompGenomics/data/finalAssemblies_highQual_1kbFilter_161101.hal data/fullAlignment.hal
ln -s /n/holylfs/INTERNAL_REPOS/PCACTUS/edelman/genomes/1kbFilter/Hmel2.fa_1kb.fa data/refGenome.fa
ln -s /n/mallet_lab/edelman/18Genomes/results/DiscovarCompGenomics/data/Hmel2pt5/Hmel2.transitions.tsv data/mapFile.tsv

chmod u+x code/*

module load gcc/6.3.0-fasrc01 AdmixTools/20171018-fasrc01

################ Define Variables ###############
refGenome=HmelRef
chromMap=data/mapFile.tsv

################# Run Code ######################
#first, extract MAF for each chromosome
chroms=$(grep ">" data/refGenome.fa |cut -d ">" -f 2|cut -d " " -f 1|tail -573)
for chrom in $chroms
do extract=sbatch code/extractChromMaf.slurm data/fullAlignment.hal $refGenome $chrom | cut -d " " -f 4
echo $extract: >> extractJobs.txt
mv $chrom_to$refGenome.maf mafs
done

#########re-ran, restricting analysis to only single copy. did:
for maf in mafs
do code/ $maf singleCopyMafs/$(basename $maf .maf)
done
#and then ran the rest of the code on

#then, convert maf to fasta
extractJobs=$(tr -d "\n" < extractJobs.txt)
extractJobs=$(basename $extractJobs :)
maf2Fasta=sbatch --dependency=afterok:$extractJobs code/mafToFasta.slurm mafs| cut -d " " -f 4

#then, convert FASTA to VCF and Eigenstrat
fasta2Eigenstrat=sbatch --dependency=afterok:$maf2Fasta code/fastaToEigenstrat.slurm fastas| cut -d " " -f 4

#In this case, since we're mapping to Hmel and have a reference chromosomal order, convert the scaffold-level eigenstrat files to chromosomes.

code/scafNamesToNums.py allGenomes.snp allGenomes.geno allHeliconiini_toHmel_placed $chromMap

qpDstat -p parFiles/parDstat_allHeliconius > allHeliconius.D.out

=== code/extractChromMaf.slurm ===

#!/bin/bash

#SBATCH -J extractChromMaf
#SBATCH -p general
#SBATCH -t 0-5:00
#SBATCH -n 1
#SBATCH --mem=5000
#SBATCH -e extractChromMaf.err
#SBATCH -o extractChromMaf.out

source /n/sw/progressiveCactus-latest/progressiveCactus/environment

halFile=$1
refGenome=$2
refSequenceList=$3

for refSequence in $refSequenceList
do hal2maf --refGenome $refGenome --refSequence $refSequence --onlyOrthologs --noAncestors $halFile $refSequence_to$refGenome.maf
done

=== code/mafToFasta.slurm ===
#!/bin/bash

#SBATCH -J mafToFasta
#SBATCH -p general
#SBATCH -t 0-10:00
#SBATCH -n 1
#SBATCH --mem=10000
#SBATCH -e mafToFasta.err
#SBATCH -o mafToFasta.out

source /n/sw/progressiveCactus-latest/progressiveCactus/environment

mafDir=$1
fastaDir=$2

for maf in $mafDir/*
do fasta=$fastaDir/$(basename $maf .maf).fa
msa_view --in-format MAF --out-format FASTA -G 1 -m $maf > $fasta
done

=== code/fastaToEigenstrat.slurm ===
#!/bin/bash

#SBATCH -J fastaToEigenstrat
#SBATCH -p general,shared,serial_requeue
#SBATCH -t 0-01:00
#SBATCH -n 1
#SBATCH --mem=10000
#SBATCH -e fastaToEigenstrat.err
#SBATCH -o fastaToEigenstrat.out

module load Anaconda

source activate my_root

export PYTHONPATH=$PYTHONPATH:/n/mallet_lab/edelman/software/gdc

module load phast/1.4-fasrc01
module load vcftools

fastaDir=$1
base=$2

mkdir -p $base_VCFs

for fastaFile in $fastaDir/*.fa
do numSeqs=$(grep ">" $fastaFile|wc -l)
if [ $numSeqs -ge 17 ]; then
VCFfile=$base_VCFs/$(basename $fastaFile .fa).vcf
scaffold=$(basename $fastaFile|cut -d "_" -f 1|cut -d "." -f 1)
msa_view --seqs Etal,Ldor,Htel,Hhsa,HeraRef,HeraDisco,Hhim,HeraHhimHyb,Hsar,Hdem,Hbes,Hpar,Hnum,Htim,Hcyd,HmelRef,HmelDisco $fastaFile|
sed 's/ //g' > $fastaFile_tmp
numSeqs=$(grep ">" $fastaFile_tmp|wc -l)
if [ $numSeqs -eq 17 ]; then
snp-sites -cv -o $VCFfile $fastaFile_tmp
sed -i "s/^1/$scaffold/g" $VCFfile

###This part is specific to Hmel2###
chrom=${scaffold:0:7}
awk -v chr=$chrom '$1=chr' $VCFfile|awk $3=$1:$2
####################################
fi
fi
rm -f $fastaFile_tmp
done

vcf-concat $base_VCFs/* > allGenomes.$base.vcf

python code/vcfToEigenstrat.py -v allGenomes.$base.vcf -o allGenomes.$base
=== code/scafNamesToNums.py ===
#!/usr/bin/env python

import sys

snpFile=sys.argv[1]
genoFile=sys.argv[2]
outBase=sys.argv[3]
mapFile=sys.argv[4]

def makeConversionKey(mapFile):
'''use a map file to create a key that converts scaffold coordinates to chromosomes'''
convertDict={}
m=open(mapFile, "r")
#ignore header line
m.readline()
for line in m:
atts=line.split()
chrom=atts[0]
scafStart=int(atts[1])
scafEnd=int(atts[2])
scafName=atts[6]
strand=atts[9]
convertDict[scafName]=[chrom,scafStart,scafEnd,strand]
m.close()
return convertDict

def scafSnpToChromSnp(snpFile,genoFile,outBase,conversionKey):
s=open(snpFile, "r")
g=open(genoFile, "r")
snpOut=open(outBase+".snp", "w")
genoOut=open(outBase+".geno", "w")
scafDict={}
for line in s:
geno=g.readline()
atts=line.split()
scaf=atts[1]
position=int(atts[3])
chrom=conversionKey[scaf][0]
if chrom != "0":
if conversionKey[scaf][3]=="+":
newPosition=conversionKey[scaf][1]+position-1
else:
newPosition=conversionKey[scaf][2]-position+1
snpOut.write('''%s\t%s\t%s\t%s\t%s\t%s\n''' % (atts[0],chrom,atts[2],newPosition,atts[4],atts[5]))
genoOut.write(geno)
g.close()
s.close()
snpOut.close()
genoOut.close()

conversionKey=makeConversionKey(mapFile)
scafSnpToChromSnp(snpFile,genoFile,outBase,conversionKey)

[bumblenick]

Please send me the output log (stdout) from the q[Dstat run you don't like. N

…

On Thu, May 14, 2026 at 9:40 PM Osteomeles ***@***.***> wrote: *Osteomeles* left a comment (DReichLab/AdmixTools#125) <#125 (comment)> Dear Professor, Thank you very much for your prompt reply. Currently, for each species I have only a single whole-genome FASTA file and the corresponding MAF files generated from Cactus alignment. Two of these species are actually geographic subspecies (i.e., they belong to the same species but were sampled from geographically distant locations), so I may not be able to calculate Fst. My pipeline follows the methods described in the paper "Genomic architecture and introgression shape a butterfly radiation", available at: https://zenodo.org/records/3401692 (specifically, the approach under nbedelman-DiscovarCompGenomics-f73a9aa/introgression/ADMIXTOOLS/allHeliconius_toHmel, as can be seen in their runAll.sh script). I have pasted their runAll.sh and the associated scripts below. === runAll.sh === #!/bin/bash ###################### Setup Environment ################# mkdir -p data mkdir -p code mkdir -p mafs mkdir -p fastas mkdir -p VCFs ln -s /n/mallet_lab/edelman/18Genomes/results/DiscovarCompGenomics/data/finalAssemblies_highQual_1kbFilter_161101.hal data/fullAlignment.hal ln -s /n/holylfs/INTERNAL_REPOS/PCACTUS/edelman/genomes/1kbFilter/Hmel2.fa_1kb.fa data/refGenome.fa ln -s /n/mallet_lab/edelman/18Genomes/results/DiscovarCompGenomics/data/Hmel2pt5/Hmel2.transitions.tsv data/mapFile.tsv chmod u+x code/* module load gcc/6.3.0-fasrc01 AdmixTools/20171018-fasrc01 ################ Define Variables ############### refGenome=HmelRef chromMap=data/mapFile.tsv ################# Run Code ###################### #first, extract MAF for each chromosome chroms=$(grep ">" data/refGenome.fa |cut -d ">" -f 2|cut -d " " -f 1|tail -573) for chrom in $chroms do extract=sbatch code/extractChromMaf.slurm data/fullAlignment.hal $refGenome $chrom | cut -d " " -f 4 echo $extract: >> extractJobs.txt mv $chrom_to$refGenome.maf mafs done #########re-ran, restricting analysis to only single copy. did: for maf in mafs do code/ $maf singleCopyMafs/$(basename $maf .maf) done #and then ran the rest of the code on #then, convert maf to fasta extractJobs=$(tr -d "\n" < extractJobs.txt) extractJobs=$(basename $extractJobs :) maf2Fasta=sbatch --dependency=afterok:$extractJobs code/mafToFasta.slurm mafs| cut -d " " -f 4 #then, convert FASTA to VCF and Eigenstrat fasta2Eigenstrat=sbatch --dependency=afterok:$maf2Fasta code/fastaToEigenstrat.slurm fastas| cut -d " " -f 4 #In this case, since we're mapping to Hmel and have a reference chromosomal order, convert the scaffold-level eigenstrat files to chromosomes. code/scafNamesToNums.py allGenomes.snp allGenomes.geno allHeliconiini_toHmel_placed $chromMap qpDstat -p parFiles/parDstat_allHeliconius > allHeliconius.D.out === code/extractChromMaf.slurm === #!/bin/bash #SBATCH -J extractChromMaf #SBATCH -p general #SBATCH -t 0-5:00 #SBATCH -n 1 #SBATCH --mem=5000 #SBATCH -e extractChromMaf.err #SBATCH -o extractChromMaf.out source /n/sw/progressiveCactus-latest/progressiveCactus/environment halFile=$1 refGenome=$2 refSequenceList=$3 for refSequence in $refSequenceList do hal2maf --refGenome $refGenome --refSequence $refSequence --onlyOrthologs --noAncestors $halFile $refSequence_to$refGenome.maf done === code/mafToFasta.slurm === #!/bin/bash #SBATCH -J mafToFasta #SBATCH -p general #SBATCH -t 0-10:00 #SBATCH -n 1 #SBATCH --mem=10000 #SBATCH -e mafToFasta.err #SBATCH -o mafToFasta.out source /n/sw/progressiveCactus-latest/progressiveCactus/environment mafDir=$1 fastaDir=$2 for maf in $mafDir/* do fasta=$fastaDir/$(basename $maf .maf).fa msa_view --in-format MAF --out-format FASTA -G 1 -m $maf > $fasta done === code/fastaToEigenstrat.slurm === #!/bin/bash #SBATCH -J fastaToEigenstrat #SBATCH -p general,shared,serial_requeue #SBATCH -t 0-01:00 #SBATCH -n 1 #SBATCH --mem=10000 #SBATCH -e fastaToEigenstrat.err #SBATCH -o fastaToEigenstrat.out module load Anaconda source activate my_root export PYTHONPATH=$PYTHONPATH:/n/mallet_lab/edelman/software/gdc module load phast/1.4-fasrc01 module load vcftools fastaDir=$1 base=$2 mkdir -p $base_VCFs for fastaFile in $fastaDir/*.fa do numSeqs=$(grep ">" $fastaFile|wc -l) if [ $numSeqs -ge 17 ]; then VCFfile=$base_VCFs/$(basename $fastaFile .fa).vcf scaffold=$(basename $fastaFile|cut -d "_" -f 1|cut -d "." -f 1) msa_view --seqs Etal,Ldor,Htel,Hhsa,HeraRef,HeraDisco,Hhim,HeraHhimHyb,Hsar,Hdem,Hbes,Hpar,Hnum,Htim,Hcyd,HmelRef,HmelDisco $fastaFile| sed 's/ //g' > $fastaFile_tmp numSeqs=$(grep ">" $fastaFile_tmp|wc -l) if [ $numSeqs -eq 17 ]; then snp-sites -cv -o $VCFfile $fastaFile_tmp sed -i "s/^1/$scaffold/g" $VCFfile ###This part is specific to Hmel2### chrom=${scaffold:0:7} awk -v chr=$chrom '$1=chr' $VCFfile|awk $3=$1:$2 #################################### fi fi rm -f $fastaFile_tmp done vcf-concat $base_VCFs/* > allGenomes.$base.vcf python code/vcfToEigenstrat.py -v allGenomes.$base.vcf -o allGenomes.$base === code/scafNamesToNums.py === #!/usr/bin/env python import sys snpFile=sys.argv[1] genoFile=sys.argv[2] outBase=sys.argv[3] mapFile=sys.argv[4] def makeConversionKey(mapFile): '''use a map file to create a key that converts scaffold coordinates to chromosomes''' convertDict={} m=open(mapFile, "r") #ignore header line m.readline() for line in m: atts=line.split() chrom=atts[0] scafStart=int(atts[1]) scafEnd=int(atts[2]) scafName=atts[6] strand=atts[9] convertDict[scafName]=[chrom,scafStart,scafEnd,strand] m.close() return convertDict def scafSnpToChromSnp(snpFile,genoFile,outBase,conversionKey): s=open(snpFile, "r") g=open(genoFile, "r") snpOut=open(outBase+".snp", "w") genoOut=open(outBase+".geno", "w") scafDict={} for line in s: geno=g.readline() atts=line.split() scaf=atts[1] position=int(atts[3]) chrom=conversionKey[scaf][0] if chrom != "0": if conversionKey[scaf][3]=="+": newPosition=conversionKey[scaf][1]+position-1 else: newPosition=conversionKey[scaf][2]-position+1 snpOut.write('''%s\t%s\t%s\t%s\t%s\t%s\n''' % (atts[0],chrom,atts[2],newPosition,atts[4],atts[5])) genoOut.write(geno) g.close() s.close() snpOut.close() genoOut.close() conversionKey=makeConversionKey(mapFile) scafSnpToChromSnp(snpFile,genoFile,outBase,conversionKey) — Reply to this email directly, view it on GitHub <#125 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEE77B2UI4CGRBQXIICRGGL42ZYSRAVCNFSM6AAAAACY6BFLOGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHM2DINJWGE2DINZQHE> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>

[Osteomeles]

log.zip
Here are the qpDstat results for all relevant four-species combinations involving Syh and Syg, which are two individuals sampled from different geographic locations of the same species. Thank you.

[bumblenick]

Thanks, I'll check them out.

…

On Fri, May 15, 2026 at 8:45 AM Osteomeles ***@***.***> wrote: *Osteomeles* left a comment (DReichLab/AdmixTools#125) <#125 (comment)> log.zip <https://github.com/user-attachments/files/27801660/log.zip> Here are the qpDstat results for all relevant four-species combinations involving Syh and Syg, which are two individuals sampled from different geographic locations of the same species. Thank you. — Reply to this email directly, view it on GitHub <#125 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEE77B3J7FIDJ775OJG6M33424GO3AVCNFSM6AAAAACY6BFLOGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHM2DINJZHA3DEMBWGU> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>

[bumblenick]

I certainly don't want to look at more than 300 output files but I note you are using 150M snps which feels like probably the whole genome. Surely not all these sites are polymorphic and qpDstat needs huge amounts of memory. Probably this does no harm -- but good style would be to downsample. Note that even if syg and syh are close genetically they may introgress differently to other species. Nick

…

On Fri, May 15, 2026 at 8:45 AM Osteomeles ***@***.***> wrote: *Osteomeles* left a comment (DReichLab/AdmixTools#125) <#125 (comment)> log.zip <https://github.com/user-attachments/files/27801660/log.zip> Here are the qpDstat results for all relevant four-species combinations involving Syh and Syg, which are two individuals sampled from different geographic locations of the same species. Thank you. — Reply to this email directly, view it on GitHub <#125 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEE77B3J7FIDJ775OJG6M33424GO3AVCNFSM6AAAAACY6BFLOGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHM2DINJZHA3DEMBWGU> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>

[Osteomeles]

Thank you for your patience and thoughtful response. I will attempt downsampling and rerun the analysis.

I have a follow-up question. For some of my species pairs, the D-statistic values reach 0.7 or even exceed 0.8. Is this considered reasonable? I understand that values above 0.1 are typically regarded as significant, but would values as high as 0.7 or 0.8 be considered too extreme?