Evaluating the accuracy of AIM panels at quantifying genome ancestry

13 Pages·2014·2.66 MB·English

Evaluating the accuracy of AIM panels at
quantifying genome ancestry
Pardo-Seco et al.
Pardo-Secoetal.BMCGenomics2014,15:543
http://www.biomedcentral.com/1471-2164/15/543
Pardo-Secoetal.BMCGenomics2014,15:543
http://www.biomedcentral.com/1471-2164/15/543
RESEARCH ARTICLE Open Access
Evaluating the accuracy of AIM panels at
quantifying genome ancestry
Jacobo Pardo-Seco1,2†, Federico Martinón-Torres2,3 and Antonio Salas1,2*†
Abstract
Background: There is a growing interest among geneticists indeveloping panels of AncestryInformativeMarkers
(AIMs) aimed at measuring thebiogeographical ancestry of individual genomes. The efficiency of thesepanels is
commonly tested empirically bycontrasting self-reported ancestry with theancestryestimatedfrom these panels.
Results: UsingSNPdatafromHapMapwecarriedoutasimulation-basedstudyaimedatmeasuringtheeffectof
SNPcoverageontheestimationofgenomeancestry.Forthreeofthemaincontinentalgroups(Africans,EastAsians,
Europeans)ancestrywasfirstestimatedusingthewholeHapMapSNPdatabaseasaproxyforglobalgenomeancestry;
theseestimatesweresubsequentlycomparedtothoseobtainedfrompre-designedAIMpanels.Panelsthatconsider
>400AIMscapturegenomeancestryreasonablywell,whilethosecontainingafewdozenAIMsshowalargevariability
inancestryestimates.Curiously,500-1,000SNPsselectedatrandomfromthegenomeprovideanunbiasedestimateof
genomeancestryandperformaswellasanyAIMpanelofsimilarsize.Insimulatedscenariosofpopulationadmixture,
panelscontainingfewAIMsalsoshowimportantdeficienciestomeasuregenomeancestry.
Conclusions:Theresultsindicatethattheabilitytoestimategenomeancestryisstronglydependentonthenumber
ofAIMsused,andnotprimarilyontheirindividualinformativeness.Cautionshouldbetakenwhenmakingindividual
(medical,forensic,oranthropological)inferencesbasedonAIMs.
Keywords:Genomics,SNPs,AIMs,Ancestry
Background question is far from trivial. Considering the way in which
With the publication of the Human Genome Project the DNA material is inherited through generations,
(http://www.ornl.gov/sci/techresources/Human_Genome/ most of it from both parents (the exception being the
home.shtml) in 2000 and the pioneering high-throughput uniparental markers), entire blocks of our genome can
single nucleotide polymorphism (SNP) genotyping pro- have different ancestral origins. In the words of Svante
jects (such as HapMap; www.hapmap.org) our perception Pääbo, “to understand what make us unique, both as
of human genome has changed, as well as our under- individuals and as a species, we need to consider the
standing of human evolution and genome ancestry. The genomeasamosaicofdiscretesegments,eachwithitsown
term ancestry refers to “the origin or background of unique history and relatedness to different contemporary
something” (http://oxforddictionaries.com). Accordingly, andancestralindividuals” [1].
in human genetics, ancestry is generally understood as Althoughgeneticvariationinhumansshowsgradientsof
the origin or background of our genomes. However, the allele frequencies extending over the entire world (within
and among continents or among groups of individuals
[2]), there is empirical evidence indicating that the most
*Correspondence:[email protected]
†Equalcontributors contrastinggenomicpatternsofdiversityinhumansoccur
1UnidadedeXenética,DepartamentodeAnatomíaPatolóxicaeCiencias at an inter-continental level; e.g. Africa, Europe, and Asia
Forenses,andInstitutodeCienciasForenses,GrupodeMedicinaXenómica (often erroneously interpreted as genetic support for
(GMX),FacultadedeMedicina,UniversidadedeSantiagodeCompostela, “races”[2]).Thebestwaytocharacterizethesecontinental
15872SantiagodeCompostela,Galicia,Spain
2GrupodeInvestigaciónenGenética,Vacunas,InfeccionesyPediatría patterns (as discrete clustersof variation) is by examining
(GENVIP),HospitalClínicoUniversitarioandUniversidadedeSantiagode them to a genomic scale, given that single locus could
Compostela(USC),SantiagodeCompostela,Galicia,Spain
Fulllistofauthorinformationisavailableattheendofthearticle
©2014Pardo-Secoetal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsofthe
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DomainDedicationwaiver(http://creativecommons.org/publicdomain/zero/1.0/)appliestothedatamadeavailableinthis
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notnecessarilycapturetheglobalgenomicscenario.With in reality they could reveal information relevant for the
thearrivalofnewgenotypingtechnologiesandlarge-scale customer’s health. This is due to the fact that there are
genomic projects, it is now possible to measure genomic health disorders that can be more highly correlated with
ancestry using large genome-wide SNP panels or, more certain ancestries than others. However, the accuracy of
recently, next generation sequencing data (NGS; e.g. DTCT has been questioned on several grounds. For in-
http://www.1000genomes.org) [3,4]. However, this gen- stance, these companies often offer only to genotype the
omic approach is not always cost-effective and it can uniparental markers (the mitochondrial DNA [mtDNA]
also represent a handicap in particular scenarios (low and/or the Y-chromosome [23]); however, these markers
amount and/or degraded DNA; e.g. population and behaveassinglelocusandthereforecanonlyreflectavery
forensic routine casework). Alternatively, ancestry can tiny portion of the genomic individual ancestry [24,25].
be estimated using a selected number of SNPs ranging Ancestry inferences made using autosomal markers have
rom a few dozens to several hundreds; this option has beenconflictingtoo[26].
been favored in different areas of biomedical research, Most of the AIM panels available in the literature have
including case-control association studies of complex been designed by way of selecting SNPs from large
disease(e.g.admixturemapping)[5-8],humanpopulation genomic databases (e.g. HapMap) showing skewed popu-
studies [9-11], and forensic genetics and police investiga- lation frequencies between the ancestral populations
tion [12-14]. The selected SNPs are commonly known targeted. Usually, researchers do not evaluate the amount
as Ancestry Informative Markers (AIMs) and received of genetic informativeness provided individually by the
this name because they exhibit large differences in selectedSNPs.AnexceptionisthestudybyGalenteretal.
allele frequencies between populations from different [5], who used a multi-step algorithm that weighs the
geographical or ethnic groups. By genotyping a number amount of information provided by their AIMs regarding
of AIMs, it seems possible to estimate the most likely theancestralpopulationsbeingconsidered.
geographical or ethnic origin of a given genomic profile, The ability of an AIM panel to measure ancestry is
or to ascertain what proportion of ancestry in this profile generally evaluated empirically, that is, by examining its
is derived from different geographical regions or source performance on a given set of DNA samples for which
populations. a given ancestry is already assumed. Several statistical
Measuring ancestry is important in biomedical studies techniques (such as principal component analysis [PCA],
for a number of reasons. For instance, it has been dem- and admixture analysis) are then used to evaluate their
onstrated that population stratification represents an efficiency. An AIM panel is generally considered to be
important confounding effect in case-control association efficient if e.g. it can differentiate the targeted populations
studies of complex and multi-factorial diseases [15-19]. intheEuclidianspacerepresentedbytwoorthreeprincipal
Estimating ancestry using AIMs panels can be used in components (PC) or if the inferred ancestry is consistent
these studies to control for population sub-structure with some expectation (e.g. self-reported ancestry). The
in medical studies. Some companies have developed majority of the panels are designed with the aim of distin-
commercial kits (http://www.illumina.com/products/ guishing main continental groups (e.g. Africans, Asians,
dna_test_panel.ilmn) aimed at measuring the ancestry Europeans, Americans) owing to the known difficulties of
of samples as a screening method before proceeding using small SNP panels to classify individuals when they
withtheirhigh-throughputgenotypingormassiveparallel belongtocloselyrelatedpopulations.
sequencing. The number of SNPs incorporated into AIM panels
The search for autosomal ancestry has also been a variesfromafewdozenstoafewhundreds(Table1);and
focus of attention in the forensic community [13,20,21]. this number is generally constrained by the genotyping
Forensic geneticists have to deal with evidentiary samples techniqueemployed.Forinstance,mostofthetechniques
containing little amounts of, and/or poorly preserved, allow genotyping only a few dozen SNPs in a single PCR
DNA.Inthesecases,thelimitedamountofDNAavailable reaction (e.g. SNaPshot [13], mass array spectrometry
often allows a singlePCRreaction only or, in cases where [27]). Although it seems reasonable to consider that the
moreDNAisavailable,itisgenerallypreferredtopreserve number of AIMs in a panel could be relevant when
itinordertoallowasecondandindependenttestinadif- estimating ancestry, a comprehensive evaluation of this
ferent laboratory. Forensic geneticists have also designed factor has not been carried out to date. Questions arise
their own panels of AIMs allowing estimation of ancestry tooabouthowmanyautosomalmarkerswouldbeneeded
basedonsingle-plexassays[13,21]. in scenarios of population admixture, where the use of a
At the same time, many private companies offer direct- dense panel of AIMs could be even more important in
to-consumer-tests(DTCT)specificallydesignedtomeasure order tobetterrepresent the admixed profiles. Difficulties
ancestry [22,23]. Although most of these tests do not aim in correctly estimating genome ancestry could also derive
to provide specific information about disease conditions, from the application of an AIM panel to a set of samples
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Table1CorrespondingancestryestimatesinthreecontinentalHapMapgrouCEpUs,CEU(Europe),CHB(EastAsiaC),HaBndYRI(Africa)usingdifferentSYRNIPsets /1471-22014,s
11
GenomeAncestry S1,N44P0s,616 ATrFaRi/nAinSIg/EsUeRtpopulations 1%00 0S.D1 19050%-1C00I 1R0a0n-g1e00 1%00 0SD 19050%-1C00I 1R0a0n-g1e00 1%00 0SD 19050%-1C00I 9R9a.n4-g1e00 64/155:543
/5
10,000rSNPs1 10,000 AFR/ASI/EUR 99.7 0.7 99.9-100 98.5-100 100 0.2 99.9-100 98.6-100 99.9 0.3 99.5-99.9 96.9-100 43
1,000rSNPs1 1,000 AFR/ASI/EUR 97.0 3.5 98.1-99.4 91.0-100 98.8 2.2 97.7-98.9 91.7-100 98.3 2.2 96.1-98.0 87.0-100
500rSNPs1 500 AFR/ASI/EUR 94.8 1.2 96.5-98.5 86.1-100 97.5 3.6 95.8-97.8 86.8-100 96.8 3.6 93.3-96.2 80.4-100
GAL 446 AFR/AME/EUR 92.7 3.1 91.9-93.6 86.6-100 96.0 3.0 95.1-96.8 89.5-100 99.1 1.2 98.8-99.4 95.8-100
ILU 360(310)2 CEU/CHB+JPT/YRI 87.8 4.7 86.5-89.1 73.9-98.1 97.0 2.5 96.3-97.7 90.1-100 98.7 1.7 98.2-99.2 92.9-100
HAL 1763(162)2 AFR/AME/EUR/ASI 87.0 7.2 85.0-89.0 70.2-100 93.9 5.1 92.4-95.3 77.6-100 96.2 3.7 95.2-97.2 86.6-100
KOS 128 AFR/ASI/EUR/ASI/SAS/AME/MEX/PRI 87.4 6.5 85.6-89.2 73.4-100 90.7 5.4 89.2-92.2 77.9-100 97.9 2.8 97.1-98.7 90.6-100
NAS 93 OCE/ASI/AFR/SAM/EUR 87.5 7.4 85.4-89.5 70.2-100 89.0 6.1 87.3-90.6 77.0-100 97.7 3.2 96.8-98.6 88.1-100
PHI 34(27)2 AFR/EUR/ASI 93.8 8.2 91.5-96.1 67.5-100 92.7 8.3 90.4-95.0 73.6-100 90.1 9.0 87.6-92.5 65.0-100
COR 24(23)2 SAM/EUR/AFR 90.9 9.7 88.2-93.5 58.4-100 89.5 12.7 86.0-93.0 37.5-100 92.7 8.1 90.5-95.0 57.7-100
LAO 10 AFR/EUR/ASI/AME 83.6 14.5 79.6-87.7 48.5-100 85.9 19.0 80.7-91.2 30.4-100 81.0 16.5 76.4-85.5 33.9-100
CEUcolumnshowsthepercentages(%)ofEuropeanancestryinCEU,CHBcolumnshowsthepercentageofAsianancestryinCHB,andYRIshowsthepercentageofAfricanancestryinYRI.Foreachpopulationgroup
thetableshowsalsothestandarddeviations(SD),95%confidenceintervals(95%CI),andranges(minimum-maximumvalues).GenomeancestryreferstotheancestrymeasuredusingthefullsetofSNPsinHapMap.
TrainingsetpopulationsrefertothepopulationgroupsusedtodesigntheAIMpanels.AFR:Africa,ASI:Asia,SAS:SouthAsia,EUR:Europe,AME:America,MEX:Mexico,SAM:SouthAmerica,OCE:Oceania,PRI:
PuertoRico.
1Averagedvaluesoverallthere-samples.
2NumberofSNPsindicatedinroundbracketsarethosecontainedintheHapMapdatabase.
3Halderetal.[37]referto170AIMs;however,theirsupplementarydatafilerefersto176AIMs.
P
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3
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1
2
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different from that used duringthe SNPselection process to measure genome ancestry could be justified in some
and the training tests (‘lack of portability’ [28]). At the genetic contexts [29], but not when trying to infer global
same time, using mtDNA and Y-chromosome markers individualgenomeancestry[24].
Figure1PCAplotsofYRI,CHBandCEUcarriedoutusing500re-samplesofrSNPsfromHapMaptakingatrandom10,000(1A),1,000
(1B)and500(1C)SNPs.Onlyoneofthere-samplesishighlightedincolor;theresultsfortheremaining499re-samplesareindicatedingrey
withtheaimofillustratingthevariabilityonancestryestimatesassociatedtotherandomsub-setsofSNPs.Thebox-plots(rightpanels)indicate
theEuropean,AsianandAfricanancestriesinCEU,CHB,andYRI,respectively,asobtainedinthedifferentre-samples.Astatisticalsummaryof
thesebox-plotsisgiveninTable1.
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Methods Samplesizeandancestryestimates
Populationsamples Thedependence of ancestryinferenceon sample sizewas
The HapMap SNP database was retrieved from its repo- estimated through simulation experiments using a similar
sitory (http://hapmap.ncbi.nlm.nih.gov). This database proceduretothat in Heinz etal. [30].Inbrief,for each of
contains 1,440,616 SNPs genotyped in a total of 1,218 the three main continental populations, we randomly
individual samples belonging to the following main selected 1,000 sub-samples of variable sizes (from five
continental groups: 472 Africans, 58 Americans, 101 to 40 profiles; in stepwise increments of five and taken
Central-SouthAsians,364EastAsiansand223Europeans. without replacement). Thus, for example, we obtained
Unless specified, for most of the simulation experiments, 1,000sub-samples ofsizefive, 1,000sub-samples ofsize
only three populations representing the main continental ten,andsoonuntilamaximumsamplesizeof40.Foreach
groups were taken from the full HapMap data, namely of the sub-samples we computed ancestry proportions as
CEU (Europeanancestry),CHB (East Asian ancestry) and indicated below. Continental ancestry was estimated as
YRI (African ancestry), with 50 individuals in each group. themeanvalueobtainedforthe1,000sub-samplesineach
ThisdecisionwasbasedonthefactthatmostoftheAIM sample window, and bootstrapping intervals were built
panels available were designed to identify ancestry from accordingly.
mainpopulationgroups.
For some simulation experiments, we created artificial
scenarios of admixture by mixing at random the same Statisticalanalysis
proportion of SNPs from the following three HapMap ThesoftwareAdmixture v.1.22[31]wasusedtoestimate
datasets:CEU,CHB,andYRI.Therefore,theexpectedgen- individual and population ancestries. This software was
ome admixture in these artificially created hybrid genomic runusingdefaultparameters.Crossvalidationerrorswere
profiles (henceforthreferred to asthe “AA-genomes”) is 1/ obtained from Admixture in order to determine the most
3 of ancestry from each of the main continental groups likelyKvalue(Kindicatingthenumberofinferredclusters
(Asia,Europe,andAfrica). showingthelowestcrossvalidationerror).
Figure2Bar-plotsofancestrymembershipsinferredforYRI,CHB,andCEU,considering1,000and500rSNPssets(eachconsidering
onesampletakenatrandomfromHapMap)andthedifferentAIMpanels.
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PLINKv.1.07 [32] was used to obtain Identity-By-State
(IBS) values between individuals, and IBS values were
used to carry out two-dimensional PCA. PLINK was
used with default settings. Only when calculating the
effect of population sample size on the estimation of
ancestry,individualprofileswithmissingdata>10%were
filteredout(callrates<90%couldbecriticalwhendealing
withAIMpanelscontaininglownumberofSNPs).
Locus specific branch length (LSBL) statistics was esti-
matedusingpairwiseF distancesascarriedoutinShriver
ST
et al. [33]. LSBL aims to assist in the selection of AIMs in
panels taking into account their level of individual inform-
ativeness with regards to the classification population sets.
F valuesweretakenfromSPSmartandENGINES[34,35].
ST
In-house R 2.13.0 (http://www.r-project.org) and Perl
(http://www.perl.org) scripts were used to display results
obtainedfrom thedifferent softwarepackagesused.
Pre-designedAIMpanels
Ancestry of the selected HapMap datasets was estimated
using different AIM panels (Table 1): Corach et al. [36]
(COR),Galenteretal.[5](GAL),Halderetal.[37](HAL),
Kosoyetal.[38](KOS),Laoetal.[11](LAO),Nassiretal.
[39] (NAS), Phillips et al. [13] (PHI), and the commercial
DNATestPanelfromIllumina(ILU;http://www.illumina.
com/products/dna_test_panel.ilmn). All of these panels
were originally designed to differentiate the three main
continental groups (Europe, East Asia and Sub-Saharan
Africa), some of them also including other ancestral
groups (seeTable 1).
Results
Estimatinggenomeancestry
Thegenome-wideset ofSNPs in HapMapcan be usedto
estimate (global) genome ancestry of continental groups.
Admixture analysis shows an optimum value of K=3
when considering CEU, CHB, and YRI. Each individual
profile received virtually 100% of the expected genomic
ancestryinonecluster(inagreementwiththeirgeographic
origin/self-declared ancestry); that is, for example, a
Yorubanprofilereceivesa~100%membershipinacluster
that groups all samples of African ancestry (Additional
file 1).
A simulation experiment was carried out by selecting
subsets of random SNPs (henceforth rSNPs) from the
whole HapMap database and obtaining estimates of
ancestry from these subsets. This procedure allows us
to investigate the extent to which the estimation of Figure3PCAplotsobtainedforYRI,CHB,andCEUconsidering
1,000and500rSNPs(onesampleeach)takenatrandomfrom
genome ancestry degenerates when using decreasing
HapMapaswellasdifferentAIMpanels.Theinferencescarried
amounts of SNPs. Panels of 10,000, 1,000, and 500 rSNPs
outonAA-genomesareshowningrey;notethatthevariation(sizeof
whererandomly selected;each panel size wassampled 500 thegreypointcloud)increasesasfewerrSNPsorAIMsareconsidered.
times each in order to account for sampling variability.
The simulations indicate that the inferred genome an-
cestry degenerates slightly as fewer rSNPs enter in a set
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(Figure 1). However, ancestry estimates using panels of MeasuringinformativenessofSNPsinAIMpanels
500 rSNPs approach well the genome ancestry (although LSBL can be used to measure the informativeness of
theestimatesshowamoderatedispersion). specificAIMsinthepanelsandtheirpotentialtomeasure
The estimates above were obtained considering the different ancestries when applied to the HapMap popula-
three main continental groups: Europe, Asia, and Africa. tions. To the best of our knowledge, only the GAL panel
However, the number of SNPs needed to infer ancestry was designed using LSBL as a criterion to balance the
strongly depends on the evolutionary relatedness of the informativeness of the AIMs incorporated in the panels
populations being considered: the closer the population (Table 2). As shown in Additional file 3, all the panels
under study, the larger the number of SNPs needed. show unbalanced accumulated LSBL values, including
The PCA plot in Additional file 2 indicates that the GAL.However,bothofthepanelsthatcontainthelargest
wholesetofSNPsinHapMapclearlyseparatesEastAsian number of AIMs (GAL and ILL) perform reasonably
populations CHB+CHD (Chinese) from JPT (Japanese). well whenestimating genome ancestry (see above). This
However, using panels of 100,000, 50,000,10,000, 1,000 evidencesuggeststhat,eventhoughmeasuringtheinform-
and 500 rSNPs, differentiating these two populations ativenessoftheAIMswouldseemalogicalwaytoproceed
groups becomes increasingly difficult; see e.g. the over- when designing a panel, ensuring that a large number of
lapping patterns of profiles in the PCAs of Additional AIMs is considered constitutes at least an equally import-
file 2 when using 500 rSNPs. In population scenarios antparameter.
considering very closely related groups, the whole power WealsofollowedtheLSBLcriteriaforselectingthebest
of a genome-wide dataset would be needed in order to AIMs from the HapMap database (as done in Galanter
differentiate populations; e.g. see the case for European et al. [5]); in each case, the number of simulated AIMs
populations in Novembre et al. [4]. selectedwasthesameasthenumberusedinthedifferent
panelstested.Additionalfile3showsthatthesetestpanels
Pre-designedAIMpanels work better than their pre-designed counterpart panel
Ancestry estimates were obtained using different pre- usinganalogouscontinentalpopulations(TSIasrepresen-
designed panels and compared to the genome ancestry tative of Europe; CHD from East Asia and LWK from
inferred using sets of 10,000, 1,000 and 500 rSNPs. As Africa). However, those containing a higher number of
shown in the bar-plots in Figure 2, inference of ancestry HapMap-AIMs perform much better than those con-
degenerates as fewer AIMs are considered in the panels. sidering lower numbers of SNPs.
The PCA plots of Figure 3 mirror the same scenario. It
is noticeable that the panel of 500 rSNPs yields broadly Effectofpopulationsamplesizeontheestimation
similar results to the GAL and ILL panels, that is, the ofancestry
panels containing a similar number of AIMs. PCA clearly Simulations were carried out in order to estimate the
shows that the panels containing fewer AIMs show more effect of population sample size when inferring genome
disperse patterns. The LAO panel, which considers only ancestry using different AIM panels. These analyses are
10 AIMs, represents the most extreme case, showing an significantbecausegeneticistsareofteninterestedinmeas-
extremelylargevariability(Table1,Figure3). uring the average genetic ancestry of a given population
Table2LSBLvaluesfortheAIMsconsideredinthedifferentSNPpanelsandwhenconsideringHapMappopulations
AccumulatedLSBL AverageLSBL
PanelID AFR ASI EUR AFR ASI EUR
HapMap 143264.06 91184.56 50587.85 0.037 0.024 0.013
GAL 71.08 23.35 46.67 0.159 0.052 0.105
ILU 37.11 29.82 17.28 0.120 0.096 0.056
HAL 13.21 13.90 5.66 0.084 0.089 0.036
KOS 14.18 7.41 9.08 0.111 0.058 0.071
NAS 12.87 6.32 6.58 0.138 0.068 0.071
PHI 3.29 3.08 3.66 0.122 0.077 0.136
COR 4.96 2.41 1.74 0.216 0.105 0.076
LAO 1.31 1.30 0.56 0.131 0.130 0.056
Presentstudy(595SNPs) 98.38 98.40 98.35 0.165 0.165 0.165
Theterm“averageLSBL”referstotheLSBLaccumulatedandstandardizedbythenumberofAIMsineachpanel.LSBLinHapMapwascalculatedusingthe
HapMapdatabasethatconsidersallAfrican,EuropeanandAsianpopulationstogether.
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(Seefigureonpreviouspage.)
Figure4EffectofsamplesizeontheinferenceofancestryusingdifferentAIMpanels.Thehorizontalbarindicatesthegenomeancestry
asestimatedusingalltheHapMapindividualsforeach(CEU,CHB,YRI)anditmarksthereforethevaluetowhichalltheancestryestimatesfrom
AIMpanelsshouldconverge.Asthenumberofindividualsincreases,theestimatesofancestryusingthedifferentpanelsapproachthegenome
ancestry.Colorcodesareasfollows,red:Africanancestry;green:Asianancestry;andblue:Europeanancestry.
(see e.g. Heinz et al. [30]), but sample sizes are very low in investigation, and anthropological studies. It is also of
severalstudies. interest for private companies, given the growing social
As expected, simulations indicate that as the sample interestinknowingmoreaboutancestrycoupledwiththe
size increases the variability of the ancestry estimations progressivereductionofthecostofDNAtests.
decreases (Figure 4). Themost remarkablefindingis that, The present study aims to estimate the number of SNPs
for equal sample sizes, the largest AIM panels, GAL and needed to reliably infer genome ancestry using unbiased
ILL,showlowervariabilitythansmallerpanels. setsofSNPs(rSNPs)andsetsofpre-fabricatedAIMpanels.
Theresultsindicatethat10,000SNPsselectedatrandom
Inferringancestryinadmixedgenomes from an individual can be used to infer genome ancestry
The hybrid AA-genomes were created in order to allow with negligible error when considering the three HapMap
some simulations to investigate the number of SNPs populations CEU, CHB, and YRI. Even so, panels of 500
needed to capturegenomeancestryin admixture scenar- rSNPs perform reasonably well in thispopulation scenario.
ios. Figure 5 shows the patterns of ancestry estimated Below this number, errors in the inference of ancestry
using the different panels and compared to the expected increase noticeably as the number of rSNPs is reduced. As
ancestry in these genomes (equal membership in Africa, expected, the number of rSNPs needed to infer ancestry
East Asia and Europe; ~33%). The estimates of individ- strongly depends on the evolutionary proximity of the
ual ancestry are stable and close to expectation when populations under study. For instance, we made simula-
using rSNPs (>1000) and also using GAL (and slightly tions to test the number of rSNPs needed to differentiate
worse with ILL); while other panels such as PHI, COR ancestry in two different East Asian populations, Chinese
andparticularly LAOshowmore arbitrary patterns. andJapanese.HerethenumberofrSNPsneededtodiffer-
entiatethesepopulationsincreasessignificantlymorethan
Quantifyingerrorsinancestryestimates one order of magnitude; therefore, the need for searching
Thestandard deviation canbeusedto measuretheerror panels of highly discriminating AIMs is more justified.
oftheestimatedancestryobtainedfrompanels,compared The distinction between individual ancestries within Asian
toglobalgenomeancestry(Figure6).Theerrorsinances- populations(orothercloselyrelatedgroups)wouldrequire
try estimates are more pronounced when the number genome-wide screenings [4] or very large panels of AIMs
of rSNPs falls to ~500 SNPs (Figure 6). The ability of (probablycontainingthousandsofSNPs).
panels to capture different proportions of ancestry varies During the last few years, several panels of SNPs have
significantlyfrompaneltopanel,butthepanelswithmore been designed in order to estimate ancestry using only a
AIMs (specially GAL and ILL) perform much better than few markers (AIM panels). Analyses were carried out in
those containing fewer SNPs. For instance, panels PHI the present study in order to assess the performance of
andCORshowverylargevariabilityinancestryestimates, these panels when applied to three main HapMap con-
and this variability is extremely large in the case of LAO tinental populations, CEU, CHB, and YRI. The results
(Figure 6). Additional file 4 shows that there is a negative indicate that inference of ancestry can be seriously com-
correlation between the number of SNPs in a panel and promised when using panels containing small numbers
the error associated to the estimates of ancestry. Further- of AIMs. For instance, out of the panels tested in the
more, the error differs regarding the kindof ancestrythat present study, those showing the best performance are
is measured: for instance, the panels with the larger ILU and GAL, that is, those that have more AIMs, while
numbers of AIMs have more difficulties to measure the the ones including only a few dozen AIMs show higher
European ancestry than the African one. Curiously, the errorsandvariability(Additional file 4).
errorinancestryestimatesseemstohaveamorebalanced ItisinterestingtonotethatneitherGALnorILUwere
behavior regarding the inferences of the different ances- specifically conceived to discriminate exactly between the
triesinscenariosofadmixture(usingAA-genomes). three tested populations from HapMap (Table 1). In fact,
the cumulative LSBLvalue for the three HapMap popu-
Discussion lations indicates that these AIMs are not balanced for
Measuring genome ancestry is an issue of interest in these population groups (Figure 5). Therefore, the good
different fields of biomedical research, including case- performance of these panels is based to a great extent
control association studies, forensic casework and police on the large number of AIMs contained inthesepanels,