Analyses of a set of 128 ancestry informative single-nucleotide polymorphisms in a global set of ...

13 Pages·2011·4.65 MB·English

Kiddetal.InvestigativeGenetics2011,2:1
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RESEARCH Open Access
Analyses of a set of 128 ancestry informative
single-nucleotide polymorphisms in a global
set of 119 population samples
Judith R Kidd1*, Françoise R Friedlaender2, William C Speed1, Andrew J Pakstis1,
Francisco M De La Vega3, Kenneth K Kidd1
Abstract
Background: Using DNA to determine an individual’s ancestry from among human populations is generally
interesting and useful for many purposes, including admixture mapping, controlling for population structure in
disease or trait association studies and forensic ancestry inference. However, to estimate ancestry, including
possible admixture within an individual, as well as heterogeneity within a group of individuals, allele frequencies
are necessary for what are believed to be the contributing populations. For this purpose, panels of ancestry
informative markers (AIMs) have been developed.
Results: We are presenting our work on one such panel, composed of 128 ancestry informative single-nucleotide
polymorphisms (AISNPs) already proposed in the literature. Compared to previous studies of these AISNPs, we have
studied three times the number of individuals (4,871) in three times as many population samples (119). We have
validated this panel for many ancestry assignment and admixture studies, especially those that were the rationale
for the original selection of the 128 SNPs: African Americans and Mexican Americans. At the same time, the
limitations of the panel for distinguishing ancestry and quantifying admixture among Eurasian populations are
noted.
Conclusion: We demonstrate the simultaneous importance of the specific set of population samples and their
relative sample sizes in the use of the structure program to determine which groups cluster together and
consequently influence the ability of a marker panel to infer ancestry. We demonstrate the strengths and
weaknesses of this particular panel of AISNPs in a global context.
Background globally distributed populations [13-16]. Yet others have
In recent years, there have been many proposed ancestry focused on identifying the stratification of populations
informative markers (AIMs) and published sets of AIMs within particular geographic areas [e.g., [17-19] or
useful for particular purposes. Some sets have focused within a clinical association study sample [20-22]. What-
on estimating the admixture between specific ancestral ever the purpose, the general usefulness of such AIMS
populations such as the African and European genetic depends very much on the set of populations used to
contributions to African Americans or European, Native identify and characterize them. Some global studies have
American and African contributions to Latino popula- used only a few but widely separated population samples
tions [e.g., [1-7]. Others have focused on distinguishing [e.g., [15]. Others have used the HGDP-CEPH panel [23]
ancestral origins from three or four continental regions, of about 1,000 samples from 52 populations to select
such as sub-Saharan Africa, Europe, East Asia and the AIMs [10,11]. All approaches provide useful data but
Americas [8-12], or more broadly between many may also have weaknesses due to sampling error, either
because the population samples used may not be highly
*Correspondence:[email protected] representative of a broader geographic area or because
1DepartmentofGenetics,YaleUniversitySchoolofMedicine,333Cedar the individual sample sizes are very small and subject to
Street,NewHaven,CT06510,USA
very large sampling errors. The same criticism applies to
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studies attempting to identify markers that provide Methods
ancestry information within a region, such as East Asia Samples
[24] or Europe [17,18]. We assembled a data set of samples of 4,871 indivi-
Whatever the strategy for identifying them, AIMs are duals: those from the HapMap 3 [28], the Human Gen-
necessarily selected because they distinguish the specific ome Diversity Project (HGDP) [29,30] and our lab, all
population samples used. Therefore, replication with typed for the 128 SNPs of Kosoy et al. [6]. Some of the
other samples of individuals from the same and/or clo- HGDP samples used by Nassir et al. [27] are also
sely related populations is necessary to verify the robust- included in our study, and for some of their populations
ness of any set of AIMs. Such replication is onerous, we have an independent sample, e.g., Ashkenazi Jews.
costly and rarely undertaken. The HGDP contains 355 DNA samples from our lab or
Given the broad interest in AIMs in genetics, medi- from cell lines we hold and routinely type in our lab
cine, anthropology and forensics, the development of an and another 31 HGDP DNA samples are DNA samples
optimal set of AIMs for a broad range of uses needs to we also have in our lab. When an HGDP sample is a
be based on multiple markers studied on moderate to subset of one of our population samples, we used only
large samples of multiple relevant populations; appropri- our inclusive sample. When a sample from our lab
ate resources will probably not be available in any single overlapped with an HGDP sample, the duplicates were
lab. As we advocated in the case of single-nucleotide removed from our sample and the full HGDP sample
polymorphisms (SNPs) for individual identification was included separately from our supplementary sam-
[25,26], multiple labs need to test candidate markers on ple. Thus, we occasionally have two samples from the
additional populations and for general robustness in the same population (e.g., Druze, PNG, Makrani), but no
laboratory. While very large numbers of markers can individuals from the two samples overlap. Sixteen popu-
provide quite accurate ancestry information for multiple lations are represented by two to four samples. Some of
geographic regions, small but robust sets of markers are the “duplicate” populations (e.g., Han, Russians, Maasai)
especially useful. were sampled in different areas or countries, and some
Seldin’s group [6,27] identified a set of 128 SNPs that of the “duplicate” populations are independent samples
they showed is useful for identification of the continen- from the same locale. Finally, the offspring in the Hap-
tal origin of people and in estimating the admixture pro- Map 3 samples (ASW, CEU, MKK, YRI, and MEX)
portions of these individuals. Thus, a particular aim was were removed so that the samples include only unre-
to develop a set of SNPs whose allele frequencies had lated people. Table 1 provides the name, sources of the
major differences between the continental populations data and sample size for each of the final set of 119
for use in matching controls and subjects in association population samples. All samples from our lab were col-
studies. They validated all and various subsets of the lected with informed consent under protocols approved
128 SNPs in their initial study of 825 individuals from by the Institutional Review Board (IRB) at Yale Univer-
20 designated populations and subsequently in a study sity and other relevant IRBs; the other data are in the
of 1,620 individuals from 48 population samples using a public domain. Descriptions of all of the population
subset of 93 of the 128 SNPs [27]. samples are in ALFRED [28] associated with the allele
UnderstandingthatasetofAIMs(orancestryidentifica- frequencies.
tionSNPs,AISNPs)willonlybebroadlyusefulforpopula-
tionrelationshipsandforidentifyingadmixtureifthatset Marker Data
canbeshownonaverylargedatasettobevalid,wehave The polymorphic sites were those reported by Kosoy
tripled the size of the Nassir et al. [27] population set, et al. [6]. The 3,071 samples from our lab were typed
increased the number of population samples to 119 and by TaqMan SNP Genotyping Assays® (Applied Biosys-
analyzed this sample with the 128 AIMs of Kosoy et al. tems, Foster City, California, USA). The HGDP marker
[6].Wefindthatthissetof128AISNPsisnotonlyglob- data were downloaded from http://hagsc.org/hgdp/files.
allyinformativefororiginsfrommajorgeographicregions html[31]. The HapMap data were downloaded from
butalsoinformativefordistinguishingrelationshipswithin http://hapmap.ncbi.nlm.nih.gov/index.html.yo [28]. Of
severalofthoseregions.Thisprovidesfurthersupportfor the 128 SNPs typed for 119 population samples, only
theusefulnessofthissetofSNPsinsomeancestry/admix- 16 instances (one AISNP for one population) of miss-
tureanalyses.WealsonotethattheseAISNPsarenotpar- ing data existed in the public data. Eleven SNPs in
ticularly good at distinguishing within certain groups of seven HapMap 3 populations do not have genotype
populations, and a comparison of the Nassir et al. [27] data available, and our estimates for those frequencies
results with ours illustrates effects of choice and size of do not significantly affect the PCA results (Additional
thepopulationsamplesanalyzed. File 1).
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Table 1Name, sourceofdata, andsample sizefor the Table 1 Name, source of data, and sample size for the
119 population samples* 119 population samples* (Continued)
Population Abbreviation N Source KomiZyriane KMZ 47 Yale
Biaka BIA 67 Yale* Chuvash CHV 42 Yale
Mbuti MBU 39 Yale* Makrani1 MKR-2 26 Yale
Mandenka MND 24 HGDP* Makrani2 MKR-1 25 HGDP
Lisongo LSG 8 Yale Kalash KLS 25 HGDP*
Yoruba YOR 77 Yale Brahui BRH 25 HGDP
YorubaYRI YRI 113 HapMap* Balochi BCH 25 HGDP*
Ibo IBO 48 Yale Sindhi SDI 25 HGDP
Zaramo ZRM 36 Yale Keralite KER 30 Yale
Hausa HAS 39 Yale Thoti THT 14 Yale
Bantu_NE BTN 12 HGDP* Kachari KCH 17 Yale
Bantu_S BTS 8 HGDP* GujaratiGIH GIH 88 HapMap
San SAN 6 HGDP* Pathan1 PTH-1 75 Yale
LuhyaLWK LWK 90 HapMap Pathan2 PTH-2 23 HGDP
AfricanAmerican1 AAM 90 Yale Mohanna MHN 48 HGDP
AfricanAmericanASW ASW 56 HapMap Burusho BSH 25 HGDP*
Chagga CGA 45 Yale Khanty KTY 50 Yale
Maasai,T MAS 20 Yale Hazara1 HZR-1 87 Yale
MaasaiMKK MKK 144 HapMap Hazara2 HZR-2 24 HGDP
Sandawe SND 40 Yale Uygur2 UYG 10 HGDP*
EthiopianJews ETH 32 Yale Uygur1 UIG 45 Yale
Somali SML 12 Yale Khazak KAZ 44 Yale
Mozabite MOZ 30 HGDP* KhambaTibetan KHG 27 Yale
Kuwaiti KWT 16 Yale Mongolians1 MVF 62 Yale
Samaritans SAM 40 Yale Mongolians2 MGL 10 HGDP*
YemeniteJews YMJ 42 Yale HmongBlack HMQ 46 Yale
Palestinian1 PLA-1 49 Yale BaimaDee BQH 40 Yale
Palestinian2 PLA-2 51 HGDP* Qiang QMR 38 Yale
Druze1 DRU-1 75 Yale Hlai LIC 47 Yale
Druze2 DRU-2 47 HGDP* Yakut YAK 51 Yale*
Bedouin BDN 48 HGDP* Dai DAI 10 HGDP
RomanJews RMJ 26 Yale Lahu LHU 10 HGDP*
Adygei ADY 54 Yale* Miaozu MIZ 10 HGDP
Greeks GRK 53 Yale Naxi NXI 9 HGDP
AshkenaziJews ASH 79 Yale Oroqen OQN 10 HGDP
Tuscan1 Tus 8 HGDP She SHE 10 HGDP
TuscanTSI TSI 88 Hapmap Tu TU 10 HGDP
Sardinian1 SRD-1 34 Yale Tujia TUJ 10 HGDP
Sardinian2 SRD-2 28 HGDP Xibo XBO 9 HGDP
Orcadian ORC 16 HGDP Yizu YIZ 10 HGDP
North_Italian ITN 13 HGDP Daur DUR 9 HGDP*
French_Basque FRB 24 HGDP* Hezhen HEZ 9 HGDP
French FRN 29 HGDP Han,SF HAN 43 HGDP
Hungarians HGR 89 Yale HanCHD CHD 85 HapMap
Irish IRI 114 Yale HanCHB CHB 84 HapMap*
EuropeanAmerican1 EAM 89 Yale Han,Taiwan CHT 50 Yale
EuropeanAmerCEU CEU 115 HapMap* Hakka HKA 41 Yale
Russians1 RUA 33 Yale Koreans KOR 54 Yale
Russians2 RUV 47 Yale* Japanese JPN 50 Yale
Finns FIN 34 Yale JapaneseJPT JPT 86 HapMap*
Danes DAN 51 Yale Laotians LAO 118 Yale
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Table 1 Name, source of data, and sample size for the umich.edu/clumpp.html) [36]. Specific solutions have
119 population samples* (Continued) beenplottedusingDISTRUCT1.1;softwarefreelyavail-
able at http://rosenberglab.bioinformatics.med.umich.
Cambodians CBD 24 Yale*
edu/distruct.html) [37]. The matrix of pairwise similari-
Ami AMI 40 Yale
ties among replicate runs was used to identify different
Atayal ATL 42 Yale
overallpatternsbasedonhighGvaluesamongrunswith
Malaysians MLY 11 Yale
the“same”patternandlowervaluesforrunswithdiffer-
Micronesians MCR 34 Yale
entpatterns.
Samoans SMO 8 Yale
P-NG1 PNG 13 Yale
Results
P-NG2 PNG 17 HGDP*
New data
Nasioi NAS 22 Yale
The allele frequencies for the 128 AISNPs for all 119
MexicanAmerMEX MEX 49 HapMap*
population samples are given in Additional file 2, and
PimaMexico PMM 53 Yale*
the allele frequencies of the 69 population samples
Maya MAY 51 Yale*
tested in our lab have all been entered into the ALFRED
Quechua QUE 22 Yale
database [30] and can be readily accessed using the rs#
Colombians COL-2 13 HGDP*
of each SNP.
Guihiba COL-1 11 Yale
Ticuna TIC 65 Yale
Fst
SuruiR SUR 45 Yale
The Fst distribution of the 128 AISNPs was compared
Karitiana KAR 55 Yale
to the distribution of 2,327 non-AISNPs typed in our
*TheseorsubsetsofthesesampleswereincludedinNassiretal.(2008).
lab (Figure 1 and Additional file 3). Although Kosoy
DescriptionsofthepopulationsandsamplesareinALFRED.
et al. [6] selected their 128 AISNPs not on the basis of
Fst, but rather on the Informativeness statistic (I ) of
n
Fst Rosenberg et al. [38,39], Fst clearly separates the two
Fst was calculated across all populations for each mar-
distributions by 1.25 standard deviations. The null
2 hypothesis that the two distributions are the same is
ker using the simple formula of Wright [32]: . For
pq rejected with a probability considerably less than 0.001.
comparison, Fst was calculated for 2,327 other poly- Outliers in the two distributions are given in Additional
morphisms typed on our samples. None of these 2,327 file 4. At the high-Fst end of the distributions, there are
polymorphisms included sites specifically selected for nine sites with Fst greater than 0.48: seven are in the
admixture or ancestry identification, or for individual reference distribution, and two are in the AISNP distri-
identification; instead, they were all selected for other bution. Of the seven in the reference distribution, five
ongoing projects in our lab (i.e., linkage disequilibrium, are located in or near genes of known phenotypic effect
disease/disorder association). (SLC24A5, OCA2 (two SNPs), HERC2 and EDAR), and
each of these genes is well known to have SNPs with
PCA marked global variation in allele frequency; but the best
Principal component analysis (PCA) analyses of popula- “known” SNPs are not part of this 128 AISNP set (Addi-
tion sample allele frequencies were performed using tional file 4). Though not associated with a phenotype,
XLSTAT (version 2009.4.07; Addinsoft SARL, http:// the remaining two “outliers” in the reference distribu-
www.xlstat.com/en/company/)) as one method to evalu- tion have comparably high Fst values (Additional file 4).
ate effectiveness of these SNPs for distinguishing among The two outliers at the high end of the AISNP distribu-
populations and to determine the major factors account- tion are sites in or near EDAR (rs260690, Fst = 0.5205)
ing for the population frequencies. and RTTN (rs4891825, Fst = 0.5176). There are 10 out-
Structure liers at the low end of the reference Fst distribution
Structure(version2.3.3;softwarefreelyavailableathttp:// with Fst <0.04. Only one of the AISNPs falls below the
pritch.bsd.uchicago.edu/structure.html[33-35]) was also mode of the reference distribution: TWGS1 (rs4798812,
used to evaluate and illustrate the effectiveness of these Fst = 0.08753).
sites to distinguish among these populations. The burn-
in was set at 20,000 followed by 10,000 iterations, and a PCA
modelofcorrelated allelefrequencieswasspecified.Ten Figure 2 presents the first three factors of the PCA ana-
replicatesateach“K”levels2-6and20replicatesatK=7 lysis based on allele frequencies of each of the 119 sam-
and K = 8 were evaluated using CLUMPP; (software ples. The first two factors account for more than 72% of
freelyavailableathttp://rosenberglab.bioinformatics.med. the variance. Factor 3 accounts for an additional 8.7% of
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Comparison of Reference Distribution (N=2327) and
Forensic Locus Distribution (N=128)
0.25
AISNP loci
mean=.25
median=.23
st. dev.=.08
0.2
Reference loci
mean-.15
median=.13
st.dev.=.08
0.15
ci AISNPs
o
e l Reference SNPs
h
f t
o
n
o
rti 0.1
o
p
o
r
P
0.05
0
0 0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.3 0.33 0.36 0.39 0.42 0.45 0.48 0.51 0.54 0.57 0.6 0.63 0.66 0.69 0.72 0.75 0.78 0.81
Fst
Figure 1 Comparisons of Fst distributions for the 128 ancestry informative single-nucleotide polymorphisms (AISNPs) and for a
referencesetof2327SNPs.
the total variance. Factor 1 clearly separates the Native stopping point. For a better understanding of the ability
Americans from all other groups, and factor 2 clearly of the data to distinguish most likely ancestry at the
separatestheAfricanpopulationsfromtherest.Factor3 higher K values, we ran structure a total of 20 times,
emphasizesthedifferencebetweenNativeAmericansand and the patterns seen more than once are illustrated in
EastAsians.ThissetofAISNPswasselectedbyKosoyet Figure 4; the patterns and likelihoods of the individual
al. [6] to maximize the differences among European runs are given in Table 2. As is obvious from the pat-
Americans, Africans and Native Americans; those three terns and the likelihoods of the individual runs, some
groupsclearlyareattheverticesofthetriangularpattern distinctions are quite consistent while others generate
based on factors 1 and 2 (Figure 2). We also note that similar likelihoods with combinatorial alternatives for a
Eurasianpopulationsshowlessclearseparation. few different groups of populations.
Structure AtK=7,thereisnosinglesolutionclearlyidentifiableas
Results for the specific structure runs with the highest best.Five differentoverallpatternsoccurinthe20runs.
likelihood at each K value, K = 2-8, are shown in Figure ThepatternillustratedinFigure3hasthehighestlikeli-
3 along with the number of times the particular overall hood but is not the most common pattern. The next
pattern occurred. At all the K values, there are popula- highestlikelihoodisnearlyidenticalandoccursforapat-
tions in each of the groups that seem quite homoge- ternthatoccursin6ofthe20runsand differsby subdi-
neous. By K = 8, the likelihoods began to plateau vidingEastAsianpopulationsandnotdistinguishingthe
(Additional file 5), providing a statistically reasonable Pacificpopulations.Themostfrequentpattern,foundfor
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(a)
16
14
12
10
8
%) 6
5
3. 4
3
2 (
FF 22
0
-2
-4
-6
-8
-22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10
F1 (38.8 %)
(b) 6
4
2
0
%)% -22
7
8 .
3 ( -4
F
-6
-8
-10
-12
-22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10
F1 (38.8 %)
Figure2Principalcomponentanalysis(PCA)of119populationsamplesbasedonallelefrequenciesof128AISNPs.
7 of the 20 runs, does not have the highest likelihoods commonly found and does have the highest likelihood
and differs in separating East African populations from among the 20 runs. A nearly equal likelihood occurred
thePacificpopulations. for a somewhat different overall pattern that
At K = 8, eight different overall patterns occur among subdivides East Asia rather than separating East African
the 20 separate runs. The pattern shown is the most populations.
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a
c a
K W. & C. Afri East AfricaS.W. Asia Europe S.C. Asia S.E & E. Asi OceaniaAmericas
2 4/10
3 10/10
4 10/10
5 8/10
6 3/10
7 3/20
8 6/20
BiakaMbutiMandenkaLisongoYorubaYorubaYRIIboZaramoHausaBantu_N.E.Bantu_SSanLuhya LWKAfrican Amer 1African Amer ASW ChaggaMaasai, TMaasai MKKSandaweEthiopian JewsSomaliMozabiteKuwaitiSamaritansYemenite JewsPalestinian 1Palestinian 2Druze 1Druze 2BedouinRoman JewsAdygeiAshkenazi JewsSardinian 1Sardinian 2Tuscan 1Tuscan TSINorth_ItalianGreeksOrcadianFrench_BasqueFrenchHungariansIrishEuropean Amer 1Europ Amer CEURussians 1Russians 2FinnsDanesKomi ZyrianeChuvashMakrani 1Makrani 2KalashBrahuiBalochiSindhiKeraliteThotiKachariGujarati GIHPathan 1Pathan 2MohannaBurushoYakutKhantyKhamba TibetanHazara 1Mongolians 1Hazara 2Mongolians 2Uygur 2HmongBlackUygur 1BaimaDeeKhazakQiangHlaiDaiLahuMiaozuNaxiOroqenSheTuTujiaXiboYizuDaurHezhenHan, S.F.Han CHDHan CHBChinese, TaiwanHakkaKoreansJapaneseJapanese JPTLaotiansCambodiansAmiAtayal MalaysiansMicronesiansSamoansMex Amer MEXP-NG 1Pima MexicoP-NG 2MayaNasioiQuechuaColombiansGuihibaTicunaSurui RKaritiana
Figure3StructureanalysesatK=2-8for119populationsamples.
AtK=8,theresultscanbesummarizedwithrespectto theKhamba-Tibet throughSoutheast Asiaalltheway to
the pattern shown in Figure 3 (pattern A in Figure 4). East Asia but the alternative (pattern B in Figure 4) has
Starting from the left, the sub-Saharan Africans, espe- the western Chinese groups at one end of a more clinal
cially the West Africans, seem relatively homogeneous pattern with the southeastern Asians at the other end.
(red). The East Africans, especially the MKK, Sandawe Interestingly, this alternative depicts the Han, Koreans
and EthiopianJews,can form adistinctgrouping(pink), and Japanese as admixed. The next clear cluster (light
in which case other Tanzanian populations, the Maasai blue)isPacificandconsistsofthreeMelanesiansamples:
and Chagga, and the Somali (now living in Pakistan) TheSamoans,MicronesiansandMalaysiansappearinter-
appearintermediate between East and West Africa. The mediatebetweenEastAsiaandthePacific.Thefinalclear
next consistent cluster includes the Mozabites and cluster(pink)consistsofNativeAmericansamples.
Southwest Asians (green). There is then a more-or-less ThedifferentpatternsatK=7andK=8showfinedis-
gradient across Europe from southeastern and southern tinctions even among the regions that are superficially
Europe(mostlygreen),throughtonorthwesternEurope, similar. To make some of these clearer, we have gener-
andendinginnortheasternEurope (mostly yellow). The ated the population averages for the best result (highest
south-centralAsianpopulationsformanother(darkblue) likelihood) for each of the patterns (Figure 5). These
consistentandrelativelyhomogeneousclusterofpopula- emphasizethevariationamongindividualsineachpopu-
tions,includingEastIndiansandseveral Pakistanipopu- lationsamplebyshowingthepopulationasmultiplecol-
lations (dark blue). The Khazaks, Uyghur, Hazara and ors. Thesefiguresalso emphasize the southwestern Asia
Khantyforma“group”thatisdepictedasadmixedunder throughnorthernEuropeclineseeninallpatterns.
anyofthe alternative commonpatterns.The nextgroup Clusters that emerge at even higher values of K
of populations (dark gray) appears homogeneous from includeaPygmy/San/SBantuclusterinAfrica,aKhanty/
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K = 7
a
W. & C. Afric East Africa S.W. Asia Europe S.C. Asia S.E & E. Asia Oceania Americas
A (3/20)
B (6/20)
C (7/20)
D (3/20)
BiakaMbutiMandenkaLisongoYorubaYorubaYRIIboZaramoHausaBantu_N.E.Bantu_SSanLuhya LWKAfrican Amer 1African Amer ASW ChaggaMaasai, TMaasai MKKSandaweEthiopian JewsSomaliMozabiteKuwaitiSamaritansYemenite JewsPalestinian 1Palestinian 2Druze 1Druze 2BedouinRoman JewsAdygeiAshkenazi JewsSardinian 1Sardinian 2Tuscan 1Tuscan TSINorth_ItalianGreeksOrcadianFrench_BasqueFrenchHungariansIrishEuropean Amer 1Europ Amer CEURussians 1Russians 2FinnsDanesKomi ZyrianeChuvashMakrani 1Makrani 2KalashBrahuiBalochiSindhiKeraliteThotiKachariGujarati GIHPathan 1Pathan 2MohannaBurushoYakutKhantyKhamba TibetanHazara 1Mongolians 1Hazara 2Mongolians 2Uygur 2HmongBlackUygur 1BaimaDeeKhazakQiangHlaiDaiLahuMiaozuNaxiOroqenSheTuTujiaXiboYizuDaurHezhenHan, S.F.Han CHDHan CHBChinese, TaiwanHakkaKoreansJapaneseJapanese JPTLaotiansCambodiansAmiAtayal MalaysiansMicronesiansSamoansMex Amer MEXP-NG 1Pima MexicoP-NG 2MayaNasioiQuechuaColombiansGuihibaTicunaSurui RKaritiana
K = 8
a
W. & C. Afric East Africa S.W. Asia Europe S.C. Asia S.E & E. Asia OceaniaAmericas
A (6/20)
B (5/20)
C (3/20)
D (2/20)
BiakaMbutiMandenkaLisongoYorubaYorubaYRIIboZaramoHausaBantu_N.E.Bantu_SSanLuhya LWKAfrican Amer 1African Amer ASW ChaggaMaasai, TMaasai MKKSandaweEthiopian JewsSomaliMozabiteKuwaitiSamaritansYemenite JewsPalestinian 1Palestinian 2Druze 1Druze 2BedouinRoman JewsAdygeiAshkenazi JewsSardinian 1Sardinian 2Tuscan 1Tuscan TSINorth_ItalianGreeksOrcadianFrench_BasqueFrenchHungariansIrishEuropean Amer 1Europ Amer CEURussians 1Russians 2FinnsDanesKomi ZyrianeChuvashMakrani 1Makrani 2KalashBrahuiBalochiSindhiKeraliteThotiKachariGujarati GIHPathan 1Pathan 2MohannaBurushoYakutKhantyKhamba TibetanHazara 1Mongolians 1Hazara 2Mongolians 2Uygur 2HmongBlackUygur 1BaimaDeeKhazakQiangHlaiDaiLahuMiaozuNaxiOroqenSheTuTujiaXiboYizuDaurHezhenHan, S.F.Han CHDHan CHBChinese, TaiwanHakkaKoreansJapaneseJapanese JPTLaotiansCambodiansAmiAtayal MalaysiansMicronesiansSamoansMex Amer MEXP-NG 1Pima MexicoP-NG 2MayaNasioiQuechuaColombiansGuihibaTicunaSurui RKaritiana
Figure4Thedifferentpatternsseenmorethanonceinsolutionsfrom20runsofstructureatK=7andK=8.
Khazak/Yakut cluster in Asia and a vaguely central Discussion
Asian group consisting of, for example, the Khamba- A set of markers particularly useful for determining in
Tibet,Mongolian,BaimaDeeandQiang.Theseclusters, detail the genetic distinctions among populations should
though reasonable, are not strongly supported also be useful in an examination of admixture. However,
statistically. “admixture” is not a singular phenomenon: A sample of
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Table 2Patternsandlikelihoods of20 structurerunsat K= 7andK= 8
PatternK=7 LnP(D) Run Bestperpattern PatternK=8 LnP(D) Run Bestperpattern
A -591354 run13 * A -590090 run13 *
B -591528 run1 * B -590185 run1 *
B -591555 run2 B -590570 run6
A -591571 run3 B -590605 run16
B -591707 run12 A -590606 run15
B -591724 run8 C -590867 run4 *
C -591822 run7 * A -591033 run18
A -591855 run17 A -591053 run2
B -591944 run15 C -591080 run20
C -591949 run5 A -591090 run10
C -591957 run9 E -591160 run5
C -592012 run11 C -591298 run14
B -592017 run4 C -591371 run3
D -592137 run20 * D -591512 run7 *
D -592272 run18 D -591689 run17
E -592309 run6 A -591744 run12
C -592342 run16 C -591745 run8
C -592548 run19 F -592008 run19
C -592605 run14 G -592162 run11
D -593102 run10 H -592261 run9
individuals might be considered admixed if it is com- good at distinguishing among other populations, per-
posed of (1) samples from two or more different popula- haps even from the same geographic regions.
tions, (2) the descendants of people from two or more It is important to realize that the outcome of any ana-
populations who have “recently” intermarried, (3) des- lysis of admixture or other population structure depends
cendants of people from two or more populations who heavily on both the population samples and the markers
have intermarried in the ancient past and (4) people dis- used. Though we have included some of the same
cretely sampled from a single region along a geographic HGDP populations as Kosoy et al. [6] did in their ana-
allele frequency cline established predominantly by ran- lyses, the outcome is always a function of which samples
dom genetic drift. are included. Thus, in our selection of samples, we have
The American Society of Human Genetics Ancestry also included samples that overlap with those reported
and Ancestry Testing Task Force, in its white paper by Nassir et al. [27] as well as others not part of either
[40], sets forth caveats to be kept in mind in ancestry the Kosoy et al. [6] or Nassir et al. [27] reports.
inference, perhaps the foremost of which is that ances-
tral populations cannot be observed directly and that PCA
even surrogates for those ancestral populations may not The results shown in Figure 2 clearly reflect the criteria
be included in any given study. Therefore, the “gold used to select this set of AISNPs [6]. The strongest dis-
standard” analytic programs such as structure (version crimination reflects the geographic and ancestral origins
2.3.3; http://pritch.bsd.uchicago.edu/structure.html of those populations (Africa, Europe, East Asia and the
[32-34]) will cause individuals in some populations to Americas), even though this analysis included none of
appear as an “admixture” of the population samples that the original samples used to select the SNPs. The first
are in the analysis. Even in analyses of principal compo- two components provide strong support for these SNPs
nents, it is not possible to distinguish whether a popula- in studies involving African, European and Native
tion is admixed or simply intermediate. Thus, a set of American populations. The relatively poorer separation
AIMs estimating the ancestry of an individual whose among Eurasian and Pacific populations reflects the
ancestry involves populations other than the majority of absence of Central, South and East Asian and Pacific
the populations in an analysis may be unsatisfactory by populations in the selection of these AIMs as well as
forcing that individual to be explained by the ancestry their distinct evolutionary relationships relative to Afri-
inferred for the majority. Further, a set of AIMs selected can and Native American populations. It is logical to
for one set of populations cannot be expected to be as expect that if more SNPs with large allele frequency
Kiddetal.InvestigativeGenetics2011,2:1 Page10of13
http://www.investigativegenetics.com/content/2/1/1
K = 8
a
c a
W. & C. Afri East Africa S.W. Asia Europe S.C. Asia S.E & E. Asi OceaniaAmericas
A
(6/20)
B
(3/20)
C
(5/20)
D
(2/20)
BiakaMbutiMandenkaLisongoYorubaYorubaYRIIboZaramoHausaBantu_N.E.Bantu_SSanLuhya LWKAfrican Amer 1African Amer ASW ChaggaMaasai, TMaasai MKKSandaweEthiopian JewsSomaliMozabiteKuwaitiSamaritansYemenite JewsPalestinian 1Palestinian 2Druze 1Druze 2BedouinRoman JewsAdygeiAshkenazi JewsSardinian 1Sardinian 2Tuscan 1Tuscan TSINorth_ItalianGreeksOrcadianFrench_BasqueFrenchHungariansIrishEuropean Amer 1Europ Amer CEURussians 1Russians 2FinnsDanesKomi ZyrianeChuvashMakrani 1Makrani 2KalashBrahuiBalochiSindhiKeraliteThotiKachariGujarati GIHPathan 1Pathan 2MohannaBurushoYakutKhantyKhamba TibetanHazara 1Mongolians 1Hazara 2Mongolians 2Uygur 2HmongBlackUygur 1BaimaDeeKhazakQiangHlaiDaiLahuMiaozuNaxiOroqenSheTuTujiaXiboYizuDaurHezhenHan, S.F.Han CHDHan CHBChinese, TaiwanHakkaKoreansJapaneseJapanese JPTLaotiansCambodiansAmiAtayal MalaysiansMicronesiansSamoansMex Amer MEXP-NG 1Pima MexicoP-NG 2MayaNasioiQuechuaColombiansGuihibaTicunaSurui RKaritiana
Figure 5 Average population assignment to clusters for structure analyses at K = 8. The data are the same as theK = 8 analysis in
Figures3and4.
differences across Eurasia were included, factor 3 would to “fall off” and become a separate cluster. The cluster
show greater separation between west and east Eurasia. assignmentsatK =4andK=5illustratethis(Figure 3).
Structure In other words, conclusions about groupings at a given
Structure attempts to find the set of K population allele value of K are a function of the populations sampled
frequencies that will give the best fit to all individual and their relative sample sizes. Thus, it is not necessa-
samples assuming Hardy-Weinberg ratios for each of rily correct that the estimated allele frequencies for a
the K populations. Structure does not consider or pro- given cluster represent the ancestral population, nor
duce analyses of population relationships. Fortunately, can one automatically interpret a partial assignment to
this is not an issue of interest to forensic science. two or more clusters as admixture. In addition, as
Rather, structure assigns individuals to clusters of shown in Figure 4 and Table 2, there is a stochastic
genetically similar individuals. Obviously, if numbers of element in each structure run such that the relative
individuals differ greatly among different populations, a likelihoods of different patterns from different runs
population sample with a large number of individuals depend on the particular outcomes that happen to
will influence the allele frequencies of the particular occur. Thus, the point of using structure is not the
cluster into which it falls more than a population with a single best run or the most common pattern seen, but
small number of individuals. Thus, a small population the stability of aspects of the patterns and of the indi-
from the middle of a cline with larger numbers in popu- vidual runs within each pattern among the runs with
lations from the more extreme parts of the cline will the higher likelihoods. Kalinowski [41] has recently
appear “admixed.” Such is seen at K = 3 for the South, published studies making additional relevant points on
Central and East Asian populations. However, a large the interpretation of structure results.
population from that middle region will, at the same K An example of the sample size effect appears to be
value, cause the allele frequency estimates of the flank- found in Nassir et al. [27]. That study contains 49 popu-
ing clusters to move toward the center even if cluster lations with a total sample size of 1,620. Their Ashke-
assignments do not change. The consequences at higher nazi sample of 240 individuals (two population samples
K values may be that the “middle” population is a dis- pooled: Ashkenazi AM 4 GP and Ashkenazi AM)
tinct group or, by shifting the estimates for the flanking constitutes about 15% of the total. Similarly, the 259
clusters, cause a population at the extreme of the cline European Americans (two population samples pooled: