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Published in final edited form as: Leg Med (Tokyo). 2015 Feb 25;17(4):245–250. doi: 10.1016/j.legalmed.2015.02.004

Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets

Kornkiat Vongpaisarnsin 1,*, Jennifer Beth Listman 2, Robert T Malison 2,3, Joel Gelernter 2,3,4,5
PMCID: PMC4480199  NIHMSID: NIHMS673434  PMID: 25759192

Abstract

The main purpose of this work was to identify a set of AIMs that stratify the genetic structure and diversity of the Thai population from a high-throughput autosomal genome-wide association study. In this study, more than one million SNPs from the International HapMap database and the Thai depression genome-wide association study have been examined to identify ancestry informative markers (AIMs) that distinguish between Thai populations. An efficient strategy is proposed to identify and characterize such SNPs and to test high-resolution SNP data from international HapMap populations. The best AIMs are identified to stratify the population and to infer genetic ancestry structure. A total of 124 AIMs were clearly clustered geographically across the continent, whereas only 89 AIMs stratified the Thai population from East Asian populations. Finally, a set of 273 AIMs was able to distinguish northern from southern Thai subpopulations. These markers will be of particular value in identifying the ethnic origins in regions where matching by self-reports is unavailable or unreliable, which usually occurs in real forensic cases.

Introduction

A new genotype sequencing technology providing large human genome information in a genome-wide association (GWA) study identified many candidate single nucleotide polymorphisms (SNPs). These SNPs could serve as potential genetic markers to assess the vulnerability to a variety of common human diseases (1) and could be useful in forensic human identification (2,3). An abundance of SNPs demonstrate allelic frequency differences at either the individual or the population level. SNPs are useful markers for population genetic, anthropological and forensic studies that observe the genetic components that are shared in particular populations. SNPs expected to have high heterozygosity are proposed as informative loci to identify individuals, whereas those with low heterozygosity loci that are restricted for each ethnic group are valuable for distinguishing populations or for ancestry studies (4).

Most GWA studies ascertain population stratification with minimal errors by employing universal SNP markers from various population groups and ancestries that reflect global ancestry information; however, there are no local ancestral variations. Regional assessment in the latter group is more considerable, particularly when populations have recently been admixed (5). Selecting the appropriate SNPs and ancestry informative markers (AIMs) to evaluate the overall genetic admixture proportion could help to identify individual geographic origins and distinguish between diverse population groups. Many highlighted AIM panels have been studied to classify subgroups within European populations (6–11),while Southeast Asian populations have rarely been studied genetically. The purpose of this study is to describe the genetic admixture and identify the best AIMs to classify Thai population. We used a population statistics model to cluster groups with a particular set of markers, and we present a genetic structural pattern that explains the origin of ancestry in this area.

Materials and methods

Population datasets

Genetic sequences of the following 11 human populations (1,301 individuals) were obtained from the international HapMap phase 3 release 2 NCBI build 36 (12): individuals of African descent in the southwest USA (ASW, 90 individuals), Utah residents of Northern and Western European descent from the CEPH collection (CEU, 180 individuals), Han Chinese in Beijing, China (CHB, 90 individuals), Chinese in Metropolitan Denver, Colorado (CHD, 100 individuals), Yoruba in Ibadan, Nigeria (YRI, 180 individuals), Gujarati Indians in Houston, Texas (GIH, 100 individuals), Tuscans in Italy (TSI, 100 individuals), Japanese in Tokyo, Japan (JPT, 91 individuals), Maasai in Kinyawa, Kenya (MKK 180 individuals), individuals of Mexican descent in Los Angeles, California (MEX, 90 individuals) and Luhya in Webuye, Kenya (LWK, 100 individuals). The Thai population data (THA, 374 individuals) were obtained from a Thai depression GWA study (186 cases and 188 controls) (13) with self-identified ethnic groups in accordance with Thai geographic sub-regions comprising Northeast (THA-NOE, 150 individuals), North (THA-NOR, 64 individuals), South (THA-SOU, 67 individuals) and Central (THA-CEN, 93 individuals).

Genotyping and data filtering

The international HapMap dataset comprised 1,440,616 SNPs. Thai population data covering 570,706 SNPs were genotyped on Illumina 650Y platforms. Sex chromosomes and individuals who originated from the same family were excluded from the analysis. Large-scale genotype data were filtered using PLINK software (14). The SNPs were subsequently excluded according to the following criteria: SNPs with more than 10% missing genotypes, SNPs with a minor allele frequency less than 0.05, SNPs with a genotype frequency that failed a Hardy-Weinberg test at a significant threshold (p <10−7 by the chi-square test) and SNPs with A/T or C/G variants. The SNPs merged with some mismatch strands between each set were flipped.

Selected AIMs

The measurement of genetic differentiation was conducted using Fst estimation, which evaluates the allele frequency differences between major populations and subpopulations and was originally defined by Weir and Cockerham (15). Using two data sets (HapMap-Thai and Asian-Thai), SNPs on each chromosome were independently extracted to calculate the Fst using GENEPOP (16). The high-Fst SNPs in each set were used to determine AIMs. SNPs that had strong linkage disequilibrium (LD) were removed, with a SNP window of 50 bases and sliding window of 5 SNPs. One SNP was also removed from each pair of SNPs using a variance inflation factor (VIF) value of 2, as described by Purcell (14). To determine population differences, the average pairwise Fst values over loci were calculated using the Arlequin software with bootstrap resampling using 1,000 replications (17).

Population model studies

For the population simulation used in this study, the two most popular approaches were structured association mappings, which use model-based clustering, and principal component analysis (PCA), which uses top principal components (PCs) as covariates for stratification. PCA was analyzed by EIGENSTRAT to calculate the eigenvalues and PCs (18). The results of the top PCs are presented in a two-dimensional scatter plot to identify clustering. The graphical results are shown using R software (19). Admixture mapping was conducted using ADMIXTURE to estimate ancestry in unrelated individuals with 10,000 burn-in and 10,000 Markov chain Monte Carlo (MCMC) iterations (20). The values of predefined cluster numbers (K) were analyzed to select a sensible modeling choice, and parameter standard errors were estimated using bootstrapping with 1,000 replications. The suggested K was determined using the lowest cross-validation (CV) values of the number of assumptions. The results are presented in boxplot matrices that show the genetic admixture pattern across populations. Computerized operations were carried out using Information Technology Services by Yale University (http://its.yale.edu/).

Results

Genetic differences among geographic regions

A total of 1,389,511 SNPs were apparent from the HapMap population (809 individuals) and 560,311 SNPs from the Thai population (374 individuals). The data were merged, and 421,925 overlapping SNPs were selected to be included in this analysis. A total of 1,012 SNPs with Fst values over 0.20 were selected to generate HapMap-Thai AIMs (Supplement 1.1). A total of 124 unique HapMap-Thai AIMs that did not overlap with any previous study (9, 20) were identified, except for one, rs12913832, which was presented in the SNPforID 34-plex (9). An Fst value of 1 was observed in 11 markers (rs2224545, rs34019675, rs2810204, rs2233971, rs11744792, rs12166946, rs10282720, rs12750376, rs35726748, rs13194134 and rs8854) (Supplement 1.4). The level of genetic differentiation between populations existing with pairwise Fst values revealed a close group within each continent (Table 1). The highest genetic distance between the Thai and the African, European and South American clusters were observed in YRI (Fst = 0.6864, p < 0.05), MEX (Fst = 0.5067, p < 0.05) and CEU (Fst = 0.5447, p < 0.05), respectively.

Table 1.

Pairwise Fst values across populations with different numbers of AIM sets

124 AIMs
POPULATIONS CHB/CHD JPT GIH THAI ASW LWK YRI MEX MKK CEU TSI
CHB/CHD + + + + + + + + + +
JPT 0.0117 + + + + + + + + +
GIH 0.1426 0.1174 + + + + + + + +
THAI 0.3977 0.4049 0.4643 + + + + + + +
ASW 0.4487 0.4156 0.2301 0.6388 + + + + + +
LWK 0.4446 0.4148 0.2498 0.6343 0.0065 + + + + +
YRI 0.5389 0.5114 0.3450 0.6864 0.0309 0.0256 + + + +
MEX 0.2176 0.1800 0.0419 0.5067 0.1885 0.2133 0.3084 + + +
MKK 0.3701 0.3378 0.1718 0.5901 0.0218 0.0221 0.0844 0.1404 + +
CEU 0.2989 0.2576 0.0743 0.5447 0.1716 0.2026 0.2931 0.0471 0.1318 +
TSI 0.2703 0.2329 0.0583 0.5294 0.1639 0.1915 0.2831 0.0363 0.1183 0.0055
89 AIMs
POPULATIONS CHB/CHD JPT GIH THAI
CHB/CHD + + +
JPT 0.0140 + +
GIH 0.1817 0.1429 +
THAI 0.1868 0.2223 0.3669
273 AIMs
POPULATIONS CHB/CHD JPT GIH THA-SOU THA-NOR
CHB/CHD + + + +
JPT 0.0001 + + +
GIH 0.0250 0.0256 + +
THA-SOU 0.0071 0.0093 0.0247 +
THA-NOR 0.0109 0.0140 0.0298 0.0283
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“+” = Fst P values with a significance level<0.05.

Within an Asian population study, CHB, GIH, JPT and THA were grouped as an Asian-Thai cluster (655 individuals) that covered 1,446,473 SNPs. After data filtering, 463,265 overlapping SNPs were identified in a total of 632 individuals. A total of 1,506 SNPs had an Fst value over 0.10 (Supplement 1.2). After LD removal, 89 SNPs were selected as Asian-Thai AIMs. Seven markers (rs186154, rs1447826, rs1036819, rs1975920, rs9572312, rs12884681 and rs8063779) were identified as a subset of 124 HapMap-Thai AIMs. The highest Fst value was 0.88, observed for rs1455311 (Supplement 1.5). The pairwise Fst values across the Asian population are presented in Table 1. A relatively high genetic distance was observed between THA and GIH (Fst = 0.3669, p < 0.05), JPT (Fst = 0.2223, p < 0.05) and CHB/CHD (Fst = 0.1868, p < 0.05).

Using a similar reference Asian population, the Thai population was classified into northern and southern groups. There were formerly 554,292 SNPs from the southern and northern Thai population. A total of 3,373 SNPs had an Fst over 0.02 (Supplement 1.3). The highest Fst value was 0.11, for rs12094795. The pairwise Fst values were calculated and are presented in Table 1. The southern and northern Thai samples showed slightly different Fst values (0.0283). The allele frequencies and heterozygosities of 273 AIMs are presented in Supplement 1.6.

Analysis of genetic structure

In evaluating the genetic structure across the studied populations from K = 2 to K= 7 using different sets of AIMs, the admixture model results of minimal K values revealed a homogenous pattern within geographic continents and closely related groups. The set of 124 AIMs had clearly differentiated clustering for the Thai population (THA) out of the East Asian and other populations at K = 4 (cross validation = 0.422), as shown in Fig. 1A. Separation of the South Asian population (GIH) also resulted in an admixed genetic proportion between European (CEU and TSI) and East Asian (CHB/CHD and JPT). When analysis of the Thai population was conducted with four reference Asian populations (CHB/CHD, JPT and GIH) (Figs. 1B and 1C), the set of 89 AIMs separated the Thai population from other Asian populations, but it was not possible to stratify a Thai subpopulation. An analysis of the Thai subpopulation was performed without reference populations (data not shown), and a homogenous admixture pattern of northern (THA-NOR), northeast (THA-NOE), southern (THASOU) and central (THA-CEN) Thai regions revealed the co-ancestry of the Thai population. After excluding the central and northeast groups, a clear stratification between the southern and northern Thai populations was presented with a set of 273 AIMs, including an Asian reference population, as shown in Fig. 1C. A block plot revealed the individual genetic components within the Asian group, in which CHB/CHD and GIH shared a common genetic component with both northern Thai (THA-NOR) and southern Thai (THA-SOU) populations. However, the northern and southern Thai individuals had different common genetic proportions shared by CHB/CHD and GIH, as depicted by the different block colors (Fig. 1C).

Fig. 1.

Fig. 1

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(A, Band C) Population structure results using an admixture population model. Each vertical line represents an individual who is proportionally assigned to one of the K clusters, with the proportions represented by the relative lengths of the K in different colors. The populations are identified below the figures. Three sets using 124, 89 and 273 AIMs were analyzed for continental, sub-continental and Thai subpopulation clustering, respectively.

Population divergence analyzed with principal component analysis

The PCA results are presented in plots of two-dimensional principal components (PCs), with similar AIM sets as analyzed in the genetic structure examination. The first and second PCs (PC1 = 40.06%, PC2 = 9.79%) with 124 AIMs were obviously clustered as Thai (THA), East Asian (CHB/CHD and JPT) and African groups (ASW, MKK, LWK and YRI), whereas the overlapping South Asian (GIH), South America (MEX) and European groups (CEU and TSI) had relatively low population heterogeneity (Fig. 2.1). The eigenvalue of each PC was 26.40% for PC1 and 5.93% for PC2. PCA performed within an Asian reference population using 89 AIMs markers notably demonstrated divergence between THA, GIH and East Asian (CHB, JPT) individuals with PC1 = 25.2% and PC2 = 4.6%, whereas strong homogeneity was observed in CHB and JPT individuals (Fig. 2.2). Analysis within the Thai population (excluding CE_THA and NE_THA) using 273 AIMs (PC1 = 4.9%, PC2 = 3.4%), visibly differentiated between a northern (THA-NOR) and southern (THA-SOU) Thai population (Fig. 2.3).

Fig. 2.1.

Fig. 2.1

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Principal component analyses of the studied population (1,183 individuals) with 124 AIMs. The different symbols indicate the samples in each population. There are four corresponding clusters: African (right upper, ASW, LWK, MKK and YRI), East Asian (left lower, CHB, CHD and JPT), Southeast Asian (left upper, THA) and combined European, South Asian and South America (right lower, CEU, TSI, GIH and MEX).

Fig. 2.2.

Fig. 2.2

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Two-dimensional plot using 89 AIMs of the East Asian and Thai populations. The samples are clusterings of the Thai (red, triangle), GIH (green, plus), JPT (blue, cross) and CHB (black, circle) populations.

Fig. 2.3.

Fig. 2.3

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PC1 and PC2 plot of 273 AIMs for the stratification of northern and southern Thai population. The northern (blue, cross) and southern (light blue, diamond) Thai samples are clustered as two separate groups, with GIH (red, triangle), JPT (green, plus) and CHB (black, circle) used as reference populations.

Discussion

The developing panel of individualized and ancestry informative SNPs was introduced in the forensic community within the past few years (8–11). In earlier studies, a 128-SNP panel was demonstrated to have the capability to cluster continentals corresponding to European, East Asian, Amerindian, African, South Asian, Mexican, and Puerto Rican (21) ancestry. A similar panel studied in large populations (4,871 individuals: included from the HapMap 3 (12), the Human Genome Diversity Project (HGDP) (22) and ALFRED (23) databases) provided the ability to distinguish between African American and Mexican Americans ancestries (24). Another study used the Eurasiaplex panel to stratify within continents and to estimate the admixture proportions of European and South Asian populations (11). Southeast Asian populations with small sample sizes were included in the analysis; nevertheless, genetic structure and diversity studies of Han Chinese, Japanese, Korean, Vietnamese and Filipino individuals identified substructures with a set of 1,500 AIMs (25). Genetic studies in Southeast Asia mainly focused on tracing part of the migration routes based on Y chromosome haplotypes and mitochondrial DNA haplogroups (26–30), but these studies did not provide strong inferences regarding the genetic population structure.

Our analysis shows that the Thai population is notably stratified into the following two groups using 273 AIMs (Fig. 2.3): northern Thai and southern Thai. Indian and Chinese genetic influences exist in mainland Southeast Asia. Upon investigating the ancestry proportion of GIH in both groups (Fig. 1C), the Indian genetic influence seemed to be more dominant in the southern Thai population compared with the northern population, which is particularly apparent in K = 3 (green color) and K = 4 (light blue color). This evidence suggests the migration of the Austroasiatic linguistic population along the coastal South and Southeast Asian territory (31–33). In addition, the diversity of genetic components to reference individuals (Fig. 1C) in K=3 for THA-NOR (blue color) and THA-SOU (red color) apparently revealed a genetic proportion suggesting an influence of Chinese ancestry in both groups. These data are supported by a record of Chinese military history described that these tribes migrated throughout Asia for up to 5,000 years (34, 35), moving continuously southward into mainland Southeast Asia, including Thailand and its neighbors, over the past one thousand years (36). However, the northern has geographically characterized by complex mountain ranges, there has a gene flow from a northern to southern migration from southern China (Yunnan Province) to northern Thailand that consistent with the earlier study of Y chromosomes and mitochondrial SNPs of hill tribes in northern Thailand (30, 32,37). In addition to the Fst of 273 AIMs (Table 1), a southern Thai population has a stronger affinity with Chinese Han (Fst=0.0071) and Japan population (Fst=0.0093) than northern Thai population (Fst=0.0109). It could be inferred that Chinese Han may have a migratory route from eastern China (Fujian and Guangdong provinces) to islands southeast Asia and Malay Peninsula, the southernmost point of the Asian mainland, which comprised of Myanmar, Malaysia, and Southern Thailand (38,39). The close relatedness of Chinese ethnic in this group has revealed with a similar culture, religion as well as linguistic as Austronesian language family.

In conclusion, the analysis of genome-wide SNP genotypes with a few hundred AIMs apparently differentiates the Thai population from other HapMap populations. The ancestry informative SNPs (AIMs) of this study are suitable for successfully stratifying the Thai population. A total of 124 AIMs represent clusters of the geographic continents. It appears that the use of the small number of 89 AIMs could stratify the Thai population and East Asian populations. Moreover, a set of 273 AIMs could also distinguish northern from southern Thai subpopulations. This study also provides a list of AIMs (as shown in the supplemental data) that could be employed as markers of choice to develop multiplex ancestry panels. Using the current next-generation sequencing technology, it could be inferred that our AIMs might be useful in identifying an unidentified body in regions where ethnic origin matching by self-reporting is not available or unreliable for use in anthropology or forensics.

Supplementary Material

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Acknowledgements

This study was supported by an NIH/Fogarty Drug Dependence Through the Lifespan: US– Thai training grant (D43 TW009087). The sample collection and genotyping of Thai depression samples were supported by Rachanukul Hospital and BIOTEC Thailand (http://www4a.biotec.or.th/thaisnp2/).

Footnotes

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Conflict of interest

The authors declare that they have no conflicts of interest.

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NIHMS673434-supplement-1.doc (1.1MB, doc)
2
NIHMS673434-supplement-2.doc (1.3MB, doc)
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NIHMS673434-supplement-3.doc (3.5MB, doc)
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