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Analysis of East Asia genetic substructure using genome-wide SNP arrays - PubMed

Analysis of East Asia genetic substructure using genome-wide SNP arrays

Chao Tian et al. PLoS One. 2008.

Abstract

Accounting for population genetic substructure is important in reducing type 1 errors in genetic studies of complex disease. As efforts to understand complex genetic disease are expanded to different continental populations the understanding of genetic substructure within these continents will be useful in design and execution of association tests. In this study, population differentiation (Fst) and Principal Components Analyses (PCA) are examined using >200 K genotypes from multiple populations of East Asian ancestry. The population groups included those from the Human Genome Diversity Panel [Cambodian, Yi, Daur, Mongolian, Lahu, Dai, Hezhen, Miaozu, Naxi, Oroqen, She, Tu, Tujia, Naxi, Xibo, and Yakut], HapMap [ Han Chinese (CHB) and Japanese (JPT)], and East Asian or East Asian American subjects of Vietnamese, Korean, Filipino and Chinese ancestry. Paired Fst (Wei and Cockerham) showed close relationships between CHB and several large East Asian population groups (CHB/Korean, 0.0019; CHB/JPT, 00651; CHB/Vietnamese, 0.0065) with larger separation with Filipino (CHB/Filipino, 0.014). Low levels of differentiation were also observed between Dai and Vietnamese (0.0045) and between Vietnamese and Cambodian (0.0062). Similarly, small Fst's were observed among different presumed Han Chinese populations originating in different regions of mainland of China and Taiwan (Fst's <0.0025 with CHB). For PCA, the first two PC's showed a pattern of relationships that closely followed the geographic distribution of the different East Asian populations. PCA showed substructure both between different East Asian groups and within the Han Chinese population. These studies have also identified a subset of East Asian substructure ancestry informative markers (EASTASAIMS) that may be useful for future complex genetic disease association studies in reducing type 1 errors and in identifying homogeneous groups that may increase the power of such studies.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Principal component analyses of substructure in a diverse set of subjects of East Asian descent.

Graphic representation of the first two PCs based on analysis with >200 K SNPs are shown. Color code shows subgroup of subjects for each population group. The subjects included Filipino (FIL), Vietnamese (VIET), Lahu, Dai, Cambodian (CAMB), Han Chinese (CHB), Mongola (MGL), Oroqen (ORQ), Daur, Korean (KOR), Chinese Americans from Taiwan (TWN),Yi, Hezhen (HEZ), Miaozu (MIAO), Naxi, She, Tu, Tujia (TUJ), Xibo, Chinese Americans (CHA), Japanese (JPT), and Yakut (YAK). A, Analyses including the Yakut population group. B, Analysis without Yakut is shown. C, Approximate geographic origin of population group is depicted on a map of East Asia (downloaded from University of Texas Library website). The positions of the HGDP population groups are based on the collection site information and the other population groups were placed based on self-identified country or region of origin. [Note: Yakut are not shown on the map since this population is from Siberia and is a considerable distance north of the depicted region.] D, Shows rotated results of PC1 and PC2 to assist illustration of geographic correspondence of ethnic group locations.

Figure 2
Figure 2. Graphic representation of additional principal components (PCs 3–8) in a diverse set of subjects of East Asian Descent.

Color key shows groups as defined in Fig 1. A, PC3 and PC4. B, PC5 and PC6. C, PC7 and PC8.

Figure 3
Figure 3. Eigenvalue distribution for principal components.

A, The eigenvalues for each PC are shown for both the entire group of EAS (excluding Yakut), and for the five most populous ethnic groups (Chinese, Korean, Japanese, Filipino and Vietnamese). B, The proportion of the adjusted eigenvalue for each PC for the first 10 PCs is shown. For this measurement the PC10 eigenvalue for each group was used as the baseline. [Note: the eigenvalues plateau as shown in panel A and there is no discernable substructure beyond PC10 for these analyses (Table 2)]. For each PC, the PC10 eigen value was subtracted to determine an “adjusted” eigenvalue. The % substructure variation measurement was the proportion of each adjusted eigenvalue divided by the sum of the adjusted eigenvalues (PC1 through PC10).

Figure 4
Figure 4. PCA analyses of Han Chinese and Chinese American population groups.

A, Results from PCA performed together with EAS populations. B, PCA performed using only Chinese and Chinese American participants. The color coded population groups included the HapMap Han Chinese from Beijing (CHB), HGDP Han Chinese (HAN), HGDP North Han Chinese (HAN_N), Chinese American North (CHAN), Chinese American South (CHAS), Chinese American Central (CHAC), Taiwan Chinese American (TWN), Korean (KOR), and Hezhen (HEZ).

Figure 5
Figure 5. Ability of EAS-AIMs to discern population substructure.

A, PCA analysis of tester population samples (see Table 3) using 200 K SNPs. B, PCA analysis of same tester population samples using 1500 EAS-AIMs.

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