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Genome diversity and signatures of natural selection in mainland Southeast Asia

Nature volume 643, pages 417–426 (2025)Cite this article

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Abstract

Mainland Southeast Asia (MSEA) has rich ethnic and cultural diversity with a population of nearly 300 million1,2. However, people from MSEA are underrepresented in the current human genomic databases. Here we present the SEA3K genome dataset (phase I), generated by deep short-read whole-genome sequencing of 3,023 individuals from 30 MSEA populations, and long-read whole-genome sequencing of 37 representative individuals. We identified 79.59 million small variants and 96,384 structural variants, among which 22.83 million small variants and 24,622 structural variants are unique to this dataset. We observed a high genetic heterogeneity across MSEA populations, reflected by the varied combinations of genetic components. We identified 44 genomic regions with strong signatures of Darwinian positive selection, covering 89 genes involved in varied physiological systems such as physical traits and immune response. Furthermore, we observed varied patterns of archaic Denisovan introgression in MSEA populations, supporting the proposal of at least two distinct instances of Denisovan admixture into modern humans in Asia3. We also detected genomic regions that suggest adaptive archaic introgressions in MSEA populations. The large number of novel genomic variants in MSEA populations highlight the necessity of studying regional populations that can help answer key questions related to prehistory, genetic adaptation and complex diseases.

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Fig. 1: Statistics of the SEA3K genomic variants.
Fig. 2: SV discovery based on the long-read genome data of 37 MSEA individuals.
Fig. 3: Genetic structure and population history of MSEA populations.
Fig. 4: Genomic signals of positive selection in MSEA populations.
Fig. 5: The landscape of archaic introgression in MSEA populations.

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Data availability

Datasets generated in this study were deposited in public repositories. WGS data are archived at the Genome Sequence Archive under the accession HRA007135. Genome assemblies are archived at Genome Warehouse (GWH) under the accession PRJCA028104. Variant data are archived at Genome Variation Map under the accession number GVM000730. To protect participant confidentiality, the raw sequencing data are available to the scientific community for general research through a controlled access process. Access can be requested by submitting an application that includes a detailed research proposal and an IRB approval from the applicant’s home institute to the Data Access Committee of Kunming Institute of Zoology, Chinese Academy of Sciences (KIZ, CAS). All other data are open access. Datasets obtained from publicly available sources include: human reference genome GRCh38 (https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa), human reference genome T2T-CHM13 (https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz), 1KGP phase 3 dataset (https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/), high-coverage 1KGP dataset (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_raw_GT_with_annot/), HGDP dataset (https://rosenberglab.stanford.edu/data/conradEtAl2006/data1_1.tar.gz), SGDP dataset (https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/vcf_variants/vcfs.variants.public_samples.279samples.tar), HGSVC3 (freeze4) (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGSVC3/working/20240415_Freeze4/), HPRC (v1.1) (https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-grch38/hprc-v1.1-mc-grch38.vcfbub.a100k.wave.vcf.gz), Genome of Altai Neanderthal and Denisovan (https://www.eva.mpg.de/genetics/genome-projects/), RefSeq genes (https://www.cog-genomics.org/static/bin/plink/glist-hg38), GWAS summary data (https://www.ebi.ac.uk/gwas/api/search/downloads/full), cCREs from ENCODE (https://downloads.wenglab.org/V3/GRCh38-cCREs.bed), ClinVar, https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/), OMIM database (https://www.ncbi.nlm.nih.gov/omim/) and eQTLs from GTEx (https://storage.googleapis.com/adult-gtex/bulk-qtl/v8/single-tissue-cis-qtl/GTEx_Analysis_v8_eQTL.tar).

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Acknowledgements

The authors thank all participants in the project, and S.-F. Wu for assistance in sample collection. This study was supported by National Natural Science Foundation of China (32288101 to B.S.; 32170632 to Y.H.; T2222030 and U23A20161 to X.Z.; 32170633 to Y.-C.L.), Major Scientific Project of Yunnan Province (202305AH340007 to B.S.), Yunnan Revitalization Talent Support Program Science and Technology Champion Project (202005AB160004 to B.S.), Yunnan Revitalization Talent Support Program Innovation Team (202405AS350008), Yunnan Scientist Workshops (to B.S.), Science and Technology General Program of Yunnan Province (202301AW070010 to Y.H.), High-level Talent Promotion and Training Projects of Kunming (2022SCP001 to M.-S.P., 2022SCP001 to Y.-P.Z. and 2020SCP001 to Q.-P.K.), Animal Branch of the Germplasm Bank of Wild Species, Chinese Academy of Sciences (the Large Research Infrastructure Funding), the National Key R&D Program of China (2022YFC3302004 to Y.-C.L.), Yunling Scholar of the Yunnan Province (Q.-P.K.) and the Yunnan Ten Thousand Talents Plan Young and Elite Talents Project (Y.-C.L.).

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  1. These authors contributed equally: Yaoxi He, Xiaoming Zhang, Min-Sheng Peng, Yu-Chun Li, Kai Liu

Authors and Affiliations

  1. State Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China

    Yaoxi He, Xiaoming Zhang, Min-Sheng Peng, Yu-Chun Li, Kai Liu, Yu Zhang, Leyan Mao, Yongbo Guo, Yujie Ma, Bin Zhou, Wangshan Zheng, Tian Yue, Yuwen Liao, Weijie Zhang, Shanshan Dai, Zongliang Gao, Li-Qin Yang, Jianxin Guo, Jiangguo Li, Qing-Peng Kong, Ya-Ping Zhang & Bing Su

  2. Yunnan Key Laboratory of Integrative Anthropology, Kunming, China

    Yaoxi He, Xiaoming Zhang, Li-Qin Yang & Bing Su

  3. KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, China

    Min-Sheng Peng, Yu-Chun Li, Zongliang Gao, Qing-Peng Kong & Ya-Ping Zhang

  4. University of Chinese Academy of Sciences, Beijing, China

    Min-Sheng Peng, Kai Liu, Yu Zhang, Leyan Mao, Yujie Ma, Bin Zhou, Wangshan Zheng, Tian Yue, Yuwen Liao, Weijie Zhang, Zongliang Gao, Jiangguo Li & Ya-Ping Zhang

  5. Kunming Key Laboratory of Healthy Aging Study, Kunming, China

    Yu-Chun Li, Zongliang Gao, Li-Qin Yang & Qing-Peng Kong

  6. State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Science, Fudan University, Shanghai, China

    Shen-Ao Liang & Lu Chen

  7. National Genomics Data Center, China National Center for Bioinformation, Beijing, China

    Xiaoning Chen & Bixia Tang

  8. School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Xiaofei Yang, Shenghan Gao & Shijie Wan

  9. MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Xiaofei Yang, Kai Ye & Shenghan Gao

  10. Center for Mathematical Medical, the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China

    Xiaofei Yang & Kai Ye

  11. School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Kai Ye & Shenghan Gao

  12. Genome Institute, the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China

    Kai Ye

  13. School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China

    Kai Ye

  14. Faculty of Science, Leiden University, Leiden, The Netherlands

    Kai Ye

  15. CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Yurun Lu & Yong Wang

  16. School of Medicine, Yunnan University, Kunming, China

    Rushan Hao

  17. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China

    Xuankai Wang & Yafei Mao

  18. Center for Genomic Research, International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University, Yiwu, China

    Yafei Mao

  19. Laboratory Animal Center, Kunming Institute of Zoology, the Chinese Academy of Sciences, Kunming, China

    Chao Liu

  20. National Resource Center for Non-Human Primates, Kunming, China

    Chao Liu

  21. Department of Anthropology, School of Sociology, Yunnan Minzu University, Kunming, China

    Jianhua Wang

  22. Department of Geography and Land Management, Royal University of Phnom Penh, Phnom Penh, Cambodia

    Tuot Sovannary & Long Bunnath

  23. Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

    Jatupol Kampuansai & Angkhana Inta

  24. Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand

    Metawee Srikummool

  25. Department of Biology, Faculty of Science, Naresuan University, Phitsanulok, Thailand

    Wibhu Kutanan

  26. Department of Immunology, Ha Noi Medical University, Ha Noi, Vietnam

    Huy Quang Ho & Khoa Dang Pham

  27. National University of Laos (NUOL), Vientiane, Laos

    Sommay Singthong

  28. Muang Sing District Hospital, Luang Namtha, Laos

    Somphad Sochampa

  29. Field School of Archaeology, Paukkhaung, Myanmar

    U. Win Kyaing

  30. Queen Sirikit Botanic Garden (QSBG), The Botanical Garden Organization, Chiang Mai, Thailand

    Wittaya Pongamornkul

  31. Inter Mountain Peoples Education and Culture in Thailand Association (IMPECT), Sansai, Thailand

    Chutima Morlaeku

  32. Asia Indigenous Peoples Pact (AIPP), Chiang Mai, Thailand

    Kittisak Rattanakrajangsri

  33. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Qing-Peng Kong & Bing Su

  34. State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, China

    Ya-Ping Zhang

Authors
  1. Yaoxi He
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Consortia

Consortium of Anthropological Research in Southeast Asia and Southwest China (CASEAC)

  • Yaoxi He
  • , Xiaoming Zhang
  • , Min-Sheng Peng
  • , Yu-Chun Li
  • , Jianxin Guo
  • , Lu Chen
  • , Yafei Mao
  • , Jianhua Wang
  • , Tuot Sovannary
  • , Long Bunnath
  • , Jatupol Kampuansai
  • , Metawee Srikummool
  • , Wibhu Kutanan
  • , Huy Quang Ho
  • , Khoa Dang Pham
  • , Sommay Singthong
  • , Somphad Sochampa
  • , U. Win Kyaing
  • , Qing-Peng Kong
  •  & Bing Su

Contributions

B.S., Y.-P.Z. and Q.-P.K. conceived and designed the study. B.S. and Y.H. coordinated and supervised the project. T.S., L.B. and X.Z. collected samples from Cambodia. J.K., M.S., W.K. and J.W. collected samples from Thailand. H.Q.H., K.D.P., S.D. and M.-S.P. collected samples from Vietnam. S. Singthong, S. Sochampa, C.L., Z.G., L.-Q.Y. and Y.-C.L. collected samples from Laos. U.W.K. and M.-S.P. collected samples from Myanmar. X.Z. collected samples from China. A.I., W.P., C.M. and K.R. were responsible for the ethical approval work, personnel coordination and volunteer organization for sample collection. Y.H., X.Z., M.-S.P., Y.-C.L., Y.Z., K.L., Y. Ma and T.Y. prepared the samples and processed them for sequencing. Y.H. and K.L. contributed to data processing, QC, variant analysis and genome assembly. Y.H., Y. Ma, W. Zheng, Y. Liao, L.M., J.G., J.L., R.H., K.L., Y. Lu and Y.W. contributed to the population genetics analysis. Y.Z., T.Y. and Y.H. contributed to construction of the imputation reference panel. W. Zhang, X.C. and B.T. contributed to construction of the SEA3K Imputation Server. Y.G., Y.H, X.Y., K.Y., S.G., S.W., B.Z., Y. Mao and X.W. contributed to structural variants analysis. L.C., S.-A.L, Y.Z. and Y.H. contributed to archaic introgression analysis. Y.H. and X.Z. were responsible for organizing the CASEAC. Y.H., M.-S.P. and Y.-C.L. were responsible for ethical, legal and social implications. Y.H. and B.S. wrote the manuscript. Y.-P.Z., Q.-P.K., X.Z., M.-S.P., Y.-C.L., L.C. and Y. Mao edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qing-Peng Kong, Ya-Ping Zhang or Bing Su.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Statistics of small variant calls.

The sample-level counts of small variants (SNVs and indels) (panel-a) and the calculated heterozygosity ratios of small variants (panel-b) cross populations, stratified by super-populations. A totoal of 29 MSEA populations (CMKL was excluded due to the small sample size of two individuals) in the SEA3K dataset and 26 populations (belong to five super-populations) from 1KGP dataset were included in this analysis. Panel-a shows a higher average number of variants per individual genome in MSEA populations than those in Eurasian populations of 1KGP, and panel-b shows a comparable heterozygosity ratio in MSEA populations with East Asian populations. The genome-wide heterozygosity was calculated by the ratio of the number of heterozygous SNVs divided by the number of non-reference homozygous SNVs. For each boxplot, we drew a box from the first quartile to the third quartile. A horizontal line across the box indicates the median. The whiskers go from each quartile to the minimum or the maximum. Abbreviations in 1KGP: EAS-East Asians, AMR-Americans, SAS-South Asians, EUR-Europeans, and AFR-Africans.

Extended Data Fig. 2 Schematic diagram of SV merging and SV novelty evaluation.

a, The SV calls from Sniffles2 and PAV of each individual were merged using Jasmine, and then we merged the 37 individual calls to obtain the population calls (120,063 SVs in total). b, The counts of individual SVs in the Sniffles2 calls, the PAV calls and the merged calls. The mean SV counts per individual at each step are labeled and denoted using dashed red lines. c, Repeat annotation of MSEA SVs. d, The HGSVC3 and HPRC SV callsets are considered as the reference panel. The detailed information of evaluation procedure is provided in Methods.

Extended Data Fig. 3 Principal component analysis (PCA) of the SEA3K and worldwide population samples.

The plots were constructed using 4,587,143 biallelic SNVs among 5,938 global individuals, including 2,183 samples drawn from SEA3K, 2,504 samples from 1KGP, 828 samples from HGDP, 279 samples from SGDP, and other representative populations from the published data: Malays (n = 96), Andamanese (n = 10) and Tibetans (n = 38) (see Methods). The upper-panel shows the global pattern, and the bottom-panel shows the regional pattern including only East Asians, South Asians and SEA3K. The SEA3K dataset was categorized by language families.

Extended Data Fig. 4 Genetic relatedness of MSEA populations with other global populations.

The Neighbor-Joining (NJ) trees and Maximum-Likelihood (ML) trees were constructed using the merged SNV data from SEA3K (marked in red font) and 1KGP (or HGDP). The NJ trees showing the genetic relationship that was determined using genetic distances, and the unit of branch length for genetic distance is shown in the bottom of the tree. In the ML trees, the standard errors and 100 bootstrap replicates were used to evaluate the confidence in the inferred tree topology.

Extended Data Fig. 5 Pairwise FST between MSEA and East Asian populations.

Heatmaps showing the pairwise FST differences between East Asian populations from SGDP, and MSEA populations in SEA3K. Color scales for both heatmaps are the same. Genetic differences between MSEA populations are greater than between population from East Asia distributed over a comparable geographic range. For example, FST between MYNA and CMKR (0.051) is significantly higher than is the highest FST between East Asian groups (0.037, Yakut vs. She), given the two populations are separated by three to four times the geographical distance of the MSEA populations.

Extended Data Fig. 6 Historical population dynamics of MSEA populations.

The plots show the results of the estimated effective population size (Ne) of the ancestral populations in MSEA populations (stratified by countries) using SMC + + (dotted lines) and MSMC2 (solid lines) methods. The generation time and the mutation rate per generation per site (μ) in the analyses were set as 29 years and 1.25 × 10−8 in both MSMC2 and SMC + +, respectively.

Extended Data Fig. 7 GWAS annotation and simulation of PS-SNVs in MSEA populations.

a, Manhattan plot of the CMS scores of the genome-wide SNVs in MSEA populations. The reported GWAS hits are highlighted by red dots and labeled with GWAS information (variants, associated traits and mapped genes). There are six positive-selection regions harboring the GWAS hits of the top PS-SNVs (with the top 0.01% CMS scores), including two regions covering genes (GDF5, UQCC1 and ZRANB3) related to body height, one region covering genes (SLC24A5 and MYEF2) related to skin pigmentation, one region covering PNPT1 related to hip circumference, one region covering WDPCP related to diet measurement, and the MHC region related to diverse phenotypes. The dot size denotes to the P-value of the CMS score. Statistical significance was assessed using one-sided chi-squared (χ2) test. b, Permutation test of height-associated SNVs. Among the 5,505 PS-SNVs in the 44 regions, we found a significant excess of variants related to body height, with 21 height-associated SNVs falling in the PS-SNV set (P-value = 0.005 based on 1000 one-sided permutation tests) (see details in Methods).

Extended Data Fig. 8 Signature of natural selection in the FLG gene region.

a, The regional plot of CMS scores and recombination rates in the FLG region, in which the peaks indicate the selective signals. The peak SNVs are marked with colors. The bottom panel shows the LD blocks of the 262 PS-SNVs with MSEA-enriched alleles. The dot size denotes to the P-value of the CMS score, and statistical significance was assessed using chi-squared (χ2) test. The calculated recombination rates (r2) indicate the estimated linkage disequilibrium (LD) degree between the peak SNV and the other SNVs and are coded in colors. b, The TCS network of the FLG region showing a MSEA-specific haplogroup. Each node represents a haplotype, and the size is proportional to its frequency. The MSEA-specific haplogroup is highlighted.

Extended Data Fig. 9 Identification of archaic introgression in MSEA populations.

a, Comparison of the archaic-introgression sequences (from Neanderthal and Denisovan) between MSEA and global populations in 1KGP. EAS-East Asians (CDX and KHV were excluded since they due belong to MSEA), AMR-Americans, SAS-South Asians and EUR-Europeans. The y-axis indicates the mean detected Neanderthal sequences (or Denisovan sequences) per individual from different populations, stratified by super populations. We compared MSEA with EAS and SAS, and evaluated significance using two-sided unpaired t-test. For each boxplot, we drew a box from the first quartile to the third quartile. A horizontal line across the box indicates the median. The whiskers go from each quartile to the minimum or the maximum. b, Mean amounts of the detected introgressed sequences per individual in the 1KGP populations, categorized by affinity to the Altai Neanderthal and Altai Denisovan genomes. c, Intersection of the Neanderthal introgression callsets between Sprime and IBDmix. The callsets were merged for all identified introgression segments in MSEA individuals. d, Violin plots of the Neanderthal sequences per individual in MSEA populations identified by Sprime and IBDmix. For each boxplot, we drew a box from the first quartile to the third quartile. A horizontal line across the box indicates the median. The whiskers go from each quartile to the minimum or the maximum.

Extended Data Fig. 10 Hierarchical clustering of the haplotypes spanning the adaptive introgression region on Chr1.

The rows illustrate the individual haplotypes. A total of 2,116 individuals from 23 populations in SEA3K (MSEA population) were include, and 2,504 individuals from 26 geographically diverse populations in 1KGP (including SAS, EAS, EUR and AMR populations) were used as the control groups. The Denisovan-derived PS-SNVs are denoted by rhombus in red. The colors of gray and black represent the ancestral and the derived alleles, respectively. DEN, Denisovan. The introgressed Denisovan-like haplotypes were marked in the plot.

Extended Data Fig. 11 Detection of disease-associated variants and protein-truncating variants in MSEA populations.

a, Frequency distribution of the ClinVar pathogenic variants in SEA3K. The classifications of autosomal-dominant (AD), autosomal recessive (AR) and unknown were based on the OMIM database. b, Number of pathogenic variants carried by each MSEA individual. c, Ten pathogenic variants specifically enriched in MSEA populations. The mapped gene, variants and risk alleles, and frequencies of risk alleles in SEA3K and other datasets are indicated. The clinical significance is indicated by the exclamation marks (pathogenic level) and stars (times of classified by previous submitter). d, Frequency distribution of an alpha-thalassemia variant in HBA2 (chromosome 16: 173598; c.427 T > C) in world populations and MSEA populations. NA, not available. e, The proportion of genes with at least one high-confidence PTVs (pie on the left), and the proportions of novel, known, heterozygous and homozygous PTVs (pie on the right) in the SEA3K dataset. f, The counts of the identified novel homozygous PTVs per individual across MSEA populations.

Supplementary information

Supplementary Information

Supplementary Figs 1–18 and a list of members of the Consortium of Anthropological Research in Southeast Asia and Southwest China (CASEAC).

Reporting Summary

Supplementary Tables

Supplementary Tables 1–20.

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He, Y., Zhang, X., Peng, MS. et al. Genome diversity and signatures of natural selection in mainland Southeast Asia. Nature 643, 417–426 (2025). https://doi.org/10.1038/s41586-025-08998-w

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