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Mobile element variation contributes to population-specific genome diversification, gene regulation and disease risk

Nature Genetics volume 55, pages 939–951 (2023)Cite this article

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Abstract

Mobile genetic elements (MEs) are heritable mutagens that recursively generate structural variants (SVs). ME variants (MEVs) are difficult to genotype and integrate in statistical genetics, obscuring their impact on genome diversification and traits. We developed a tool that accurately genotypes MEVs using short-read whole-genome sequencing (WGS) and applied it to global human populations. We find unexpected population-specific MEV differences, including an Alu insertion distribution distinguishing Japanese from other populations. Integrating MEVs with expression quantitative trait loci (eQTL) maps shows that MEV classes regulate tissue-specific gene expression by shared mechanisms, including creating or attenuating enhancers and recruiting post-transcriptional regulators, supporting class-wide interpretability. MEVs more often associate with gene expression changes than SNVs, thus plausibly impacting traits. Performing genome-wide association study (GWAS) with MEVs pinpoints potential causes of disease risk, including a LINE-1 insertion associated with keloid and fasciitis. This work implicates MEVs as drivers of human divergence and disease risk.

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Fig. 1: Discovery and accurate genotyping of MEVs in global and Japanese populations.
Fig. 2: Biased distribution of MEVs.
Fig. 3: eQTL analysis with MEVs.
Fig. 4: Alu insertions in regulatory elements.
Fig. 5: Alu insertions in 3’UTRs.
Fig. 6: MEVs associate with disease.

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Data availability

MEVs identified from 1000GP and summary statistics of eQTL analysis were uploaded in Zenodo (https://doi.org/10.5281/zenodo.7703708). The positions of and allele frequencies of MEVs identified from Japanese recruited by BBJ are available from National Bioscience Database Center (https://humandbs.biosciencedbc.jp/en/, accession ID: hum0014.v28) without any access restrictions. Summary statistics of GWAS are publicly available from our website (JENGER, http://jenger.riken.jp/en). Human reference genomes, human_g1k_v37, hs37d5, and GRCh38DH, are available from 1000GP repository (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz, ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz, and http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa, respectively). The 30× WGS data from 3,202 individuals recruited by 1000GP were downloaded from the 1000GP website (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/). SV callset generated by the phased assembly variant caller (PAV) in part of 1000GP is available from 1000GP repository (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGSVC2/release/v2.0/integrated_callset/variants_freeze4_sv_insdel.tsv.gz). SV callset generated by Panenie in part of 1000GP is available from 1000GP repository (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGSVC2/release/v1.0/PanGenie_results). GATK-SV callset generated by Panenie in part of 1000GP is available from 1000GP repository (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20210124.SV_Illumina_Integration/1KGP_3202.Illumina_ensemble_callset.freeze_V1.vcf.gz). Human repeat library is available from RepBase (https://www.girinst.org/repbase). Gene models, GenCode GTF v26 and v26lift37, are available from GenCode (https://www.gencodegenes.org/human). SNVs found from participants in 1000GP are available from 1000GP repository (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502). ENCODE cCRE dataset is available from ENCODE repository (https://screen.encodeproject.org). Accession numbers of histone ChIP-seq data from ENCODE are summarized in Supplementary Table 7. Other datasets of ESCs we used are available from ENCODE repository (https://screen.encodeproject.org) and deposited under accession numbers ENCFF601NBW (CpG methylation), ENCFF524BMX (CpG methylation), ENCFF379ZXG (CHG methylation), ENCFF086MMC (CHG methylation), ENCFF417VRB (CHH methylation), ENCFF918PML (CHH methylation), ENCFF000KUF (Repli-Chip), ENCFF000KUG (Repli-Chip), ENCFF000KUK (Repli-Chip), ENCFF905XDS (DNase-seq), ENCFF574LKL (DNase-seq), ENCFF338KTY (DNase-seq), ENCFF821AQO (CTCF ChIP-seq), ENCFF418QVJ (phospho-Pol-II A ChIP-seq), ENCFF422HDN (Pol-ll ChIP-seq), and ENCFF834UVX (EP300 ChIP-seq). Annotations of DHS were obtained from data deposited under accession number ENCFF503GCK. Methylation data of iPSCs and ECSs taken by genome tiling array deposited under number accession GSE60821 was used. Hi-C data of H1-hESCs deposited under accession numbers GSM5057489 and GSM5057481 was used. Gene expression profiles during spermatogenesis and early embryo deposited under accession numbers GSE120508 and GSE36552 were used. Read count tables of RNA-sequencing done by GTEx are available from GTEx repository (https://storage.googleapis.com/gtex_analysis_v8/rna_seq_data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_exon_reads.parquet and GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_reads.gct.gz). Covariates for eQTL analysis and phased SNVs and indels in GTEx (that is PEER factors, genetic PCs, library preparation methods, sequencing platforms, and sex) are available from NCBI under dbGaP accession number phs000424. The script used to collapse GenCode GTF file is available from the URL below: https://github.com/broadinstitute/gtex-pipeline/blob/master/gene_model/collapse_annotation.py. The script used to apply TMM normalization is available from the URL below: https://github.com/broadinstitute/gtex-pipeline/blob/master/qtl/src/eqtl_prepare_expression.py. MEVs identified in Cao et al. are available from the following URLs: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02101-4. Gene sets used for gene-set enrichment analysis is available from msigdb (https://www.gsea-msigdb.org/gsea/msigdb/). 7221 summary statistics of GWAS done by Pan-UKB were downloaded from the URLs listed in a file: https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_release/phenotype_manifest.tsv.bgz. The ‘tophit’ variants in BBJ were downloaded from http://jenger.riken.jp:8080/top_hits and https://pheweb.jp.

Code availability

MEGAnE is deposited in Zenodo (https://doi.org/10.5281/zenodo.7703696) and available from GitHub and Dockerhub (https://github.com/shohei-kojima/MEGAnE, https://hub.docker.com/repository/docker/shoheikojima/megane). A complete environment including MEGAnE and other required software is available from Dockerhub.

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Acknowledgements

We are grateful to all of the participants in BBJ, as well as the staff of BBJ for their assistance. BBJ is supported by the Japan Agency for Medical Research and Development (AMED) (grant JP19km0605001). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The GTEx data used for the analyses described in this study were obtained from dbGaP accession number phs000424.v8.p2 We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren and E. Wijsman). We appreciate obtaining access to genetic and pedigree data on SFARI Base. We acknowledge the resources of the 1000 Genomes Project and HGDP-CEPH Human Genome Diversity Cell Line Panel. We are grateful to the UK Biobank participants contributing to the results made public via the Pan-UK Biobank Resource and acknowledge the Pan-UKBB team (https://pan.ukbb.broadinstitute.org/team). Super-computing resources were provided by Human Genome Center, the Institute of Medical Science, the University of Tokyo (SHIROKANE), and the Office for Information Systems and Cybersecurity, RIKEN (HOKUSAI General Use project G20021 and Q21537). We acknowledge H. Yoshida and T. Suzuki of RIKEN Center for Integrative Medical Sciences for providing plasmids, X. Chen and G. Bourque of Kyoto University and N. Sasa and Y. Okada of Osaka University for testing prerelease versions of MEGAnE, K. Sato and J. Ito of University of Tokyo for helpful discussion, and M. Yoshioka for outstanding administrative support. S. Kojima, M.N. and A.J.K. acknowledge funding from the Incentive Research Projects of RIKEN, supported in part by Resona Bank. S. Kojima acknowledges a funding from Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Early-Career Scientists 22K15385. S. Kojima and S.M.H. acknowledge the RIKEN Special Postdoctoral Researcher Program. K. Ito acknowledges fundings from JSPS KAKENHI Grant-in-Aid for Scientific Research(B) 21H02919, AMED (JP22ek0210164, JP21tm0724601, JP20km0405209 and JP20ek0109487) and Research Funding for Longevity Sciences from the NCGG. N.F.P. acknowledges funding from JSPS KAKENHI Grants-in-Aid for Scientific Research(S) 20H05682, JSPS KAKENHI Grants-in-Aid for Scientific Research(B) 21H02972, RIKEN-McGill International Collaborative grant, Gout and Uric Acid Foundation of Japan, Cluster for Pioneering Research under the Hakubi fellowship program and from the discretionary budget of the Director of the RIKEN Center for Integrative Medical Sciences, K. Yamamoto.

Author information

Author notes

  1. Mirei Ka

    Present address: Next-Generation Precision Medicine Development, Integrative Genomics Laboratory, Graduate School of Medicine, Department of Medical Science, The University of Tokyo, Tokyo, Japan

  2. Anselmo J. Kamada

    Present address: Paleovirology Lab, Department of Biology, University of Oxford, Oxford, UK

Authors and Affiliations

  1. Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences and RIKEN Cluster for Pioneering Research, Yokohama, Japan

    Shohei Kojima, Mirei Ka, Yuka Saito, Erica H. Parrish, Asami F. Gelinas, Steven M. Heaton, Rie Koide, Anselmo J. Kamada & Nicholas F. Parrish

  2. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Satoshi Koyama & Kaoru Ito

  3. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Satoshi Koyama

  4. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    Satoshi Koyama

  5. Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan

    Yuka Saito

  6. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Mikiko Endo, Sadaaki Takata, Misaki Mizukoshi & Yukihide Momozawa

  7. Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Keiko Hikino

  8. Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan

    Atsushi Takeda & Michiaki Hamada

  9. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Atsushi Takeda & Michiaki Hamada

  10. Cell Engineering Division, BioResource Research Center, RIKEN, Tsukuba, Japan

    Michiya Noguchi & Yukio Nakamura

  11. Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani

  12. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yoichiro Kamatani & Chikashi Terao

  13. RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yasuhiro Murakawa

  14. Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan

    Yasuhiro Murakawa

  15. IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy

    Yasuhiro Murakawa

  16. Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Kazuyoshi Ishigaki

  17. Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan

    Chikashi Terao

  18. The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan

    Chikashi Terao

  19. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  20. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  21. Division of Genetics, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yuji Yamanashi

  22. Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoichi Furukawa

  23. Division of Molecular Pathology, IMSUT Hospital, Department of Internal Medicine, Project Division of Genomic Medicine and Disease Prevention, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  24. Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  25. Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Kaori Muto & Akiko Nagai

  26. Department of Urology, Iwate Medical University, Iwate, Japan

    Wataru Obara

  27. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Yamaji

  28. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Kazuhisa Takahashi

  29. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai

  30. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai & Yasuo Takahashi

  31. Tokushukai Group, Tokyo, Japan

    Takao Suzuki & Nobuaki Sinozaki

  32. Department of Hematology, Nippon Medical School, Tokyo, Japan

    Hiroki Yamaguchi

  33. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan

    Shiro Minami

  34. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan

    Shigeo Murayama

  35. Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan

    Kozo Yoshimori

  36. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan

    Satoshi Nagayama

  37. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan

    Daisuke Obata

  38. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan

    Masahiko Higashiyama

  39. Iizuka Hospital, Fukuoka, Japan

    Akihide Masumoto

  40. National Hospital Organization Osaka National Hospital, Osaka, Japan

    Yukihiro Koretsune

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  1. Shohei Kojima
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  2. Satoshi Koyama
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  10. Atsushi Takeda
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  11. Asami F. Gelinas
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  14. Anselmo J. Kamada
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  17. Yoichiro Kamatani
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  19. Kazuyoshi Ishigaki
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  21. Kaoru Ito
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Biobank Japan Project Consortium

  • Koichi Matsuda
  • , Yuji Yamanashi
  • , Yoichi Furukawa
  • , Takayuki Morisaki
  • , Yoshinori Murakami
  • , Kaori Muto
  • , Akiko Nagai
  • , Wataru Obara
  • , Ken Yamaji
  • , Kazuhisa Takahashi
  • , Satoshi Asai
  • , Yasuo Takahashi
  • , Takao Suzuki
  • , Nobuaki Sinozaki
  • , Hiroki Yamaguchi
  • , Shiro Minami
  • , Shigeo Murayama
  • , Kozo Yoshimori
  • , Satoshi Nagayama
  • , Daisuke Obata
  • , Masahiko Higashiyama
  • , Akihide Masumoto
  •  & Yukihiro Koretsune

Contributions

S. Kojima and N.F.P. designed this study. S. Kojima, A.T., M.H., and N.F.P. developed MEGAnE. S. Kojima, S. Koyama, and N.F.P. analyzed data. N.F.P., K.H., K. Ishigaki, K. Ito, C.T., and Y.K. organized data in BBJ. S. Kojima, Y.S., M.E., M.M., S.T., Y.M., Y. Momozawa, and N.F.P. performed targeted deep sequencing. S.M.H., A.J.K., Y.M., Y. Murakawa, C.T., M.N., and Y.N. managed cell culture. S. Kojima, E.H.P., M.K., A.F.G., and R.K. performed wet experiments. S. Kojima and N.F.P. wrote and all authors checked the manuscript.

Corresponding authors

Correspondence to Shohei Kojima or Nicholas F. Parrish.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Clement Goubert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Note and Supplementary Figures 1–47

Reporting Summary

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Supplementary Table 1

Supplementary Tables 1–18

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Kojima, S., Koyama, S., Ka, M. et al. Mobile element variation contributes to population-specific genome diversification, gene regulation and disease risk. Nat Genet 55, 939–951 (2023). https://doi.org/10.1038/s41588-023-01390-2

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