Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk

Nature Genetics volume 57, pages 1881–1889 (2025)Cite this article

Subjects

Abstract

Multiancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. Here we present the sum of shared single effects (SuShiE) model, which leverages linkage disequilibrium heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations and estimate ancestry-specific expression prediction weights. Through extensive simulations, we find that SuShiE consistently outperforms existing methods. We apply SuShiE to 36,907 molecular phenotypes including mRNA expression and protein levels from individuals of diverse ancestries in the TOPMed-MESA and GENOA studies. SuShiE fine-maps cis-molQTLs for 18.2% more genes compared with existing methods while prioritizing fewer variants and exhibiting greater functional enrichment. While SuShiE infers highly consistent cis-molQTL architectures across ancestries, it finds evidence of heterogeneity at genes with predicted loss-of-function intolerance. Lastly, using SuShiE-derived cis-molQTL effect sizes, we perform transcriptome- and proteome-wide association studies on six white blood cell-related traits in the All of Us biobank and identify 25.4% more genes compared with existing methods. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SuShiE infers credible sets with PIPs, cross-ancestry effect size correlations and ancestry-specific effect sizes by leveraging shared genetic architectures and LD heterogeneity.
Fig. 2: SuShiE outperforms other methods, estimates accurate effect size correlation and boosts higher power of TWAS in realistic simulations.
Fig. 3: SuShiE reveals cis-regulatory mechanisms for mRNA expression and protein abundance.
Fig. 4: SuShiE identifies cis-eQTL rs2528382 for URGCP with functional support.
Fig. 5: SuShiE identifies more TWAS and PWAS genes and higher chi-square statistics compared with SuSiE and MESuSiE.

Similar content being viewed by others

Data availability

SuShiE-derived prediction models (in both tsv format and FUSION format) for TWAS, PWAS, fine-mapping and other analyzed results across cis-molQTL datasets are available via Zenodo at https://doi.org/10.5281/zenodo.10963033 (ref. 69). The TOPMed-MESA data can be found and requested at dbGaP: phs000209.v13.p3, phs001416.v3.p1 and phs001416.v1.p1. The GENOA data can be found and requested at dbGaP: phs001238.v2.p1 and GEO: GSE138914. The GEUVADIS data can be found at https://www.internationalgenome.org/data-portal/data-collection/geuvadis. The INTERVAL data can be found and requested at https://ega-archive.org/datasets/EGAD00001004080. The summary statistics in Chen et al. can be found at https://doi.org/10.1016/j.cell.2020.06.045. The LDSC annotation files can be found at https://console.cloud.google.com/storage/browser/broad-alkesgroup-public-requester-pays/. The ENCODE cCRE v3 can be found at https://screen.encodeproject.org/index/cversions. The snATAC-seq cCRE can be found at (ref. 24). The scATAC-seq cCRE can be found at (ref. 25). The All of Us data can be requested through https://allofus.nih.gov. The 1000G project data can be found at https://www.internationalgenome.org. The gnomAD v4.0 dataset for pLI and LOEUF is available at https://gnomad.broadinstitute.org/news/2023-11-gnomad-v4-0/. The RVIS dataset can be found at (ref. 39). The shet dataset can be found at (ref. 70). The EDS dataset can be found at (ref. 38).

Code availability

SuShiE v0.16 software is available via GitHub at https://github.com/mancusolab/sushie. The analysis codes for simulation and real-data analysis of this Article are available via GitHub at https://github.com/mancusolab/sushie-project-codes and https://doi.org/10.5281/zenodo.10963033 (ref. 69). The twas_sim software is available via GitHub at https://github.com/mancusolab/twas_sim. TOPMed RNA-seq Harmonization pipeline instructions are available via GitHub at https://github.com/broadinstitute/gtex-pipeline/blob/master/TOPMed_RNAseq_pipeline.md. The GTEx eQTL analysis pipeline is available at https://www.gtexportal.org/home/methods. The PLINK2 software is available at https://www.cog-genomics.org/plink/2.0. The BCFTOOLS v1.21 software is available at https://samtools.github.io/bcftools/bcftools.html. The FUSION pipeline is available at http://gusevlab.org/projects/fusion/. The LiftOver software is available at https://genome.ucsc.edu/cgi-bin/hgLiftOver. The WashU Epigenome Browser is available at https://epigenomegateway.wustl.edu/. The Plotgardener v1.8.3 software is available via GitHub at https://github.com/PhanstielLab/plotgardener/. The AnnoQ is available at http://annoq.org. The SuSiEx v1.1.2 software is available via GitHub at https://github.com/getian107/SuSiEx. The MESuSiE software is available via GitHub at https://github.com/borangao/MESuSiE. The XMAP v1.0.1 software is available via GitHub at https://github.com/YangLabHKUST/XMAP.

References

  1. Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Aguet, F. et al. Molecular quantitative trait loci. Nat. Rev. Methods Prim. 3, 4 (2023).

    Article  CAS  Google Scholar 

  3. Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B 82, 1273–1300 (2020).

    Article  Google Scholar 

  4. Wen, X., Luca, F. & Pique-Regi, R. Cross-population joint analysis of eQTLs: fine mapping and functional annotation. PLoS Genet. 11, e1005176 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kichaev, G. & Pasaniuc, B. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97, 260–271 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. LaPierre, N. et al. Identifying causal variants by fine mapping across multiple studies. PLoS Genet. 17, e1009733 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Yuan, K. et al. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Nat. Genet. 56, 1841–1850 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Gao, B. & Zhou, X. MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies. Nat. Genet. 56, 170–179 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Cai, M. et al. XMAP: cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nat. Commun. 14, 6870 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Bild, D. E. et al. Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 111, 1313–1320 (2005).

    Article  PubMed  Google Scholar 

  13. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Shang, L. et al. Genetic architecture of gene expression in European and African Americans: an eQTL mapping study in GENOA. Am. J. Hum. Genet. 106, 496–512 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature 627, 340–346 (2024).

  16. Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the ‘Sum of Single Effects’ model. PLoS Genet. 18, e1010299 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Tibshirani, R. Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Ser. B 58, 267–288 (1996).

    Article  Google Scholar 

  18. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B 67, 301–320 (2005).

    Article  Google Scholar 

  19. Clark, S. A. & van der Werf, J. Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values. Methods Mol. Biol. 1019, 321–330 (2013).

    Article  PubMed  Google Scholar 

  20. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  21. Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mostafavi, H., Spence, J. P., Naqvi, S. & Pritchard, J. K. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat. Genet. 55, 1866–1875 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  24. Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Cai, J. et al. URGCP promotes non-small cell lung cancer invasiveness by activating the NF-κB–MMP-9 pathway. Oncotarget 6, 36489–36504 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kachuri, L. et al. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat. Genet. 55, 952–963 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Shi, H. et al. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. Am. J. Hum. Genet. 106, 805–817 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 1098 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Hou, K. et al. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat. Genet. 55, 549–558 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Saito, S. et al. Gene-specific somatic epigenetic mosaicism of FDFT1 underlies a non-hereditary localized form of porokeratosis. Am. J. Hum. Genet. 111, 896–912 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Taylor, D. J. et al. Sources of gene expression variation in a globally diverse human cohort. Nature 632, 122–130 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Wang, X. & Goldstein, D. B. Enhancer domains predict gene pathogenicity and inform gene discovery in complex disease. Am. J. Hum. Genet. 106, 215–233 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Zeng, T., Spence, J. P., Mostafavi, H. & Pritchard, J. K. Bayesian estimation of gene constraint from an evolutionary model with gene features. Nat. Genet. 56, 1632–1643 (2024).

    Article  PubMed  CAS  Google Scholar 

  41. Lu, Z. et al. Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Am. J. Hum. Genet. 109, 1388–1404 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Tapia, A. L. et al. A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping. Genet. Epidemiol. 46, 3–16 (2022).

    Article  PubMed  Google Scholar 

  43. Wen, J. et al. Transcriptome-wide association study of blood cell traits in African ancestry and Hispanic/Latino populations. Genes 12, 1049 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Rowland, B. et al. Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits. Hum. Mol. Genet. 31, 2333–2347 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Chen, M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213.e14 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Ding, Y. et al. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature 618, 774–781 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Mester, R. et al. Impact of cross-ancestry genetic architecture on GWASs in admixed populations. Am. J. Hum. Genet. 110, 927–939 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Zhang, J. & Stram, D. O. The role of local ancestry adjustment in association studies using admixed populations. Genet. Epidemiol. 38, 502–515 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Wang, L. et al. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat. Commun. 15, 4260 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Bhattacharya, A. et al. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: lessons from the Global Biobank Meta-analysis Initiative. Cell Genom. 2, 100180 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet. 51, 675–682 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017).

    Article  CAS  Google Scholar 

  54. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems Vol. 14, 601–608 (MIT Press, 2002).

  55. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    Article  PubMed  CAS  Google Scholar 

  56. Frankish, A. et al. GENCODE 2021. Nucleic Acids Res. 49, D916–D923 (2021).

    Article  PubMed  CAS  Google Scholar 

  57. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Hujoel, M. L. A., Gazal, S., Hormozdiari, F., van de Geijn, B. & Price, A. L. Disease heritability enrichment of regulatory elements is concentrated in elements with ancient sequence age and conserved function across species. Am. J. Hum. Genet. 104, 611–624 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).

    Article  Google Scholar 

  64. Liu, Z. et al. Annotation Query (AnnoQ): an integrated and interactive platform for large-scale genetic variant annotation. Nucleic Acids Res. 50, W57–W65 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Li, D. et al. WashU Epigenome Browser update 2022. Nucleic Acids Res. 50, W774–W781 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Kramer, N. E. et al. Plotgardener: cultivating precise multi-panel figures in R. Bioinformatics 38, 2042–2045 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Hudson, R. R., Slatkin, M. & Maddison, W. P. Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583–589 (1992).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Bhatia, G., Patterson, N., Sankararaman, S. & Price, A. L. Estimating and interpreting FST: the impact of rare variants. Genome Res. 23, 1514–1521 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Lu, Z. et al. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. Zenodo https://doi.org/10.5281/zenodo.10963033 (2025).

  70. Zeng, T., Spence, J. P., Mostafavi, H. & Pritchard, J. K.and found highly significantraint from an evolutionary model with gene features. Nat. Genet. 56, 1632–1643 (2024).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of the Mancuso and Gazal laboratories for fruitful discussions regarding this Article. We also thank M. D. Edge for his thoughtful comments and suggestions. This work was funded in part by National Institutes of Health (NIH) under awards R01HG012133 (N.M.), R01CA258808 (N.M.), R01GM140287 (P.M.), R35GM142783 (N.M.), R01GM140287 (P.M.), U54HG013243 (L.W.), R35GM147789 (S.G.), K08HL159346 (J.P.), R00CA246076 (L.K.) and R01MH125252 (A.G.). MESA phenotypes (dbGaP: phs000209.v13.p3): MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-001079, UL1-TR000040, UL1-TR-001420, UL1-TR-001881 and DK063491. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. TOPMed-MESA WGS genotype, mRNA and protein expression data (dbGaP: phs001416.v3.p1): molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS genotype data for NHLBI TOPMed: MESA (phs001416.v3.p1) was performed at Broad Genomics (HHSN268201600034I). mRNA expression data for NHLBI TOPMed: MESA (phs001416.v3.p1) was performed at NWGC (HHSN268201600032I). SOMAscan proteomics for NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA) (phs001416.v1.p1) was performed at the Broad Institute and Beth Israel Proteomics Platform (HHSN268201600034I). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity quality control, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. GENOA genotype (dbGaP: phs001238.v2.p1) and gene expression (GEO: GSE138914) data were supported by grants from NIH NHLBI (HL054457, HL054464, HL054481, HL119443 and HL087660). We acknowledge S. Kardia and J. Smith in preparing GENOA eQTL data. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants.

Author information

Authors and Affiliations

  1. Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Zeyun Lu, Xinran Wang, Matthew Carr, Artem Kim, Steven Gazal & Nicholas Mancuso

  2. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Zeyun Lu, Xinran Wang, Steven Gazal & Nicholas Mancuso

  3. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Zeyun Lu & Alexander Gusev

  4. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA

    Steven Gazal & Nicholas Mancuso

  5. Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA

    Pejman Mohammadi

  6. Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA

    Pejman Mohammadi

  7. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Pejman Mohammadi

  8. Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Lang Wu

  9. Division of Cardiology, University of California San Francisco, San Francisco, CA, USA

    James Pirruccello

  10. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

    Linda Kachuri

  11. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Linda Kachuri

Authors
  1. Zeyun Lu
    View author publications

    Search author on:PubMed Google Scholar

  2. Xinran Wang
    View author publications

    Search author on:PubMed Google Scholar

  3. Matthew Carr
    View author publications

    Search author on:PubMed Google Scholar

  4. Artem Kim
    View author publications

    Search author on:PubMed Google Scholar

  5. Steven Gazal
    View author publications

    Search author on:PubMed Google Scholar

  6. Pejman Mohammadi
    View author publications

    Search author on:PubMed Google Scholar

  7. Lang Wu
    View author publications

    Search author on:PubMed Google Scholar

  8. James Pirruccello
    View author publications

    Search author on:PubMed Google Scholar

  9. Linda Kachuri
    View author publications

    Search author on:PubMed Google Scholar

  10. Alexander Gusev
    View author publications

    Search author on:PubMed Google Scholar

  11. Nicholas Mancuso
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Z.L. and N.M. developed the model and study design. Z.L. performed simulations and fine-mapping analyses. Z.L., X.W., J.P. and L.K. performed TWAS and AoU analyses. Z.L., M.C. and N.M. developed the model and inference scheme. Z.L. and A.K. prepared functional genomic annotations and performed heritability enrichment analyses. Z.L. and N.M. wrote the initial paper. Z.L., X.W., M.C., A.K., S.G., P.M., L.W., J.P., L.K., A.G. and N.M. edited the final paper.

Corresponding authors

Correspondence to Zeyun Lu or Nicholas Mancuso.

Ethics declarations

Competing interests

L.W. provided consulting service to Pupil Bio Inc. and reviewed manuscripts for Gastroenterology Report, not related to this study, and received honorarium. S.G. received consulting fees from Eleven Therapeutics unrelated to this work. The other authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, and Figs. 1–39.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–15.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Z., Wang, X., Carr, M. et al. Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. Nat Genet 57, 1881–1889 (2025). https://doi.org/10.1038/s41588-025-02262-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41588-025-02262-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing