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Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Nature Genetics volume 55, pages 549–558 (2023)Cite this article

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Abstract

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93–0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.

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Fig. 1: Concepts of estimating similarity in the causal effects across local ancestries.
Fig. 2: Results of genetic correlation radmix estimation in genome-wide simulations.
Fig. 3: Similarity of causal effects and marginal effects across local ancestries meta-analyzed across PAGE, UKBB and AoU.
Fig. 4: Induced heterogeneities in marginal effects across local ancestries.
Fig. 5: Pitfalls of including local ancestry in estimating heterogeneity.
Fig. 6: Miscalibration of HET test/Deming regression/OLS regression in simulations with radmix = 1.

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Data availability

PAGE individual-level genotype and phenotype data are available through dbGaP https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v2.p1. UKBB individual-level genotype and phenotype data are available through application at https://www.ukbiobank.ac.uk/. AoU individual-level genotype and phenotype are available through application at https://www.researchallofus.org/. The set of preprocessed HapMap3 variants used in this manuscript is retrieved from https://ndownloader.figshare.com/files/25503788.

Code availability

Software implementing genome-wide genetic correlation estimation method: https://github.com/kangchenghou/admix-kit (ref. https://doi.org/10.5281/ZENODO.7482679) Code for replicating analyses: https://github.com/kangchenghou/admix-genet-cor (ref. https://doi.org/10.5281/ZENODO.7482683).

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Acknowledgements

We thank A. Price, M. J. Zhang, R. Patel, J. Pritchard, A. Durvasula, J. Cai and E. Petter for helpful suggestions. This research was funded in part by the National Institutes of Health under awards U01-HG011715 (B.P.), R01-HG009120 (B.P.), R01-MH115676 (B.P.), R01-HL151152 (C.K.), P01-CA196569 (D.V.C.) and U01-CA261339 (D.V.C.). Y.W. and S.S. were supported in part by NIH R35-GM125055 and NSF CAREER-1943497. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. PAGE is supported by the National Institutes of Health under awards R01-HG010297. This research was conducted using the UKBB Resource under application 33297. We thank the participants of UKBB for making this work possible. 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

Author notes

  1. These authors contributed equally: Gillian M. Belbin, Steve Buyske, David V. Conti, Burcu F. Darst, Myriam Fornage, Chris Gignoux, Xiuqing Guo, Christopher Haiman, Eimear E. Kenny, Michelle Kim, Charles Kooperberg, Leslie Lange, Ani Manichaikul, Kari E. North, Ulrike Peters, Laura J. Rasmussen-Torvik, Stephen S. Rich, Jerome I. Rotter, Heather E. Wheeler, Genevieve L. Wojcik, Ying Zhou.

Authors and Affiliations

  1. Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA

    Kangcheng Hou, Yi Ding, Sriram Sankararaman & Bogdan Pasaniuc

  2. Department of Computer Science, UCLA, Los Angeles, CA, USA

    Ziqi Xu, Yue Wu & Sriram Sankararaman

  3. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Arjun Bhattacharya & Bogdan Pasaniuc

  4. Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Rachel Mester

  5. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Gillian M. Belbin & Eimear E. Kenny

  6. The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Gillian M. Belbin

  7. Department of Statistics, Rutgers University, Piscataway, NJ, USA

    Steve Buyske & Kari E. North

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

    David V. Conti & Christopher Haiman

  9. Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Burcu F. Darst, Michelle Kim, Charles Kooperberg, Ulrike Peters & Ying Zhou

  10. Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA

    Myriam Fornage

  11. Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA

    Chris Gignoux

  12. Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    Xiuqing Guo & Jerome I. Rotter

  13. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  14. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  15. Department of Medicine, University of Colorado, Aurora, CO, USA

    Leslie Lange

  16. Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    Ani Manichaikul & Stephen S. Rich

  17. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kari E. North

  18. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Laura J. Rasmussen-Torvik

  19. Department of Biology, Loyola University Chicago, Chicago, IL, USA

    Heather E. Wheeler

  20. Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA

    Heather E. Wheeler

  21. Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Genevieve L. Wojcik

  22. Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Sriram Sankararaman & Bogdan Pasaniuc

  23. Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Sriram Sankararaman & Bogdan Pasaniuc

Authors
  1. Kangcheng Hou
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  2. Yi Ding
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  3. Ziqi Xu
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Contributions

K.H. and B.P. conceived and designed the experiments. K.H. performed the experiments and statistical analyses with assistance from Y.D., Z.X., Y.W., A.B., R.M., S.S. and B.P. G.M.B., S.B., D.V.C., B.F.D., M.F., C.G., X.G., C.H., E.E.K., M.K., C.K., L.L., A.M., K.E.N., U.P., L.J.R.-T., S.S.R., J.I.R., H.E.W., G.L.W. and Y.Z. provided data and feedback on analysis. K.H. and B.P. wrote the manuscript with feedback from all authors.

Corresponding authors

Correspondence to Kangcheng Hou or Bogdan Pasaniuc.

Ethics declarations

Competing interests

E.E.K. has received personal fees from Regeneron Pharmaceuticals, 23&Me and Illumina, and serves on the advisory boards for Encompass Biosciences, Foresite Labs and Galateo Bio. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Loïc Yengo 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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Extended data

Extended Data Fig. 1 Consistency of radmix for shared traits across studies.

We compared estimated radmix for shared traits across studies. We compared both \(\hat r_{{{{\mathrm{admix}}}}}\) (a-c) and \(- \log _{10}\left( p \right)\) (for one-sided test of \(H_0:r_{{{{\mathrm{admix}}}}} = 1\); Methods) (d-f). Three traits (Height, Triglycerides, Total cholesterol) with the most significant p-values for \(H_0:r_{{{{\mathrm{admix}}}}} = 1\) were annotated. Number of common traits shared across studies (ncommon) and Spearman correlation p-value were shown in the title for each panel. Overall, there were weak consistency of estimated \(\hat r_{{{{\mathrm{admix}}}}}\) for shared traits across studies (although 𝑝-values for \(H_0:r_{{{{\mathrm{admix}}}}} = 1\) were consistent significantly). Numerical results are reported in Supplementary Table 7.

Extended Data Fig. 2 radmix estimation is robust to the assumption of radmix > 0.

We performed radmix estimation using alternative assumption of \(- 1 \le r_{{{{\mathrm{admix}}}}} \le 1\) in real trait analysis in PAGE in light of potential scenarios of effect sizes in opposite directions36,63. We compared estimated radmix when assuming \(0 \le r_{{{{\mathrm{admix}}}}} \le 1\) (default method) and when assuming \(- 1 \le r_{{{{\mathrm{admix}}}}} \le 1\). Left: comparing point estimates of 𝑟admix across 24 traits in PAGE. Right: comparing the meta-analyzed log-likelihood. Results obtained from two methods are highly consistent.

Extended Data Fig. 3 radmix estimation is robust to genetic architecture and SNP set.

We performed radmix estimation under the assumption of alternative genetic architecture and SNP set on real trait analysis across PAGE and UKBB. We compared p-values (for one-sided test of \(H_0:r_{{{{\mathrm{admix}}}}} = 1\)) of our default setting (using frequency-dependent genetic architecture and imputed SNPs; Table 1) to those obtained using GCTA genetic architecture and imputed SNPs (a), and to those obtained using frequency-dependent genetic architecture and HM3 SNPs (b). Numerical results are reported in Supplementary Table 8.

Extended Data Fig. 4 radmix estimation is robust to subsetting PAGE African American individuals based on genotype PCs.

We subsetted PAGE individuals with self-identified race/ethnicity label of ‘African American’ (total N = 17,327) based on genotype PCs and retained N = 17,167 individuals (a). We found that the estimated 𝑟admix were highly consistent between using all PAGE African American individuals (default) and using subset of PAGE African American individuals based on genotype PCs. (b) comparing point estimates of 𝑟admix across 24 traits in PAGE. (Dot on the bottom left of the figure corresponds to MCHC trait, with a small sample size of 3,650.) (c) comparing the meta-analyzed log-likelihood. Results obtained from two sets of individuals are highly consistent.

Extended Data Fig. 5 Comparing estimated radmix between alternative method formulations and default method.

Each dot corresponds to a trait. (a) Comparing results of default method and of directly optimizing and estimating \(\sigma _g^2,\rho _g\). (b) Comparing results of default method and of directly optimizing and estimating \(\sigma _{g,1}^2,\sigma _{g,2}^2\) (different variance components per ancestry) and \(\rho _g\). See Supplementary Table 9 and Supplementary Note for details.

Extended Data Fig. 6 Multiple conditionally independent association signals for loci with heterogeneity by ancestry.

Upper panel corresponds to the two-sided association p-values and lower panel corresponds to the fine-mapping PIP. Different colors in the PIP plot corresponds to different credible sets. (a) MCH at 16p13.3 for UK Biobank European-African admixed individuals. (b) RBC at 16p13.3 for UK Biobank European-African admixed individuals. (c) CRP at 1q23.2 for PAGE European-African admixed individuals.

Extended Data Fig. 7 Simulations with single causal variant.

Simulations were based on 100 regions each spanning 20 Mb on chromosome 1 and 17,299 PAGE individuals. In each simulation, we randomly selected single causal variant and simulated quantitative phenotypes where these causal variants had same causal effects across ancestries and each causal variant was expected to explain a fixed amount of heritability (0.2%, 0.6%, 1.0%). Each panel corresponds to one metric for both causal and clumped variants. (a) False positive rate (FPR) of HET test. (b) Deming regression slope with \(\beta _{{{{\mathrm{afr}}}}} \sim \beta _{{{{\mathrm{eur}}}}}\). (c) Deming regression slope with \(\beta _{{{{\mathrm{eur}}}}} \sim \beta _{{{{\mathrm{afr}}}}}\). (d) Pearson correlation. (e) OLS regression slope with \(\beta _{{{{\mathrm{afr}}}}} \sim \beta _{{{{\mathrm{eur}}}}}\). (f) OLS regression slope with \(\beta _{{{{\mathrm{eur}}}}} \sim \beta _{{{{\mathrm{afr}}}}}\). 95% confidence intervals were based on 100 random sub-samplings with each sample consisted of 500 SNPs (Methods). Numerical results are reported in Supplementary Table 13.

Extended Data Fig. 8 Simulation with multiple causal variants at other sample sizes (Fig. 6d–f).

Simulations were based on chromosome 1 (515,087 SNPs) and 17,299 PAGE individuals. We drew 62, 125, 250, 500, 1000 causal variants to simulate different level of polygenicity, such that on average there were approximately 0.25, 0.5, 1.0, 2.0, 4.0 causal variants per Mb. The heritability explained by all causal variants was fixed at \(h_g^2 = 10\%\). (a-c) False positive rate of HET test for the causal variants and clumped variants. (d-f) Deming regression slope of estimated ancestry-specific effects (βeur ~ βaf) for the causal variants and clumped variants. 95% confidence intervals were based on 100 random sub-samplings with each sub-sample consisted of n = 50, 100, 500 SNPs (instead of n = 1,000 SNPs in Fig. 6c, d) (Methods).

Extended Data Fig. 9 Additional results for simulations with single causal variant with varying βeur:βafr and \(h_g^2\).

Simulations were based on 100 regions each spanning 20 Mb on chromosome 1 from 17299 PAGE individuals. In each simulation, we randomly selected single causal variant and simulated quantitative phenotypes where these causal variants had varying causal effects across ancestries and each causal variant was expected to explain a fixed amount of heritability (0.2%, 0.6%, 1.0%, 2.0%, 5.0%). We provide results for both causal variants and LD-clumped variants. We separate results into two rows for better visualization: upper row (a-c): βeur:βafr = 0.9, 1.0, 1.1; lower row (d-f): βeur:βafr = 0.0, 0.5, 1.0. We show results for False positive rate (FPR) of HET test, Deming regression slope with βeur ~ βafr, and OLS regression slope with βeur ~ βafr. 95% confidence intervals were based on 100 random sub-samplings with each sample consisted of 500 SNPs (Methods). Numerical results and further discussions are provided in Supplementary Table 15.

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Supplementary Note and Tables 1–18.

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Supplementary Tables 8 and 11.

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Hou, K., Ding, Y., Xu, Z. 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). https://doi.org/10.1038/s41588-023-01338-6

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