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Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies

Nature Genetics volume 54, pages 593–602 (2022)Cite this article

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Abstract

Improved understanding of genetic regulation of the proteome can facilitate identification of the causal mechanisms for complex traits. We analyzed data on 4,657 plasma proteins from 7,213 European American (EA) and 1,871 African American (AA) individuals from the Atherosclerosis Risk in Communities study, and further replicated findings on 467 AA individuals from the African American Study of Kidney Disease and Hypertension study. Here, we identified 2,004 proteins in EA and 1,618 in AA, with most overlapping, which showed associations with common variants in cis-regions. Availability of AA samples led to smaller credible sets and notable number of population-specific cis-protein quantitative trait loci. Elastic Net produced powerful models for protein prediction in both populations. An application of proteome-wide association studies to serum urate and gout implicated several proteins, including IL1RN, revealing the promise of the drug anakinra to treat acute gout flares. Our study demonstrates the value of large and diverse ancestry study to investigate the genetic mechanisms of molecular phenotypes and their relationship with complex traits.

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Fig. 1: cis-pQTL analysis.
Fig. 2: Fine-mapping analysis.
Fig. 3: cis-h2 and evaluation of models for genetic prediction of proteins.
Fig. 4: Miami plots for PWAS and TWAS analyses for serum urate level and gout.

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Data availability

Genome-wide summary-level statistics for all single SNP cis-pQTL analysis, irrespective of significance level, and data required to perform PWAS, are available from http://nilanjanchatterjeelab.org/pwas. For individual-level plasma protein data, pre-existing data access policies for each of the parent cohort studies (ARIC and AASK) specify that research data requests can be submitted to each steering committee; these will be reviewed promptly for confidentiality or intellectual property restrictions and will not unreasonably be refused. Please refer to the data-sharing policies of these studies. Individual-level patient or protein data may further be restricted by consent, confidentiality or privacy laws/considerations. These policies apply to both clinical and proteomic data. The CKDGen Consortium makes all data reported in its original publications publicly available (https://ckdgen.imbi.uni-freiburg.de/). For European-specific gout GWAS data, further data requests can be submitted to the CKDGen steering committee; these will be reviewed promptly for confidentiality or intellectual property restrictions and will not unreasonably be refused. GRCh38 reference genome data from Phase-3 1000 Genome Project is available from https://www.internationalgenome.org/data. Access to UK Biobank individual-level data can be requested from https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Gene expression imputation models previously built based on data from the GTEx V7 and data required to perform TWAS are available from http://gusevlab.org/projects/fusion/#reference-functional-data, accession date 28 July 2021; models based on GTEx V8 are available on request from . Mancuso and A. Gusev. cis-eQTL summary statistics are available from https://gtexportal.org/home/. VEP was obtained from https://useast.ensembl.org/index.html. Therapeutic target database was downloaded from http://db.idrblab.net/ttd/full-data-download. Source data are provided with this paper.

Code availability

All custom codes used to perform data analysis relevant to this paper, including protein data cleaning, cis-pQTL mapping, building PWAS models, etc., are available at Zenodo with https://zenodo.org/record/6332981 and https://doi.org/10.5281/zenodo.6332981. Example codes to perform PWAS using external GWAS data are available from http://nilanjanchatterjeelab.org/pwas. Most of our statistical analysis was performed using R v.3.6.1 and R v.4.0.2, and R packages biomaRt v.2.42.1, peer v.1.0, plink2R v.1.1, glmnet v.4.0, ggplot2 v.3.3.3, gaston v.1.5.6, GGally v.2.0.0, ggpubr v.0.4.0, readr v.1.3.1, bigreadr v.0.2.0, readxl v.1.3.1, xlsx v.0.6.3, dplyr v.1.0.4, stringr v.1.4.0, latex2exp v.0.4.0. cis-pQTL mapping was performed using QTLtools v.1.2 (Binary CentOS 7.8). The publicly available summary-level statistics and analysis relevant to analyzing genotype data were performed by PLINK v.2.0 and PLINK v.1.9. cis-h2 analysis was performed using GCTA v.1.93.0 beta. Plasma protein imputation models were trained using FUSION available from https://github.com/gusevlab/fusion_twas. Downstream analysis including enrichment and colocalization was performed using VEP (v.85), TORUS (https://github.com/xqwen/torus), and coloc v.3.2.1. Fine-mapping was performed using SuSIE v.0.11.42 for ancestry-specific analysis, and MANTRA [1.0; February 2012] (available on request from A.P. Morris) for transancestry analysis.

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Acknowledgements

The ARIC study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I). The authors thank the staff and participants of the ARIC study for their important contributions. SomaLogic Inc. conducted the SomaScan assays in exchange for use of ARIC data. This work was supported in part by NIH/NHLBI grant R01 HL134320. The UK Biobank data was obtained under the UK Biobank resource application 17712. Research of J.Z., D.D. and N.C. was supported by grant R01 from the National Human Genome Research Institute [1 R01 HG010480-01]. B.H. was supported by the Bloomberg Distinguished Professorship Endowment fund available to N.C. The work of A.K. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 431984000—SFB 1453. The work of P.S. was funded by the EQUIP Program for Medical Scientists, Faculty of Medicine, University of Freiburg. The work of A.T. was funded by R01 AR073178. The work of J.C. and E.B. was funded by the ARIC contract. The work of M.G. and J.C. was funded by the multiomics grant R01 DK124399. The work of B.Y. was funded by HL148218. We acknowledge N. Mancuso and A. Gusev for providing preliminary TWAS models built with GTEx V8 data.

Author information

Authors and Affiliations

  1. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Jingning Zhang, Diptavo Dutta, Benjamin Harvey, Josef Coresh & Nilanjan Chatterjee

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

    Anna Köttgen, Adrienne Tin, Pascal Schlosser, Morgan E. Grams & Josef Coresh

  3. Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany

    Anna Köttgen & Pascal Schlosser

  4. MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS, USA

    Adrienne Tin

  5. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Morgan E. Grams & Josef Coresh

  6. Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

    Bing Yu & Eric Boerwinkle

  7. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Eric Boerwinkle

  8. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Nilanjan Chatterjee

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  • , Adrienne Tin
  • , Eric Boerwinkle
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Contributions

J.Z., J.C. and N.C. conceived the project. J.Z. and D.D. carried out all data analyses with supervision from N.C. B.H. developed online resources for data visualization and sharing, J.Z., D.D., A.K. and N.C. drafted the manuscript, and A.T., P.S., M.E.G., B.Y. and E.B. provided comments. All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Nilanjan Chatterjee.

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Competing interests

Proteomic assays in ARIC were conducted free of charge as part of a data exchange agreement with SomaLogic. The authors declare no other competing interests.

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Nature Genetics thanks Anders Mälarstig 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 Cis-pQTLs’ effect sizes across two populations.

Effect sizes for common (MAF> 1%) sentinel cis-pQTLs across EA and AA populations. Each dot represents a common sentinel SNP detected through either the EA (left panel) or the AA population (right panel). x-axis shows the effect size in the population through which the cis-pQTL is identified, and y-axis shows effect size in the other population. Minor allele frequencies (MAF) are checked for some outliers corresponding to large difference in allele frequency across populations (marked with orange). Red line is diagonal.

Source data

Extended Data Fig. 2 Overlap and colocalization of cis-pQTLs and cis-eQTLs.

(a) Proportion of sentinel cis-pQTLs in EA (including their LD proxies; SNPs with LD > 0.8) that are identified as cis-eQTLs across 49 different tissues in GTEx V8. Results are ordered by the size of overlap. (b) proportion of SOMAmers showing high colocalization probability (PP.H4 > 0.8) of underlying cis-pQTLS and cis-eQTLs in the same gene across tissues in GTEx (V8). Results are ordered by the size of overlap reported in (a) for ease of comparison.

Source data

Extended Data Fig. 3 Cis-pQTLs tended to be significant cis-eQTLs across multiple tissues.

Distribution of number of tissues with significant cis-eQTL effects in GTEx V8 for the sentinel cis-pQTLs (and SNPs in high LD) (blue) compared to that of cis-eQTLs in GTEx V8 irrespective of their cis-pQTL status (red). Sentinel cis-pQTLs are restricted to those which show cis-eQTL effect in at least one tissue. cis-eQTL effects are evaluated for the same underlying genes for which significant cis-pQTLs are detected.

Source data

Extended Data Fig. 4 Functional enrichment.

Functional enrichment of all sentinel cis-pQTLs and SNPs in high LD with them (r2 > 0.8) for EA (a) and AA (b). Functional enrichment of sentinel cis-pQTLs which have effects independent of protein altering variants are shown for EA (c) and AA (d). The red dots denote the estimated log2-enrichment statistic, and the black lines represent the corresponding 95% confidence intervals using TORUS (See Methods for details). Sample sizes for EA and AA population are n=7,213 and 1,871, respectively.

Source data

Extended Data Fig. 5 Cis-heritability comparison between gene expression and plasma protein levels.

Comparison of cis- heritability (cis-h2) estimates of plasma protein (P) and gene expression (T) for a common set of overlapping genes. For each population, the overlap is defined by the set of genes that have significant cis-h2 for both plasma protein and gene expression in the given tissue (liver and whole blood) in GTEx (a) V7 and (b) V8. Sample sizes for EA and AA populations are n=7,213 and 1,871, respectively. In boxplots, the boxes are drawn from first and third quartiles, with the median at the center, and the whiskers extending to 1.5 times the interquartile range from the box boundaries. Figures are truncated in the y-axis at cis-h2=0 and 0.5 for better display.

Source data

Extended Data Fig. 6 Correlation between imputed gene expression and measured plasma protein levels in ARIC EA samples.

Measured plasma protein levels are pre-processed by inverse-rank normalization and adjusted for covariates and 90 PEER factors. Gene expression imputation models for TWAS analyses across all tissues are built based on GTEx V7 datasets (see Supplementary Table 13 for available sample sizes). The imputation models for plasma proteins are built based n=7,213 EA individuals in the ARIC study. In boxplots, the boxes are drawn from first and third quartiles, with the median at the center, and the whiskers extending to 1.5 times the interquartile range from the box boundaries. Figure is truncated in the y-axis at correlation= −0.15 and 0.45 for better display.

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Extended Data Fig. 7 Control of type-1 error of PWAS.

Quantile-quantile plot (red diagonal line) of p-values are shown for a continuous phenotype that is simulated under the null hypothesis of no genetic association for unrelated European ancestry individuals in the UK Biobank study (n=337,484). Results are based on two-sided z-tests of association between the cis-genetic regulated plasma protein level and the simulated null trait. The diagonal line represents expected p-values under the null hypothesis of no genetic association and the 95% confidence band, which is calculated based on standard errors of order statistics under normal approximation, represents regions of uncertainty in the q-q plot under the null hypothesis of no association.

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Extended Data Fig. 8 PWAS of serum urate level and gout.

Quantile-quantile plots of PWAS p-values obtained from two-sided z-tests of association between the cis-genetic regulated plasma protein levels and the trait of interest, serum urate level (n=288,649) and gout (n=754,056). The diagonal lines represent expected p-values under the null hypothesis of no genetic association and the 95% confidence bands, which is calculated based on standard errors of order statistics under normal approximation, represent regions of uncertainty in the q-q plot under the null hypothesis of no association.

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Extended Data Fig. 9 PWAS identify repurposing opportunity for anakinra to treat gout.

Blue particle is interleukin-1 (IL-1) which produces proinflammatory effect of interleukin-1 signaling. Green particle is interleukin-1 receptor antagonist protein (IL1RN) which competes for binding but does not lead to a signal. Red particle is anakinra which has same shape as IL1RN and can also bind to the IL1R1 without eliciting a signal. Anakinra is a synthetic drug that mimics the function of the natural protein IL1RN. It is approved for treating rheumatoid arthritis. Our study shows that genetically higher IL1RN levels show protection from gout. This suggests that anakinra may also be effective to treat gout (repurposing). Plot was created with BioRender.com.

Extended Data Fig. 10 Top five genetic principal components (PC) of ARIC data.

Genetic PCs represent the major population structure in the aggregated sample of EA (blue) and AA (green) populations, colored by self-reported ancestry.

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Zhang, J., Dutta, D., Köttgen, A. et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat Genet 54, 593–602 (2022). https://doi.org/10.1038/s41588-022-01051-w

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