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A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts

Nature Genetics volume 53, pages 972–981 (2021)Cite this article

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Abstract

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

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Fig. 1: Workflow to identify lipid-associated genes, suggestive pleiotropy between lipids and diseases and putative diseases for which lipids are modifiable exposures.
Fig. 2: Replication of lipid-associated genes across four cohorts from lipid TWAS.
Fig. 3: Comparison of results between Xpress-PheWAS and lipid-guided PheWAS in eMERGE and UKB.
Fig. 4: Lipid–disease pleiotropy from Xpress-PheWAS and colocalization in either eMERGE or the UKB.
Fig. 5: Concordant/discordant pleiotropy for SNPs that replicate in both eMERGE and the UKB for the same lipids/diseases.
Fig. 6: Protective/risk effect genes from Xpress-PheWAS and colocalization analyses that replicate in both eMERGE and the UKB for the same lipids/diseases.
Fig. 7: Two-sample univariable Mendelian randomization.
Fig. 8: Pictorial depiction of suggestive genetic mechanisms underlying the analyses conducted in this study.

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Data availability

This project corresponds to UKB application ID 32133 and eMERGE Network phase III (dbGaP study accession no. phs001584.v1.p1). Lipid GWAS summary statistics for GLGC 2013 (ref. 3) are publicly available for download (http://csg.sph.umich.edu/willer/public/lipids2013/). Lipid GWAS summary statistics for GERA5 are available via dbGaP (accession no. phs000674.v2.p2). Expression prediction models with LD reference data using MASHR are available on Zenodo (https://zenodo.org/record/3518299/files/mashr_eqtl.tar?download=1). GTEx Analysis Release v8 (dbGaP accession no. phs000424.v8.p2) is available for download via the GTEx Portal (https://gtexportal.org/home/datasets/). Summary statistics for lipid GWAS, lipid TWAS, lipid-guided PheWAS and Xpress-PheWAS generated in this study are available on Figshare (https://figshare.com/s/d62961bbc6c45c8dc2b0).

Code availability

Code for identifying LD-contaminated genes and detecting secondary independent associations at a locus is shared on GitHub (https://github.com/RitchieLab/Gene-level-statistical-colocalization/).

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Acknowledgements

Phase III of the eMERGE Network was initiated and funded by the NHGRI through the following grants: U01HG8657 (Group Health Cooperative/University of Washington); U01HG8685 (Brigham and Women’s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children’s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children’s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine). For the UKB, all data for this cohort pertained to project 32133: ‘Integration of multi-organ imaging phenotypes, clinical phenotypes and genomic data’. Y.V., R.M.K., T.J.H., N.R., M.W.M., E.T. and M.D.R. acknowledge funding from the National Institutes of Health (NIH GM115318: Pharmacogenomics of Statin Therapy (POST)). Y.V. and M.D.R. also acknowledge NIH AI077505 (Pharmacogenomics of HIV Therapy). J.E.M. acknowledges NHGRI T32HG009495–01; C.M.S. acknowledges R35GM131770 (Pharmacogenetics to improve Drug Therapy). B.F.V. acknowledges NIH DK101478, NIH HG010067 and a Linda Pechenik Montague Investigator Award for their time on this project.

Author information

Authors and Affiliations

  1. Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yogasudha Veturi, Anastasia Lucas, Yuki Bradford, Daniel Hui, Scott Dudek, Anurag Verma, Jason E. Miller, Daniel J. Rader & Marylyn D. Ritchie

  2. Department of Pediatrics, University of California, San Francisco, Oakland, CA, USA

    Elizabeth Theusch, Marisa W. Medina & Ronald M. Krauss

  3. Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA

    Iftikhar Kullo

  4. Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Hakon Hakonarson & Patrick Sleiman

  5. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Daniel Schaid

  6. Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Charles M. Stein & QiPing Feng

  7. Department of Biomedical Informatics in School of Medicine, Vanderbilt University, Nashville, TN, USA

    Digna R. Velez Edwards & Wei-Qi Wei

  8. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA

    Digna R. Velez Edwards

  9. Division of Quantitative Science, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA

    Digna R. Velez Edwards

  10. Institute for Human Genetics, and Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Thomas J. Hoffmann & Neil Risch

  11. Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin F. Voight & Daniel J. Rader

  12. Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin F. Voight

Authors
  1. Yogasudha Veturi
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  2. Anastasia Lucas
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  23. Marylyn D. Ritchie
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Contributions

Y.V. and M.D.R. conceptualized and designed the study. Y.V. conducted all statistical analyses. Y.V. and D.H. conducted phase III analyses. Y.V., A.L. and S.D. performed data visualization. Y.V., Y.B. and A.L. conducted phenotype curation. Y.V., M.D.R. and A.V. performed data acquisition for the UKB. H.H., P.S., I.K., D.S., C.M.S., D.R.V.E., Q.F. and W.-Q.W. performed data acquisition for eMERGE. T.J.H., N.R., R.M.K., M.W.M. and E.T. performed data acquisition for GERA. Y.V. and B.F.V. conceptualized phase III of this study. Y.V. and J.E.M. performed overrepresentation analysis. D.J.R. provided guidance for phases I and II. Y.V. and M.D.R. wrote the manuscript. All authors provided interpretation of the results and critical feedback on the manuscript.

Corresponding author

Correspondence to Marylyn D. Ritchie.

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Competing interests

M.D.R. is on the scientific advisory board for Goldfinch Bio and Cipherome. D.J.R. serves on Scientific Advisory Boards for Alnylam, Novartis, Pfizer and Verve and is a founder of Staten Biotechnology. The other co-authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Case-control distribution for ICD codes.

Distribution of cases (blue) and controls (yellow) for the collapsed 3-digit ICD codes in eMERGE (top) and UKB (bottom). eMERGE has predominantly ICD-9 codes whereas UKB has predominantly ICD-10 codes.

Extended Data Fig. 2 Lipid GWAS in eMERGE.

Manhattan plots from GWAS (two-sided linear regression) conducted on the four plasma lipid traits (HDL-C, LDL-C, TC, TG) for the eMERGE cohort. In each plot we have chromosomes 1 to 22 on the x-axis and -log(P) value on the y-axis.

Extended Data Fig. 3 Lipid GWAS in UKB.

Manhattan plots from GWAS (two-sided linear regression) conducted on the four plasma lipid traits (HDL-C, LDL-C, TC, TG) for the UKB cohort. In each plot we have chromosomes 1 to 22 on the x-axis and -log(P) value on the y-axis.

Extended Data Fig. 4 Lipid TWAS P-values for novel lipid genes.

Synthesis-view plot indicating -log10 P-values for Bonferroni-significant ‘novel’ genes (two-sided gene-based tests: P < 5.57 × 10−7) from lipid TWAS. These genes passed coloc P[H3] < 0.5 filter in at least one cohort. The direction of triangle corresponds to the direction of gene-effect from TWAS (left facing-negative and right facing-positive). Colors indicate the five selected tissues from GTEx v8 (adipose subcutaneous, adipose visceral omentum, liver, small intestine terminal ileum, whole blood).

Extended Data Fig. 5 Colocalization probabilities of shared causal variant between lipids and gene expression for novel lipid genes.

Synthesis-view plot indicating coloc P[H4] for Bonferroni-significant ‘novel’ genes (two-sided gene-based tests: P < 5.57 × 10−7) obtained from lipid TWAS. These genes passed coloc P[H3] < 0.5 filter in at least one cohort. The direction of triangle corresponds to the direction of gene-effect from TWAS (left facing-negative and right facing-positive). Colors indicate the five selected tissues from GTEx v8 (adipose subcutaneous, adipose visceral omentum, liver, small intestine terminal ileum, whole blood). We present coloc results for all regions corresponding to a gene.

Extended Data Fig. 6 Overlap of detected ICD codes between cohorts.

UpSet plot indicating overlap of diseases (ICD codes) with Bonferroni-significant genes between PheWAS and Xpress-PheWAS conducted on eMERGE and UKB, respectively.

Extended Data Fig. 7 Overlap of significant SNPs between lipid GWAS and lipid-guided PheWAS across cohorts.

UpSet plot indicating overlap of GWAS-significant SNPs (Bonferroni threshold) between each of the four plasma lipids (HDL-C, LDL-C, TC, TG) aggregated across the four considered cohorts (eMERGE, GERA, GLGC, UKB) and lipid-guided PheWAS conducted in eMERGE and UKB, respectively.

Extended Data Fig. 8 Lipid-disease pleiotropy from lipid-guided PheWAS in either eMERGE or UKB.

Circos plot indicates Bonferroni-significant SNPs in either cohort (eMERGE or UKB) from lipid-guided PheWAS (two-sided logistic regression). Outer track, the number of SNPs detected in either cohort; inner track, significant ICD codes per disease category. Links, SNPs connecting lipids (in salmon) to diseases (in blue); link thickness, # SNPs; link color, chromosome. Due to large number of SNP associations involved, this plot does not show associations (links) in the HLA region (chromosome 6).

Extended Data Fig. 9 Overlap of significant genes between lipid TWAS and Xpress-PheWAS across cohorts.

UpSet plot indicating overlap of detected Bonferroni-significant genes between lipid TWAS and Xpress-PheWAS conducted on eMERGE and UKB, respectively. Lipid TWAS genes have been split into two categories: (1) novel; (2) previously reported.

Extended Data Fig. 10 Effect sizes and confidence intervals from two-sample univariable Mendelian randomization analyses.

Mendelian randomization funnel plots depicting MR effect size (using two-sided IVW and Egger approaches) across ICD codes detected as FDR significant (excluding proof-of-concept diseases such as E78 Disorders of lipoprotein metabolism and other lipidemias and I10 Essential primary hypertension; see Fig. 7 for a full list of FDR-significant diseases). Top 5 plots: exposure dataset (lipid), GERA; outcome dataset, UKB. Remaining plots: exposure dataset (lipid), UKB; outcome dataset, eMERGE.

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Veturi, Y., Lucas, A., Bradford, Y. et al. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts. Nat Genet 53, 972–981 (2021). https://doi.org/10.1038/s41588-021-00879-y

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