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Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Nature Genetics volume 55, pages 1267–1276 (2023)Cite this article

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Abstract

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

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Fig. 1: Overview of PoPS.
Fig. 2: Evaluation of PoPS and comparison to other similarity-based methods.
Fig. 3: Most informative gene features used by PoPS.
Fig. 4: Comparing and combining PoPS with locus-based methods.
Fig. 5: High-confidence genes for selected traits.
Fig. 6: Known and new biological examples.

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Data availability

A repository of processed gene features, visualizations of top derived features and code to reproduce these analyses are available on GitHub at https://github.com/FinucaneLab/gene_features. Complete PoPS results for 95 complex traits in the UK Biobank and 18 additional disease traits, as well as results for PoPS and locus-based methods in genome-wide significant loci, are available at https://www.finucanelab.org/data.

Code availability

PoPS is available as an open-source Python package at https://github.com/FinucaneLab/pops. A static version of the PoPS method used in the present study is available at https://doi.org/10.5281/zenodo.8002379.

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Acknowledgements

We thank K. Aragam, A. Butterworth, M. Daly, N. Artomov, Y. Reshef and all members of the Finucane lab for helpful discussions. This research was conducted using the UK Biobank Resource under project 31063. H.K.F. was funded by a National Institutes of Health (NIH) grant (no. DP5 OD024582) and by Eric and Wendy Schmidt. J.M.E. was supported by a Pathway to Independence Award (grant nos. K99HG00917 and R00HG009917), the Harvard Society of Fellows and the Base Research Initiative at Stanford University. J.M. and J.N.H. were supported by an NIH grant (no. R01DK075787). R.S.F. was supported by National Human Genome Research Institute, NIH (grant no. F31HG009850). J.O.-M. was supported by the Richard and Susan Smith Family Foundation, the HHMI Damon Runyon Cancer Research Foundation Fellowship (no. DRG-2274-16), the AGA Research Foundation’s AGA-Takeda Pharmaceuticals Research Scholar Award in Inflammatory Bowel Disease (grant no. AGA2020-13-01), the HDDC Pilot and Feasibility (grant no. P30 DK034854) and the Food Allergy Science Initiative.

Author information

Author notes

  1. Jacob C. Ulirsch

    Present address: Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA

  2. Rebecca S. Fine

    Present address: Vertex Pharmaceuticals Incorporated, Boston, MA, USA

  3. Charles P. Fulco

    Present address: Bristol Myers Squibb, Cambridge, MA, USA

  4. Moshe Biton

    Present address: Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel

  5. Aviv Regev

    Present address: Genentech, San Francisco, CA, USA

  6. These authors contributed equally: Elle M. Weeks, Jacob C. Ulirsch.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Elle M. Weeks, Jacob C. Ulirsch, Nathan Y. Cheng, Rebecca S. Fine, Jenkai Miao, Tejal A. Patwardhan, Masahiro Kanai, Joseph Nasser, Charles P. Fulco, Katherine C. Tashman, Francois Aguet, Taibo Li, Jose Ordovas-Montanes, Christopher S. Smillie, Moshe Biton, Alex K. Shalek, Ramnik J. Xavier, Aviv Regev, Rajat M. Gupta, Kasper Lage, Kristin G. Ardlie, Joel N. Hirschhorn, Eric S. Lander, Jesse M. Engreitz & Hilary K. Finucane

  2. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA

    Jacob C. Ulirsch

  3. Program in Computational & Systems Biology, MIT, Cambridge, MA, USA

    Brian L. Trippe & Christopher S. Smillie

  4. Computer Science & Artificial Intelligence Lab, MIT, Cambridge, MA, USA

    Brian L. Trippe

  5. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, USA

    Rebecca S. Fine, Jenkai Miao & Joel N. Hirschhorn

  6. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Rebecca S. Fine & Joel N. Hirschhorn

  7. Department of Statistics, Harvard University, Cambridge, MA, USA

    Tejal A. Patwardhan

  8. Analytic and Translational Genetics Unit, MGH, Boston, MA, USA

    Masahiro Kanai & Hilary K. Finucane

  9. Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA

    Masahiro Kanai

  10. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Masahiro Kanai

  11. MD-PhD Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Taibo Li

  12. Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital, Boston, MA, USA

    Jose Ordovas-Montanes

  13. Program in Immunology, Harvard Medical School, Boston, MA, USA

    Jose Ordovas-Montanes

  14. Harvard Stem Cell Institute, Cambridge, MA, USA

    Jose Ordovas-Montanes

  15. Department of Molecular Biology, MGH, Boston, MA, USA

    Moshe Biton & Ramnik J. Xavier

  16. Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA

    Alex K. Shalek

  17. Department of Chemistry, MIT, Cambridge, MA, USA

    Alex K. Shalek

  18. Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA

    Alex K. Shalek & Aviv Regev

  19. Ragon Institute of MGH, MMIT, Cambridge, MA, USA

    Alex K. Shalek

  20. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA

    Ashwin N. Ananthakrishnan & Ramnik J. Xavier

  21. Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Ramnik J. Xavier

  22. Howard Hughes Medical Institute, MIT, Cambridge, MA, USA

    Aviv Regev

  23. Division of Cardiovascular Medicine and Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Rajat M. Gupta

  24. Harvard Medical School, Boston, MA, USA

    Rajat M. Gupta

  25. Department of Surgery, MGH, Boston, MA, USA

    Kasper Lage

  26. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Kristin G. Ardlie

  27. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Joel N. Hirschhorn

  28. Department of Biology, MIT, Cambridge, MA, USA

    Eric S. Lander

  29. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Eric S. Lander

  30. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Jesse M. Engreitz

  31. BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA, USA

    Jesse M. Engreitz

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  1. Elle M. Weeks
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  2. Jacob C. Ulirsch
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Contributions

E.M.W. and H.K.F. conceived of the study. E.M.W., J.C.U., N.Y.C. and H.K.F. designed the research, performed the experiments, analyzed the data and interpreted the results. B.L.T. and R.S.F. designed and performed the enrichment-based validations. J.M., T.A.P., M.K., J.N., C.P.F., K.C.T., F.A., T.L., J.O.-M., C.S.S., M.B., A.K.S., A.N.A., R.J.X., A.R., R.M.G., K.L., K.G.A., J.N.H. and J.M.E. provided data or analysis tools used by PoPS or other gene prioritization methods. E.S.L. helped advise the project. E.M.W., J.C.U. and H.K.F. wrote the manuscript with input from all authors. H.K.F. supervised the project.

Corresponding authors

Correspondence to Elle M. Weeks or Hilary K. Finucane.

Ethics declarations

Competing interests

J.C.U. reports compensation from consulting services with Goldfinch Bio and is an employee of Illumina. R.S.F. is an employee of Vertex Pharmaceuticals Incorporated. C.P.F. is an employee of Bristol Myers Squibb. J.O.-M. reports compensation for consulting services with Cellarity. A.R. is a cofounder and equity holder of Celsius Therapeutics and an equity holder in Immunitas, and was an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. From 1 August 2020, A.R. is an employee of Genentech. J.N.H. served on the Scientific Advisory Board of and consults for Camp4 Therapeutics. E.S.L. serves on the Board of Directors for Codiak BioSciences and Neon Therapeutics, and serves on the Scientific Advisory Board of F-Prime Capital Partners and Third Rock Ventures; he is also affiliated with several nonprofit organizations including serving on the Board of Directors of the Innocence Project, Count Me In and Biden Cancer Initiative, and the Board of Trustees for the Parker Institute for Cancer Immunotherapy. He has served and continues to serve on various federal advisory committees. The remaining authors declare no competing interests.

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Nature Genetics thanks the anonymous reviewers 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 PoPS model parameter choices and feature selection.

a-c, Results using Benchmarker to compare different parameter choices for fitting the PoPS model, meta-analyzed across independent traits (n = 46). Error bars represent 95% confidence intervals around the meta-analyzed point estimate. a, Feature selection: GLS with an L1 penalty on the full set of features performs less well than GLS after marginal selection using a P value < 0.05 threshold from the two-sided Wald test. b, Error model: ordinary least squares (OLS) performs less well than generalized least squares (GLS) using marginal selection from a. c, Joint model regularization: GLS after marginal feature selection with an L2 penalty performs better than similar models with an L1 penalty or no penalty. d, Number of features selected (marginal P value < 0.05 from the two-sided Wald test) and included in the joint predictive model for PoPS for each trait. A legend for trait domain colors is provided in Fig. 2.

Extended Data Fig. 2 Additional comparisons using closest gene metric.

a, Results using closest gene enrichment to compare similarity-based gene prioritization methods, meta-analyzed within each trait domain across independent traits (n = 46). Error bars represent 95% confidence intervals around the meta-analyzed point estimate. b, Results using closest gene enrichment to compare PoPS results using different feature sets, meta-analyzed within each trait domain across independent traits (n = 46). Error bars represent 95% confidence intervals around the meta-analyzed point estimate.

Extended Data Fig. 3 Comparison of gene expression features derived from bulk and single-cell RNA seq datasets.

a, Results using Benchmarker to compare PoPS results using different feature sets, meta-analyzed within each trait domain across independent traits (n = 46). Error bars represent 95% confidence intervals around the meta-analyzed point estimate. b, Results using closest gene enrichment to compare PoPS results using different feature sets, meta-analyzed within each trait domain across independent traits (n = 46). Error bars represent 95% confidence intervals around the meta-analyzed point estimate.

Extended Data Fig. 4 Comparison of similarity-based methods using precision and recall.

Precision-recall plot showing performance of similarity-based methods.

Extended Data Fig. 5 Comparing prioritization criteria.

Precision-recall plots for each method with varying prioritization criteria. Each point shows the precision and recall for a set of prioritized genes selected using prioritization criteria based on absolute thresholds and/or relative rank in a locus. For all methods, the star represents the final chosen criteria. a, Circles: PoP scores ranked ≤ 2–5 in the locus. Star: highest PoPS score in the locus. b, Plus: significant TWAS P value after Bonferroni correction (P < 0.05/235,584). Circles: TWAS P values ranked ≤ 2–5 in the locus. Star: significant TWAS P value after Bonferroni correction (P < 0.05/235,584) and the most significant in the locus. c, Pluses: CLPP > 0.01, 0.1, 0.5, 0.9, and 0.99. Circles: CLPP > 0.01, 0.1, 0.5, 0.9, and 0.99 and also the highest CLPP in the locus. Star: CLPP > 0.1 and also the highest CLPP in the locus. d, Plus: any predicted connection from ABC. Circles: ABC connection strength ranked ≤ 2–5 in the locus. Star: highest ABC connection strength in the locus. e, Pluses: any predicted connection from PCHiC for individual datasets. Triangle: any predicted connection from PCHi-C in any dataset. Circles: highest connection strength in the locus for individual datasets. Star: highest connection strength in the locus in any dataset. f, Pluses: any predicted connection from E-P correlation for individual datasets. Triangle: any predicted connection from E-P correlation in any dataset. Circles: highest connection strength in the locus for individual datasets. Star: highest connection strength in the locus in any dataset. g, Circle: closest gene by distance to the transcription start site. Star: closest gene by distance to the gene body. h, Circles: MAGMA z-scores ranked ≤ 2–5 in the locus. Star: highest MAGMA score in the locus. i, Plus: significant SMR P value after Bonferroni correction (P < 0.05/18,383). Circles: SMR P values ranked ≤ 2–5 in the locus. Star: significant SMR P value after Bonferroni correction (P < 0.05/18,383) and the most significant in the locus.

Extended Data Fig. 6 Performance of PoPS and locus-based gene prioritization methods by trait.

Precision-recall plots for each method. Each point represents a single trait colored by trait domain. Only traits for which the method prioritized at least five genes in the validation loci were included. A legend for trait domain colors is provided in Fig. 2.

Extended Data Fig. 7 Additional performance metrics using evaluation gene set in 1,348 non-coding loci containing genes that harbor fine-mapped protein coding variants.

a, Sensitivity-specificity plot showing performance of locus-based methods, PoPS, intersections of pairs of locus-based methods, and intersections of PoPs with locus-based methods on the evaluation gene set of 589 genes with fine-mapped protein coding variants. b, Heatmap showing performance using the F-score of locus-based methods, PoPS, intersections of pairs of locus-based methods, and intersections of PoPs with locus-based methods.

Extended Data Fig. 8 Number of prioritized genes for non-UK Biobank traits.

Number of unique gene-trait pairs prioritized by PoPS, locus-based gene prioritization methods, and their intersections, sorted by estimated precision. The full height of each bar represents the total number of genes prioritized. The opaque portion of each bar represents the expected number of true causal genes prioritized. Methods to the left of the dashed line achieve precision greater than 75%.

Extended Data Fig. 9 Known example RBM38.

Top: summary statistics colored by LD to the lead variant and fine-mapping results for variants in the locus colored by credible set. Bottom: results from PoPS and locus-based methods for all genes in the locus. Genes are colored by strength of prediction for each method with a star denoting the prioritized gene. Variant rs737092, RBM38 for mean corpuscular hemoglobin (MCH).

Extended Data Fig. 10 Sensitivity of precision and recall estimates to locus definition.

a, Loci defined as +/− 100 kb on either side of the lead variant. b, Loci defined as +/− 1 Mb on either side of the lead variant. c, Results restricted to loci in fine-mapped regions with three or fewer independent credible sets. d, Results restricted to loci in fine-mapped regions with five or fewer independent credible sets.

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Weeks, E.M., Ulirsch, J.C., Cheng, N.Y. et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet 55, 1267–1276 (2023). https://doi.org/10.1038/s41588-023-01443-6

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