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


Journal

Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904

Informations de publication

Date de publication:
08 2023
Historique:
received: 14 08 2020
accepted: 09 06 2023
medline: 11 8 2023
pubmed: 14 7 2023
entrez: 13 7 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 37443254
doi: 10.1038/s41588-023-01443-6
pii: 10.1038/s41588-023-01443-6
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1267-1276

Subventions

Organisme : NIH HHS
ID : DP5 OD024582
Pays : United States
Organisme : NHLBI NIH HHS
ID : DP2 HL152423
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK075787
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK034854
Pays : United States
Organisme : NHGRI NIH HHS
ID : F31 HG009850
Pays : United States
Organisme : NHGRI NIH HHS
ID : K99 HG009917
Pays : United States
Organisme : NHGRI NIH HHS
ID : R00 HG009917
Pays : United States

Informations de copyright

© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.

Références

Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
pubmed: 28686856 pmcid: 5501872 doi: 10.1016/j.ajhg.2017.06.005
Donnelly, P. Progress and challenges in genome-wide association studies in humans. Nature 456, 728–731 (2008).
pubmed: 19079049 doi: 10.1038/nature07631
Gallagher, M. D. & Chen-Plotkin, A. S. The post-GWAS era: from association to function. Am. J. Hum. Genet. 102, 717–730 (2018).
pubmed: 29727686 pmcid: 5986732 doi: 10.1016/j.ajhg.2018.04.002
Reich, D. E. et al. Linkage disequilibrium in the human genome. Nature 411, 199–204 (2001).
pubmed: 11346797 doi: 10.1038/35075590
van Arensbergen, J., van Steensel, B. & Bussemaker, H. J. In search of the determinants of enhancer-promoter interaction specificity. Trends Cell Biol. 24, 695–702 (2014).
pubmed: 25160912 pmcid: 4252644 doi: 10.1016/j.tcb.2014.07.004
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
pubmed: 25597830 doi: 10.1038/ncomms6890
Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).
pubmed: 27866706 pmcid: 5142122 doi: 10.1016/j.ajhg.2016.10.003
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
pubmed: 26854917 pmcid: 4767558 doi: 10.1038/ng.3506
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
pubmed: 25885710 pmcid: 4401657 doi: 10.1371/journal.pcbi.1004219
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
pubmed: 25915600 pmcid: 4828725 doi: 10.1038/ng.3259
Fulco, C. P. et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).
pubmed: 31784727 pmcid: 6886585 doi: 10.1038/s41588-019-0538-0
Jung, I. et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat. Genet. 51, 1442–1449 (2019).
pubmed: 31501517 pmcid: 6778519 doi: 10.1038/s41588-019-0494-8
Ulirsch, J. C. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51, 683–693 (2019).
pubmed: 30858613 pmcid: 6441389 doi: 10.1038/s41588-019-0362-6
Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).
pubmed: 27863249 pmcid: 5123897 doi: 10.1016/j.cell.2016.09.037
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
pubmed: 24670763 pmcid: 5215096 doi: 10.1038/nature12787
Liu, Y., Sarkar, A., Kheradpour, P., Ernst, J. & Kellis, M. Evidence of reduced recombination rate in human regulatory domains. Genome Biol. 18, 193 (2017).
pubmed: 29058599 pmcid: 5651596 doi: 10.1186/s13059-017-1308-x
Fine, R. S., Pers, T. H., Amariuta, T., Raychaudhuri, S. & Hirschhorn, J. N. Benchmarker: an unbiased, association-data-driven strategy to evaluate gene prioritization algorithms. Am. J. Hum. Genet. 104, 1025–1039 (2019).
pubmed: 31056107 pmcid: 6556976 doi: 10.1016/j.ajhg.2019.03.027
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
pubmed: 33499903 pmcid: 7836161 doi: 10.1186/s13059-020-02252-4
Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).
pubmed: 30239796 doi: 10.1093/nar/gky837
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
pubmed: 20562875 pmcid: 3232052 doi: 10.1038/ng.608
Kanai, M. et al. Insights from complex trait fine-mapping across diverse populations. Preprint at medRxiv https://doi.org/2021.09.03.21262975 (2021).
The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).
pubmed: 27892958 doi: 10.1038/nmeth.4083
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651 pmcid: 3037419 doi: 10.1038/75556
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).
pubmed: 22080510 doi: 10.1093/nar/gkr988
Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691–D697 (2011).
pubmed: 21067998 doi: 10.1093/nar/gkq1018
Blake, J. A. et al. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res. 42, D810–D817 (2014).
pubmed: 24285300 doi: 10.1093/nar/gkt1225
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
pubmed: 20686565 pmcid: 3039276 doi: 10.1038/nature09270
Wheeler, E. et al. Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 14, e1002383 (2017).
pubmed: 28898252 pmcid: 5595282 doi: 10.1371/journal.pmed.1002383
Kurkó, J. et al. Genetics of rheumatoid arthritis—a comprehensive review. Clin. Rev. Allergy Immunol. 45, 170–179 (2013).
pubmed: 23288628 pmcid: 3655138 doi: 10.1007/s12016-012-8346-7
Gejman, P. V., Sanders, A. R. & Duan, J. The role of genetics in the etiology of schizophrenia. Psychiatr. Clin. North Am. 33, 35–66 (2010).
pubmed: 20159339 pmcid: 2826121 doi: 10.1016/j.psc.2009.12.003
Heyes, S. et al. Genetic disruption of voltage-gated calcium channels in psychiatric and neurological disorders. Prog. Neurobiol. 134, 36–54 (2015).
pubmed: 26386135 pmcid: 4658333 doi: 10.1016/j.pneurobio.2015.09.002
GTEx, Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
doi: 10.1038/nature24277
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
pubmed: 27019110 doi: 10.1038/ng.3538
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Wang, Q. S. et al. Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs. Nat. Commun. 12, 3394 (2021).
pubmed: 34099641 pmcid: 8184741 doi: 10.1038/s41467-021-23134-8
Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527–1533 (2021).
pubmed: 34711957 pmcid: 7611956 doi: 10.1038/s41588-021-00945-5
Dron, J. S. & Hegele, R. A. Genetics of lipid and lipoprotein disorders and traits. Curr. Genet. Med. Rep. 4, 130–141 (2016).
pubmed: 28286704 pmcid: 5325854 doi: 10.1007/s40142-016-0097-y
Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood. Nature 575, 652–657 (2019).
pubmed: 31748747 pmcid: 6887549 doi: 10.1038/s41586-019-1765-3
Brisch, R. et al. The role of dopamine in schizophrenia from a neurobiological and evolutionary perspective: old fashioned, but still in vogue. Front. Psychiatry 5, 47 (2014).
pubmed: 24904434 pmcid: 4032934
Basak, A. et al. BCL11A deletions result in fetal hemoglobin persistence and neurodevelopmental alterations. J. Clin. Invest. 125, 2363–2368 (2015).
pubmed: 25938782 pmcid: 4497765 doi: 10.1172/JCI81163
Quednow, B. B., Brzózka, M. M. & Rossner, M. J. Transcription factor 4 (TCF4) and schizophrenia: integrating the animal and the human perspective. Cell. Mol. Life Sci. 71, 2815–2835 (2014).
pubmed: 24413739 doi: 10.1007/s00018-013-1553-4
Ulirsch, J. C. et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell 165, 1530–1545 (2016).
pubmed: 27259154 pmcid: 4893171 doi: 10.1016/j.cell.2016.04.048
Cvejic, A. et al. SMIM1 underlies the Vel blood group and influences red blood cell traits. Nat. Genet. 45, 542–545 (2013).
pubmed: 23563608 pmcid: 4179282 doi: 10.1038/ng.2603
Cawley, N. X. et al. Obese carboxypeptidase E knockout mice exhibit multiple defects in peptide hormone processing contributing to low bone mineral density. Am. J. Physiol. Endocrinol. Metab. 299, E189–E197 (2010).
pubmed: 20460579 pmcid: 2928512 doi: 10.1152/ajpendo.00516.2009
Kato, S. et al. Leucine-rich repeat-containing G protein-coupled receptor-4 (LGR4, Gpr48) is essential for renal development in mice. Nephron Exp. Nephrol. 104, e63–e75 (2006).
pubmed: 16785743 doi: 10.1159/000093999
Budnik, I. & Brill, A. Immune factors in deep vein thrombosis initiation. Trends Immunol. 39, 610–623 (2018).
pubmed: 29776849 pmcid: 6065414 doi: 10.1016/j.it.2018.04.010
Lambert, M. P., Sachais, B. S. & Kowalska, M. A. Chemokines and thrombogenicity. Thromb. Haemost. 97, 722–729 (2007).
pubmed: 17479182 doi: 10.1160/TH07-01-0046
Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901 pmcid: 1950838 doi: 10.1086/519795
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).
pubmed: 29892013 pmcid: 6309610 doi: 10.1038/s41588-018-0144-6
Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).
pubmed: 30104761 pmcid: 6119127 doi: 10.1038/s41588-018-0184-y
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
pubmed: 31178118 pmcid: 6687398 doi: 10.1016/j.cell.2019.05.031
Baglama, J. & Reichel, L. Restarted block Lanczos bidiagonalization methods. Numer. Algorithms 43, 251–272 (2007).
doi: 10.1007/s11075-006-9057-z
Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999).
pubmed: 18252563 doi: 10.1109/72.761722
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678 pmcid: 4626285 doi: 10.1038/ng.3404
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
pubmed: 26414676 pmcid: 4797329 doi: 10.1038/ng.3406
UK10K Consortium et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).
doi: 10.1038/nature14962
Csárdi, G. & Nepusz, T. The igraph software package for complex network research. Int. J. complex syst. 1695, 1–9 (2006).
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. Series B Stat. Methodol. 82, 1273–1300 (2020).
pubmed: 37220626 pmcid: 10201948 doi: 10.1111/rssb.12388
Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).
pubmed: 28942963 pmcid: 5630179 doi: 10.1016/j.ajhg.2017.08.012
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795 pmcid: 4893825 doi: 10.1186/s13059-016-0974-4
Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127 (2016).
pubmed: 27306882 pmcid: 4908757 doi: 10.1186/s13059-016-0992-2
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
pmcid: 4530010 doi: 10.1038/nature14248
Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).
pubmed: 31570894 pmcid: 6858557 doi: 10.1038/s41588-019-0505-9

Auteurs

Elle M Weeks (EM)

Broad Institute of MIT and Harvard, Cambridge, MA, USA. eweeks@broadinstitute.org.

Jacob C Ulirsch (JC)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.
Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.

Nathan Y Cheng (NY)

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

Brian L Trippe (BL)

Program in Computational & Systems Biology, MIT, Cambridge, MA, USA.
Computer Science & Artificial Intelligence Lab, MIT, Cambridge, MA, USA.

Rebecca S Fine (RS)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA.
Department of Genetics, Harvard Medical School, Boston, MA, USA.
Vertex Pharmaceuticals Incorporated, Boston, MA, USA.

Jenkai Miao (J)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA.

Tejal A Patwardhan (TA)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Statistics, Harvard University, Cambridge, MA, USA.

Masahiro Kanai (M)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Analytic and Translational Genetics Unit, MGH, Boston, MA, USA.
Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.

Joseph Nasser (J)

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

Charles P Fulco (CP)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Bristol Myers Squibb, Cambridge, MA, USA.

Katherine C Tashman (KC)

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

Francois Aguet (F)

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

Taibo Li (T)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
MD-PhD Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Jose Ordovas-Montanes (J)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA, USA.
Program in Immunology, Harvard Medical School, Boston, MA, USA.
Harvard Stem Cell Institute, Cambridge, MA, USA.

Christopher S Smillie (CS)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Program in Computational & Systems Biology, MIT, Cambridge, MA, USA.

Moshe Biton (M)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Molecular Biology, MGH, Boston, MA, USA.
Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.

Alex K Shalek (AK)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA.
Department of Chemistry, MIT, Cambridge, MA, USA.
Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA.
Ragon Institute of MGH, MMIT, Cambridge, MA, USA.

Ashwin N Ananthakrishnan (AN)

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

Ramnik J Xavier (RJ)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Molecular Biology, MGH, Boston, MA, USA.
Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA.
Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Aviv Regev (A)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA.
Howard Hughes Medical Institute, MIT, Cambridge, MA, USA.
Genentech, San Francisco, CA, USA.

Rajat M Gupta (RM)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Cardiovascular Medicine and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.

Kasper Lage (K)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Surgery, MGH, Boston, MA, USA.

Kristin G Ardlie (KG)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Joel N Hirschhorn (JN)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA.
Department of Genetics, Harvard Medical School, Boston, MA, USA.
Department of Pediatrics, Harvard Medical School, Boston, MA, USA.

Eric S Lander (ES)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Biology, MIT, Cambridge, MA, USA.
Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Jesse M Engreitz (JM)

Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.

Hilary K Finucane (HK)

Broad Institute of MIT and Harvard, Cambridge, MA, USA. finucane@broadinstitute.org.
Analytic and Translational Genetics Unit, MGH, Boston, MA, USA. finucane@broadinstitute.org.

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