Abstract
Here, in a multi-ancestry genome-wide association study meta-analysis of kidney cancer (29,020 cases and 835,670 controls), we identified 63 susceptibility regions (50 novel) containing 108 independent risk loci. In analyses stratified by subtype, 52 regions (78 loci) were associated with clear cell renal cell carcinoma (RCC) and 6 regions (7 loci) with papillary RCC. Notably, we report a variant common in African ancestry individuals (rs7629500) in the 3′ untranslated region of VHL, nearly tripling clear cell RCC risk (odds ratio 2.72, 95% confidence interval 2.23–3.30). In cis-expression quantitative trait locus analyses, 48 variants from 34 regions point toward 83 candidate genes. Enrichment of hypoxia-inducible factor-binding sites underscores the importance of hypoxia-related mechanisms in kidney cancer. Our results advance understanding of the genetic architecture of kidney cancer, provide clues for functional investigation and enable generation of a validated polygenic risk score with an estimated area under the curve of 0.65 (0.74 including risk factors) among European ancestry individuals.
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Data availability
All GWAS summary statistics are available on dbGaP (phs003505.v1.p1) and GWAS Catalog (GCST90320043–GCST90320065). Individual-level data from the new NCI-3 scan are available on dbGaP (phs003505.v1.p1). Data from previously published scans are also available on dbGaP (NCI-1, phs000351.v1.p1; NCI-2, phs001736.v2.p1; USKC, phs000863.v1.p1; IARC-2, phs001271.v1.p1; MDA and MDA-OncoArray, phs003505.v1.p1). The individual-level data from the IARC-1 and UK scans have not been deposited in dbGaP or any other data archive site given decisions by the institutional ethics review boards for these projects. The data from these scans are available upon reasonable request through internal processes unique to each institution. Such requests can be made in writing to the principal investigators (IARC: P. Brennan, pbrennan@iarc.fr and UK: R.H., richard.houlston@icr.ac.uk); the time frame from request to receipt of data is approximately 4–6 weeks. The UK Biobank analysis was conducted via application number 86140 (https://www.ukbiobank.ac.uk/). The Finnish biobank data included in FinnGen can be accessed via Fingenious services at https://site.fingenious.fi/en/ (ref. 80) managed by FINBB. Finnish Health register data can be applied for via Findata at https://findata.fi/en/data/ (ref. 81). The full GWAS results of the BBJ are available via the website of the Japanese ENcyclopedia of GEnetic Associations by Riken (JENGER) at http://jenger.riken.jp/en/ (ref. 82; case–control GWAS no. 156). Function annotation enrichment was performed with the annotation data provided via the GARFIELD package at https://www.ebi.ac.uk/birney-srv/GARFIELD/ (ref. 83). Position weight matrices for transcription factor-binding sites as cataloged in HOCOMOCO were provided along with the motifbreakR R package, in the associated MotifDb database. ChIP-Seq data reported by Schmid et al.18 are publicly available through the Gene Expression Omnibus (GEO) database under the accession codes: GSE120885 (HIF-1α, HIF-2α and HIF-1β ChIP-seq in RCC4 cells) and GSE67237 (HIF-2α and HIF-1β ChIP-seq in 786-O cells). Epigenomic charting data (H3K27ac peaks) generated by Nassar et al.37 are publicly available through GEO database under accession code GSE188486; the sample attributes are mentioned in Supplementary Table 1 of the corresponding paper. GTEx v8 and TCGA data can be accessed via GTEx and Genomic Data Commons at https://gtexportal.org/home/ (ref. 84) and https://portal.gdc.cancer.gov/repository (ref. 85), respectively. Additionally, eQTLs for TCGA were queried via the PancanQTL database at http://gong_lab.hzau.edu.cn/PancanQTL/ (ref. 86).
Code availability
Code used in performing the liftover of summary statistics and fixed-effects GWAS meta-analyses (version 2022-12-23) is available via GitHub at https://github.com/freeseek/score (ref. 72). No previously unreported custom computer code or algorithm was used to generate results.
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Acknowledgements
We thank O. Jahagirdar and A. Klein for their efforts in implementing various computational pipelines used in this project. This research was supported by the Intramural Research Program of the National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, as well as with Federal funds from the National Cancer Institute under contract no. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. Acknowledgments and funding sources for participating centers are provided in Supplementary Table 17.
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M.P.P., D.D., M.J.M., B.R.G., P. Brennan, J.G. and S.J.C. contributed to the design and execution of the overall study. M.Y., N.R.C., B.D.H., M.R.M. and A.A.H. performed the experiments. M.P.P., D.D., M.J.M., B.R.G., T.W., D.O., S.C., A. Liu, K.H., K.M.B., J.R. and S.J.C. contributed to the design and execution of the statistical analysis. M.P.P., D.D., M.J.M., B.R.G. and S.J.C. contributed to the first draft of the paper. P. Brennan, J.G. and S.J.C. edited the paper. A.F.-I., P.S., A. Liu, C.W., S.O.A., J. Larkin, S.C.Z., M.S., K.H., A.H., K.A.L., F. Cárcano, O.B., B.S., K.G.N., G.M., D.S., W.R.D., M.A.A.K.F., A.v.B., F.N., J.N.H., N.R., W.Y.H., W.M.L., A. Lori, M.F., M.Z.-M., S.V.S., W.J.M., Biobank Japan Project, A.V., R.D., F. Carusso, L.S.G., K.A., M.A.B., C.A., I.P., S. Ricard, FinnGen, G.S., R.E.B., N.S.V., N.S., G.D.S., A.A., S.B., D.H., N.G., P.P., M.S., A.P., F.I.N., M.J.F., X.Z., L.J.M., M.K., T.E., S.A.C., D.C.C., R.G.U., D.Z., A.M., I.H., A.H., L.F., V. Janout, D.M., V. Jinga, S. Rascu, M.M., S.S., S.M., V.G., B.A.-A., J.M., M.J., L.P., L.H., J. Li, I.L., S.M.B., A.G.S., C.T.G.S., R.B.R., F.P.G., M.D.C., M.P., G.-S.M.L., M.L.F., A.J., S.E.G., A.S., R.H.T., V.S., D.D.T., C.T.B., D.A., E.T.L., W.C.N., V.A.M., A.V.P., J.-C.B., N.D.F., P. Bigot, R.M.R., L.M.C., A.F., B.J.M., C.T., T.K.C., D.M.C., R.H., J.E.E.-P., P.H.A., A.G., P. Brennan and J.G. contributed samples and/or data. All authors critically reviewed the paper. The following authors contributed equally as co-first authors: M.P.P., D.D., M.J.M. and B.R.G. The following authors contributed equally to the work as co-second authors: T.W., D.O., S.C., A.F.I., P.A.S., A. Liu, C.W., S.O.A., J. Larkin, S.C.Z., M.S., K.H., A.H., K.A.L., F. Cárcano, O.B., B.S., K.G.N., G.M., D.S., W.R.D., M.A.A.K.F., A.v.B. and F.N. The following authors contributed equally as co-second-last authors: D.A., E.T.L., W.C.N., V.A.M., A.V.P., L.M.C., J.C.B., N.F., P. Bigot, R.M.R., A.F., B.J.M., C.T., T.K.C., D.M., R.H., J.E.E.-P., P.H.A. and A.G. The following authors jointly supervised this work: P. Brennan, J.G. and S.J.C.
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D.S. has received funds from Janssen for consulting outside of the submitted work. N.S.V. has received grants, personal fees and non-financial support from Bristol Myers Squibb; personal fees and non-financial support from Ipsen and EUSA Pharma; and personal fees from Merck Serono, Pfizer, Eisai Ltd and 4D Pharma, all outside the submitted work. G.D.S. has received educational grants from Pfizer and AstraZeneca; consultancy fees from Pfizer, Merck, EUSA Pharma and MSD; travel expenses from Pfizer and speaker fees from Pfizer, all outside the submitted work. M.S. has received honoraria from Covidien/Medtronic for teaching on courses and speaker fees from Pfizer, all outside the submitted work. L.M.C. has received research funding from BMS, Novartis and GSK, all outside the submitted work. B.J.M. has received funding from the NCCN Hereditary Kidney Cancer Panel and Merck, all outside the submitted work. T.K.C. has received funding from Alkermes, AstraZeneca, Aravive, Aveo, Bayer, Bristol Myers Squibb, Calithera, Circle Pharma, Deciphera Pharmaceuticals, Eisai, EMD Serono, Exelixis, GlaxoSmithKline, Gilead, IQVIA, Infinity, Ipsen, Jansen, Kanaph, Lilly, Merck, Nikang, Nuscan, Novartis, Oncohost, Pfizer, Roche, Sanofi/Aventis, Scholar Rock, Surface Oncology, Takeda, Tempest, Up-To-Date, CME events (Peerview, OncLive, MJH, CCO and others), outside the submitted work. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Pleiotropic effects of kidney cancer loci for other cancers and risk factors.
Pleiotropy matrix summarizing kidney cancer susceptibility loci with evidence of pleiotropic effects for other cancers and/or selected risk factors (body mass index, hypertension, blood pressure, smoking) from searches of GWAS Catalog and UK Biobank GWAS summary statistics. Cell colors indicating associations with specific traits: red, other cancers; blue, body mass index; orange, hypertension or blood pressure; violet, smoking. Locus-trait summary statistics listed in Supplementary Table 11 (GWAS Catalog) and 12 (UK Biobank).
Extended Data Fig. 2 In silico analysis of enrichment for putative regulatory annotations among kidney cancer loci.
Enrichment of variants associated with overall RCC in (a) DNAse Hotspots (b) Histone modification sites (c) Chromatin states (d) different genic locations. The enrichments are depicted for variants at different p-value thresholds denoted by the colors and across different categories in each panel. The results were computed using GARFIELD v2.
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Purdue, M.P., Dutta, D., Machiela, M.J. et al. Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions. Nat Genet 56, 809–818 (2024). https://doi.org/10.1038/s41588-024-01725-7
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DOI: https://doi.org/10.1038/s41588-024-01725-7
