Abstract
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84–5.29) for men of European ancestry to 3.74 (95% CI, 3.36–4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14–2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71–0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
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Data availability
The full summary statistics resulting from this investigation are available through dbGaP under accession code phs001120.v2.p1. The genotype data and relevant covariate information (ancestry, country, principal components and so on) used in this study are deposited in dbGaP under accession codes phs001391.v1.p1, phs000306.v4.p1, phs001120.v1.p1, phs001221.v1.p1, phs000812.v1.p1 and phs000838.v1.p1. Publicly available data described in this manuscript can be found from the following websites: 1000 Genomes Project (https://www.internationalgenome.org/); SEER (https://seer.cancer.gov/); National Center for Health Statistics, CDC (https://www.cdc.gov/nchs/index.htm); Cistrome Data Browser (http://cistrome.org/db/); GTEx (https://gtexportal.org/home/datasets); and TCGA (https://portal.gdc.cancer.gov/).
Code availability
Imputation was performed using IMPUTE2, MACH 1.0, Minimac3 and Minimac4. Association testing was performed using PLINK 1.07, SNPtest v.2.5.2, and R v.3.5. Meta-analyses were conducted using METAL v.2011-03-25 and fine-mapping with JAM. Other analyses were performed with PriorityPruner v.0.1.4, RFMix v.1.0.2 and wANNOVAR (accessed 21 April 2020). Custom code modifying the JAM approach was developed for these analyses and is available on GitHub (https://github.com/USCmec/Conti_NatGen_2020). Code for analyses using other indicated software is readily available from the websites of the corresponding software.
Change history
20 January 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41588-021-00786-2.
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Acknowledgements
This project was support by US National Institutes of Health grants no. U19CA148537 (C.A.H.), no. U01CA194393 (S. Lindstrom) and no. K99CA246063 (B.F. Darst). We acknowledge the ARCS Foundation, Inc., Los Angeles Chapter, for their generous support of L.C.M. through the Margaret Kirsten Ponty Fellowship and B.F. Darst through the John and Edith Leonis Family Foundation. This research has been conducted using the UK Biobank Resource under application no. 42195. A full description of funding and acknowledgements for each of the contributing studies can be found in the Supplementary Information.
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D.V.C., R.A.E., Z.K.-J. and C.A.H. contributed to study conception, and D.V.C., B.F. Darst, R.A.E., Z.K.-J. and C.A.H. contributed to interpretation and wrote the manuscript. E.J.S. performed a literature search. M.B., T.D., S.B. and X.S. provided data management and bioinformatics support. D.V.C., L.C.M., B.F. Darst, E.J.S., T.D., Z.K.-J. and C.A.H. contributed to data analysis and interpretation. All authors contributed data to the study, and revised, critically reviewed and approved the final version of the manuscript: D.V.C., B.F. Darst, L.C.M., E.J.S., X.S., A.C., F.R.S., A.A.A.O., S.B., T.D., M.N.B., A.S., T.J.H., A.T., K. Matsuda, Y.M., M.F., K. Muir, A. Lophatananon, P.W., L.L.M., L.R.W., V.L.S., S.M.G., B.D.C., J.S., T.L.J.T., C. Sipeky, A.A., G.G.G., M.C. Southey, R.J.M., C.C., D.W., J. Lubiński, D.E.N., J.L.D., F.C.H., R.M.M., B.G.N., S.F.N., M.W., S.E.B., M.A.R., P.I., J.B., S.C., L. Moya, L.H., J.A.C., W.T., G.P.R., H.G., M.A., R.S., M.E., T.N., N.P., A.M.D., M. Ghoussaini, R.C.T., T.J.K., E.R., J.Y.P., T.A.S., H.-Y.L., D.A., S.J.W., L.A.M., E.G., S. Lindstrom, P.K., D.J.H., K.L.P., C.T., C.M.T., P.J.G., I.M.T., R.J.H., N.E.F., A.F., M.-É.P., J.L.S., E.A.O., M.S.G., S.K., L.E.B.F., M. Stampfer, A.W., N.H., G.L.A., R.N.H., M.J.M., K.D.S., M.B., W.J.B., W.Z., E.D.Y., J.E.M., Y.-J.L., H.-W.Z., N.F., X.M., Y.W., S.-C.Z., Z.S., S.N.T., S.K.M., D.J.S., C.M.L.W., N.B., G.B., C.M., T.S., M.L., A.S.K., B.F. Drake, O.C., G.C.-T., F.M., T.T., Y.A.K., E.M.J., E.M.G., L. Maehle, K.-T.K., S.A.I., M.C. Stern, A.V., A.G.-C., L.F., B.S.R., S.L.K., H.O., M.R.T., P.P., A.B., S.W., A. Lubwama, J.T.B., E.T.H.F., J.M., J.A.T., M.K., J. Llorca, G.C.-V., L.C.-A., C.C.T., C.D.H., S.S.S., L. Multigner, P.B., L.B., R.K., C. Slavov, V.M., R.J.L., B.W., H.B., K.C., B.H., K.-U.S., E.A.K., A.W.H., R.A.K., A.B.M., C.J.L., J.K., S.L.N., L.S., Y.C.D., W.B.I., B.N., A.J.M.H., J.C., H.P., A.M., K.D.R., G.D.M., P.O., J.X., A.R., J. Lim, S.-H.T., L.F.N., D.W.L., J.H.F., C.N.-D., B.A.R., M. Ghoussaini, D.L., T.K., N.U., S. Singhal, M.P., F.C., S.J., T.V.B., M.G.-D., J.E.C., M.E.M., S. Larkin, P.A.T., C.A.-H., W.S.B., M.C.A., D.C.C., S. Srivastava, J.C.C., G.P., G.C., M.J.R., G.J., R.H.N.S., J.J.H., M. Sanderson, R.V., R.M.-C., M.T., N.M., S.I.B., S.K.V.D.E., D.F.E., S.J.C., M.B.C., F.W., H.N., J.S.W., R.A.E., Z.K.-J. and C.A.H. C.A.H. and R.A.E. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
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R.A.E. reports the following disclosures: (1) GU-ASCO meeting in San Francisco (January 2016)—received US$500 honorarium as speaker; (2) RMH FR meeting (November 2017)—received support from Janssen and £1,100 honorarium as speaker; (3) University of Chicago invited talk (May 2018)—received US$1,000 honorarium as speaker; (4) EUR 200 education honorarium paid by Bayer & Ipsen to attend GU Connect ‘Treatment sequencing for mCRPC patients within the changing landscape of mHSPC’ at a venue at ESMO, Barcelona (September 2019); and (5) Prostate Dx Advisory Panel—Member of External Expert Committee (June 2020), 3 hours, £900. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Effect comparisons of the 269 prostate cancer risk variants between younger (age ≤ 55) and older (age > 55) men of European and African ancestry.
Variants above the identity line have larger effects in younger men, and variants below the identity line have larger effects in older men. Blue dots indicate effect differences with an unadjusted P-value < 0.05. 188/269 (69.9%) of tested variants have larger effects in younger vs. older men and 31/269 (11.5%) of tested variants have larger effects in younger vs. older men at a P-value < 0.05 threshold. All statistical tests were two-sided. Results presented in the figure are also provided in Supplementary Table 8. SE: standard error.
Extended Data Fig. 2 Effect correlations of the 269 prostate cancer risk variants between populations.
Effects and RAF are compared between European (EUR) ancestry men and a) African (AFR) ancestry men, b) East Asian (EAS) ancestry men, and c) Hispanic (HIS) men. Figure is annotated to show risk allele frequency (RAF) differences between Europeans and non-Europeans for each variant. SE: standard error.
Extended Data Fig. 3 Discriminative ability and highest GRS decile odds ratio of the multiethnic genome-wide GRS upon iteratively adding each variant to the GRS model.
a) European ancestry men from the UK Biobank and b) African ancestry men from the California Uganda (CA UG) study. Variants are sorted first within the 269-variant genetic risk score (GRS) then for other genome-wide variants by the multiethnic genome-wide association study (GWAS) meta-analysis P-values (with four P-value thresholds indicated by dotted vertical lines), and GRS weights are based on multiancestry GWAS meta-analysis results. Black lines represent the area under the curve (AUC) and correspond to the left y-axis, while blue lines represent the 90-100% GRS odds ratio (OR; relative to 40-60% GRS) and correspond to the right y-axis. All statistical tests were two-sided. PCs: principal components.
Extended Data Fig. 4 Discriminative ability and highest GRS decile odds ratio of the African ancestry genome-wide GRS upon iteratively adding each variant to the GRS model.
a) European ancestry men from the UK Biobank and b) African ancestry men from the California Uganda (CA UG) study. Variants are sorted first within the 269-variant genetic risk score (GRS) then for other genome-wide variants by the African ancestry genome-wide association study (GWAS) meta-analysis P-values (with four P-value thresholds indicated by dotted vertical lines), and GRS weights are based on African ancestry GWAS meta-analysis results. Black lines represent the area under the curve (AUC) and correspond to the left y-axis, while blue lines represent the 90-100% GRS odds ratio (OR; relative to 40-60% GRS) and correspond to the right y-axis. All statistical tests were two-sided. PCs: principal components.
Extended Data Fig. 5 Distribution of age at prostate cancer diagnosis by GRS category and population.
Differences between populations reflect sampling differences rather than population differences in age at diagnosis. SE: standard deviation, GRS: genetic risk score.
Extended Data Fig. 6 Distribution of cases with a first-degree family history of prostate cancer by GRS decile and population.
The percentage of family history-positive cases in each genetic risk score (GRS) category are shown in men of European and African ancestry. The x-axis indicates the GRS category and the y-axis is the percentage of family history-positive prostate cancer cases.
Extended Data Fig. 7 Comparison of the GRS distributions between cases and controls.
a) Men of European ancestry, b) Men of African Ancestry, c) Men of Asian ancestry and d) Hispanic men. The x-axis indicates the relative risk calculated by exponentiation of the difference in the mean genetic risk score (GRS) in controls and the mean GRS in cases for each population. The y-axis indicates the GRS density. Solid areas and corresponding percentages are the proportion of cases and controls with a GRS above 20% in the controls.
Extended Data Fig. 8 Distribution of aggressive and non-aggressive prostate cancer cases by GRS category.
a) Men of European ancestry and b) Men of African ancestry. The x-axis indicates the percentage of aggressive or non-aggressive prostate cancer cases and the y-axis indicates the genetic risk score (GRS) category.
Extended Data Fig. 9 Absolute risks of prostate cancer by GRS category.
a) Men of European ancestry from the UK Biobank and b) Men of African ancestry from the California Uganda (CA UG) study. The x-axis indicates the age of an individual and the y-axis indicates the absolute risk by a given age. Colored lines correspond to the indicated genetic risk score (GRS) category.
Extended Data Fig. 10 Absolute risks of prostate cancer by GRS category including individuals with a positive first-degree family history for prostate cancer (FH+).
a) Men of European ancestry and b) Men of African ancestry. The x-axis indicates the age of an individual and the y-axis indicates the absolute risk by a given age. Colored lines correspond to the indicated genetic risk score (GRS) category. FH+: family history positive.
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Conti, D.V., Darst, B.F., Moss, L.C. et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet 53, 65–75 (2021). https://doi.org/10.1038/s41588-020-00748-0
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DOI: https://doi.org/10.1038/s41588-020-00748-0
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