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Meta-Analysis
. 2021 Jan;53(1):65-75.
doi: 10.1038/s41588-020-00748-0. Epub 2021 Jan 4.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

David V Conti #  1 , Burcu F Darst #  1 , Lilit C Moss  1 , Edward J Saunders  2 , Xin Sheng  1 , Alisha Chou  1 , Fredrick R Schumacher  3   4 , Ali Amin Al Olama  5   6 , Sara Benlloch  5 , Tokhir Dadaev  2 , Mark N Brook  2 , Ali Sahimi  1 , Thomas J Hoffmann  7   8 , Atushi Takahashi  9   10 , Koichi Matsuda  11   12 , Yukihide Momozawa  13 , Masashi Fujita  14 , Kenneth Muir  15   16 , Artitaya Lophatananon  15 , Peggy Wan  1 , Loic Le Marchand  17 , Lynne R Wilkens  17 , Victoria L Stevens  18 , Susan M Gapstur  18 , Brian D Carter  18 , Johanna Schleutker  19   20 , Teuvo L J Tammela  21 , Csilla Sipeky  19 , Anssi Auvinen  22 , Graham G Giles  23   24   25 , Melissa C Southey  25 , Robert J MacInnis  23   24 , Cezary Cybulski  26 , Dominika Wokołorczyk  26 , Jan Lubiński  26 , David E Neal  27   28   29 , Jenny L Donovan  30 , Freddie C Hamdy  31   32 , Richard M Martin  30   33   34 , Børge G Nordestgaard  35   36 , Sune F Nielsen  35   36 , Maren Weischer  36 , Stig E Bojesen  35   36 , Martin Andreas Røder  37 , Peter Iversen  37 , Jyotsna Batra  38   39 , Suzanne Chambers  40 , Leire Moya  38   39 , Lisa Horvath  41   42 , Judith A Clements  38   39 , Wayne Tilley  43 , Gail P Risbridger  44   45 , Henrik Gronberg  46 , Markus Aly  46   47   48 , Robert Szulkin  46   49 , Martin Eklund  46 , Tobias Nordström  46   50 , Nora Pashayan  51   52   51 , Alison M Dunning  52 , Maya Ghoussaini  53 , Ruth C Travis  54 , Tim J Key  54 , Elio Riboli  55 , Jong Y Park  56 , Thomas A Sellers  56 , Hui-Yi Lin  57 , Demetrius Albanes  58 , Stephanie J Weinstein  58 , Lorelei A Mucci  59 , Edward Giovannucci  59 , Sara Lindstrom  60 , Peter Kraft  61 , David J Hunter  62 , Kathryn L Penney  63 , Constance Turman  61 , Catherine M Tangen  64 , Phyllis J Goodman  64 , Ian M Thompson Jr  65 , Robert J Hamilton  66   67 , Neil E Fleshner  66 , Antonio Finelli  68 , Marie-Élise Parent  69   70 , Janet L Stanford  71   72 , Elaine A Ostrander  73 , Milan S Geybels  71 , Stella Koutros  58 , Laura E Beane Freeman  58 , Meir Stampfer  63 , Alicja Wolk  74   75 , Niclas Håkansson  74 , Gerald L Andriole  76 , Robert N Hoover  58 , Mitchell J Machiela  58 , Karina Dalsgaard Sørensen  77   78 , Michael Borre  78   79 , William J Blot  80   81 , Wei Zheng  80 , Edward D Yeboah  82   83 , James E Mensah  82   83 , Yong-Jie Lu  84 , Hong-Wei Zhang  85 , Ninghan Feng  86 , Xueying Mao  84 , Yudong Wu  87 , Shan-Chao Zhao  88 , Zan Sun  89 , Stephen N Thibodeau  90 , Shannon K McDonnell  91 , Daniel J Schaid  91 , Catharine M L West  92 , Neil Burnet  93 , Gill Barnett  94 , Christiane Maier  95 , Thomas Schnoeller  96 , Manuel Luedeke  97 , Adam S Kibel  98 , Bettina F Drake  76 , Olivier Cussenot  99 , Géraldine Cancel-Tassin  99   100 , Florence Menegaux  101 , Thérèse Truong  101 , Yves Akoli Koudou  102 , Esther M John  103 , Eli Marie Grindedal  104 , Lovise Maehle  104 , Kay-Tee Khaw  105 , Sue A Ingles  106 , Mariana C Stern  106 , Ana Vega  107   108   109 , Antonio Gómez-Caamaño  110 , Laura Fachal  5   107   108   109 , Barry S Rosenstein  111   112 , Sarah L Kerns  113 , Harry Ostrer  114 , Manuel R Teixeira  115   116 , Paula Paulo  115   117 , Andreia Brandão  115   117 , Stephen Watya  118 , Alexander Lubwama  118 , Jeannette T Bensen  119   120 , Elizabeth T H Fontham  58 , James Mohler  120   121 , Jack A Taylor  122   123 , Manolis Kogevinas  124   125   126   127 , Javier Llorca  127   128 , Gemma Castaño-Vinyals  124   125   126   127 , Lisa Cannon-Albright  129   130 , Craig C Teerlink  129   130 , Chad D Huff  131 , Sara S Strom  131 , Luc Multigner  132 , Pascal Blanchet  133 , Laurent Brureau  133 , Radka Kaneva  134 , Chavdar Slavov  135 , Vanio Mitev  134 , Robin J Leach  136 , Brandi Weaver  136 , Hermann Brenner  137   138   139 , Katarina Cuk  137 , Bernd Holleczek  140 , Kai-Uwe Saum  137 , Eric A Klein  141   142 , Ann W Hsing  143 , Rick A Kittles  144 , Adam B Murphy  145 , Christopher J Logothetis  146 , Jeri Kim  146 , Susan L Neuhausen  147 , Linda Steele  147 , Yuan Chun Ding  147 , William B Isaacs  148 , Barbara Nemesure  149 , Anselm J M Hennis  149   150 , John Carpten  151 , Hardev Pandha  152 , Agnieszka Michael  152 , Kim De Ruyck  153 , Gert De Meerleer  154 , Piet Ost  154 , Jianfeng Xu  155 , Azad Razack  156 , Jasmine Lim  156 , Soo-Hwang Teo  157 , Lisa F Newcomb  72   158 , Daniel W Lin  72   158 , Jay H Fowke  159 , Christine Neslund-Dudas  160 , Benjamin A Rybicki  160 , Marija Gamulin  161 , Davor Lessel  162 , Tomislav Kulis  163 , Nawaid Usmani  164   165 , Sandeep Singhal  164 , Matthew Parliament  164   165 , Frank Claessens  166 , Steven Joniau  167 , Thomas Van den Broeck  166   167 , Manuela Gago-Dominguez  168   169 , Jose Esteban Castelao  170 , Maria Elena Martinez  171 , Samantha Larkin  172 , Paul A Townsend  152   173 , Claire Aukim-Hastie  152 , William S Bush  174 , Melinda C Aldrich  175 , Dana C Crawford  174 , Shiv Srivastava  176 , Jennifer C Cullen  176 , Gyorgy Petrovics  176 , Graham Casey  177 , Monique J Roobol  178 , Guido Jenster  178 , Ron H N van Schaik  179 , Jennifer J Hu  180 , Maureen Sanderson  181 , Rohit Varma  182 , Roberta McKean-Cowdin  1 , Mina Torres  182 , Nicholas Mancuso  1 , Sonja I Berndt  58 , Stephen K Van Den Eeden  183   184 , Douglas F Easton  5 , Stephen J Chanock  58 , Michael B Cook  58 , Fredrik Wiklund  46 , Hidewaki Nakagawa  14 , John S Witte  7   8   184 , Rosalind A Eeles  2   185 , Zsofia Kote-Jarai  2 , Christopher A Haiman  186
Affiliations
Meta-Analysis

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

David V Conti et al. Nat Genet. 2021 Jan.

Erratum in

  • Publisher Correction: Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.
    Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X, Chou A, Schumacher FR, Olama AAA, Benlloch S, Dadaev T, Brook MN, Sahimi A, Hoffmann TJ, Takahashi A, Matsuda K, Momozawa Y, Fujita M, Muir K, Lophatananon A, Wan P, Le Marchand L, Wilkens LR, Stevens VL, Gapstur SM, Carter BD, Schleutker J, Tammela TLJ, Sipeky C, Auvinen A, Giles GG, Southey MC, MacInnis RJ, Cybulski C, Wokołorczyk D, Lubiński J, Neal DE, Donovan JL, Hamdy FC, Martin RM, Nordestgaard BG, Nielsen SF, Weischer M, Bojesen SE, Røder MA, Iversen P, Batra J, Chambers S, Moya L, Horvath L, Clements JA, Tilley W, Risbridger GP, Gronberg H, Aly M, Szulkin R, Eklund M, Nordström T, Pashayan N, Dunning AM, Ghoussaini M, Travis RC, Key TJ, Riboli E, Park JY, Sellers TA, Lin HY, Albanes D, Weinstein SJ, Mucci LA, Giovannucci E, Lindstrom S, Kraft P, Hunter DJ, Penney KL, Turman C, Tangen CM, Goodman PJ, Thompson IM Jr, Hamilton RJ, Fleshner NE, Finelli A, Parent MÉ, Stanford JL, Ostrander EA, Geybels MS, Koutros S, Freeman LEB, Stampfer M, Wolk A, Håkansson N, Andriole GL, Hoover RN, Machiela MJ, Sørensen KD, Borre M, Blot WJ, Zheng W, Yeboah ED, Mensah JE, Lu YJ, Zhang HW, Feng N, Mao X, Wu Y, Zhao SC, Sun Z, Thibodeau SN, M… See abstract for full author list ➔ Conti DV, et al. Nat Genet. 2021 Mar;53(3):413. doi: 10.1038/s41588-021-00786-2. Nat Genet. 2021. PMID: 33473200 No abstract available.

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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Figures

Extended Data Fig. 1
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 figure are also provided in Supplementary Table 8. SE: standard error.
Extended Data Fig. 2
Extended Data Fig. 2. Effect correlations of the 269 prostate cancer risk variants between populations
Figure is annotated to show risk allele frequency (RAF) differences between Europeans and non-Europeans for each variant. 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. SE: standard error.
Extended Data Fig. 3
Extended Data Fig. 3. Discriminative ability and highest GRS decile odds ratio of the multiancestry genome-wide GRS upon iteratively adding each variant to the GRS model
Discriminative ability is shown in men of A) European ancestry from the UK Biobank and B) African ancestry from the California Uganda (CA UG) study. Variants are sorted first within the 269-genetic risk score (GRS) variants then for other genome-wide variants by the multiancestry 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
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
Discriminative ability is shown in men of A) European ancestry from the UK Biobank and B) African ancestry from the California Uganda (CA UG) study. Variants are sorted first within the 269-genetic risk score (GRS) variants 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
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
Extended Data Fig. 6. Distribution of cases with a 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
Extended Data Fig. 7. Comparison of the GRS distributions between cases and controls
A) Men of European ancestry and 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
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
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
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.
Figure 1:
Figure 1:
Odds ratio for prostate cancer by genetic risk score (GRS) category stratified by age. Results are shown for A. Men of European ancestry (N=124,101 from the genome-wide association study [GWAS] and 199,969 from independent replication) and B. Men of African ancestry (N=17,828 from the GWAS and 2,633 from independent replication). The x-axis indicates the GRS category [0–10% (low-risk), 40–60% (average risk), 60–70%, 80–90%, 90–100% (high-risk) and 99–100% (high-risk)]. The y-axis indicates odds ratios with error bars representing 95% confidence intervals (Cis) for each GRS category compared to the 40–60% GRS as the reference. The horizontal line corresponds to an odds ratio of one. Odds ratios and 95% CIs for each decile and strata are provided in Supplementary Table 20.
Figure 2.
Figure 2.
Comparison of prostate cancer GRS distributions for controls. A. Men of European ancestry versus men of African ancestry; B. Men of European ancestry versus men of East Asian ancestry; and C. Men of European ancestry versus Hispanic men. The x-axis indicates the relative risk calculated by exponentiation of the difference in the mean GRS in controls for men of European ancestry and the mean GRS in controls for each of the other populations. The y-axis indicates the GRS density. Solid areas and corresponding percentages indicate the proportion of a given population with a relative risk greater than or equal to 2.0 in comparison to the mean GRS for men of European ancestry.
Figure 3.
Figure 3.
Absolute risks of prostate cancer by GRS category. A. European ancestry; B. African ancestry; C. East Asian ancestry; and D. Hispanic. SEER data is used for mortality and incidence rates corresponding to non-Hispanic White, Black, Asian and Hispanic men. The x-axis indicates the age of an individual and the y-axis is the absolute risk by a given age.
See this image and copyright information in PMC

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