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. 2023 Dec;55(12):2065-2074.
doi: 10.1038/s41588-023-01534-4. Epub 2023 Nov 9.

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

Anqi Wang  1 , Jiayi Shen  1 , Alex A Rodriguez  2 , Edward J Saunders  3 , Fei Chen  1 , Rohini Janivara  4 , Burcu F Darst  1   5 , Xin Sheng  1 , Yili Xu  1 , Alisha J Chou  1 , Sara Benlloch  6 , Tokhir Dadaev  3 , Mark N Brook  3 , Anna Plym  7   8 , Ali Sahimi  9 , Thomas J Hoffman  10   11 , Atushi Takahashi  12   13 , Koichi Matsuda  14 , Yukihide Momozawa  15 , Masashi Fujita  16 , Triin Laisk  17 , Jéssica Figuerêdo  17 , Kenneth Muir  18   19 , Shuji Ito  20   21 , Xiaoxi Liu  22 ; Biobank Japan Project; Yuji Uchio  20 , Michiaki Kubo  21 , Yoichiro Kamatani  22   23 , Artitaya Lophatananon  18 , Peggy Wan  9 , Caroline Andrews  24 , Adriana Lori  25 , Parichoy P Choudhury  26 , Johanna Schleutker  27   28 , Teuvo L J Tammela  29 , Csilla Sipeky  27 , Anssi Auvinen  30 , Graham G Giles  31   32   33 , Melissa C Southey  33 , Robert J MacInnis  31   32 , Cezary Cybulski  34 , Dominika Wokolorczyk  34 , Jan Lubinski  34 , Christopher T Rentsch  35   36   37 , Kelly Cho  38   39 , Benjamin H Mcmahon  40 , David E Neal  41   42   43 , Jenny L Donovan  44 , Freddie C Hamdy  45   46 , Richard M Martin  44   47   48 , Borge G Nordestgaard  49   50 , Sune F Nielsen  49   50 , Maren Weischer  50 , Stig E Bojesen  49   50 , Andreas Røder  51   52 , Hein V Stroomberg  51 , Jyotsna Batra  53   54 , Suzanne Chambers  55 , Lisa Horvath  56   57 , Judith A Clements  54 , Wayne Tilly  58 , Gail P Risbridger  59   60 , Henrik Gronberg  7 , Markus Aly  7   61   62 , Robert Szulkin  7   63 , Martin Eklund  7 , Tobias Nordstrom  7   64 , Nora Pashayan  65   66 , Alison M Dunning  66 , Maya Ghoussaini  67 , Ruth C Travis  68 , Tim J Key  68 , Elio Riboli  69 , Jong Y Park  70 , Thomas A Sellers  70 , Hui-Yi Lin  71 , Demetrius Albanes  72 , Stephanie Weinstein  72 , Michael B Cook  72 , Lorelei A Mucci  73 , Edward Giovannucci  73 , Sara Lindstrom  74 , Peter Kraft  75 , David J Hunter  76 , Kathryn L Penney  77 , Constance Turman  75 , Catherine M Tangen  78 , Phyllis J Goodman  78 , Ian M Thompson Jr  79 , Robert J Hamilton  80   81 , Neil E Fleshner  80 , Antonio Finelli  82 , Marie-Élise Parent  83 , Janet L Stanford  5 , Elaine A Ostrander  84 , Stella Koutros  72 , Laura E Beane Freeman  72 , Meir Stampfer  77 , Alicja Wolk  85   86 , Niclas Håkansson  85 , Gerald L Andriole  87 , Robert N Hoover  72 , Mitchell J Machiela  72 , Karina Dalsgaard Sørensen  88   89 , Michael Borre  89   90 , William J Blot  91   92 , Wei Zheng  91 , Edward D Yeboah  93 , James E Mensah  93   94 , Yong-Jie Lu  95 , Hong-Wei Zhang  96 , Ninghan Feng  97 , Xueying Mao  95 , Yudong Wu  98 , Shan-Chao Zhao  99 , Zan Sun  100 , Stephen N Thibodeau  101 , Shannon K McDonnell  102 , Daniel J Schaid  102 , Catharine M L West  103 , Gill Barnett  104 , Christiane Maier  105 , Thomas Schnoeller  106 , Manuel Luedeke  105 , Adam S Kibel  107 , Bettina F Drake  108 , Olivier Cussenot  109   110 , Geraldine Cancel-Tassin  109   110 , Florence Menegaux  111 , Thérèse Truong  111 , Yves Akoli Koudou  112 , Esther M John  113 , Eli Marie Grindedal  114 , Lovise Maehle  114 , Kay-Tee Khaw  115 , Sue A Ingles  116 , Mariana C Stern  116 , Ana Vega  117   118   119 , Antonio Gómez-Caamaño  120 , Laura Fachal  117   118   119   121 , Barry S Rosenstein  122   123 , Sarah L Kerns  124 , Harry Ostrer  125 , Manuel R Teixeira  126   127   128 , Paula Paulo  127 , Andreia Brandão  127 , Stephen Watya  129 , Alexander Lubwama  129 , Jeannette T Bensen  130   131 , Ebonee N Butler  130 , James L Mohler  131   132 , Jack A Taylor  133   134 , Manolis Kogevinas  135   136   137   138 , Trinidad Dierssen-Sotos  138   139 , Gemma Castaño-Vinyals  135   136   137   138 , Lisa Cannon-Albright  140   141 , Craig C Teerlink  140   141 , Chad D Huff  142 , Patrick Pilie  143 , Yao Yu  142 , Ryan J Bohlender  142 , Jian Gu  142 , Sara S Strom  144 , Luc Multigner  145 , Pascal Blanchet  146 , Laurent Brureau  146 , Radka Kaneva  147 , Chavdar Slavov  148 , Vanio Mitev  147 , Robin J Leach  149 , Hermann Brenner  150   151   152 , Xuechen Chen  150 , Bernd Holleczek  153 , Ben Schöttker  150 , Eric A Klein  154   155 , Ann W Hsing  113 , Rick A Kittles  156 , Adam B Murphy  157 , Christopher J Logothetis  143 , Jeri Kim  143 , Susan L Neuhausen  158 , Linda Steele  158 , Yuan Chun Ding  158 , William B Isaacs  159 , Barbara Nemesure  160 , Anselm J M Hennis  160   161 , John Carpten  162 , Hardev Pandha  163 , Agnieszka Michael  163 , Kim De Ruyck  164 , Gert De Meerleer  165 , Piet Ost  165 , Jianfeng Xu  166 , Azad Razack  167 , Jasmine Lim  167 , Soo-Hwang Teo  168 , Lisa F Newcomb  5   169 , Daniel W Lin  5   169 , Jay H Fowke  170 , Christine M Neslund-Dudas  171 , Benjamin A Rybicki  171 , Marija Gamulin  172 , Davor Lessel  173 , Tomislav Kulis  174 , Nawaid Usmani  175   176 , Aswin Abraham  175   176 , Sandeep Singhal  175 , Matthew Parliament  175   176 , Frank Claessens  177 , Steven Joniau  178 , Thomas Van den Broeck  177   178 , Manuela Gago-Dominguez  179   180 , Jose Esteban Castelao  181 , Maria Elena Martinez  182 , Samantha Larkin  183 , Paul A Townsend  184 , Claire Aukim-Hastie  185 , William S Bush  186 , Melinda C Aldrich  187 , Dana C Crawford  186 , Shiv Srivastava  188 , Jennifer Cullen  186   189 , Gyorgy Petrovics  189 , Graham Casey  190 , Ying Wang  25 , Yao Tettey  94   191 , Joseph Lachance  4 , Wei Tang  192 , Richard B Biritwum  94 , Andrew A Adjei  193 , Evelyn Tay  94 , Ann Truelove  194 , Shelley Niwa  194 , Kosj Yamoah  70   195 , Koveela Govindasami  3 , Anand P Chokkalingam  196 , Jacob M Keaton  91   197 , Jacklyn N Hellwege  91   198 , Peter E Clark  199 , Mohamed Jalloh  200 , Serigne M Gueye  200 , Lamine Niang  200 , Olufemi Ogunbiyi  201 , Olayiwola Shittu  202 , Olukemi Amodu  203 , Akindele O Adebiyi  204 , Oseremen I Aisuodionoe-Shadrach  205 , Hafees O Ajibola  205 , Mustapha A Jamda  205 , Olabode P Oluwole  205 , Maxwell Nwegbu  205 , Ben Adusei  206 , Sunny Mante  206 , Afua Darkwa-Abrahams  94 , Halimatou Diop  207 , Susan M Gundell  1 , Monique J Roobol  208 , Guido Jenster  208 , Ron H N van Schaik  209 , Jennifer J Hu  210 , Maureen Sanderson  211 , Linda Kachuri  212 , Rohit Varma  213 , Roberta McKean-Cowdin  9 , Mina Torres  213 , Michael H Preuss  214 , Ruth J F Loos  214 , Matthew Zawistowski  215   216 , Sebastian Zöllner  215   216   217 , Zeyun Lu  9 , Stephen K Van Den Eeden  218 , Douglas F Easton  121 , Stefan Ambs  192 , Todd L Edwards  91 , Reedik Mägi  17 , Timothy R Rebbeck  24 , Lars Fritsche  219 , Stephen J Chanock  72 , Sonja I Berndt  72 , Fredrik Wiklund  7 , Hidewaki Nakagawa  16 , John S Witte  212   220 , J Michael Gaziano  38   39 , Amy C Justice  35 , Nick Mancuso  1 , Chikashi Terao  22   221   222 , Rosalind A Eeles  3   223 , Zsofia Kote-Jarai  3 , Ravi K Madduri  2 , David V Conti #  1 , Christopher A Haiman #  224
Collaborators, Affiliations

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

Anqi Wang et al. Nat Genet. 2023 Dec.

Abstract

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

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Conflict of interest statement

Competing interests

The authors declare no competing interests.

Figures

Extended Data Fig. 1
Extended Data Fig. 1
Venn diagram of prostate cancer risk variants common (MAF>1%) among European, African, Asian and Hispanic populations. The plot illustrates the distribution of 451 prostate cancer risk variants, highlighting the number of variants that are either unique to or shared among European, African, Asian, and Hispanic populations. Five variants with a minor allele frequency (MAF) of ≤1% across all populations are specifically included under the European population, where they have the highest MAF. Numbers in parentheses denote the total count of variants common to each respective population.
Extended Data Fig. 2
Extended Data Fig. 2
The associations of GRS451 and total prostate cancer risk in GWAS discovery and replication sub-studies and meta-analysis by ancestry. Odds ratios and 95% confidence intervals for one SD increase in GRS451 and total prostate cancer risk were calculated from logistic regression. The columns ‘case’ and ‘control’ show the case and control sample sizes, respectively. ‘META’ refers to the meta-analyzed results using the inverse-variance weighted method. The y-axis shows each individual sub-studies (details of each sub-studies are available in Supplemental Table 1 and 2) and their corresponding meta-analyzed results by ancestry and study phase (GWAS discovery or replication), as well as overall meta-analyzed results.
Figure 1.
Figure 1.
Manhattan plot of results from the multi-ancestry prostate cancer meta-analysis. Multi-ancestry meta-analysis (156,319 cases and 788,443 controls) was performed using an inverse-variance-weighted fixed-effects model. Nominal statistical significance is shown as −log10P (two-sided) of z statistics on the y axis. Purple and orange circles indicate previously known or novel risk variants, respectively, that were genome-wide significant in multi-ancestry or ancestry-specific meta-analyses. The plot is truncated at −log10P=600.
Figure 2.
Figure 2.
Comparison of the ancestry-specific results of the 451 risk variants for prostate cancer. (a) Venn diagram of genome-wide significant variants (P<5x10−8) among European, African, Asian, and Hispanic populations. (b) Venn diagram of nominally significant variants (P<0.05) among European, African, Asian, and Hispanic populations. (c) Comparison of ancestry-specific odds ratios (ORs) between European and African, Asian, and Hispanic populations, respectively. The number of variants is denoted in the lower right corner. Genome-wide significant variants among African, Asian, or Hispanic populations are highlighted in orange. Two-sided Pearson correlation tests were performed. The Pearson’s correlation coefficient between effect size and corresponding P-value are denoted in the upper left in each sub-panel. Only common variants across all populations (MAF>1%, n=370) were included in (a), (b), and (c).
Figure 3.
Figure 3.
Percentage of cases in the lowest and highest genetic risk score (GRS) quintiles based on GRS100, GRS181, GRS269, and GRS451 in the multi-ancestry sample. GRS risk quintiles were categorized based on GRS distributions among controls. Quintile 1 (orange bar) refers to the lowest quintile (0–20%), and quintile 5 (yellow bar) refers to the highest quintile (80–100%).
Figure 4.
Figure 4.
The associations of GRS and prostate cancer risk in GWAS discovery and replication samples. ORs and 95% Confidence Intervals (CIs) from logistic regression for one standard deviation (SD) increase in (a) GRS100, GRS181, GRS269, and GRS451 and total prostate cancer risk by ancestry in the GWAS discovery studies; (b) GRS269 and GRS451 and total prostate cancer risk in the replication studies: Michigan Genomics Initiative (MGI), Mass General Brigham Biobank (MGB), Estonian Biobank (EstBB ), and Men of African Descent and Carcinoma of the Prostate (MADCaP); (c) GRS451 and total prostate cancer risk by age; (d) GRS451 and GRS400 and prostate cancer aggressiveness among prostate cancer cases in the GWAS discovery studies. ‘META’ refers to the meta-analyzed results for all populations using the inverse-variance weighted method. Incremental percentage change of ORs were calculated for each comparison. The columns ‘case’ and ‘control’ show the case and control sample sizes, and the columns ‘agg’ and ‘non-agg’ show the aggressive and non-aggressive cases sample sizes, respectively.
Figure 5.
Figure 5.
Cumulative absolute risk by age. Solid lines are the cumulative absolute risk for individuals in the top 16% GRS for African ancestry and top 20% for European ancestry. These GRS categories represent the percent of individuals in each population with at least a 2-fold increase in risk in comparison to the median GRS (as indicated in the inset distributions for African and European ancestries, respectively). Dashed horizontal lines indicate the lifetime absolute risk achieved at age 85 for the average (50% GRS) in African (11.6%) and European (7.8%) ancestry populations. Solid dots indicate the ages at which lifetime absolute risk levels are achieved for men of African ancestry in the top 16% GRS (age = 66 years) and men of European ancestry in the top 20% GRS (age = 69 years).
See this image and copyright information in PMC

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