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. 2025 Jul;57(7):1598-1610.
doi: 10.1038/s41588-025-02227-w. Epub 2025 Jun 18.

Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations

Aude Nicolas #  1   2 , Richard Sherva #  3   4 , Benjamin Grenier-Boley #  5 , Yoontae Kim #  6 , Masataka Kikuchi  7 , Jigyasha Timsina  8   9 , Itziar de Rojas  10   11 , María Carolina Dalmasso  12   13 , Xiaopu Zhou  14   15   16 , Yann Le Guen  17   18 , Carlos E Arboleda-Bustos  19 , Maria Aparecida Camargos Bicalho  20   21   22 , Maëlenn Guerchet  23 , Sven van der Lee  24   25 , Monica Goss  26 , Atahualpa Castillo  27 , Céline Bellenguez  5 , Fahri Küçükali  28   29 , Claudia L Satizabal  26   30   31 , Bernard Fongang  26   32   33 , Qiong Yang  30   31 , Oliver Peters  34   35 , Anja Schneider  36   37 , Martin Dichgans  38   39   40 , Dan Rujescu  41 , Norbert Scherbaum  42 , Jürgen Deckert  43 , Steffi Riedel-Heller  44 , Lucrezia Hausner  45 , Laura Molina-Porcel  46   47 , Emrah Düzel  48   49 , Timo Grimmer  50 , Jens Wiltfang  51   52   53 , Stefanie Heilmann-Heimbach  54 , Susanne Moebus  55 , Thomas Tegos  56 , Nikolaos Scarmeas  57   58 , Oriol Dols-Icardo  11   59 , Fermin Moreno  11   60   61 , Jordi Pérez-Tur  11   62 , María J Bullido  11   63   64   65 , Pau Pastor  66   67 , Raquel Sánchez-Valle  68 , Victoria Álvarez  69   70 , Han Cao  14 , Nancy Y Ip  14   15   16 , Amy K Y Fu  14   15   16 , Fanny C F Ip  15   16 , Natividad Olivar  71 , Carolina Muchnik  71 , Carolina Cuesta  72 , Lorenzo Campanelli  73 , Patricia Solis  74 , Daniel Gustavo Politis  72 , Silvia Kochen  74 , Luis Ignacio Brusco  71 , Mercè Boada  10   11 , Pablo García-González  10 , Raquel Puerta  10 , Pablo Mir  11   75 , Luis M Real  11   76   77 , Gerard Piñol-Ripoll  78   79 , Jose María García-Alberca  11   80 , Jose Luís Royo  81 , Eloy Rodriguez-Rodriguez  11   82 , Hilkka Soininen  83 , Sami Heikkinen  84 , Alexandre de Mendonça  85 , Shima Mehrabian  86 , Latchezar Traykov  87 , Jakub Hort  88   89 , Martin Vyhnalek  88   89 , Katrine Laura Rasmussen  90   91 , Jesper Qvist Thomassen  90 , Yolande A L Pijnenburg  24 , Henne Holstege  24   92 , John C van Swieten  93 , Harro Seelaar  93 , Jurgen A H R Claassen  94   95 , Willemijn J Jansen  96 , Inez Ramakers  96 , Frans Verhey  96 , Aad van der Lugt  97 , Philip Scheltens  24 , Jenny Ortega-Rojas  19 , Ana Gabriela Concha Mera  19 , Maria F Mahecha  19 , Rodrigo Pardo  19 , Gonzalo Arboleda  19 , Shahram Bahrami  98   99 , Vera Fominykh  98   99 , Geir Selbæk  100   101   102 , Caroline Graff  103 , Goran Papenberg  104 , Vilmantas Giedraitis  105 , Anne Boland  106 , Jean-François Deleuze  106 , Luiz Armando de Marco  21   107 , Edgar Nunes de Moraes  20   22 , Bernardo de Mattos Viana  21   108   109 , Marco Túlio Gualberto Cintra  20   22 , Teresa Juarez-Cedillo  110 , Anthony J Griswold  111 , Tatiana Forund  112 , Jonathan Haines  113 , Lindsay Farrer  4   114   115   116   117 , Anita DeStefano  114 , Ellen Wijsman  118   119   120 , Richard Mayeux  121   122 , Margaret Pericak-Vance  111   123 , Brian Kunkle  111 , Alison Goate  124 , Gerard D Schellenberg  125 , Badri Vardarajan  121   122   126 , Li-San Wang  125 , Yuk Yee Leung  125 , Clifton L Dalgard  127   128 , Gael Nicolas  129 , David Wallon  129 , Carole Dufouil  130   131 , Florence Pasquier  132 , Olivier Hanon  133 , Stéphanie Debette  134   135 , Edna Grünblatt  136   137   138 , Julius Popp  139   140   141 , Bárbara Angel  142   143 , Sergio Gloger  144   145 , Maria Victoria Chacon  144 , Rafael Aranguiz  144   146   147 , Paulina Orellana  148   149 , Andrea Slachevsky  148   150 , Christian Gonzalez-Billault  142   148 , Cecilia Albala  142   143 , Patricio Fuentes  151   152 , Perminder Sachdev  153 , Karen A Mather  153 , Richard L Hauger  154   155 , Victoria Merritt  156   157   158 , Matthew Panizzon  155   157   158 , Rui Zhang  3 , J Michael Gaziano  159   160 , Roberta Ghidoni  161 , Daniela Galimberti  162   163 , Beatrice Arosio  164 , Patrizia Mecocci  165   166 , Vincenzo Solfrizzi  167 , Lucilla Parnetti  168 , Alessio Squassina  169 , Lucio Tremolizzo  170 , Barbara Borroni  171   172 , Benedetta Nacmias  173   174 , Paolo Caffarra  175 , Davide Seripa  176 , Innocenzo Rainero  177 , Antonio Daniele  178   179 , Fabrizio Piras  180 ; EADB; Hampton L Leonard  181   182 , Jenifer S Yokoyama  182   183   184   185 , Mike A Nalls  181   182   186 , Akinori Miyashita  187 , Norikazu Hara  187 , Kouichi Ozaki  188 , Shumpei Niida  188 , Julie Williams  27   189 , Carlo Masullo  190 , Philippe Amouyel  5 , Pierre-Marie Preux  23 , Pascal Mbelesso  23   191 , Bébène Bandzouzi  192 , Andy Saykin  112   193 , Frank Jessen  36   194   195 , Patrick G Kehoe  196 , Cornelia Van Duijn  197   198 , Nesrine Ben Salem  199 , Ruth Frikke-Schmidt  90   91 , Lotfi Cherni  199   200 , Michael D Greicius  17 , Magda Tsolaki  56   200 , Pascual Sánchez-Juan  11   201 , Marco Aurélio Romano Silva  21   108 , Tenielle Porter  202   203   204 , Simon M Laws  202   203   204 , Kristel Sleegers  28   29 , Martin Ingelsson  205   206   207 , Jean-François Dartigues  208 , Sudha Seshadri  26   30   31 , Giacomina Rossi  209 , Laura Morelli  73 , Mikko Hiltunen  84 , Rebecca Sims  27 , Wiesje van der Flier  24 , Ole A Andreassen  98   99 , Humberto Arboleda  19 , Carlos Cruchaga  8   9 , Valentina Escott-Price  189   210 , Agustín Ruiz  10   11   211 , Kun Ho Lee  6   212   213 , Takeshi Ikeuchi  187 , Alfredo Ramirez  211   214   215   216   217 , Jungsoo Gim  6   212   213 , Mark Logue  3   114   218   219 , Jean-Charles Lambert  220
Collaborators, Affiliations

Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations

Aude Nicolas et al. Nat Genet. 2025 Jul.

Abstract

A polygenic score (PGS) for Alzheimer's disease (AD) was derived recently from data on genome-wide significant loci in European ancestry populations. We applied this PGS to populations in 17 European countries and observed a consistent association with the AD risk, age at onset and cerebrospinal fluid levels of AD biomarkers, independently of apolipoprotein E locus (APOE). This PGS was also associated with the AD risk in many other populations of diverse ancestries. A cross-ancestry polygenic risk score improved the association with the AD risk in most of the multiancestry populations tested when the APOE region was included. Finally, we found that the PGS/polygenic risk score captured AD-specific information because the association weakened as the diagnosis was broadened. In conclusion, a simple PGS captures the AD-specific genetic information that is common to populations of different ancestries, although studies of more diverse populations are still needed to better characterize the genetics of AD.

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Conflict of interest statement

Competing interests: S.v.d.L. is a recipient of funding from ABOARD, which is a public–private partnership financed by ZonMW (no. 73305095007) and Health–Holland, Topsector Life Sciences & Health (PPP-allowance; no. LSHM20106). C.C. has received research support from GSK and EISAI. The study’s funders had no role in the collection, analysis or interpretation of data; in the writing of the report or in the decision to submit the paper for publication. C.C. is an advisory board member for Vivid Genomics and Circular Genomics and owns stock. L.M.-P. received personal fees from Biogen for consulting activities unrelated to the submitted work. T.G. received consulting fees from AbbVie, Alector, Anavex, Biogen, Cogthera, Eli Lilly, Functional Neuromodulation, Grifols, Iqvia, Janssen, Noselab, Novo Nordisk, NuiCare, Orphanzyme, Roche Diagnostics, Roche Pharma, UCB and Vivoryon; lecture fees from Biogen, Eisai, Grifols, Medical Tribune, Novo Nordisk, Roche Pharma, Schwabe and Synlab; and has received grants to his institution from Biogen, Eisai and Roche Diagnostics. O.A.A. is a consultant to Cortechs and Precision Health AS, and has received speaker’s honoraria from Lundbeck, Sunovion, Otsuka and Janssen. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Associations between the various PGSs and the risk of developing AD as a function of APOE status (25,782 AD cases and 35,280 controls).
a, The risk of developing AD, by PGSALZ stratum (0–2%, 2–5%, 10–20%, 20–40%, 60–80%, 80–90%, 90–95%, 95–98% and 98–100%). The 40–60% PGSALZ stratum was used as the reference. b, Risk of developing AD, by PGSALZ stratum (0–20%, 20–40%, 60–80% and 80–100%) and by APOE genotype (by grouping together the ε2ε2/ε2ε3, ε3ε3, ε2ε4/ε3ε4 and ε4ε4 carriers). The 40–60% PGSALZ stratum was used as the reference. OR per s.d. was calculated by logistic regression adjusted for age, gender, 14 first PCs and chip center if necessary. The lines indicate the 95% CI of each OR. ε2ε2/ε2ε3 carriers (960 AD cases and 3,604 controls), ε3ε3 (15,623 AD cases and 17,782 controls), ε2ε4/ε3ε4 (8,780 AD cases and 6,242 controls) and ε4ε4 carriers (2,309 AD cases and 479 controls).
Fig. 2
Fig. 2. Association of PGSALZ with Aβ42 and p-tau in cerebrospinal fluid.
a–c, Association of PGSALZ with the level of normalized Aβ42 (a) and p-tau (b) in cerebrospinal fluid (n = 13,004) across European ancestry populations and according to PGSALZ strata (0–20%, 20–40%, 60–80% and 80–100%) (c); the 40–60% PGSALZ stratum was used as the reference. β values were calculated by general linear model and logistic regression adjusted for APOE, age, gender, ten first PCs and chip center if necessary. The horizontal lines in the forest plots indicate the 95% CI of each β value. If heterogeneity P (HetP) < 0.05, a random effect is shown for the meta-analysis results. I2; heterogeneity.
Fig. 3
Fig. 3. Association of PGSALZ across multiancestry populations.
a, Association of PGSALZ with the risk of developing AD in multiancestry populations. The European ancestry meta-analysis includes MVP and Australia. The African American ancestry (more than 75% AA ancestry) meta-analysis includes MVP and ADSP. The East Asia meta-analysis includes China, Korea and Japan. The Latin American ancestry (self-reported) meta-analysis includes MVP and ADSP. The South America meta-analysis includes Argentina, Brazil, Chile and Colombia. b, The risk of developing AD, according to PGSALZ (logistic regression adjusted or not for APOE or included APOE variants) strata (0–20%, 20–40%, 60–80% and 80–100%) in multiancestry populations. The 40–60% PGSALZ stratum was used as the reference in each population, and results were meta-analyzed. The European ancestry meta-analysis includes MVP and Australia. The African American ancestry meta-analysis includes MVP and ADSP. The East Asia meta-analysis includes China, Korea and Japan. The Latin American ancestry meta-analysis includes MVP and ADSP. The South America meta-analysis includes Argentina, Brazil, Chile and Colombia. Ncases, number of cases; Ncontrols, number of controls. OR per s.d. was calculated by logistic regression adjusted for APOE, age, sex and specific PCs according to the study (Supplementary Table 2). The lines in the Forest plots indicate the 95% CI of each OR. If HetP < 0.05, a random effect is shown for the meta-analysis results. AA, African American; EUR, European; LA, Latin American.
Fig. 4
Fig. 4. Comparison of the association of PGSALZ or PRS (excluding the APOE region) with the AD risk and the corresponding predictive values (adjusted Nagelkerke R2 and liability R2).
All PGSALZ and PRS values were adjusted for interpopulation differences in distribution; PRSEUR were generated by using only European ancestry summary statistics; PRSCOMB were generated by combining European, African American, Latin American and East Asian ancestry summary statistics. The sparseness parameter was set to 10−8, 10−7, 10−6, 10−5, 10−4, 10−2 or 1. OR per s.d. was calculated by logistic regression adjusted for age, sex and specific PCs according to the study (Supplementary Table 2). MVP EUR (4,561 AD cases and 84,587 controls), MVP LA (375 AD cases and 7,166 controls), MVP AA (713 AD cases and 19,405 controls) and South Korea (1,119 AD cases and 1,172 controls).
Fig. 5
Fig. 5. Association of PRS (including the APOE region) with the AD risk and the corresponding predictive values (adjusted Nagelkerke R2 and liability R2).
All PRS were adjusted for interpopulation differences in distribution; PRSEUR were generated by using only European ancestry summary statistics; PRSCOMB were generated by combining European, African American, Latin American and East Asian ancestry summary statistics. The sparseness parameter was set to 10−8, 10−7, 10−6, 10−5, 10−4, 10−2 or 1. OR per s.d. was calculated by logistic regression adjusted for age, sex and specific PCs according to the study (Supplementary Table 2). MVP EUR (4,561 AD cases and 84,587 controls), MVP LA (375 AD cases and 7,166 controls), MVP AA (713 AD cases and 19,405 controls) and South Korea (1,119 AD cases and 1,172 controls).
Fig. 6
Fig. 6. Association of PGSALZ or PRSCOMB (excluding the APOE region) with AD, AD and related dementia (ADRD) and dementia in MVP, and the corresponding predictive values (adjusted Nagelkerke R2 and liability R2).
PGSALZ and PRSCOMB were adjusted for interpopulation differences in distribution; PRSCOMB were generated by combining European, African American and Latin American and East Asian ancestry summary statistics. The sparseness parameter was set to 10−8 and 10−6. OR per s.d. was calculated by logistic regression adjusted for age, sex and specific PCs according to the study (Supplementary Table 2). MVP EUR (4,561 AD, 17,519 ADRD, 26,473 dementia cases and 84,587 controls), MVP LA (375 AD; 1,527 ADRD; 1,981 dementia cases and 7,166 controls), MVP AA (713 AD; 4,016 ADRD; 4,702 dementia cases and 19,405 controls).
Extended Data Fig. 1
Extended Data Fig. 1. Association of PGSALZ with the risk of developing AD (a) in 17 European countries and (b) in Men and Women.
Ncases, number of cases; Ncontrols, number of controls; OR, Odds ratio per Standard deviation were calculated using logistic regressions adjusted for age, gender and PCs according to the population studied (Supplementary Table 2). The lines in the Forest plots indicate the 95% confidence interval for the ORs.
Extended Data Fig. 2
Extended Data Fig. 2. Associations between (a) PGSALZ or (b) PGSALZ adjusted for APOE and age at onset of AD in European countries.
Ncases, the number of cases. Since HetP <0.05, the random effect is shown for the meta-analysis results. βs were calculated using a general linear model adjusted for APOE, gender and PCs according to the population studied (Supplementary Table 2).
Extended Data Fig. 3
Extended Data Fig. 3. Distribution and association of APOE ε2/ε3/ε4 alleles with AD risk worldwide.
(a) World map showing the populations analyzed. A color gradient indicates the strength of the association between APOE ε2/ε3/ε4 alleles and the risk of developing AD in different countries (b) frequencies of APOE ε2/ε3/ε4 alleles in case and controls as well association of APOE ε4 alleles with the risk of developing AD in different countries. OR, Odds ratio were calculated using logistic regressions adjusted for age, gender and PCs according to the population studied (Supplementary Table 2). Sample sizes are reported in Supplementary Table 2. The map was generated using ggplot2 and royalty-free data from rnaturalearth (https://www.naturalearthdata.com/about/terms-of-use/).
Extended Data Fig. 4
Extended Data Fig. 4. Association between (a) PGSALZ or (b) PGSALZ (adjusted for APOE) and age at onset of AD in multi-ancestry populations.
Ncases, number of cases. The African-American-ancestry meta-analysis (more than 75% of the population with African-American ancestry) included the MVP and ADSP datasets. The East Asia meta-analysis included datasets from China, Korea, and Japan. The Latin American (LA) ancestry (self-reporting) meta-analysis included the MMVP and ADSP datasets. The South America meta-analysis included the datasets from Argentina, Brazil, Chile, and Colombia. * not used in the meta-analysis. βs were calculated using a general linear model adjusted for gender and PCs according to the population studied (Supplementary Table 2).
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