Медицинская библиотека · medRxiv : the preprint server for health sciences · 2026

PRANA: A Deep Learning Method for Adapting Polygenic Risk Scores to Diverse Ethnic Groups.

Levi Hagai, Wang Qin, Bolla Manjeet K, Dennis Joe, Andrulis Irene L, Antonenkova Natalia, Au Chun Hang, Augustinsson Annelie, Beane Freeman Laura E, Behrens Sabine, Bermisheva Marina, Bodelon Clara, Bogdanova Natalia V, Bojesen Stig E, Brenner Hermann, Brock Ian W, Brüning Thomas, Byers Helen, Camp Nicola J, Castelao Jose E, Choi Ji-Yeob, Chung Wendy K, NBCS Collaborators, Colonna Sarah V, Couch Fergus J, Czene Kamila, Daly Mary B, Devilee Peter, Dörk Thilo, Eliassen A Heather, Eriksson Mikael, Evans D Gareth, Fasching Peter A, Faw Kierstin, Gago-Dominguez Manuela, García-Closas Montserrat, Haiman Christopher A, Hamann Ute, Hartman Mikael, Ho Vikki, Ho Peh Joo, Hooning Maartje J, Hoppe Reiner, Howell Sacha J, ABCTB Investigators, SGBCC Investigators, Ito Hidemi, Iwasaki Motoki, Jakubowska Anna, Jernström Helena, Joseph Vijai, Kaaks Rudolf, Kang Daehee, Khusnutdinova Elza K, Kiprijanovska Sanja, Koutros Stella, Kristensen Vessela N, Kurian Allison W, Kwong Ava, Lambrechts Diether, Larson Nicole L, Marchand Loic Le, Li Shuai, Li Jingmei, Lophatananon Artitaya, Mannermaa Arto, Matsuo Keitaro, Maxwell Whitney, Muir Kenneth, Nevelsteen Ines, Obi Nadia, O'Brien Katie M, Offit Kenneth, Ottewell Penelope D, Patel Alpa V, Peterlongo Paolo, Plaseska-Karanfilska Dijana, Prajzendanc Karolina, Radice Paolo, Ramachandran Dhanya, Rashid Muhammad U, Romero Atocha, Saloustros Emmanouil, Sandler Dale P, Schöttker Ben, Simard Jacques, Southey Melissa C, Stone Jennifer, Sugier Pierre-Emmanuel, Taylor Jack A, Teras Lauren R, Truong Thérèse, Weinberg Clarice R, Yadav Siddhartha, Yamaji Taiki, Zheng Wei, Dunning Alison M, Mavaddat Nasim, Easton Douglas F, Michailidou Kyriaki, Elkon Ran, Shamir Ron

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Аннотация

Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke's R2, and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Для цитирования: Levi Hagai, Wang Qin, Bolla Manjeet K, Dennis Joe, Andrulis Irene L, Antonenkova Natalia, Au Chun Hang, Augustinsson Annelie, Beane Freeman Laura E, Behrens Sabine, Bermisheva Marina, Bodelon Clara, Bogdanova Natalia V, Bojesen Stig E, Brenner Hermann, Brock Ian W, Brüning Thomas, Byers Helen, Camp Nicola J, Castelao Jose E, Choi Ji-Yeob, Chung Wendy K, NBCS Collaborators, Colonna Sarah V, Couch Fergus J, Czene Kamila, Daly Mary B, Devilee Peter, Dörk Thilo, Eliassen A Heather, Eriksson Mikael, Evans D Gareth, Fasching Peter A, Faw Kierstin, Gago-Dominguez Manuela, García-Closas Montserrat, Haiman Christopher A, Hamann Ute, Hartman Mikael, Ho Vikki, Ho Peh Joo, Hooning Maartje J, Hoppe Reiner, Howell Sacha J, ABCTB Investigators, SGBCC Investigators, Ito Hidemi, Iwasaki Motoki, Jakubowska Anna, Jernström Helena, Joseph Vijai, Kaaks Rudolf, Kang Daehee, Khusnutdinova Elza K, Kiprijanovska Sanja, Koutros Stella, Kristensen Vessela N, Kurian Allison W, Kwong Ava, Lambrechts Diether, Larson Nicole L, Marchand Loic Le, Li Shuai, Li Jingmei, Lophatananon Artitaya, Mannermaa Arto, Matsuo Keitaro, Maxwell Whitney, Muir Kenneth, Nevelsteen Ines, Obi Nadia, O'Brien Katie M, Offit Kenneth, Ottewell Penelope D, Patel Alpa V, Peterlongo Paolo, Plaseska-Karanfilska Dijana, Prajzendanc Karolina, Radice Paolo, Ramachandran Dhanya, Rashid Muhammad U, Romero Atocha, Saloustros Emmanouil, Sandler Dale P, Schöttker Ben, Simard Jacques, Southey Melissa C, Stone Jennifer, Sugier Pierre-Emmanuel, Taylor Jack A, Teras Lauren R, Truong Thérèse, Weinberg Clarice R, Yadav Siddhartha, Yamaji Taiki, Zheng Wei, Dunning Alison M, Mavaddat Nasim, Easton Douglas F, Michailidou Kyriaki, Elkon Ran, Shamir Ron. PRANA: A Deep Learning Method for Adapting Polygenic Risk Scores to Diverse Ethnic Groups.. medRxiv : the preprint server for health sciences. 2026. DOI: 10.64898/2026.07.12.26357860.

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