TY - JOUR
T1 - Polygenic score distribution differences across European ancestry populations
T2 - implications for breast cancer risk prediction
AU - NBCS Collaborators
AU - ABCTB Investigators
AU - kConFab Investigators
AU - Yiangou, Kristia
AU - Mavaddat, Nasim
AU - Dennis, Joe
AU - Zanti, Maria
AU - Wang, Qin
AU - Bolla, Manjeet K.
AU - Abubakar, Mustapha
AU - Ahearn, Thomas U.
AU - Andrulis, Irene L.
AU - Anton-Culver, Hoda
AU - Antonenkova, Natalia N.
AU - Arndt, Volker
AU - Aronson, Kristan J.
AU - Augustinsson, Annelie
AU - Baten, Adinda
AU - Behrens, Sabine
AU - Bermisheva, Marina
AU - de Gonzalez, Amy Berrington
AU - Białkowska, Katarzyna
AU - Boddicker, Nicholas
AU - Bodelon, Clara
AU - Bogdanova, Natalia V.
AU - Bojesen, Stig E.
AU - Brantley, Kristen D.
AU - Brauch, Hiltrud
AU - Brenner, Hermann
AU - Camp, Nicola J.
AU - Canzian, Federico
AU - Castelao, Jose E.
AU - Cessna, Melissa H.
AU - Chang-Claude, Jenny
AU - Chenevix-Trench, Georgia
AU - Chung, Wendy K.
AU - Colonna, Sarah V.
AU - Couch, Fergus J.
AU - Cox, Angela
AU - Cross, Simon S.
AU - Czene, Kamila
AU - Daly, Mary B.
AU - Devilee, Peter
AU - Dörk, Thilo
AU - Dunning, Alison M.
AU - Eccles, Diana M.
AU - Eliassen, A. Heather
AU - Engel, Christoph
AU - Eriksson, Mikael
AU - Evans, D. Gareth
AU - Fasching, Peter A.
AU - Fletcher, Olivia
AU - Flyger, Henrik
AU - Fritschi, Lin
AU - Gago-Dominguez, Manuela
AU - Gentry-Maharaj, Aleksandra
AU - González-Neira, Anna
AU - Guénel, Pascal
AU - Hahnen, Eric
AU - Haiman, Christopher A.
AU - Hamann, Ute
AU - Hartikainen, Jaana M.
AU - Ho, Vikki
AU - Hodge, James
AU - Hollestelle, Antoinette
AU - Honisch, Ellen
AU - Hooning, Maartje J.
AU - Hoppe, Reiner
AU - Hopper, John L.
AU - Howell, Sacha
AU - Howell, Anthony
AU - Jakovchevska, Simona
AU - Jakubowska, Anna
AU - Jernström, Helena
AU - Johnson, Nichola
AU - Kaaks, Rudolf
AU - Khusnutdinova, Elza K.
AU - Kitahara, Cari M.
AU - Koutros, Stella
AU - Kristensen, Vessela N.
AU - Lacey, James V.
AU - Lambrechts, Diether
AU - Lejbkowicz, Flavio
AU - Lindblom, Annika
AU - Lush, Michael
AU - Mannermaa, Arto
AU - Mavroudis, Dimitrios
AU - Menon, Usha
AU - Murphy, Rachel A.
AU - Nevanlinna, Heli
AU - Obi, Nadia
AU - Offit, Kenneth
AU - Park-Simon, Tjoung Won
AU - Patel, Alpa V.
AU - Peng, Cheng
AU - Peterlongo, Paolo
AU - Pita, Guillermo
AU - Plaseska-Karanfilska, Dijana
AU - Pylkäs, Katri
AU - Radice, Paolo
AU - Rashid, Muhammad U.
AU - Rennert, Gad
AU - Roberts, Eleanor
AU - Rodriguez, Juan
AU - Romero, Atocha
AU - Rosenberg, Efraim H.
AU - Saloustros, Emmanouil
AU - Sandler, Dale P.
AU - Sawyer, Elinor J.
AU - Schmutzler, Rita K.
AU - Scott, Christopher G.
AU - Shu, Xiao Ou
AU - Southey, Melissa C.
AU - Stone, Jennifer
AU - Taylor, Jack A.
AU - Teras, Lauren R.
AU - van de Beek, Irma
AU - Willett, Walter
AU - Winqvist, Robert
AU - Zheng, Wei
AU - Vachon, Celine M.
AU - Schmidt, Marjanka K.
AU - Hall, Per
AU - MacInnis, Robert J.
AU - Milne, Roger L.
AU - Pharoah, Paul D.P.
AU - Simard, Jacques
AU - Antoniou, Antonis C.
AU - Easton, Douglas F.
AU - Michailidou, Kyriaki
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - Background: The 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Methods: We explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction. Results: The mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment. Conclusions: Our results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.
AB - Background: The 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Methods: We explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction. Results: The mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment. Conclusions: Our results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.
KW - Breast cancer
KW - Polygenic risk scores
KW - Risk calibration
KW - Risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85213697828&partnerID=8YFLogxK
U2 - 10.1186/s13058-024-01947-x
DO - 10.1186/s13058-024-01947-x
M3 - Article
C2 - 39734228
AN - SCOPUS:85213697828
SN - 1465-5411
VL - 26
JO - Breast Cancer Research
JF - Breast Cancer Research
IS - 1
M1 - 189
ER -