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Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. / Wang, Ying; Namba, Shinichi; Lopera-Maya, Esteban A.; Kerminen, Sini; Tsuo, Kristin; Läll, Kristi; Kanai, Masahiro; Zhou, Wei; Wu, Kuan Han H.; Favé, Marie Julie; Bhatta, Laxmi; Awadalla, Philip; Brumpton, Ben M.; Deelen, Patrick; Hveem, Kristian; Lo Faro, Valeria; Mägi, Reedik; Murakami, Yoshinori; Sanna, Serena; Smoller, Jordan W.; Uzunovic, Jasmina; Wolford, Brooke N.; Wu, Kuan Han H.; Rasheed, Humaira; Hirbo, Jibril B.; Bhattacharya, Arjun; Zhao, Huiling; Surakka, Ida; Lopera-Maya, Esteban A.; Chapman, Sinéad B.; Karjalainen, Juha; Kurki, Mitja; Mutaamba, Maasha; Partanen, Juulia J.; Brumpton, Ben M.; Chavan, Sameer; Chen, Tzu Ting; Daya, Michelle; Ding, Yi; Feng, Yen Chen A.; Gignoux, Christopher R.; Graham, Sarah E.; Hornsby, Whitney E.; Ingold, Nathan; Johnson, Ruth; Laisk, Triin; Lin, Kuang; Lv, Jun; Millwood, Iona Y.; Loos, Ruth J.F.; BBJ; BioMe; BioVU; Canadian Partnership for Tomorrow's Health/OHS; China Kadoorie Biobank Collaborative Group; Colorado Center for Personalized Medicine; deCODE Genetics; ESTBB; FinnGen; Generation Scotland; Genes & Health; LifeLines; Mass General Brigham Biobank; Michigan Genomics Initiative; QIMR Berghofer Biobank; Taiwan Biobank; The HUNT Study; UCLA ATLAS Community Health Initiative; UKBB.
In:
Cell Genomics, Vol. 3, No. 1, 100241, 2023.
Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
Wang, Y, Namba, S, Lopera-Maya, EA, Kerminen, S, Tsuo, K, Läll, K, Kanai, M, Zhou, W, Wu, KHH, Favé, MJ, Bhatta, L, Awadalla, P, Brumpton, BM, Deelen, P, Hveem, K, Lo Faro, V, Mägi, R, Murakami, Y, Sanna, S, Smoller, JW, Uzunovic, J, Wolford, BN, Wu, KHH, Rasheed, H, Hirbo, JB, Bhattacharya, A, Zhao, H, Surakka, I, Lopera-Maya, EA, Chapman, SB, Karjalainen, J, Kurki, M, Mutaamba, M, Partanen, JJ, Brumpton, BM, Chavan, S, Chen, TT, Daya, M, Ding, Y, Feng, YCA, Gignoux, CR, Graham, SE, Hornsby, WE, Ingold, N, Johnson, R, Laisk, T, Lin, K, Lv, J, Millwood, IY
, Loos, RJF, BBJ, BioMe, BioVU, Canadian Partnership for Tomorrow's Health/OHS, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, ESTBB, FinnGen, Generation Scotland, Genes & Health, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, QIMR Berghofer Biobank, Taiwan Biobank, The HUNT Study, UCLA ATLAS Community Health Initiative & UKBB 2023, '
Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts',
Cell Genomics, vol. 3, no. 1, 100241.
https://doi.org/10.1016/j.xgen.2022.100241APA
Wang, Y., Namba, S., Lopera-Maya, E. A., Kerminen, S., Tsuo, K., Läll, K., Kanai, M., Zhou, W., Wu, K. H. H., Favé, M. J., Bhatta, L., Awadalla, P., Brumpton, B. M., Deelen, P., Hveem, K., Lo Faro, V., Mägi, R., Murakami, Y., Sanna, S., ... UKBB (2023).
Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.
Cell Genomics,
3(1), [100241].
https://doi.org/10.1016/j.xgen.2022.100241Vancouver
Wang Y, Namba S, Lopera-Maya EA, Kerminen S, Tsuo K, Läll K et al.
Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.
Cell Genomics. 2023;3(1). 100241.
https://doi.org/10.1016/j.xgen.2022.100241Author
Wang, Ying ; Namba, Shinichi ; Lopera-Maya, Esteban A. ; Kerminen, Sini ; Tsuo, Kristin ; Läll, Kristi ; Kanai, Masahiro ; Zhou, Wei ; Wu, Kuan Han H. ; Favé, Marie Julie ; Bhatta, Laxmi ; Awadalla, Philip ; Brumpton, Ben M. ; Deelen, Patrick ; Hveem, Kristian ; Lo Faro, Valeria ; Mägi, Reedik ; Murakami, Yoshinori ; Sanna, Serena ; Smoller, Jordan W. ; Uzunovic, Jasmina ; Wolford, Brooke N. ; Wu, Kuan Han H. ; Rasheed, Humaira ; Hirbo, Jibril B. ; Bhattacharya, Arjun ; Zhao, Huiling ; Surakka, Ida ; Lopera-Maya, Esteban A. ; Chapman, Sinéad B. ; Karjalainen, Juha ; Kurki, Mitja ; Mutaamba, Maasha ; Partanen, Juulia J. ; Brumpton, Ben M. ; Chavan, Sameer ; Chen, Tzu Ting ; Daya, Michelle ; Ding, Yi ; Feng, Yen Chen A. ; Gignoux, Christopher R. ; Graham, Sarah E. ; Hornsby, Whitney E. ; Ingold, Nathan ; Johnson, Ruth ; Laisk, Triin ; Lin, Kuang ; Lv, Jun ; Millwood, Iona Y. ; Loos, Ruth J.F. ; BBJ ; BioMe ; BioVU ; Canadian Partnership for Tomorrow's Health/OHS ; China Kadoorie Biobank Collaborative Group ; Colorado Center for Personalized Medicine ; deCODE Genetics ; ESTBB ; FinnGen ; Generation Scotland ; Genes & Health ; LifeLines ; Mass General Brigham Biobank ; Michigan Genomics Initiative ; QIMR Berghofer Biobank ; Taiwan Biobank ; The HUNT Study ; UCLA ATLAS Community Health Initiative ; UKBB. / Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. In: Cell Genomics. 2023 ; Vol. 3, No. 1.
Bibtex
@article{f71c1e9796f14a81b706c1af9339b101,
title = "Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts",
abstract = "Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.",
keywords = "accuracy heterogeneity, Global-Biobank Meta-analysis Initiative, multi-ancestry genetic prediction, polygenic risk scores",
author = "Ying Wang and Shinichi Namba and Lopera-Maya, {Esteban A.} and Sini Kerminen and Kristin Tsuo and Kristi L{\"a}ll and Masahiro Kanai and Wei Zhou and Wu, {Kuan Han H.} and Fav{\'e}, {Marie Julie} and Laxmi Bhatta and Philip Awadalla and Brumpton, {Ben M.} and Patrick Deelen and Kristian Hveem and {Lo Faro}, Valeria and Reedik M{\"a}gi and Yoshinori Murakami and Serena Sanna and Smoller, {Jordan W.} and Jasmina Uzunovic and Wolford, {Brooke N.} and Wu, {Kuan Han H.} and Humaira Rasheed and Hirbo, {Jibril B.} and Arjun Bhattacharya and Huiling Zhao and Ida Surakka and Lopera-Maya, {Esteban A.} and Chapman, {Sin{\'e}ad B.} and Juha Karjalainen and Mitja Kurki and Maasha Mutaamba and Partanen, {Juulia J.} and Brumpton, {Ben M.} and Sameer Chavan and Chen, {Tzu Ting} and Michelle Daya and Yi Ding and Feng, {Yen Chen A.} and Gignoux, {Christopher R.} and Graham, {Sarah E.} and Hornsby, {Whitney E.} and Nathan Ingold and Ruth Johnson and Triin Laisk and Kuang Lin and Jun Lv and Millwood, {Iona Y.} and Loos, {Ruth J.F.} and BBJ and BioMe and BioVU and {Canadian Partnership for Tomorrow's Health/OHS} and {China Kadoorie Biobank Collaborative Group} and {Colorado Center for Personalized Medicine} and {deCODE Genetics} and ESTBB and FinnGen and {Generation Scotland} and {Genes & Health} and LifeLines and {Mass General Brigham Biobank} and {Michigan Genomics Initiative} and {QIMR Berghofer Biobank} and {Taiwan Biobank} and {The HUNT Study} and {UCLA ATLAS Community Health Initiative} and UKBB",
note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2023",
doi = "10.1016/j.xgen.2022.100241",
language = "English",
volume = "3",
journal = "Cell Genomics",
issn = "2666-979x",
publisher = "Elsevier",
number = "1",
}
RIS
TY - JOUR
T1 - Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
AU - Wang, Ying
AU - Namba, Shinichi
AU - Lopera-Maya, Esteban A.
AU - Kerminen, Sini
AU - Tsuo, Kristin
AU - Läll, Kristi
AU - Kanai, Masahiro
AU - Zhou, Wei
AU - Wu, Kuan Han H.
AU - Favé, Marie Julie
AU - Bhatta, Laxmi
AU - Awadalla, Philip
AU - Brumpton, Ben M.
AU - Deelen, Patrick
AU - Hveem, Kristian
AU - Lo Faro, Valeria
AU - Mägi, Reedik
AU - Murakami, Yoshinori
AU - Sanna, Serena
AU - Smoller, Jordan W.
AU - Uzunovic, Jasmina
AU - Wolford, Brooke N.
AU - Wu, Kuan Han H.
AU - Rasheed, Humaira
AU - Hirbo, Jibril B.
AU - Bhattacharya, Arjun
AU - Zhao, Huiling
AU - Surakka, Ida
AU - Lopera-Maya, Esteban A.
AU - Chapman, Sinéad B.
AU - Karjalainen, Juha
AU - Kurki, Mitja
AU - Mutaamba, Maasha
AU - Partanen, Juulia J.
AU - Brumpton, Ben M.
AU - Chavan, Sameer
AU - Chen, Tzu Ting
AU - Daya, Michelle
AU - Ding, Yi
AU - Feng, Yen Chen A.
AU - Gignoux, Christopher R.
AU - Graham, Sarah E.
AU - Hornsby, Whitney E.
AU - Ingold, Nathan
AU - Johnson, Ruth
AU - Laisk, Triin
AU - Lin, Kuang
AU - Lv, Jun
AU - Millwood, Iona Y.
AU - Loos, Ruth J.F.
AU - BBJ
AU - BioMe
AU - BioVU
AU - Canadian Partnership for Tomorrow's Health/OHS
AU - China Kadoorie Biobank Collaborative Group
AU - Colorado Center for Personalized Medicine
AU - deCODE Genetics
AU - ESTBB
AU - FinnGen
AU - Generation Scotland
AU - Genes & Health
AU - LifeLines
AU - Mass General Brigham Biobank
AU - Michigan Genomics Initiative
AU - QIMR Berghofer Biobank
AU - Taiwan Biobank
AU - The HUNT Study
AU - UCLA ATLAS Community Health Initiative
AU - UKBB
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2023
Y1 - 2023
N2 - Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.
AB - Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.
KW - accuracy heterogeneity
KW - Global-Biobank Meta-analysis Initiative
KW - multi-ancestry genetic prediction
KW - polygenic risk scores
U2 - 10.1016/j.xgen.2022.100241
DO - 10.1016/j.xgen.2022.100241
M3 - Journal article
C2 - 36777179
AN - SCOPUS:85147104921
VL - 3
JO - Cell Genomics
JF - Cell Genomics
SN - 2666-979x
IS - 1
M1 - 100241
ER -
