Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

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

doi:10.1016/j.xgen.2022.100241

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

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

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

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.

Original language	English (US)
Article number	100241
Journal	Cell Genomics
Volume	3
Issue number	1
DOIs	https://doi.org/10.1016/j.xgen.2022.100241
State	Published - Jan 11 2023
Externally published	Yes

Keywords

accuracy heterogeneity
Global-Biobank Meta-analysis Initiative
multi-ancestry genetic prediction
polygenic risk scores

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology (miscellaneous)
Genetics

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10.1016/j.xgen.2022.100241

Cite this

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, 3(1), Article 100241. https://doi.org/10.1016/j.xgen.2022.100241

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.2022.100241

@article{540ed31c064c4957910c286d111cc58f,

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 = "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 and Ying Wang and Shinichi Namba and Esteban Lopera and Sini Kerminen and Kristin Tsuo and Kristi L{\"a}ll and Masahiro Kanai and Wei Zhou and Wu, {Kuan Han} and Fav{\'e}, {Marie Julie} and Laxmi Bhatta and Philip Awadalla and Ben Brumpton 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 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 Priit Palta and Anita Pandit",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2023",

month = jan,

day = "11",

doi = "10.1016/j.xgen.2022.100241",

language = "English (US)",

volume = "3",

journal = "Cell Genomics",

issn = "2666-979X",

publisher = "Cell Press",

number = "1",

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TY - JOUR

T1 - Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

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

AU - Wang, Ying

AU - Namba, Shinichi

AU - Lopera, Esteban

AU - Kerminen, Sini

AU - Tsuo, Kristin

AU - Läll, Kristi

AU - Kanai, Masahiro

AU - Zhou, Wei

AU - Wu, Kuan Han

AU - Favé, Marie Julie

AU - Bhatta, Laxmi

AU - Awadalla, Philip

AU - Brumpton, Ben

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 - 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 - Palta, Priit

AU - Pandit, Anita

PY - 2023/1/11

Y1 - 2023/1/11

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

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UR - http://www.scopus.com/inward/citedby.url?scp=85147104921&partnerID=8YFLogxK

U2 - 10.1016/j.xgen.2022.100241

DO - 10.1016/j.xgen.2022.100241

M3 - Article

C2 - 36777179

AN - SCOPUS:85147104921

SN - 2666-979X

VL - 3

JO - Cell Genomics

JF - Cell Genomics

IS - 1

M1 - 100241

ER -

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Abstract

Keywords

ASJC Scopus subject areas

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