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A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

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Harvard

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium & Werge, TM 2021, 'A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts', Biological Psychiatry, bind 90, nr. 9, s. 611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

APA

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, & Werge, T. M. (2021). A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry, 90(9), 611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

CBE

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Werge TM. 2021. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry. 90(9):611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

MLA

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium og Thomas Mears Werge. "A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts". Biological Psychiatry. 2021, 90(9). 611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

Vancouver

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Werge TM. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry. 2021 nov 1;90(9):611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

Author

Schizophrenia Working Group of the Psychiatric Genomics Consortium ; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium ; Werge, Thomas Mears. / A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. I: Biological Psychiatry. 2021 ; Bind 90, Nr. 9. s. 611-620.

Bibtex

@article{0c93ff596ee043a99e21c49cd03059c2,
title = "A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts",
abstract = "BACKGROUND: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.METHODS: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.RESULTS: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.CONCLUSIONS: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.",
keywords = "Lassosum, LDpred2, Major depressive disorder, MegaPRS, Polygenic scores, PRS-CS, Psychiatric disorders, Risk prediction, SBayesR, Schizophrenia",
author = "Guiyan Ni and Jian Zeng and Revez, {Joana A} and Ying Wang and Zhili Zheng and Tian Ge and Restuadi Restuadi and Jacqueline Kiewa and Nyholt, {Dale R} and Coleman, {Jonathan R I} and Smoller, {Jordan W} and Jian Yang and Visscher, {Peter M} and Wray, {Naomi R} and {Schizophrenia Working Group of the Psychiatric Genomics Consortium} and {Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium} and Werge, {Thomas Mears}",
note = "Copyright {\textcopyright} 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.",
year = "2021",
month = nov,
day = "1",
doi = "10.1016/j.biopsych.2021.04.018",
language = "English",
volume = "90",
pages = "611--620",
journal = "Biological Psychiatry",
issn = "0006-3223",
publisher = "Elsevier Inc",
number = "9",

}

RIS

TY - JOUR

T1 - A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

AU - Ni, Guiyan

AU - Zeng, Jian

AU - Revez, Joana A

AU - Wang, Ying

AU - Zheng, Zhili

AU - Ge, Tian

AU - Restuadi, Restuadi

AU - Kiewa, Jacqueline

AU - Nyholt, Dale R

AU - Coleman, Jonathan R I

AU - Smoller, Jordan W

AU - Yang, Jian

AU - Visscher, Peter M

AU - Wray, Naomi R

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

AU - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

AU - Werge, Thomas Mears

N1 - Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

PY - 2021/11/1

Y1 - 2021/11/1

N2 - BACKGROUND: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.METHODS: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.RESULTS: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.CONCLUSIONS: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

AB - BACKGROUND: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.METHODS: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.RESULTS: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.CONCLUSIONS: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

KW - Lassosum

KW - LDpred2

KW - Major depressive disorder

KW - MegaPRS

KW - Polygenic scores

KW - PRS-CS

KW - Psychiatric disorders

KW - Risk prediction

KW - SBayesR

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85107783986&partnerID=8YFLogxK

U2 - 10.1016/j.biopsych.2021.04.018

DO - 10.1016/j.biopsych.2021.04.018

M3 - Journal article

C2 - 34304866

VL - 90

SP - 611

EP - 620

JO - Biological Psychiatry

JF - Biological Psychiatry

SN - 0006-3223

IS - 9

ER -

ID: 67622255