Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability

Xiaotong Wang; Alicia Walker; Joana A. Revez; Guiyan Ni; Mark J. Adams; Andrew M. McIntosh; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

doi:10.1016/j.ajhg.2023.06.006

Polygenic risk prediction: why and when out-of-sample prediction R² can exceed SNP-based heritability

Xiaotong Wang, Alicia Walker, Joana A. Revez, Guiyan Ni, Mark J. Adams, Andrew M. McIntosh, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

5 Citationer (Scopus)

Abstract

In polygenic score (PGS) analysis, the coefficient of determination (R²) is a key statistic to evaluate efficacy. R² is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (h_SNP², the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R². However, in real data analyses R² has been reported to exceed h_SNP², which occurs in parallel with the observation that h_SNP² estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific h_SNP² exist, or if genetic correlations between cohorts are less than one, h_SNP² estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R² will be greater than h_SNP² and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

Originalsprog	Engelsk
Tidsskrift	American Journal of Human Genetics
Vol/bind	110
Nummer	7
Sider (fra-til)	1207-1215
Antal sider	9
ISSN	0002-9297
DOI	https://doi.org/10.1016/j.ajhg.2023.06.006
Status	Udgivet - jul. 2023

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Citationsformater

@article{3554a23a4eac4827898b689748b1876f,

title = "Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability",

abstract = "In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.",

keywords = "meta-analysis, out-of-sample prediction R, polygenic risk prediction, SNP-based heritability, Genome-Wide Association Study, Polymorphism, Single Nucleotide/genetics, Phenotype, Humans, Computer Simulation, Multifactorial Inheritance/genetics",

author = "Xiaotong Wang and Alicia Walker and Revez, {Joana A.} and Guiyan Ni and Adams, {Mark J.} and McIntosh, {Andrew M.} and Wray, {Naomi R.} and Stephan Ripke and Manuel Mattheisen and Maciej Trzaskowski and Byrne, {Enda M.} and Abdel Abdellaoui and Esben Agerbo and Air, {Tracy M.} and Andlauer, {Till F.M.} and Bacanu, {Silviu Alin} and Marie B{\ae}kvad-Hansen and Beekman, {Aartjan T.F.} and Bigdeli, {Tim B.} and Binder, {Elisabeth B.} and Julien Bryois and Buttensch{\o}n, {Henriette N.} and Jonas Bybjerg-Grauholm and Na Cai and Enrique Castelao and Christensen, {Jane Hvarregaard} and Clarke, {Toni Kim} and Coleman, {Jonathan R.I.} and Luc{\'i}a Colodro-Conde and Baptiste Couvy-Duchesne and Nick Craddock and Crawford, {Gregory E.} and Gail Davies and Franziska Degenhardt and Derks, {Eske M.} and Nese Direk and Dolan, {Conor V.} and Dunn, {Erin C.} and Eley, {Thalia C.} and Jakob Grove and {B{\o}cker Pedersen}, Carsten and {Gi{\o}rtz Pedersen}, Marianne and Per Qvist and Ole Mors and {Bo Mortensen}, Preben and B{\o}rglum, {Anders D.} and {Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium}",

year = "2023",

month = jul,

doi = "10.1016/j.ajhg.2023.06.006",

language = "English",

volume = "110",

pages = "1207--1215",

journal = "American Journal of Human Genetics",

issn = "0002-9297",

publisher = "Cell Press",

number = "7",

}

Polygenic risk prediction: why and when out-of-sample prediction R² can exceed SNP-based heritability. / Wang, Xiaotong; Walker, Alicia; Revez, Joana A. et al.
I: American Journal of Human Genetics, Bind 110, Nr. 7, 07.2023, s. 1207-1215.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Polygenic risk prediction

T2 - why and when out-of-sample prediction R2 can exceed SNP-based heritability

AU - Wang, Xiaotong

AU - Walker, Alicia

AU - Revez, Joana A.

AU - Ni, Guiyan

AU - Adams, Mark J.

AU - McIntosh, Andrew M.

AU - Wray, Naomi R.

AU - Ripke, Stephan

AU - Mattheisen, Manuel

AU - Trzaskowski, Maciej

AU - Byrne, Enda M.

AU - Abdellaoui, Abdel

AU - Agerbo, Esben

AU - Air, Tracy M.

AU - Andlauer, Till F.M.

AU - Bacanu, Silviu Alin

AU - Bækvad-Hansen, Marie

AU - Beekman, Aartjan T.F.

AU - Bigdeli, Tim B.

AU - Binder, Elisabeth B.

AU - Bryois, Julien

AU - Buttenschøn, Henriette N.

AU - Bybjerg-Grauholm, Jonas

AU - Cai, Na

AU - Castelao, Enrique

AU - Christensen, Jane Hvarregaard

AU - Clarke, Toni Kim

AU - Coleman, Jonathan R.I.

AU - Colodro-Conde, Lucía

AU - Couvy-Duchesne, Baptiste

AU - Craddock, Nick

AU - Crawford, Gregory E.

AU - Davies, Gail

AU - Degenhardt, Franziska

AU - Derks, Eske M.

AU - Direk, Nese

AU - Dolan, Conor V.

AU - Dunn, Erin C.

AU - Eley, Thalia C.

AU - Grove, Jakob

AU - Bøcker Pedersen, Carsten

AU - Giørtz Pedersen, Marianne

AU - Qvist, Per

AU - Mors, Ole

AU - Bo Mortensen, Preben

AU - Børglum, Anders D.

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

PY - 2023/7

Y1 - 2023/7

N2 - In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

AB - In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

KW - meta-analysis

KW - out-of-sample prediction R

KW - polygenic risk prediction

KW - SNP-based heritability

KW - Genome-Wide Association Study

KW - Polymorphism, Single Nucleotide/genetics

KW - Phenotype

KW - Humans

KW - Computer Simulation

KW - Multifactorial Inheritance/genetics

UR - http://www.scopus.com/inward/record.url?scp=85164270154&partnerID=8YFLogxK

U2 - 10.1016/j.ajhg.2023.06.006

DO - 10.1016/j.ajhg.2023.06.006

M3 - Journal article

C2 - 37379836

AN - SCOPUS:85164270154

SN - 0002-9297

VL - 110

SP - 1207

EP - 1215

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

IS - 7