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    Rights statement: This is the author’s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001

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Modeling linkage disequilibrium increases accuracy of polygenic risk scores

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Modeling linkage disequilibrium increases accuracy of polygenic risk scores. / Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study.
In: American Journal of Human Genetics, Vol. 97, No. 4, 01.10.2015, p. 576-592.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study 2015, 'Modeling linkage disequilibrium increases accuracy of polygenic risk scores', American Journal of Human Genetics, vol. 97, no. 4, pp. 576-592. https://doi.org/10.1016/j.ajhg.2015.09.001

APA

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics, 97(4), 576-592. https://doi.org/10.1016/j.ajhg.2015.09.001

Vancouver

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics. 2015 Oct 1;97(4):576-592. doi: 10.1016/j.ajhg.2015.09.001

Author

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study. / Modeling linkage disequilibrium increases accuracy of polygenic risk scores. In: American Journal of Human Genetics. 2015 ; Vol. 97, No. 4. pp. 576-592.

Bibtex

@article{f74e69f9119744bcbed206f8ef86ffb6,
title = "Modeling linkage disequilibrium increases accuracy of polygenic risk scores",
abstract = "Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.",
keywords = "Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Models, Theoretical, Multifactorial Inheritance, Multiple Sclerosis, Phenotype, Polymorphism, Single Nucleotide, Prognosis, Quantitative Trait Loci, Schizophrenia",
author = "Vilhj{\'a}lmsson, {Bjarni J.} and Jian Yang and Finucane, {Hilary K.} and Alexander Gusev and Sara Lindstr{\"o}m and Stephan Ripke and Giulio Genovese and Po-Ru Loh and Gaurav Bhatia and Ron Do and Tristan Hayeck and Hong-Hee Won and Sekar Kathiresan and Michele Pato and Carlos Pato and Rulla Tamimi and Eli Stahl and Noah Zaitlen and Bogdan Pasaniuc and Gillian Belbin and Kenny, {Eimear E.} and Schierup, {Mikkel H.} and {De Jager}, Philip and Patsopoulos, {Nikolaos A.} and Steve McCarroll and Mark Daly and Shaun Purcell and Daniel Chasman and Benjamin Neale and Michael Goddard and Visscher, {Peter M.} and Peter Kraft and Nick Patterson and Price, {Alkes L.} and Jo Knight and {Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001",
year = "2015",
month = oct,
day = "1",
doi = "10.1016/j.ajhg.2015.09.001",
language = "English",
volume = "97",
pages = "576--592",
journal = "American Journal of Human Genetics",
issn = "0002-9297",
publisher = "Cell Press",
number = "4",

}

RIS

TY - JOUR

T1 - Modeling linkage disequilibrium increases accuracy of polygenic risk scores

AU - Vilhjálmsson, Bjarni J.

AU - Yang, Jian

AU - Finucane, Hilary K.

AU - Gusev, Alexander

AU - Lindström, Sara

AU - Ripke, Stephan

AU - Genovese, Giulio

AU - Loh, Po-Ru

AU - Bhatia, Gaurav

AU - Do, Ron

AU - Hayeck, Tristan

AU - Won, Hong-Hee

AU - Kathiresan, Sekar

AU - Pato, Michele

AU - Pato, Carlos

AU - Tamimi, Rulla

AU - Stahl, Eli

AU - Zaitlen, Noah

AU - Pasaniuc, Bogdan

AU - Belbin, Gillian

AU - Kenny, Eimear E.

AU - Schierup, Mikkel H.

AU - De Jager, Philip

AU - Patsopoulos, Nikolaos A.

AU - McCarroll, Steve

AU - Daly, Mark

AU - Purcell, Shaun

AU - Chasman, Daniel

AU - Neale, Benjamin

AU - Goddard, Michael

AU - Visscher, Peter M.

AU - Kraft, Peter

AU - Patterson, Nick

AU - Price, Alkes L.

AU - Knight, Jo

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study

N1 - This is the author’s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001

PY - 2015/10/1

Y1 - 2015/10/1

N2 - Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

AB - Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

KW - Genome-Wide Association Study

KW - Genotype

KW - Humans

KW - Linkage Disequilibrium

KW - Models, Theoretical

KW - Multifactorial Inheritance

KW - Multiple Sclerosis

KW - Phenotype

KW - Polymorphism, Single Nucleotide

KW - Prognosis

KW - Quantitative Trait Loci

KW - Schizophrenia

U2 - 10.1016/j.ajhg.2015.09.001

DO - 10.1016/j.ajhg.2015.09.001

M3 - Journal article

C2 - 26430803

VL - 97

SP - 576

EP - 592

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

SN - 0002-9297

IS - 4

ER -