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Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

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 associati... Full description

Journal Title: American journal of human genetics 2015-10-01, Vol.97 (4), p.576-592
Main Author: Ripke, Stephan
Other Authors: Neale, Benjamin M , Holmans, Peter A , Collier, David A , Agartz, Ingrid , Albus, Margot , Bacanu, Silviu A , Bene, Judit , Bevilacqua, Elizabeth , Bruggeman, Richard , Buckner, Randy L , Chan, Raymond C.K , Cheng, Wei , Cohen, Nadine , Curtis, David , Dikeos, Dimitris , Dinan, Timothy , Eriksson, Johan , Escott-Price, Valentina , Farrell, Martilias S , Franke, Lude , Gershon, Elliot S , Godard, Stephanie , de Haan, Lieuwe , Henskens, Frans A , Hoffmann, Per , Hofman, Andrea , Ikeda, Masashi , Keller, Matthew C , Kim, Yunjung , Knowles, James A , Kucinskas, Vaidutis , Laurent, Claudine , Limborska, Svetlana , Macek, Milan , Maher, Brion S , Marsal, Sara , Mattheisen, Manuel , McIntosh, Andrew M , Meijer, Carin J , Michie, Patricia T , Milanova, Vihra , Mors, Ole , Murray, Robin M , Olsen, Line , Papadimitriou, George N , Parkhomenko, Elena , Pato, Michele T , Paunio, Tiina , Pulver, Ann E , Reichenberg, Abraham , Roffman, Joshua L , Sanders, Alan R , Scolnick, Edward M , Sigurdsson, Engilbert , Silverman, Jeremy M , So, Hon-Cheong , Spencer, Chris C.A , Strohmaier, Jana , Tosato, Sarah , Veijola, Juha , Walsh, Dermot , Wang, Dai , Webb, Bradley T , Weiser, Mark , Witt, Stephanie H , Wolen, Aaron R , Wormley, Brandon K , Blackwood, Douglas H.R , Børglum, Anders D , Ehrenreich, Hannelore , Gill, Michael , McCarroll, Steven A , McQuillin, Andrew , Moran, Jennifer L , Ophoff, Roel A , Rietschel, Marcella , Riley, Brien P , Sklar, Pamela , Daly, Mark J , Sullivan, Patrick F , Crisponi, Laura , Figueroa, Jonine , Haiman, Christopher A , Hall, Per , Irwanto, Astrid , Johansson, Mattias , Lund, Eiliv , Peeters, Petra H , Peto, Julian , Rahman, Nazneen , Travis, Ruth , van der Luijt, Rob B , Waisfisz, Quinten , Pato, Carlos , Stahl, Eli , Belbin, Gillian , Schierup, Mikkel H , Purcell, Shaun , Chasman, Daniel
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: United States: Elsevier Inc
ID: ISSN: 0002-9297
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title: Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
format: Article
creator:
  • Ripke, Stephan
  • Neale, Benjamin M
  • Holmans, Peter A
  • Collier, David A
  • Agartz, Ingrid
  • Albus, Margot
  • Bacanu, Silviu A
  • Bene, Judit
  • Bevilacqua, Elizabeth
  • Bruggeman, Richard
  • Buckner, Randy L
  • Chan, Raymond C.K
  • Cheng, Wei
  • Cohen, Nadine
  • Curtis, David
  • Dikeos, Dimitris
  • Dinan, Timothy
  • Eriksson, Johan
  • Escott-Price, Valentina
  • Farrell, Martilias S
  • Franke, Lude
  • Gershon, Elliot S
  • Godard, Stephanie
  • de Haan, Lieuwe
  • Henskens, Frans A
  • Hoffmann, Per
  • Hofman, Andrea
  • Ikeda, Masashi
  • Keller, Matthew C
  • Kim, Yunjung
  • Knowles, James A
  • Kucinskas, Vaidutis
  • Laurent, Claudine
  • Limborska, Svetlana
  • Macek, Milan
  • Maher, Brion S
  • Marsal, Sara
  • Mattheisen, Manuel
  • McIntosh, Andrew M
  • Meijer, Carin J
  • Michie, Patricia T
  • Milanova, Vihra
  • Mors, Ole
  • Murray, Robin M
  • Olsen, Line
  • Papadimitriou, George N
  • Parkhomenko, Elena
  • Pato, Michele T
  • Paunio, Tiina
  • Pulver, Ann E
  • Reichenberg, Abraham
  • Roffman, Joshua L
  • Sanders, Alan R
  • Scolnick, Edward M
  • Sigurdsson, Engilbert
  • Silverman, Jeremy M
  • So, Hon-Cheong
  • Spencer, Chris C.A
  • Strohmaier, Jana
  • Tosato, Sarah
  • Veijola, Juha
  • Walsh, Dermot
  • Wang, Dai
  • Webb, Bradley T
  • Weiser, Mark
  • Witt, Stephanie H
  • Wolen, Aaron R
  • Wormley, Brandon K
  • Blackwood, Douglas H.R
  • Børglum, Anders D
  • Ehrenreich, Hannelore
  • Gill, Michael
  • McCarroll, Steven A
  • McQuillin, Andrew
  • Moran, Jennifer L
  • Ophoff, Roel A
  • Rietschel, Marcella
  • Riley, Brien P
  • Sklar, Pamela
  • Daly, Mark J
  • Sullivan, Patrick F
  • Crisponi, Laura
  • Figueroa, Jonine
  • Haiman, Christopher A
  • Hall, Per
  • Irwanto, Astrid
  • Johansson, Mattias
  • Lund, Eiliv
  • Peeters, Petra H
  • Peto, Julian
  • Rahman, Nazneen
  • Travis, Ruth
  • van der Luijt, Rob B
  • Waisfisz, Quinten
  • Pato, Carlos
  • Stahl, Eli
  • Belbin, Gillian
  • Schierup, Mikkel H
  • Purcell, Shaun
  • Chasman, Daniel
subjects:
  • Accuracy
  • Article
  • Gene expression
  • Genetic disorders
  • Genetic linkage
  • Genetic research
  • Genetics
  • Genetics & Heredity
  • Genetics(clinical)
  • genome
  • Genome-Wide Association Study
  • Genotype
  • heritability
  • Humans
  • linkage disequilibrium
  • Linkage Disequilibrium - genetics
  • Medicin och hälsovetenskap
  • Methods
  • Models, Theoretical
  • Multifactorial Inheritance - genetics
  • Multiple sclerosis
  • Multiple Sclerosis - genetics
  • Phenotype
  • polygenic risk scores
  • Polymorphism, Single Nucleotide - genetics
  • Prognosis
  • Quantitative Trait Loci
  • Risk factors (Health)
  • Sample size
  • Schizophrenia
  • Schizophrenia - genetics
  • wide association study
ispartof: American journal of human genetics, 2015-10-01, Vol.97 (4), p.576-592
description: 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 R2 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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0002-9297
fulltext: fulltext
issn:
  • 0002-9297
  • 1537-6605
  • 1537-6605
url: Link


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titleModeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
sourceAlma/SFX Local Collection
creatorRipke, Stephan ; Neale, Benjamin M ; Holmans, Peter A ; Collier, David A ; Agartz, Ingrid ; Albus, Margot ; Bacanu, Silviu A ; Bene, Judit ; Bevilacqua, Elizabeth ; Bruggeman, Richard ; Buckner, Randy L ; Chan, Raymond C.K ; Cheng, Wei ; Cohen, Nadine ; Curtis, David ; Dikeos, Dimitris ; Dinan, Timothy ; Eriksson, Johan ; Escott-Price, Valentina ; Farrell, Martilias S ; Franke, Lude ; Gershon, Elliot S ; Godard, Stephanie ; de Haan, Lieuwe ; Henskens, Frans A ; Hoffmann, Per ; Hofman, Andrea ; Ikeda, Masashi ; Keller, Matthew C ; Kim, Yunjung ; Knowles, James A ; Kucinskas, Vaidutis ; Laurent, Claudine ; Limborska, Svetlana ; Macek, Milan ; Maher, Brion S ; Marsal, Sara ; Mattheisen, Manuel ; McIntosh, Andrew M ; Meijer, Carin J ; Michie, Patricia T ; Milanova, Vihra ; Mors, Ole ; Murray, Robin M ; Olsen, Line ; Papadimitriou, George N ; Parkhomenko, Elena ; Pato, Michele T ; Paunio, Tiina ; Pulver, Ann E ; Reichenberg, Abraham ; Roffman, Joshua L ; Sanders, Alan R ; Scolnick, Edward M ; Sigurdsson, Engilbert ; Silverman, Jeremy M ; So, Hon-Cheong ; Spencer, Chris C.A ; Strohmaier, Jana ; Tosato, Sarah ; Veijola, Juha ; Walsh, Dermot ; Wang, Dai ; Webb, Bradley T ; Weiser, Mark ; Witt, Stephanie H ; Wolen, Aaron R ; Wormley, Brandon K ; Blackwood, Douglas H.R ; Børglum, Anders D ; Ehrenreich, Hannelore ; Gill, Michael ; McCarroll, Steven A ; McQuillin, Andrew ; Moran, Jennifer L ; Ophoff, Roel A ; Rietschel, Marcella ; Riley, Brien P ; Sklar, Pamela ; Daly, Mark J ; Sullivan, Patrick F ; Crisponi, Laura ; Figueroa, Jonine ; Haiman, Christopher A ; Hall, Per ; Irwanto, Astrid ; Johansson, Mattias ; Lund, Eiliv ; Peeters, Petra H ; Peto, Julian ; Rahman, Nazneen ; Travis, Ruth ; van der Luijt, Rob B ; Waisfisz, Quinten ; Pato, Carlos ; Stahl, Eli ; Belbin, Gillian ; Schierup, Mikkel H ; Purcell, Shaun ; Chasman, Daniel
creatorcontribRipke, Stephan ; Neale, Benjamin M ; Holmans, Peter A ; Collier, David A ; Agartz, Ingrid ; Albus, Margot ; Bacanu, Silviu A ; Bene, Judit ; Bevilacqua, Elizabeth ; Bruggeman, Richard ; Buckner, Randy L ; Chan, Raymond C.K ; Cheng, Wei ; Cohen, Nadine ; Curtis, David ; Dikeos, Dimitris ; Dinan, Timothy ; Eriksson, Johan ; Escott-Price, Valentina ; Farrell, Martilias S ; Franke, Lude ; Gershon, Elliot S ; Godard, Stephanie ; de Haan, Lieuwe ; Henskens, Frans A ; Hoffmann, Per ; Hofman, Andrea ; Ikeda, Masashi ; Keller, Matthew C ; Kim, Yunjung ; Knowles, James A ; Kucinskas, Vaidutis ; Laurent, Claudine ; Limborska, Svetlana ; Macek, Milan ; Maher, Brion S ; Marsal, Sara ; Mattheisen, Manuel ; McIntosh, Andrew M ; Meijer, Carin J ; Michie, Patricia T ; Milanova, Vihra ; Mors, Ole ; Murray, Robin M ; Olsen, Line ; Papadimitriou, George N ; Parkhomenko, Elena ; Pato, Michele T ; Paunio, Tiina ; Pulver, Ann E ; Reichenberg, Abraham ; Roffman, Joshua L ; Sanders, Alan R ; Scolnick, Edward M ; Sigurdsson, Engilbert ; Silverman, Jeremy M ; So, Hon-Cheong ; Spencer, Chris C.A ; Strohmaier, Jana ; Tosato, Sarah ; Veijola, Juha ; Walsh, Dermot ; Wang, Dai ; Webb, Bradley T ; Weiser, Mark ; Witt, Stephanie H ; Wolen, Aaron R ; Wormley, Brandon K ; Blackwood, Douglas H.R ; Børglum, Anders D ; Ehrenreich, Hannelore ; Gill, Michael ; McCarroll, Steven A ; McQuillin, Andrew ; Moran, Jennifer L ; Ophoff, Roel A ; Rietschel, Marcella ; Riley, Brien P ; Sklar, Pamela ; Daly, Mark J ; Sullivan, Patrick F ; Crisponi, Laura ; Figueroa, Jonine ; Haiman, Christopher A ; Hall, Per ; Irwanto, Astrid ; Johansson, Mattias ; Lund, Eiliv ; Peeters, Petra H ; Peto, Julian ; Rahman, Nazneen ; Travis, Ruth ; van der Luijt, Rob B ; Waisfisz, Quinten ; Pato, Carlos ; Stahl, Eli ; Belbin, Gillian ; Schierup, Mikkel H ; Purcell, Shaun ; Chasman, Daniel ; Psychosis Endophenotypes International Consortium ; Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study ; Wellcome Trust Case Control Consortium ; Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON) ; Schizophrenia Working Group of the Psychiatric Genomics Consortium ; Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
descriptionPolygenic 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 R2 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.
identifier
0ISSN: 0002-9297
1ISSN: 1537-6605
2EISSN: 1537-6605
3DOI: 10.1016/j.ajhg.2015.09.001
4PMID: 26430803
languageeng
publisherUnited States: Elsevier Inc
subjectAccuracy ; Article ; Gene expression ; Genetic disorders ; Genetic linkage ; Genetic research ; Genetics ; Genetics & Heredity ; Genetics(clinical) ; genome ; Genome-Wide Association Study ; Genotype ; heritability ; Humans ; linkage disequilibrium ; Linkage Disequilibrium - genetics ; Medicin och hälsovetenskap ; Methods ; Models, Theoretical ; Multifactorial Inheritance - genetics ; Multiple sclerosis ; Multiple Sclerosis - genetics ; Phenotype ; polygenic risk scores ; Polymorphism, Single Nucleotide - genetics ; Prognosis ; Quantitative Trait Loci ; Risk factors (Health) ; Sample size ; Schizophrenia ; Schizophrenia - genetics ; wide association study
ispartofAmerican journal of human genetics, 2015-10-01, Vol.97 (4), p.576-592
rights
02015 The American Society of Human Genetics
1info:eu-repo/semantics/restrictedAccess
2Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
3COPYRIGHT 2015 Elsevier B.V.
4Copyright Cell Press Oct 1, 2015
52015 by The American Society of Human Genetics. All rights reserved. 2015 The American Society of Human Genetics
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creatorcontrib
0Ripke, Stephan
1Neale, Benjamin M
2Holmans, Peter A
3Collier, David A
4Agartz, Ingrid
5Albus, Margot
6Bacanu, Silviu A
7Bene, Judit
8Bevilacqua, Elizabeth
9Bruggeman, Richard
10Buckner, Randy L
11Chan, Raymond C.K
12Cheng, Wei
13Cohen, Nadine
14Curtis, David
15Dikeos, Dimitris
16Dinan, Timothy
17Eriksson, Johan
18Escott-Price, Valentina
19Farrell, Martilias S
20Franke, Lude
21Gershon, Elliot S
22Godard, Stephanie
23de Haan, Lieuwe
24Henskens, Frans A
25Hoffmann, Per
26Hofman, Andrea
27Ikeda, Masashi
28Keller, Matthew C
29Kim, Yunjung
30Knowles, James A
31Kucinskas, Vaidutis
32Laurent, Claudine
33Limborska, Svetlana
34Macek, Milan
35Maher, Brion S
36Marsal, Sara
37Mattheisen, Manuel
38McIntosh, Andrew M
39Meijer, Carin J
40Michie, Patricia T
41Milanova, Vihra
42Mors, Ole
43Murray, Robin M
44Olsen, Line
45Papadimitriou, George N
46Parkhomenko, Elena
47Pato, Michele T
48Paunio, Tiina
49Pulver, Ann E
50Reichenberg, Abraham
51Roffman, Joshua L
52Sanders, Alan R
53Scolnick, Edward M
54Sigurdsson, Engilbert
55Silverman, Jeremy M
56So, Hon-Cheong
57Spencer, Chris C.A
58Strohmaier, Jana
59Tosato, Sarah
60Veijola, Juha
61Walsh, Dermot
62Wang, Dai
63Webb, Bradley T
64Weiser, Mark
65Witt, Stephanie H
66Wolen, Aaron R
67Wormley, Brandon K
68Blackwood, Douglas H.R
69Børglum, Anders D
70Ehrenreich, Hannelore
71Gill, Michael
72McCarroll, Steven A
73McQuillin, Andrew
74Moran, Jennifer L
75Ophoff, Roel A
76Rietschel, Marcella
77Riley, Brien P
78Sklar, Pamela
79Daly, Mark J
80Sullivan, Patrick F
81Crisponi, Laura
82Figueroa, Jonine
83Haiman, Christopher A
84Hall, Per
85Irwanto, Astrid
86Johansson, Mattias
87Lund, Eiliv
88Peeters, Petra H
89Peto, Julian
90Rahman, Nazneen
91Travis, Ruth
92van der Luijt, Rob B
93Waisfisz, Quinten
94Pato, Carlos
95Stahl, Eli
96Belbin, Gillian
97Schierup, Mikkel H
98Purcell, Shaun
99Chasman, Daniel
100Psychosis Endophenotypes International Consortium
101Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
102Wellcome Trust Case Control Consortium
103Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)
104Schizophrenia Working Group of the Psychiatric Genomics Consortium
105Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
title
0Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
1American journal of human genetics
addtitleAm J Hum Genet
descriptionPolygenic 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 R2 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.
subject
0Accuracy
1Article
2Gene expression
3Genetic disorders
4Genetic linkage
5Genetic research
6Genetics
7Genetics & Heredity
8Genetics(clinical)
9genome
10Genome-Wide Association Study
11Genotype
12heritability
13Humans
14linkage disequilibrium
15Linkage Disequilibrium - genetics
16Medicin och hälsovetenskap
17Methods
18Models, Theoretical
19Multifactorial Inheritance - genetics
20Multiple sclerosis
21Multiple Sclerosis - genetics
22Phenotype
23polygenic risk scores
24Polymorphism, Single Nucleotide - genetics
25Prognosis
26Quantitative Trait Loci
27Risk factors (Health)
28Sample size
29Schizophrenia
30Schizophrenia - genetics
31wide association study
issn
00002-9297
11537-6605
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creator
0Ripke, Stephan
1Neale, Benjamin M
2Holmans, Peter A
3Collier, David A
4Agartz, Ingrid
5Albus, Margot
6Bacanu, Silviu A
7Bene, Judit
8Bevilacqua, Elizabeth
9Bruggeman, Richard
10Buckner, Randy L
11Chan, Raymond C.K
12Cheng, Wei
13Cohen, Nadine
14Curtis, David
15Dikeos, Dimitris
16Dinan, Timothy
17Eriksson, Johan
18Escott-Price, Valentina
19Farrell, Martilias S
20Franke, Lude
21Gershon, Elliot S
22Godard, Stephanie
23de Haan, Lieuwe
24Henskens, Frans A
25Hoffmann, Per
26Hofman, Andrea
27Ikeda, Masashi
28Keller, Matthew C
29Kim, Yunjung
30Knowles, James A
31Kucinskas, Vaidutis
32Laurent, Claudine
33Limborska, Svetlana
34Macek, Milan
35Maher, Brion S
36Marsal, Sara
37Mattheisen, Manuel
38McIntosh, Andrew M
39Meijer, Carin J
40Michie, Patricia T
41Milanova, Vihra
42Mors, Ole
43Murray, Robin M
44Olsen, Line
45Papadimitriou, George N
46Parkhomenko, Elena
47Pato, Michele T
48Paunio, Tiina
49Pulver, Ann E
50Reichenberg, Abraham
51Roffman, Joshua L
52Sanders, Alan R
53Scolnick, Edward M
54Sigurdsson, Engilbert
55Silverman, Jeremy M
56So, Hon-Cheong
57Spencer, Chris C.A
58Strohmaier, Jana
59Tosato, Sarah
60Veijola, Juha
61Walsh, Dermot
62Wang, Dai
63Webb, Bradley T
64Weiser, Mark
65Witt, Stephanie H
66Wolen, Aaron R
67Wormley, Brandon K
68Blackwood, Douglas H.R
69Børglum, Anders D
70Ehrenreich, Hannelore
71Gill, Michael
72McCarroll, Steven A
73McQuillin, Andrew
74Moran, Jennifer L
75Ophoff, Roel A
76Rietschel, Marcella
77Riley, Brien P
78Sklar, Pamela
79Daly, Mark J
80Sullivan, Patrick F
81Crisponi, Laura
82Figueroa, Jonine
83Haiman, Christopher A
84Hall, Per
85Irwanto, Astrid
86Johansson, Mattias
87Lund, Eiliv
88Peeters, Petra H
89Peto, Julian
90Rahman, Nazneen
91Travis, Ruth
92van der Luijt, Rob B
93Waisfisz, Quinten
94Pato, Carlos
95Stahl, Eli
96Belbin, Gillian
97Schierup, Mikkel H
98Purcell, Shaun
99Chasman, Daniel
general
0Elsevier Inc
1Elsevier B.V
2Cell Press
3Elsevier
scope
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35DJ
4QVL
5CGR
6CUY
7CVF
8ECM
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12CITATION
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20FR3
21K9.
22P64
23RC3
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25BOBZL
26CLFQK
275PM
28ADTPV
29AOWAS
30D8T
sort
creationdate20151001
titleModeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
authorRipke, Stephan ; Neale, Benjamin M ; Holmans, Peter A ; Collier, David A ; Agartz, Ingrid ; Albus, Margot ; Bacanu, Silviu A ; Bene, Judit ; Bevilacqua, Elizabeth ; Bruggeman, Richard ; Buckner, Randy L ; Chan, Raymond C.K ; Cheng, Wei ; Cohen, Nadine ; Curtis, David ; Dikeos, Dimitris ; Dinan, Timothy ; Eriksson, Johan ; Escott-Price, Valentina ; Farrell, Martilias S ; Franke, Lude ; Gershon, Elliot S ; Godard, Stephanie ; de Haan, Lieuwe ; Henskens, Frans A ; Hoffmann, Per ; Hofman, Andrea ; Ikeda, Masashi ; Keller, Matthew C ; Kim, Yunjung ; Knowles, James A ; Kucinskas, Vaidutis ; Laurent, Claudine ; Limborska, Svetlana ; Macek, Milan ; Maher, Brion S ; Marsal, Sara ; Mattheisen, Manuel ; McIntosh, Andrew M ; Meijer, Carin J ; Michie, Patricia T ; Milanova, Vihra ; Mors, Ole ; Murray, Robin M ; Olsen, Line ; Papadimitriou, George N ; Parkhomenko, Elena ; Pato, Michele T ; Paunio, Tiina ; Pulver, Ann E ; Reichenberg, Abraham ; Roffman, Joshua L ; Sanders, Alan R ; Scolnick, Edward M ; Sigurdsson, Engilbert ; Silverman, Jeremy M ; So, Hon-Cheong ; Spencer, Chris C.A ; Strohmaier, Jana ; Tosato, Sarah ; Veijola, Juha ; Walsh, Dermot ; Wang, Dai ; Webb, Bradley T ; Weiser, Mark ; Witt, Stephanie H ; Wolen, Aaron R ; Wormley, Brandon K ; Blackwood, Douglas H.R ; Børglum, Anders D ; Ehrenreich, Hannelore ; Gill, Michael ; McCarroll, Steven A ; McQuillin, Andrew ; Moran, Jennifer L ; Ophoff, Roel A ; Rietschel, Marcella ; Riley, Brien P ; Sklar, Pamela ; Daly, Mark J ; Sullivan, Patrick F ; Crisponi, Laura ; Figueroa, Jonine ; Haiman, Christopher A ; Hall, Per ; Irwanto, Astrid ; Johansson, Mattias ; Lund, Eiliv ; Peeters, Petra H ; Peto, Julian ; Rahman, Nazneen ; Travis, Ruth ; van der Luijt, Rob B ; Waisfisz, Quinten ; Pato, Carlos ; Stahl, Eli ; Belbin, Gillian ; Schierup, Mikkel H ; Purcell, Shaun ; Chasman, Daniel
facets
frbrtype5
frbrgroupidcdi_FETCH-LOGICAL-1746t-103bfda20a8bc2d858a4dcd4b35ecca6169423118cd605461d6c6210f960f3fc3
rsrctypearticles
prefilterarticles
languageeng
creationdate2015
topic
0Accuracy
1Article
2Gene expression
3Genetic disorders
4Genetic linkage
5Genetic research
6Genetics
7Genetics & Heredity
8Genetics(clinical)
9genome
10Genome-Wide Association Study
11Genotype
12heritability
13Humans
14linkage disequilibrium
15Linkage Disequilibrium - genetics
16Medicin och hälsovetenskap
17Methods
18Models, Theoretical
19Multifactorial Inheritance - genetics
20Multiple sclerosis
21Multiple Sclerosis - genetics
22Phenotype
23polygenic risk scores
24Polymorphism, Single Nucleotide - genetics
25Prognosis
26Quantitative Trait Loci
27Risk factors (Health)
28Sample size
29Schizophrenia
30Schizophrenia - genetics
31wide association study
toplevel
0peer_reviewed
1online_resources
creatorcontrib
0Ripke, Stephan
1Neale, Benjamin M
2Holmans, Peter A
3Collier, David A
4Agartz, Ingrid
5Albus, Margot
6Bacanu, Silviu A
7Bene, Judit
8Bevilacqua, Elizabeth
9Bruggeman, Richard
10Buckner, Randy L
11Chan, Raymond C.K
12Cheng, Wei
13Cohen, Nadine
14Curtis, David
15Dikeos, Dimitris
16Dinan, Timothy
17Eriksson, Johan
18Escott-Price, Valentina
19Farrell, Martilias S
20Franke, Lude
21Gershon, Elliot S
22Godard, Stephanie
23de Haan, Lieuwe
24Henskens, Frans A
25Hoffmann, Per
26Hofman, Andrea
27Ikeda, Masashi
28Keller, Matthew C
29Kim, Yunjung
30Knowles, James A
31Kucinskas, Vaidutis
32Laurent, Claudine
33Limborska, Svetlana
34Macek, Milan
35Maher, Brion S
36Marsal, Sara
37Mattheisen, Manuel
38McIntosh, Andrew M
39Meijer, Carin J
40Michie, Patricia T
41Milanova, Vihra
42Mors, Ole
43Murray, Robin M
44Olsen, Line
45Papadimitriou, George N
46Parkhomenko, Elena
47Pato, Michele T
48Paunio, Tiina
49Pulver, Ann E
50Reichenberg, Abraham
51Roffman, Joshua L
52Sanders, Alan R
53Scolnick, Edward M
54Sigurdsson, Engilbert
55Silverman, Jeremy M
56So, Hon-Cheong
57Spencer, Chris C.A
58Strohmaier, Jana
59Tosato, Sarah
60Veijola, Juha
61Walsh, Dermot
62Wang, Dai
63Webb, Bradley T
64Weiser, Mark
65Witt, Stephanie H
66Wolen, Aaron R
67Wormley, Brandon K
68Blackwood, Douglas H.R
69Børglum, Anders D
70Ehrenreich, Hannelore
71Gill, Michael
72McCarroll, Steven A
73McQuillin, Andrew
74Moran, Jennifer L
75Ophoff, Roel A
76Rietschel, Marcella
77Riley, Brien P
78Sklar, Pamela
79Daly, Mark J
80Sullivan, Patrick F
81Crisponi, Laura
82Figueroa, Jonine
83Haiman, Christopher A
84Hall, Per
85Irwanto, Astrid
86Johansson, Mattias
87Lund, Eiliv
88Peeters, Petra H
89Peto, Julian
90Rahman, Nazneen
91Travis, Ruth
92van der Luijt, Rob B
93Waisfisz, Quinten
94Pato, Carlos
95Stahl, Eli
96Belbin, Gillian
97Schierup, Mikkel H
98Purcell, Shaun
99Chasman, Daniel
100Psychosis Endophenotypes International Consortium
101Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
102Wellcome Trust Case Control Consortium
103Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)
104Schizophrenia Working Group of the Psychiatric Genomics Consortium
105Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
collection
0ScienceDirect Open Access Titles
1Elsevier:ScienceDirect:Open Access
2NARCIS
3NARCIS: Datasets
4NARCIS:Publications
5Medline
6MEDLINE
7MEDLINE (Ovid)
8MEDLINE
9MEDLINE
10PubMed
11CrossRef
12Gale General OneFile (A&I only)
13Calcium & Calcified Tissue Abstracts
14Neurosciences Abstracts
15Nucleic Acids Abstracts
16Toxicology Abstracts
17Technology Research Database
18Environmental Sciences and Pollution Management
19Engineering Research Database
20ProQuest Health & Medical Complete (Alumni)
21Biotechnology and BioEngineering Abstracts
22Genetics Abstracts
23MEDLINE - Academic
24OpenAIRE (Open Access)
25OpenAIRE
26PubMed Central (Full Participant titles)
27SwePub
28SwePub Articles
29SWEPUB Freely available online
jtitleAmerican journal of human genetics
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
au
0Ripke, Stephan
1Neale, Benjamin M
2Holmans, Peter A
3Collier, David A
4Agartz, Ingrid
5Albus, Margot
6Bacanu, Silviu A
7Bene, Judit
8Bevilacqua, Elizabeth
9Bruggeman, Richard
10Buckner, Randy L
11Chan, Raymond C.K
12Cheng, Wei
13Cohen, Nadine
14Curtis, David
15Dikeos, Dimitris
16Dinan, Timothy
17Eriksson, Johan
18Escott-Price, Valentina
19Farrell, Martilias S
20Franke, Lude
21Gershon, Elliot S
22Godard, Stephanie
23de Haan, Lieuwe
24Henskens, Frans A
25Hoffmann, Per
26Hofman, Andrea
27Ikeda, Masashi
28Keller, Matthew C
29Kim, Yunjung
30Knowles, James A
31Kucinskas, Vaidutis
32Laurent, Claudine
33Limborska, Svetlana
34Macek, Milan
35Maher, Brion S
36Marsal, Sara
37Mattheisen, Manuel
38McIntosh, Andrew M
39Meijer, Carin J
40Michie, Patricia T
41Milanova, Vihra
42Mors, Ole
43Murray, Robin M
44Olsen, Line
45Papadimitriou, George N
46Parkhomenko, Elena
47Pato, Michele T
48Paunio, Tiina
49Pulver, Ann E
50Reichenberg, Abraham
51Roffman, Joshua L
52Sanders, Alan R
53Scolnick, Edward M
54Sigurdsson, Engilbert
55Silverman, Jeremy M
56So, Hon-Cheong
57Spencer, Chris C.A
58Strohmaier, Jana
59Tosato, Sarah
60Veijola, Juha
61Walsh, Dermot
62Wang, Dai
63Webb, Bradley T
64Weiser, Mark
65Witt, Stephanie H
66Wolen, Aaron R
67Wormley, Brandon K
68Blackwood, Douglas H.R
69Børglum, Anders D
70Ehrenreich, Hannelore
71Gill, Michael
72McCarroll, Steven A
73McQuillin, Andrew
74Moran, Jennifer L
75Ophoff, Roel A
76Rietschel, Marcella
77Riley, Brien P
78Sklar, Pamela
79Daly, Mark J
80Sullivan, Patrick F
81Crisponi, Laura
82Figueroa, Jonine
83Haiman, Christopher A
84Hall, Per
85Irwanto, Astrid
86Johansson, Mattias
87Lund, Eiliv
88Peeters, Petra H
89Peto, Julian
90Rahman, Nazneen
91Travis, Ruth
92van der Luijt, Rob B
93Waisfisz, Quinten
94Pato, Carlos
95Stahl, Eli
96Belbin, Gillian
97Schierup, Mikkel H
98Purcell, Shaun
99Chasman, Daniel
aucorp
0Psychosis Endophenotypes International Consortium
1Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
2Wellcome Trust Case Control Consortium
3Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)
4Schizophrenia Working Group of the Psychiatric Genomics Consortium
5Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
formatjournal
genrearticle
ristypeJOUR
atitleModeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
jtitleAmerican journal of human genetics
addtitleAm J Hum Genet
date2015-10-01
risdate2015
volume97
issue4
spage576
epage592
pages576-592
issn
00002-9297
11537-6605
eissn1537-6605
abstractPolygenic 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 R2 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.
copUnited States
pubElsevier Inc
pmid26430803
doi10.1016/j.ajhg.2015.09.001
oafree_for_read