The University of Southampton
University of Southampton Institutional Repository

Common polygenic variation enhances risk prediction for Alzheimer's disease

Common polygenic variation enhances risk prediction for Alzheimer's disease
Common polygenic variation enhances risk prediction for Alzheimer's disease

The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Alleles, Alzheimer Disease, Apolipoproteins E, Case-Control Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Logistic Models, Multifactorial Inheritance, ROC Curve, Risk, Journal Article, Research Support, Non-U.S. Gov't
0006-8950
3673-84
Escott-Price, Valentina
ac515d63-88bb-47b8-9dc8-73fc4d6bfc4f
Sims, Rebecca
9e087008-d8a2-404f-a926-01ea46051733
Bannister, Christian
0d5fb912-5709-44b9-ac91-4c52d89f89b9
Harold, Denise
d37e566f-4ca4-49ae-8082-6368cc4c4b57
Vronskaya, Maria
0432a925-b12d-4c3f-b303-e592da16c983
Majounie, Elisa
1fbb4f6d-223c-4327-8c89-72c2f6fe3898
Badarinarayan, Nandini
1e76aafc-ebb2-4961-9be1-1e30b105ca5e
Morgan, Kevin
f4a865c9-f8cc-443c-a2e7-3e3ac5a8cf85
Passmore, Peter
175afd4d-532f-4340-b40b-ce5f4b7e8945
Holmes, Clive
ada5abf3-8459-4cf7-be40-3f4e9391cc96
Powell, John
0616c5bf-0ce6-48ef-9b89-45a72529beb1
Brayne, Carol
978cfad1-c7f6-4f79-aa1c-4f189eaaf035
Gill, Michael
408d1dfa-5205-4e50-8130-7b26aa8288e8
Mead, Simon
1e29a17b-01fa-47b2-a868-1847a7d8f7f8
Goate, Alison
53b9ac60-c863-4636-9ed2-63565ebb1a11
Cruchaga, Carlos
f649f0f3-4901-4918-ae59-a6ab5cc5471e
Lambert, Jean-Charles
f3e20429-771e-4499-96d3-dffc3e92c608
van Duijn, Cornelia
cb0de809-cb8e-4e71-b529-8db403393cd2
Maier, Wolfgang
2c2c79ec-df0d-4496-a280-6d391c96a245
Ramirez, Alfredo
88c0c21b-cf5c-4153-a46c-ace75bb7b917
Holmans, Peter
48b5a4c1-a9c4-498f-8acf-835f71957831
Jones, Lesley
74cd06b4-027a-4f92-bb21-0edd74fda98a
Hardy, John
4e432d40-5069-43f2-b716-33c9af27a6c3
Seshadri, Sudha
e2cb43f3-1baa-47b9-8fd8-e9a2199c8d26
Schellenberg, Gerard D
02af9772-3d74-44d1-b16f-060b6dbf4fe3
Amouyel, Philippe
a73c2250-a21a-4108-9f64-8be8fa432143
Williams, Julie
4752662a-f68a-40fd-bb6a-015c53d73d27
GERAD/PERADES
IGAP consortia
Escott-Price, Valentina
ac515d63-88bb-47b8-9dc8-73fc4d6bfc4f
Sims, Rebecca
9e087008-d8a2-404f-a926-01ea46051733
Bannister, Christian
0d5fb912-5709-44b9-ac91-4c52d89f89b9
Harold, Denise
d37e566f-4ca4-49ae-8082-6368cc4c4b57
Vronskaya, Maria
0432a925-b12d-4c3f-b303-e592da16c983
Majounie, Elisa
1fbb4f6d-223c-4327-8c89-72c2f6fe3898
Badarinarayan, Nandini
1e76aafc-ebb2-4961-9be1-1e30b105ca5e
Morgan, Kevin
f4a865c9-f8cc-443c-a2e7-3e3ac5a8cf85
Passmore, Peter
175afd4d-532f-4340-b40b-ce5f4b7e8945
Holmes, Clive
ada5abf3-8459-4cf7-be40-3f4e9391cc96
Powell, John
0616c5bf-0ce6-48ef-9b89-45a72529beb1
Brayne, Carol
978cfad1-c7f6-4f79-aa1c-4f189eaaf035
Gill, Michael
408d1dfa-5205-4e50-8130-7b26aa8288e8
Mead, Simon
1e29a17b-01fa-47b2-a868-1847a7d8f7f8
Goate, Alison
53b9ac60-c863-4636-9ed2-63565ebb1a11
Cruchaga, Carlos
f649f0f3-4901-4918-ae59-a6ab5cc5471e
Lambert, Jean-Charles
f3e20429-771e-4499-96d3-dffc3e92c608
van Duijn, Cornelia
cb0de809-cb8e-4e71-b529-8db403393cd2
Maier, Wolfgang
2c2c79ec-df0d-4496-a280-6d391c96a245
Ramirez, Alfredo
88c0c21b-cf5c-4153-a46c-ace75bb7b917
Holmans, Peter
48b5a4c1-a9c4-498f-8acf-835f71957831
Jones, Lesley
74cd06b4-027a-4f92-bb21-0edd74fda98a
Hardy, John
4e432d40-5069-43f2-b716-33c9af27a6c3
Seshadri, Sudha
e2cb43f3-1baa-47b9-8fd8-e9a2199c8d26
Schellenberg, Gerard D
02af9772-3d74-44d1-b16f-060b6dbf4fe3
Amouyel, Philippe
a73c2250-a21a-4108-9f64-8be8fa432143
Williams, Julie
4752662a-f68a-40fd-bb6a-015c53d73d27

Escott-Price, Valentina, Sims, Rebecca, Bannister, Christian, Harold, Denise, Vronskaya, Maria, Majounie, Elisa, Badarinarayan, Nandini, Morgan, Kevin, Passmore, Peter, Holmes, Clive, Powell, John, Brayne, Carol, Gill, Michael, Mead, Simon, Goate, Alison, Cruchaga, Carlos, Lambert, Jean-Charles, van Duijn, Cornelia, Maier, Wolfgang, Ramirez, Alfredo, Holmans, Peter, Jones, Lesley, Hardy, John, Seshadri, Sudha, Schellenberg, Gerard D, Amouyel, Philippe and Williams, Julie , GERAD/PERADES and IGAP consortia (2015) Common polygenic variation enhances risk prediction for Alzheimer's disease. Brain, 138 (12), 3673-84. (doi:10.1093/brain/awv268).

Record type: Article

Abstract

The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Full text not available from this repository.

More information

Published date: 21 October 2015
Additional Information: © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Keywords: Alleles, Alzheimer Disease, Apolipoproteins E, Case-Control Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Logistic Models, Multifactorial Inheritance, ROC Curve, Risk, Journal Article, Research Support, Non-U.S. Gov't

Identifiers

Local EPrints ID: 416111
URI: https://eprints.soton.ac.uk/id/eprint/416111
ISSN: 0006-8950
PURE UUID: dec82275-9c9f-4d77-8f9b-53d039f38f97
ORCID for Clive Holmes: ORCID iD orcid.org/0000-0003-1999-6912

Catalogue record

Date deposited: 04 Dec 2017 17:30
Last modified: 17 Jul 2019 17:42

Export record

Altmetrics

Contributors

Author: Valentina Escott-Price
Author: Rebecca Sims
Author: Christian Bannister
Author: Denise Harold
Author: Maria Vronskaya
Author: Elisa Majounie
Author: Nandini Badarinarayan
Author: Kevin Morgan
Author: Peter Passmore
Author: Clive Holmes ORCID iD
Author: John Powell
Author: Carol Brayne
Author: Michael Gill
Author: Simon Mead
Author: Alison Goate
Author: Carlos Cruchaga
Author: Jean-Charles Lambert
Author: Cornelia van Duijn
Author: Wolfgang Maier
Author: Alfredo Ramirez
Author: Peter Holmans
Author: Lesley Jones
Author: John Hardy
Author: Sudha Seshadri
Author: Gerard D Schellenberg
Author: Philippe Amouyel
Author: Julie Williams

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×