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Alzheimer's Disease polygenic risk score as a predictor of conversion for mild cognitive impairment

Alzheimer's Disease polygenic risk score as a predictor of conversion for mild cognitive impairment
Alzheimer's Disease polygenic risk score as a predictor of conversion for mild cognitive impairment
Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer’s disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer’s Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups (p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.
Brookes, Keeley
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Chaudhury, Sultan
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Fallows, Abigail
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Guetta-Baranes, Tamar
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Turton, James
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Guerreiro, Rita
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Bras, Jose
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Hardy, John
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Francis, Paul
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Croucher, Rebecca
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Holmes, Clive
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Morgan, Kevin
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Brookes, Keeley
2a5b21f5-145c-425d-85cb-b15da8a558da
Chaudhury, Sultan
272cda10-5f2b-4194-a938-b1b78de1b9ac
Fallows, Abigail
4aa7f385-5b8f-4ad7-ad62-5400ccaa8c57
Guetta-Baranes, Tamar
5077c3da-4dc9-412e-b3eb-2acb02d64998
Turton, James
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Guerreiro, Rita
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Bras, Jose
91d511fb-75e1-41d1-8212-7b83dd8a5f7a
Hardy, John
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Francis, Paul
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Croucher, Rebecca
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Holmes, Clive
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Morgan, Kevin
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Brookes, Keeley, Chaudhury, Sultan, Fallows, Abigail, Guetta-Baranes, Tamar, Turton, James, Guerreiro, Rita, Bras, Jose, Hardy, John, Francis, Paul, Croucher, Rebecca, Holmes, Clive and Morgan, Kevin (2019) Alzheimer's Disease polygenic risk score as a predictor of conversion for mild cognitive impairment. Translational Psychiatry, 9, [154]. (doi:10.1038/s41398-019-0485-7).

Record type: Article

Abstract

Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer’s disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer’s Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups (p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.

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Accepted/In Press date: 10 April 2019
e-pub ahead of print date: 24 May 2019

Identifiers

Local EPrints ID: 431599
URI: http://eprints.soton.ac.uk/id/eprint/431599
PURE UUID: 4fd12c2e-e8c7-4a77-bdff-80e440de06f4
ORCID for Clive Holmes: ORCID iD orcid.org/0000-0003-1999-6912

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Date deposited: 10 Jun 2019 16:30
Last modified: 26 Nov 2021 07:11

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Contributors

Author: Keeley Brookes
Author: Sultan Chaudhury
Author: Abigail Fallows
Author: Tamar Guetta-Baranes
Author: James Turton
Author: Rita Guerreiro
Author: Jose Bras
Author: John Hardy
Author: Paul Francis
Author: Rebecca Croucher
Author: Clive Holmes ORCID iD
Author: Kevin Morgan

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