Chaudhury, S;
Brookes, KJ;
Patel, T;
Fallows, A;
Guetta-Baranes, T;
Turton, JC;
Guerreiro, R;
... Thomas, AJ; + view all
(2019)
Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment.
Translational Psychiatry
, 9
, Article 154. 10.1038/s41398-019-0485-7.
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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.
| Type: | Article |
|---|---|
| Title: | Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41398-019-0485-7 |
| Publisher version: | https://doi.org/10.1038/s41398-019-0485-7 |
| Language: | English |
| Additional information: | © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creative commons.org/ licenses/by/4.0/ - Corrected version dated 11 June 2019 |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Psychiatry, Genome-wide Association, Genetic Risk, Memory Decline, Variants, Polymorphism, Metaanalysis, Hippocampal, Progression, Biomarkers, Dementia |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10076600 |
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