PT  - JOURNAL ARTICLE
AU  - Nicola Voyle
AU  - Willemijn Jansen
AU  - Aoife Keohane
AU  - Hamel Patel
AU  - Amos Folarin
AU  - Stephen Newhouse
AU  - Caroline Johnston
AU  - Kuang Lin
AU  - Pieter Jelle Visser
AU  - Angela Hodges
AU  - Richard JB Dobson
AU  - Steven J Kiddle
AU  - for the Alzheimer’s Disease Neuroimaging Initiative
AU  - EDAR and DESCRIPA study groups
TI  - Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach
AID  - 10.1101/174995
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 174995
4099  - http://biorxiv.org/content/early/2017/08/10/174995.short
4100  - http://biorxiv.org/content/early/2017/08/10/174995.full
AB  - INTRODUCTION In this study we investigate the association between Aβ levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area.METHODS Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF Aβ burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimer’s Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables.RESULTS The use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability.DISCUSSION A previous study indicated that a PGRS of AD case/control status was associated with CSF Aβ burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE.*‘Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response’**‘Development of screening guidelines and criteria for pre-dementia Alzheimers disease’