Alzheimer’s disease polygenic risk score as a predictor of conversion from mild-cognitive impairment

• DOI: 10.1038/s41398-019-0485-7
• Publication Date: 2019-05-24T09:04:26Z
• Authors: Sultan Chaudhury; Keeley J. Brookes; Tulsi Patel; Abigail Fallows; Tamar Guetta-Baranes; James C. Turton; Rita Guerreiro; Jose Bras; John Hardy; Paul T. Francis; Rebecca Croucher; Clive Holmes; Kevin Morgan; A. J. Thomas
• Keywords: Male; Risk; Apolipoprotein E4; Models, Neurological; 610; Article; Genetic Risk; 03 medical and health sciences; 0302 clinical medicine; Alzheimer Disease; Hippocampal; Humans; Cognitive Dysfunction; Polymorphism; Age of Onset; Aged; Psychiatry; Aged, 80 and over; Science & Technology; Progression; Models, Genetic; Variants; Metaanalysis; Prognosis; Genome-wide Association; 3. Good health; Disease Progression; Dementia; Female; Life Sciences & Biomedicine; Biomarkers; Memory Decline

AbstractMild-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.