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Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment

Sultan Chaudhury1, Keeley J Brookes2, Tulsi Patel1

  • 1Human Genetics Group, University of Nottingham, Nottingham, UK.

Translational Psychiatry
|May 26, 2019
PubMed

Insights

A new polygenic risk score (PRS) model effectively predicts late-onset Alzheimer's disease (LOAD). This PRS model can identify individuals with mild cognitive impairment (MCI) at higher risk of converting to LOAD.

Area of Science:

  • Neurogenetics
  • Alzheimer's Disease Research
  • Geriatric Medicine

Background:

  • Mild cognitive impairment (MCI) affects over 20% of individuals aged 65+, with a significant risk of progressing to dementia.
  • Early identification of dementia risk is crucial, as underlying pathological changes begin decades before symptom onset.
  • Late-onset Alzheimer's disease (LOAD) poses a growing public health challenge, necessitating improved predictive tools.

Purpose of the Study:

  • To develop and validate a predictive model for LOAD using polygenic risk scores (PRS) and clinical data.
  • To assess the model's ability to predict conversion from MCI to LOAD.
  • To investigate the association between PRS and diagnostic groups (MCI, LOAD, controls).

Main Methods:

  • Genotyping of 122 MCI patients, LOAD cases, and controls using the NeuroChip array.
  • Generation of PRS using PRSice-2 and International Genomics of Alzheimer's Disease Project (IGAP) summary statistics.
  • Development of a LOAD predictability model incorporating PRS, APOE SNPs (rs7412, rs429358), age, and gender.

Main Results:

  • The PRS model achieved 82.5% predictability for LOAD (AUPRC).
  • When applied to the MCI cohort, the model predicted MCI to LOAD conversion with 61.0% accuracy.
  • One-way ANOVA revealed significant differences in average PRS scores across diagnostic groups (p < 0.0001).

Conclusions:

  • The developed PRS model demonstrates strong predictability for LOAD.
  • The PRS model shows potential for identifying MCI individuals at elevated risk of progressing to LOAD.
  • Genetic risk profiling, including PRS, offers a promising avenue for early dementia risk assessment.

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