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Basic Science and Pathogenesis.

Eden R Martin1,2, Anthony J Griswold1,2, Farid Rajabli1,2

  • 1Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA.

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|December 25, 2025
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Summary
This summary is machine-generated.

Estimating Alzheimer's disease (AD) probability in mild cognitive impairment (MCI) individuals enhances genetic association studies. Imputing "caseness" for MCI patients improves statistical power, aiding in the discovery of genetic links to AD.

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Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Alzheimer's disease (AD) genetic studies typically exclude individuals with mild cognitive impairment (MCI) due to diagnostic uncertainty.
  • This exclusion may limit the power to detect genetic associations relevant to AD development.

Purpose of the Study:

  • To develop and validate a method for estimating the probability of AD conversion in MCI individuals.
  • To enhance genetic association analyses by incorporating MCI individuals with estimated probabilities of disease into case-control studies.

Main Methods:

  • A multi-step approach was proposed: 1. Identify a predictive model for AD using logistic regression on established AD cases and cognitively unimpaired (CU) controls. 2. Estimate AD probability (pi) for MCI individuals using this model. 3. Incorporate MCI individuals into genetic association tests via thresholding (pi > t) or probabilistic resampling.
  • The APOE-e4 allele association with AD was assessed in 1420 participants (448 AD, 714 CU, 258 MCI) as a proof of principle.

Main Results:

  • The benchmark analysis of APOE-e4 in AD cases vs. CU controls yielded an odds ratio (OR) of 3.25 (Z=9.088).
  • Including all MCI individuals as cases reduced the OR to 1.97 (Z=6.628).
  • A threshold model (t=0.8) incorporating clinical, demographic, and biomarker data (pTau-181, CDR memory score, age, sex) slightly improved the test statistic (OR=3.22, Z=9.174) by adding 24 MCI individuals as probable cases. The resampling approach was less effective than the threshold method.

Conclusions:

  • Clinical, demographic, and biomarker data can effectively impute disease probability ('caseness') in MCI individuals for genetic association studies.
  • Even a small number of imputed MCI cases significantly improved statistical test performance.
  • This imputation strategy holds promise for enhancing genetic discovery in larger Alzheimer's disease datasets.