Abstract
BackgroundAlzheimer’s disease, among other neurodegenerative disorders, spans decades in individuals’ life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer’s disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE-ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals.MethodsWe estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups.ResultsThe AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+, the probability of AD grows from 0.03 to 0.18 (without APOE) and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08–0.6 and 0.3–0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3.ConclusionsOur approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach has potential for use in a clinical setting and can easily be updated for novel rare variants and for other populations or confounding factors when appropriate genome-wide association data become available.
Highlights
Genome-wide association studies (GWAS) identified genetic risk variants of late onset “sporadic” disease beyond the APOE locus [1,2,3,4], followed by exome chip analyses identifying rare variants with moderate risk [5,6,7]
We calculated the probability of Alzheimer’s disease (AD) for 2%, 10% and 30% prevalence during lifetime and in 65+ and 85+ age groups, respectively
We have shown that (5) gives a highly accurate proxy for case-control sampling of polygenic risk score (PRS) and logistic regression if either the PRS aggregates a high number of singular nucleotide polymorphism (SNP) or very highly associated SNPs are excluded from the PRS
Summary
Genome-wide association studies (GWAS) identified genetic risk variants of late onset “sporadic” disease beyond the APOE locus [1,2,3,4], followed by exome chip analyses identifying rare variants with moderate risk [5,6,7]. While causal fully penetrant mutations almost certainly lead to development of the disease [8], The PRSs are designed to aggregate genome-wide genotype data into a single variable indicating genetic liability to a disorder or trait. PRS studies often reach sufficiently high statistical significance to suggest trait polygenicity and, the prediction accuracy is usually insufficient for clinical utility [9], PRS has been suggested as a useful tool for the selection for clinical trials of individuals of European ancestry across different traits [10,11,12,13]. Designed to capture the risk of common variants, the PRS aggregates the effects of known genome-wide associated loci [15] and of loci that do not reach genomewide statistical significance. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals
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