Age-dependent effect of APOE and polygenic component on Alzheimer's disease

Bellou, Eftychia; Baker, Emily; Leonenko, Ganna; Bracher-Smith, Matthew; Daunt, Paula; Menzies, Georgina E.; Williams, Julie; Escott‐Price, Valentina

doi:10.1016/j.neurobiolaging.2020.04.024

Cited by 40 publications

(51 citation statements)

References 38 publications

(66 reference statements)

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“…Recently another study, investigating subjects that do not overlap with our study and using different parameters, reported a greater effect of APOE in younger participants (Bellou et al, 2020). Our stratified interaction analysis indicating a larger effect of PRS non-APOE in younger APOE4 carriers leads us to a novel conclusion about APOE -independent genetic risk and provides a logical explanation for their observation: among all APOE4 cases, those with a younger onset carry the largest PRS non-APOE .…”

Section: Discussionsupporting

confidence: 53%

Greater Effect of Polygenic Risk Score for Alzheimer’s Disease Among Younger Cases who are Apolipoprotein E-ε4 Carriers

Fulton-Howard

Goate

Adelson

et al. 2020

Preprint

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Disclosure StatementAMG has consulted for Eisai, Biogen, Pfizer, AbbVie, Cognition Therapeutics and GSK.She also served on the SAB at Denali Therapeutics from 2015-2018. YFH co-owns stock and stock options of Regeneron Pharmaceuticals. All other authors have no interests to declare. AbstractPolygenic risk scores (PRS) have the potential to serve as a low-cost, non-invasive screening method for Alzheimer's disease (AD). However, to what extent age and the Apolipoprotein E-ε4 (APOE4) risk allele influence the effect of PRS is underexplored. In a cohort of 346 superager controls (age ≥ 90 years), 2,930 controls (age 60-89) and 1,760 AD cases, we computed APOE-independent PRS for AD. When using superager controls, subjects with PRS in the top decile had nearly five times greater odds of having AD than subjects in the lowest decile (OR=4.91, P=2.24x10 -6 ). In our crosssectional cohort, PRS modifies the age of onset for AD among APOE4 carriers, but not among non-carriers. Among APOE4 carriers, PRS in the top decile was associated with a five years earlier AD onset than the lowest decile (70.0 vs 75.0 years; t-test P=2.4x10 -5 ). These findings suggest that APOE-independent genetic risk disproportionally affects younger APOE4 carriers, leading to earlier disease onset, while older controls carry less genetic risk.

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Section: Discussionsupporting

confidence: 53%

Greater Effect of Polygenic Risk Score for Alzheimer’s Disease Among Younger Cases who are Apolipoprotein E-ε4 Carriers

Fulton-Howard

Goate

Adelson

et al. 2020

Preprint

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“…Future studies should further investigate this avenue of brain-behaviour relationships in older age. They should consider inclusion of the wider range of genetic 40 and environmental 41 variables, and thus probably reduce the misclassifications shown in the present study, as well as other predictive models and methods within modern machine learning frameworks 36 , 42 .…”

Section: Discussionmentioning

confidence: 99%

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Mofrad

Lundervold

Vik

et al. 2021

Sci Rep

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The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in $$F_1$$ F 1 -score from 60 to 77%. The $$F_1$$ F 1 -scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.

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“…The oligogenic risk score (ORS.no.APOE) (based on SNPs with pT ≤ 10 − 5 ) also decreases in cases with age but is on average higher than in controls, with the highest being in ORS.no.APOE for ε44 cases as reported in 8 . Contrary to ORS.no.APOE, the mean of PRS.no.APOE (blue line) is higher in older cases and lower in older controls 16 . Thus, because of the changing allelic frequencies of APOE genotypes over age, it is clear that the APOE genotype by itself and the ORS.no.APOE become much less accurate predictors in older cases, while the reverse is seen with the PRS.no.APOE score.…”

Section: Optimal P-value Thresholdmentioning

confidence: 97%

“…It has been shown that the PRS contribution to AD risk differs with age and APOE-ε4 allele status. For example, the effect of PRS (pT ≤ 0.5) is more pronounced in older people 16 , and the effect of oligogenic risk scores constructed using SNPs with an association pT ≤ 1e-5 is greater in ε4 homozygotes 8 . Based upon these observations, we hypothesised that unaccounted age-related genetic differences (in particular the APOE-ε4 agedependent frequency) lead to the disagreement about the optimal p-value threshold and the consequent debate about oligogenic (ORS) vs polygenic (PRS) disease models.…”

Section: Introductionmentioning

confidence: 99%

Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores is most accurate when using all genetic information.

Leonenko

Baker

Stevenson-Hoare

et al. 2021

Preprint

Self Cite

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There is little agreement regarding the approach and optimal p-value threshold of SNPs to calculate genetic risk scores for Alzheimer’s disease (AD). This reflects a fundamental underlying debate on the polygenic versus oligogenic disease architecture. We re-investigated the assumptions underlying the choice of specific p-value thresholds defining genetic loci used to determine polygenic risk scores (PRS). We find the optimal p-value threshold for SNP selection is 0.1, which supports the polygenic architecture of AD. We found that previous studies supporting an oligogenic model of AD did not take account of the reduction of APOE-ε4 allele frequency in older individuals, which skewed the results towards lower p-value thresholds and eclipsed the contribution of genes associated to AD with higher p-values. The polygenic approach to AD is also effective to identify individuals at high or low AD risk, when only APOE-ε3 homozygous individuals are considered. We also introduce the standardisation of PRS against a population data which ensures comparability of the PRS between studies. In conclusion, our work demonstrates that AD is fundamentally a polygenic disease and that stratifying populations for AD risk best takes the full PRS score into account.

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Age-dependent effect of APOE and polygenic component on Alzheimer's disease

Cited by 40 publications

References 38 publications

Greater Effect of Polygenic Risk Score for Alzheimer’s Disease Among Younger Cases who are Apolipoprotein E-ε4 Carriers

Greater Effect of Polygenic Risk Score for Alzheimer’s Disease Among Younger Cases who are Apolipoprotein E-ε4 Carriers

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores is most accurate when using all genetic information.

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