Polygenic Risk Scores in Alzheimer's Disease: Current Applications and Future Directions

Baker, Emily; Escott‐Price, Valentina

doi:10.3389/fdgth.2020.00014

Cited by 49 publications

(54 citation statements)

References 36 publications

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“…No associations were found between AD PRS or CAD PRS and baseline total WBV and WMH volumes across all diagnostic groups, although PRSs have been shown to be predictive of AD risk 40 . That said, AD PRS beyond APOE is correlated with white matter load in the occipital lobe in AD subjects upon the inclusion of many variants (polygenic p=0.5).…”

Section: Discussion [1771 Words]mentioning

confidence: 71%

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired

Silva

Sudre

Barnes

et al. 2022

Preprint

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The role cardiac function plays in the predilection for, and progression of, Alzheimer's disease (AD), is complex and unclear. While a number of low-frequency genetic variants of large effect-size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small-effect size, which, in aggregate, are embodied by a polygenic risk score (PRS). In this study we aim to investigate the effect of polygenic risk for coronary artery disease (CAD) on brain atrophy in AD using whole brain volume (WBV) and put our findings in context with the polygenic risk for AD and presumed small vessel disease as quantified by white matter hyperintensities (WMH). We used 730 subjects from the ADNI database to investigate PRS effects (beyond APOE) on whole brain volumes, total and regional WMH and amyloid beta across diagnostic groups. In a subset of these subjects (N=602) we utilise longitudinal changes in whole brain volume over a maximum of 24 months using the boundary shift integral approach. Linear regression and linear mixed effects models were used to investigate the effect of WMH at baseline as well as AD-PRS and CAD-PRS on whole brain atrophy and whole brain atrophy acceleration, respectively. All genetic associations were examined under oligogenic (p=1e-5) and the more variant-inclusive polygenic (p=0.5) scenarios. Our results suggest no evidence for a link between PRS score and markers of AD pathology at baseline (when stratified by diagnostic group). However, both AD-PRS and CAD-PRS were associated with longitudinal decline in WBV (AD PRS t=3.3, PFDR=0.007 over 24 months in healthy controls) and surprisingly, under certain conditions WBV atrophy is statistically more correlated with cardiac PRS than AD PRS (CAD PRS t=2.1, PFDR=0.04 over 24 months in the MCI group). Further, in our regional analysis of WMH, AD PRS beyond APOE is predictive of white matter volume in the occipital lobe in AD subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to AD polygenic risk beyond APOE in healthy individuals (t=2, p=0.04). For subjects with mild cognitive impairment (MCI), beyond APOE, a more inclusive polygenic risk score including more variants, shows CAD PRS to be more predictive of WBV atrophy, than an oligogenic approach including fewer larger effect size variants.

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Section: Discussion [1771 Words]mentioning

confidence: 71%

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired

Silva

Sudre

Barnes

et al. 2022

Preprint

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“…This has directed the focus on investigating other pathways and other possible genetic variants associated with AD. Genome Wide Association Studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing AD in late life ( Baker and Escott-Price, 2020 ). These include CLU, PICALM, and CR1 as well as BIN1, ABCA7, and EPHA1 ( Harrison and Bookheimer, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Association Between Polygenic Risk Scores for Alzheimer’s Disease With Cognitive Performance and Intrinsic Functional Connectivity in Healthy Adults

Ibnidris

Fußer

Kranz

et al. 2022

Front. Aging Neurosci.

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BackgroundAlzheimer’s disease (AD) pathology is present many years before the onset of clinical symptoms. AD dementia cannot be treated. Timely and early detection of people at risk of developing AD is key for primary and secondary prevention. Moreover, understanding the underlying pathology that is present in the earliest stages of AD, and the genetic predisposition to that might contribute to the development of targeted disease-modifying treatments.ObjectivesIn this study, we aimed to explore whether genetic disposition to AD in asymptomatic individuals is associated with altered intrinsic functional connectivity as well as cognitive performance on neuropsychological tests.MethodsWe examined 136 cognitively healthy adults (old group: mean age = 69.32, SD = 4.23; young group: mean age = 31.34, SD = 13.12). All participants had undergone resting-state functional magnetic resonance imagining (fMRI), DNA genotyping to ascertain polygenic risk scores (PRS), and neuropsychological testing for global cognition, working memory, verbal fluency, and executive functions.ResultsTwo-step hierarchical regression analysis revealed that higher PRS was significantly associated with lower scores in working memory tasks [Letter Number Span: ΔR2 = 0.077 (p < 0.05); Spatial Span: ΔR2 = 0.072 (p < 0.05)] in older adults (>60 years). PRS did not show significant modulations of the intrinsic functional connectivity of the posterior cingulate cortex (PCC) with other regions of interest in the brain that are affected in AD.ConclusionAllele polymorphisms may modify the effect of other AD risk factors. This potential modulation warrants further investigations, particularly in cognitively healthy adults.

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“…The use of predictive modeling implies further practical applications in the field of clinical assessment [ 25 ]. For example, recent attempts showed a promising advancement of using predictive pattern scores of dementia in the multidimensional dataset, such as genetic polymorphisms or brain structural imaging [ 61, 62 ]. Likewise, translation from cognitive task performance to real-world functioning requires complex translation.…”

Section: Discussionmentioning

confidence: 99%

“…When comparing the two linear models, the combination of multiple subtests of the neuropsychological battery in a linear fashion showed a marginal increase in predictive accuracy, indicating that the current The use of predictive modeling implies further practical applications in the field of clinical assessment [25]. For example, recent attempts showed a promising advancement of using predictive pattern scores of dementia in the multidimensional dataset, such as genetic polymorphisms or brain structural imaging [61,62]. Likewise, translation from cognitive task performance to real-world functioning requires complex translation.…”

mentioning

confidence: 99%

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer’s Disease

Kwak

Jeon

et al. 2022

JAD

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Background: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. Objective: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. Methods: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. Results: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. Conclusion: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

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Polygenic Risk Scores in Alzheimer's Disease: Current Applications and Future Directions

Cited by 49 publications

References 36 publications

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired

Investigating the Association Between Polygenic Risk Scores for Alzheimer’s Disease With Cognitive Performance and Intrinsic Functional Connectivity in Healthy Adults

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer’s Disease

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