Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

Leonenko, Ganna; Baker, Emily; Stevenson-Hoare, Joshua; Sierksma, Annerieke; Fiers, Mark; Williams, Julie; Strooper, Bart De; Escott‐Price, Valentina

doi:10.1038/s41467-021-24082-z

Cited by 132 publications

(140 citation statements)

References 60 publications

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“… 13 The best prediction was observed in a model combining directly measured APOE with the PRS excluding the APOE region at a threshold of P ≤ 0.10, whereas the prediction accuracy was attenuated at more relaxed thresholds despite increases in variants. 13 Altogether, recent findings suggest that Alzheimer’s disease is polygenic and the age-related nature of the risk is essential to consider. 13 …”

Section: Introductionmentioning

confidence: 94%

“… 12 However, the age distribution among Alzheimer’s disease cases versus controls, and thus differences in the prevalence of APOE ɛ2 versus ɛ4 allele frequencies can impact PRS prediction. 13 In addition to Alzheimer’s disease risk, APOE is associated with longevity where the allele frequencies for ɛ2 become more prevalent in older samples and ɛ4 alleles become less prevalent, at least in samples of European and Asian ancestries. 14–17 Moreover, the methods used to construct PRSs for Alzheimer’s disease can impact the composition of genetic variants included and hence prediction.…”

Section: Introductionmentioning

confidence: 99%

“… 13 Altogether, recent findings suggest that Alzheimer’s disease is polygenic and the age-related nature of the risk is essential to consider. 13 …”

Section: Introductionmentioning

confidence: 99%

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Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins

Karlsson

Escott‐Price

Gatz

et al. 2022

Brain Communications

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Heritability of Alzheimer’s disease estimated from twin studies is greater than heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer’s disease polygenic risk scores (PRSs) and heritability from twin models, to provide insight into the role of measured genetic variants, and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer’s disease was conducted between 1986 and 2016 and is the first study to incorporate PRSs into biometrical twin models of Alzheimer’s disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs, 688 incomplete pairs) with clinically-based diagnoses and registry follow-up (Mage = 85.28, SD = 7.02; 44% male; 431 cases, 1155 controls). We report contributions of PRSs at P < 1 × 10−05, considering a full PRS, PRS without the APOE region (PRS.no.APOE), and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer’s disease risk. The full PRS and PRS.no.APOE contributed 10.1% and 2.4% to Alzheimer’s disease risk, respectively. When APOE ε4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer’s disease risk, where APOE ε4 explained 9.3% and PRS.no.APOE dropped from 2.4% to 2.1%. The total genetic contribution to Alzheimer’s disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region explains much of the measurable genetic contribution to Alzheimer’s disease, with smaller contributions from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins

Karlsson

Escott‐Price

Gatz

et al. 2022

Brain Communications

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“…The ApoE region (Chr19:45,116,911–46,318,605) was excluded from the analysis due to its reported large effect size. PRS values with p-threshold (pT) of 0.1 were used for further statistical analysis since this pT explains the highest variance and provides the best prediction accuracy according to a study conducted by Leonenko et al (2021) . We performed the analysis using PRS that excluded ApoE region as the main explanatory variable.…”

Section: Methodsmentioning

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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“…Using PRSice-2 (28), we computed PRS in the NSHD, both including and excluding SNPs in the APOE region (chr 19, GRCh37 coordinates 44912079 to 45912079) (29). Two p-value thresholds (P T ) – previously identified to be optimal for PRS including and excluding the APOE region – were used for SNP selection: 5×10 −8 (suggested for APOE region included) and 0.1 (suggested for APOE region excluded) (30), resulting in four PRS. SNPs in linkage disequilibrium (r 2 >0.001 within a 250kb window) were clumped and the SNP with the lowest p-value was retained.…”

Section: Methodsmentioning

confidence: 99%

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

Green

Lord

Scelsi

et al. 2022

Preprint

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Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46 — the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). Following quality control, concentrations of 1019 metabolites – detected with liquid chromatography-mass spectrometry – were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures – modules and hub metabolites – and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR<0.05) modules or previously identified as a hub for cognition in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognition and social factors. Finally, AD polygenic risk scores (PRS), including and excluding the APOE region, were tested for relationships with metabolites and modules that associated (pFDR<0.05) with an imaging outcome (N=1638). In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR<0.05): one enriched in sphingolipids (hippocampal volume: β=0.14, 95%CI=[0.055,0.23]), one in several fatty acid pathways (whole-brain volume: β=-0.072, 95%CI=[-0.12,-0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: β=-0.066, 95%CI=[-0.11,-0.020]). Twenty-two hub metabolites were associated (pFDR<0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

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Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

Cited by 132 publications

References 60 publications

Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins

Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins

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

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

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