scholarly journals A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andre Altmann ◽  
Marzia A Scelsi ◽  
Maryam Shoai ◽  
Eric de Silva ◽  
Leon M Aksman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Diagnosis ◽  
Risk Scores ◽  
Imaging Biomarkers ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
Apoe Ε4 ◽  
Polygenic Scores ◽  
Apoe Locus

Abstract Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer’s disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer’s disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer’s disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer’s disease and their association (beyond the APOE locus) with a broad range of Alzheimer’s disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.

scholarly journals Association of naturally occurring antibodies to β-amyloid with cognitive decline and cerebral amyloidosis in Alzheimer’s disease

2021 ◽  
Vol 7 (1) ◽  
pp. eabb0457
Author(s):  
Yu-Hui Liu ◽  
Jun Wang ◽  
Qiao-Xin Li ◽  
Christopher J. Fowler ◽  
Fan Zeng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Plasma Levels ◽  
Imaging Biomarkers ◽  
Amino Terminus ◽  
Cross Sectional ◽  
Naturally Occurring ◽  
Β Amyloid ◽  
Naturally Occurring Antibodies

The pathological relevance of naturally occurring antibodies to β-amyloid (NAbs-Aβ) in Alzheimer’s disease (AD) remains unclear. We aimed to investigate their levels and associations with Aβ burden and cognitive decline in AD in a cross-sectional cohort from China and a longitudinal cohort from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. NAbs-Aβ levels in plasma and cerebrospinal fluid (CSF) were tested according to their epitopes. Levels of NAbs targeting the amino terminus of Aβ increased, and those targeting the mid-domain of Aβ decreased in both CSF and plasma in AD patients. Higher plasma levels of NAbs targeting the amino terminus of Aβ and lower plasma levels of NAbs targeting the mid-domain of Aβ were associated with higher brain amyloidosis at baseline and faster cognitive decline during follow-up. Our findings suggest a dynamic response of the adaptive immune system in the progression of AD and are relevant to current passive immunotherapeutic strategies.

[P3-095]: THE USE OF POLYGENIC RISK SCORES (PRS) TO CHARACTERISE THE PRECLINICAL PHASE OF ALZHEIMER's DISEASE

2017 ◽  
Vol 13 (7S_Part_20) ◽  
pp. P970-P971
Author(s):  
Michelle K. Lupton ◽  
Margie Wright ◽  
Nick Martin ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Preclinical Phase

scholarly journals Associations between Alzheimer’s disease polygenic risk scores and hippocampal subfield volumes in 17,161 UK Biobank participants

2021 ◽  
Vol 98 ◽  
pp. 108-115
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest Koch ◽  
Karen A. Mather ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk

P3-482: HIGHER ALZHEIMER'S DISEASE POLYGENIC RISK SCORES ARE ASSOCIATED WITH GREATER OBJECTIVELY MEASURED COGNITIVE EFFORT DESPITE NORMAL NEUROPSYCHOLOGICAL FUNCTION

2006 ◽  
Vol 14 (7S_Part_24) ◽  
pp. P1305-P1306
Author(s):  
William S. Kremen ◽  
Matthew S. Panizzon ◽  
Eric L. Granholm ◽  
Jeremy A. Elman ◽  
Daniel E. Gustavson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Effort ◽  
Risk Scores ◽  
Neuropsychological Function ◽  
Polygenic Risk ◽  
Objectively Measured

scholarly journals THE APOE-ε4 ALLELE AND AGE SYNERGISTICALLY DRIVE DISEASE PROGRESSION IN ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S943-S943
Author(s):  
Luca Kleineidam ◽  
Andrea R Zammit ◽  
Alyssa DeVito ◽  
Richard B Lipton ◽  
Oliver Peters ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Underlying Mechanism ◽  
Additive Models ◽  
Tau Pathology ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Apoe Ε4 ◽  
Ε4 Allele

Abstract The Apolipoprotein E (APOE)-ε4 allele is the strongest genetic risk factor for Alzheimer’s disease (AD) and other neurodegenerative dementias. Cross-sectional case-control studies suggest that the effect of APOE-ε4 decreases in old age. However, since APOE- ε4 is associated with mortality, these studies might be prone to bias due to selective survival. Therefore, we used multi-state-modeling in longitudinal cohort studies to examine the effect of APOE-ε4 on the transition through cognitive states (i.e. cognitively normal, mild cognitive impairment (MCI) and dementia) while taking death as a competing risk into account. Results from the German AgeCoDe study (n=3000, aged 75-101 years) showed that APOE-ε4 increases the risk for cognitive deterioration in all disease stages. Contrary to results from cross-sectional studies, the effect of APOE-ε4 on the transition from MCI to dementia increased with increasing age (HR=1.044, 95%-CI=1.001-1090). The direction of this effect was confirmed in a smaller sample from the Einstein Aging Study (n=744, HR=1.032, 95%-CI=0.949-1.122). To examine the pathophysiological basis of these results, generalized additive models were used to study AD biomarkers in the liquor of 1045 patients with MCI or AD-dementia. Here, increased amyloid (Abeta1-42) pathology was associated with increased tau pathology (pTau181), consistent with the amyloid-cascade-hypothesis. Interestingly, higher age and presence of the APOE-ε4 synergistically lowered the amount of amyloid required to exacerbate tau pathology (interaction p=0.012). Taken together, our results suggest that the effect of APOE-ε4 on disease progression increases with advancing age. An altered neuroinflammatory response to neurodegeneration should be further explored as potential underlying mechanism.

scholarly journals Defining Alzheimer’s disease subtypes using polygenic risk scores integrated with genomic and brain transcriptomic profiles

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Junming Hu ◽  
Jaeyoon Chung ◽  
Rebecca Panitch ◽  
Congcong Zhu ◽  
Gary W. Beecham ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Disease Subtypes

Identification of Biological Pathways to Alzheimer's Disease Using Polygenic Scores

2017 ◽  
Vol 41 (S1) ◽  
pp. S166-S167
Author(s):  
J. Harrison ◽  
E. Baker ◽  
L. Hubbard ◽  
D. Linden ◽  
J. Williams ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Folding ◽  
Late Onset ◽  
Cell Lineage ◽  
Biological Pathways ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Discovery Sample ◽  
Polygenic Scores

IntroductionSingle nucleotide polymorphisms (SNPs) contribute small increases in risk for late-onset Alzheimer's disease (LOAD). LOAD SNPs cluster around genes with similar biological functions (pathways). Polygenic risk scores (PRS) aggregate the effect of SNPs genome-wide. However, this approach has not been widely used for SNPs within specific pathways.ObjectivesWe investigated whether pathway-specific PRS were significant predictors of LOAD case/control status.MethodsWe mapped SNPs to genes within 8 pathways implicated in LOAD. For our polygenic analysis, the discovery sample comprised 13,831 LOAD cases and 29,877 controls. LOAD risk alleles for SNPs in our 8 pathways were identified at a P-value threshold of 0.5. Pathway-specific PRS were calculated in a target sample of 3332 cases and 9832 controls. The genetic data were pruned with R2 > 0.2 while retaining the SNPs most significantly associated with AD. We tested whether pathway-specific PRS were associated with LOAD using logistic regression, adjusting for age, sex, country, and principal components. We report the proportion of variance in liability explained by each pathway.ResultsThe most strongly associated pathways were the immune response (NSNPs = 9304, = 5.63 × 10−19, R2 = 0.04) and hemostasis (NSNPs = 7832, P = 5.47 × 10−7, R2 = 0.015). Regulation of endocytosis, hematopoietic cell lineage, cholesterol transport, clathrin and protein folding were also significantly associated but accounted for less than 1% of the variance. With APOE excluded, all pathways remained significant except proteasome-ubiquitin activity and protein folding.ConclusionsGenetic risk for LOAD can be split into contributions from different biological pathways. These offer a means to explore disease mechanisms and to stratify patients.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Alzheimer’s disease genetic risk and sleep phenotypes: association with more slow-waves and daytime sleepiness

2020 ◽  
Author(s):  
Vincenzo Muto ◽  
Ekaterina Koshmanova ◽  
Pouya Ghaemmaghami ◽  
Mathieu Jaspar ◽  
Christelle Meyer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Young Adults ◽  
Genetic Variants ◽  
Daytime Sleepiness ◽  
Sleep Disturbances ◽  
Cognitive Disorders ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Genome Wide

AbstractSleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease. Whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset, is currently not known. We extensively phenotyped sleep under different sleep conditions and compute whole-genome Polygenic Risk Scores (PRS) for AD in a carefully selected homogenous sample of healthy 363 young men (22.1 y ± 2.7) devoid of sleep and cognitive disorders. AD PRS was associated with more slow wave energy, i.e. the cumulated power in the 0.5-4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss. Furthermore higher AD PRS was correlated with higher habitual daytime sleepiness. These results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and reinforce the idea that sleep may be an efficient intervention target for AD.

scholarly journals Predicting Alzheimer’s disease progression using deep recurrent neural networks

2019 ◽  
Author(s):  
Minh Nguyen ◽  
Tong He ◽  
Lijun An ◽  
Daniel C. Alexander ◽  
Jiashi Feng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Missing Data ◽  
Longitudinal Data ◽  
Clinical Diagnosis ◽  
Short Term Memory ◽  
Support Vector ◽  
Training Procedure ◽  
Cross Sectional ◽  
The Future

AbstractEarly identification of individuals at risk of developing Alzheimer’s disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al. 2018) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a “preprocessing” issue, by imputing the missing data using the previous timepoint (“forward filling”) or linear interpolation (“linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing (“model filling”). Our analyses suggest that the minimalRNN with “model filling” compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.

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