scholarly journals Alzheimer’s disease heterogeneity explained by polygenic risk scores based on brain transcriptomic profiles

2021 ◽  
Vol 17 (S5) ◽  
Author(s):  
Jaeyoon Chung ◽  
Rebecca Panitch ◽  
Junming Hu ◽  
Congcong Zhu ◽  
Jesse Mez ◽  
...  
2017 ◽  
Vol 13 (7S_Part_20) ◽  
pp. P970-P971
Author(s):  
Michelle K. Lupton ◽  
Margie Wright ◽  
Nick Martin ◽  

2021 ◽  
Vol 98 ◽  
pp. 108-115
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest Koch ◽  
Karen A. Mather ◽  
...  

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 ◽  
...  

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Junming Hu ◽  
Jaeyoon Chung ◽  
Rebecca Panitch ◽  
Congcong Zhu ◽  
Gary W. Beecham ◽  
...  

2020 ◽  
Author(s):  
Vincenzo Muto ◽  
Ekaterina Koshmanova ◽  
Pouya Ghaemmaghami ◽  
Mathieu Jaspar ◽  
Christelle Meyer ◽  
...  

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.


2019 ◽  
Vol 15 ◽  
pp. P284-P285
Author(s):  
Danai Chasioti ◽  
Tanner Y. Jacobson ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Jingwen Yan ◽  
...  

2021 ◽  
pp. 1-13
Author(s):  
Hongliang Liu ◽  
Michael Lutz ◽  
Sheng Luo ◽  

Background: Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer’s disease (AD). Objective: In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. Methods: We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. Results: We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, se = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, se = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PIB and AV45), but decreased hippocampus and entorhinal cortex volumes (p <  0.05). Mediation analysis showed that the effect of PRS on the increased risk of AD may be mediated by Aβ 42 (beta = 0.056, SE = 0.026, p = 0.036). Conclusion: Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andre Altmann ◽  
Marzia A Scelsi ◽  
Maryam Shoai ◽  
Eric de Silva ◽  
Leon M Aksman ◽  
...  

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.


2020 ◽  
Vol 16 (S3) ◽  
Author(s):  
Danai Chasioti ◽  
Tanner Y. Jacobson ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Sujuan Gao ◽  
...  

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