scholarly journals Probability of the Alzheimer’s disease based on common and rare genetic variants

Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt

Abstract Background: Alzheimer’s disease, among other neurodegenerative disorders, spans decades in individuals’ life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer’s disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE- ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals. Methods: We estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups. Results: The AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+ the probability of AD grows from 0.03 to 0.18 (without APOE), and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08-0.6 and 0.3-0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3.Conclusions: Our approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach can be directly implemented in a clinical setting and easily updated for novel rare variants and for other populations when appropriate ethnic GWASes appear.

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt

Abstract Background Alzheimer’s disease, among other neurodegenerative disorders, spans decades in individuals’ life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer’s disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE-ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals. Methods We estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups. Results The AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+, the probability of AD grows from 0.03 to 0.18 (without APOE) and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08–0.6 and 0.3–0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3. Conclusions Our approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach has potential for use in a clinical setting and can easily be updated for novel rare variants and for other populations or confounding factors when appropriate genome-wide association data become available.


2008 ◽  
Vol 115 (6) ◽  
pp. 863-867 ◽  
Author(s):  
O. Combarros ◽  
P. Sánchez-Juan ◽  
J. A. Riancho ◽  
I. Mateo ◽  
E. Rodríguez-Rodríguez ◽  
...  

Author(s):  
Lubomir Balabanski ◽  
Dimitar Serbezov ◽  
Maya Atanasoska ◽  
Sena Karachanak-Yankova ◽  
Savina Hadjidekova ◽  
...  

2020 ◽  
Vol 21 (21) ◽  
pp. 8338
Author(s):  
Kimberley D. Bruce ◽  
Maoping Tang ◽  
Philip Reigan ◽  
Robert H. Eckel

Lipoprotein lipase (LPL) is a key enzyme in lipid and lipoprotein metabolism. The canonical role of LPL involves the hydrolysis of triglyceride-rich lipoproteins for the provision of FFAs to metabolic tissues. However, LPL may also contribute to lipoprotein uptake by acting as a molecular bridge between lipoproteins and cell surface receptors. Recent studies have shown that LPL is abundantly expressed in the brain and predominantly expressed in the macrophages and microglia of the human and murine brain. Moreover, recent findings suggest that LPL plays a direct role in microglial function, metabolism, and phagocytosis of extracellular factors such as amyloid- beta (Aβ). Although the precise function of LPL in the brain remains to be determined, several studies have implicated LPL variants in Alzheimer’s disease (AD) risk. For example, while mutations shown to have a deleterious effect on LPL function and expression (e.g., N291S, HindIII, and PvuII) have been associated with increased AD risk, a mutation associated with increased bridging function (S447X) may be protective against AD. Recent studies have also shown that genetic variants in endogenous LPL activators (ApoC-II) and inhibitors (ApoC-III) can increase and decrease AD risk, respectively, consistent with the notion that LPL may play a protective role in AD pathogenesis. Here, we review recent advances in our understanding of LPL structure and function, which largely point to a protective role of functional LPL in AD neuropathogenesis.


2008 ◽  
Vol 147B (5) ◽  
pp. 650-653 ◽  
Author(s):  
Eloy Rodríguez‐Rodríguez ◽  
Javier Llorca ◽  
Ignacio Mateo ◽  
Jon Infante ◽  
Coro Sánchez‐Quintana ◽  
...  

2016 ◽  
Vol 54 (7) ◽  
pp. 5192-5200 ◽  
Author(s):  
Qun Xiang ◽  
Rui Bi ◽  
Min Xu ◽  
Deng-Feng Zhang ◽  
Liwen Tan ◽  
...  

2011 ◽  
Vol 32 (3) ◽  
pp. 547.e1-547.e6 ◽  
Author(s):  
Onofre Combarros ◽  
Eloy Rodríguez-Rodríguez ◽  
Ignacio Mateo ◽  
José Luis Vázquez-Higuera ◽  
Jon Infante ◽  
...  

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Marzieh Khani ◽  
Elizabeth Gibbons ◽  
Jose Bras ◽  
Rita Guerreiro

AbstractThe search for rare variants in Alzheimer’s disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.


2018 ◽  
Author(s):  
Niccolò Tesi ◽  
Sven J. van der Lee ◽  
Marc Hulsman ◽  
Iris E. Jansen ◽  
Najada Stringa ◽  
...  

AbstractThe detection of genetic loci associated with Alzheimer’s disease (AD) requires large numbers of cases and controls because variant effect-sizes are mostly small. We hypothesized that variant effect-sizes should increase when individuals who represent the extreme ends of a disease spectrum are considered, as their genomes are assumed to be maximally enriched or depleted with disease-associated genetic variants.We used 1,073 extensively phenotyped AD cases with relatively young age at onset as extreme cases (66.3±7.9 years), 1,664 age-matched controls (66.0±6.5 years) and 255 cognitively healthy centenarians as extreme controls (101.4±1.3 years). We estimated the effect-size of 29 variants that were previously associated with AD in genome-wide association studies.Comparing extreme AD-cases with centenarian-controls increased the variant effect-size relative to published effect-sizes by on average 1.90-fold (SE=0.29,p=9.0×10−4). The effect-size increase was largest for the rare high-impactTREM2 (R74H)variant (6.5-fold), and significant for variants in/nearECHDC3(4.6-fold),SLC24A4-RIN3(4.5-fold),NME8(3.8-fold),PLCG2(3.3-fold),APOE-ε2(2.2-fold) andAPOE-ε4(2.0-fold). Comparing extreme phenotypes enabled us to replicate the AD association for 10 variants (p<0.05) in relatively small samples. The increase in effect-sizes depended mainly on using centenarians as extreme controls: the average variant effect-size was not increased in a comparison of extreme AD cases and age-matched controls (0.94-fold,p=6.8×10−1), suggesting that on average the tested genetic variants did not explain the extremity of the AD-cases. Concluding, using centenarians as extreme controls in AD case-controls studies boosts the variant effect-size by on average two-fold, allowing the replication of disease-association in relatively small samples.


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