Advances in Genetic Risk Scores for Alzheimer's Disease and Dementia: A Systematic Review
Stefanos N Sampatakakis1, Niki Mourtzi1, Alex Hatzimanolis2
11st Department of Neurology, Aiginition Hospital, Athens Medical School, National and Kapodistrian University, 11528 Athens, Greece.
Background:
Research concerning the genetic risk for dementia has recently been headed towards new directions. Novel findings from genome-wide association studies have highlighted the association of Alzheimer's disease incidence with many gene polymorphisms, apart from the Apolipoprotein-E genotype. The identification of additional genetic risk factors has led to the construction of specific genetic risk scores for dementia, considering many different genetic factors and specific biological pathways related to Alzheimer's disease.
Methods:
We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis method, summarizing existing data regarding genetic risk scores for Alzheimer's disease and dementia, in order to improve the current understanding of the genetic underpinnings of dementia. In specific, five databases (PubMed/MEDLINE, Embase, Scopus, Web of science, and Cochrane Central) were searched using the keywords "genetic risk score", "Alzheimer's disease", and "dementia" with specific inclusion and exclusion criteria.
Results:
From the 552 articles identified, we finally included 20 studies for the qualitative analysis. These reports were classified in three different categories of genetic scores: "polygenic risk scores (PRSs)" (including 11 studies), "pathway specific polygenic risk scores (p-PRSs)" (5 studies), and "complex genetic risk scores" (4 studies).
Conclusions:
Existing genetic risk scores have contributed to better dementia prediction and a better understanding of the underlying pathology. Novel approaches integrating multiple polygenic risk scores might ameliorate the accuracy of genetic risk scores. The combination of polygenic risk scores that are specific to related biological pathways or relevant biomarkers is of utmost importance to achieve a better predictive ability.
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