Abstract
In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.
Highlights
Neurodegenerative diseases are a group of disorders that are characterised by the progressive loss of the structure and function of the central nervous system
APOE is the strongest predictor of late onset Alzheimer's disease (AD), the genetic SNP-based heritability explained by this locus is not high (0.05) (Escott-Price et al, 2017b) compared to genome-wide estimates (0.24–0.53) (Lee et al, 2013a; Ridge et al, 2016; Ridge et al, 2013)
In this review we sought to 1) to summarize the statistical techniques that can be used to identify pleotropic genes and regions, 2) to discuss the biological mechanisms that are common between neurodegenerative disorders, 3) to explain how heritability estimates are related to the prediction accuracy by the polygenic risk scores (PRS) and 4) to explore how PRS analysis can be utilised to model the genetic risk of pleiotropic regions for prediction of shared sub-phenotypes in neurodegeneration
Summary
Neurodegenerative diseases are a group of disorders that are characterised by the progressive loss of the structure and function of the central nervous system. Various methods have been developed that exploit pleiotropic effects and several studies have been conducted in an effort to identify novel genetic associations in neurological disorders Despite these GWASs being conducted and hundreds of genomic regions being implicated in various neurodegeneration related traits, these findings have not been translated into clinically useful risk prediction models. In this review we sought to 1) to summarize the statistical techniques that can be used to identify pleotropic genes and regions, 2) to discuss the biological mechanisms that are common between neurodegenerative disorders, 3) to explain how heritability estimates are related to the prediction accuracy by the PRS and 4) to explore how PRS analysis can be utilised to model the genetic risk of pleiotropic regions for prediction of shared sub-phenotypes in neurodegeneration
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
