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Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease

The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art...

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Autores principales: Vlachakis, Dimitrios, Papakonstantinou, Eleni, Sagar, Ram, Bacopoulou, Flora, Exarchos, Themis, Kourouthanassis, Panos, Karyotis, Vasileios, Vlamos, Panayiotis, Lyketsos, Constantine, Avramopoulos, Dimitrios, Mahairaki, Vasiliki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305482/
https://www.ncbi.nlm.nih.gov/pubmed/34209762
http://dx.doi.org/10.3390/cells10071627
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author Vlachakis, Dimitrios
Papakonstantinou, Eleni
Sagar, Ram
Bacopoulou, Flora
Exarchos, Themis
Kourouthanassis, Panos
Karyotis, Vasileios
Vlamos, Panayiotis
Lyketsos, Constantine
Avramopoulos, Dimitrios
Mahairaki, Vasiliki
author_facet Vlachakis, Dimitrios
Papakonstantinou, Eleni
Sagar, Ram
Bacopoulou, Flora
Exarchos, Themis
Kourouthanassis, Panos
Karyotis, Vasileios
Vlamos, Panayiotis
Lyketsos, Constantine
Avramopoulos, Dimitrios
Mahairaki, Vasiliki
author_sort Vlachakis, Dimitrios
collection PubMed
description The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.
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spelling pubmed-83054822021-07-25 Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease Vlachakis, Dimitrios Papakonstantinou, Eleni Sagar, Ram Bacopoulou, Flora Exarchos, Themis Kourouthanassis, Panos Karyotis, Vasileios Vlamos, Panayiotis Lyketsos, Constantine Avramopoulos, Dimitrios Mahairaki, Vasiliki Cells Opinion The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD. MDPI 2021-06-29 /pmc/articles/PMC8305482/ /pubmed/34209762 http://dx.doi.org/10.3390/cells10071627 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Opinion
Vlachakis, Dimitrios
Papakonstantinou, Eleni
Sagar, Ram
Bacopoulou, Flora
Exarchos, Themis
Kourouthanassis, Panos
Karyotis, Vasileios
Vlamos, Panayiotis
Lyketsos, Constantine
Avramopoulos, Dimitrios
Mahairaki, Vasiliki
Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title_full Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title_fullStr Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title_full_unstemmed Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title_short Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease
title_sort improving the utility of polygenic risk scores as a biomarker for alzheimer’s disease
topic Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305482/
https://www.ncbi.nlm.nih.gov/pubmed/34209762
http://dx.doi.org/10.3390/cells10071627
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