cells Opinion Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease Dimitrios Vlachakis 1,2,3 , Eleni Papakonstantinou 1 , Ram Sagar 4 , Flora Bacopoulou 2 , Themis Exarchos 5 , Panos Kourouthanassis 5 , Vasileios Karyotis 5 , Panayiotis Vlamos 5 , Constantine Lyketsos 6,7 , Dimitrios Avramopoulos 4,6,7, * and Vasiliki Mahairaki 4,6, *   Citation: Vlachakis, D.; Papakonstantinou, E.; Sagar, R.; Bacopoulou, F.; Exarchos, T.; Kourouthanassis,P.; Karyotis, V.; Vlamos, P.; Lyketsos, C.; Avramopoulos, D.; et al. Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease. Cells 2021, 10, 1627. https://doi.org/ 10.3390/cells10071627 Academic Editors: Dimitrios Kapogiannis, Stefan Kins, Nady Braidy and Alexander E. Kalyuzhny Received: 30 March 2021 Accepted: 25 June 2021 Published: 29 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. 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/). 1 Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece; dimvl@aua.gr (D.V.); eleni.ppk@gmail.com (E.P.) 2 University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children’s Hospital, 11527 Athens, Greece; fbacopoulou@med.uoa.gr 3 Center of Clinical, Laboratory of Molecular Endocrinology, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece 4 Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA; rsagar2@jhmi.edu 5 Bioinformatics and Human Electrophysiology Laboratory, Ionian University, 49100 Corfu, Greece; themis.exarchos@gmail.com(T.E.);pkour@ionio.gr (P.K.); karyotis@ionio.gr (V.K.); vlamos@ionio.gr (P.V.) 6 The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD 21287, USA; kostas@jhmi.edu 7 Johns Hopkins Medicine and Johns Hopkins Bayview Medical Center, Department of Psychiatry and Behavioral Sciences, Baltimore, MD 21287, USA * Correspondence: adimitr1@jhmi.edu (D.A.); vmachai1@jhmi.edu (V.M.) Abstract: 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. Keywords: Alzheimer’s disease; polygenic risk scores; biomarkers Cells 2021, 10, 1627. https://doi.org/10.3390/cells10071627 https://www.mdpi.com/journal/cells