Polygenic risk and hazard scores for Alzheimer's disease prediction
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Leonenko, Ganna | Sims, Rebecca | Shoai, Maryam | Frizzati, Aura | Bossù, Paola | Spalletta, Gianfranco | Fox, Nick, C | Williams, Julie | Hardy, John | Escott-Price, Valentina | Tsolaki, Magda | Craig, David | Avramidou, Despoina | Germanou, Antonia | Koutroumani, Maria | Gkatzima, Olymbia | Hampel, Harald | Kehoe, Patrick G | Love, Seth | Rubinsztein, David C | Frölich, Lutz | Mcguinness, Bernadette | Johnston, Janet A | Passmore, Peter | Drichel, Dmitriy | Rossor, Martin | Schott, Jonathan M | Warren, Jason D | Bras, Jose | Guerreiro, Rita | Hughes, Amit Kawalia Joseph T | Patel, Yogen | Lupton, Michelle K | Proitsi, Petra | Powell, John | Kauwe, John S K | Mancuso, Michelangelo | Bonuccelli, Ubaldo | Uphill, James | Fisher, Elizabeth | Masullo, Carlo | Soininen, Hilkka | Bisceglio, Gina | Ma, Li | Dickson, Dennis W | Graff-Radford, Neill R | Carrasquillo, Minerva M | Younkin, Steven G | Sorbi, Sandro | Daniilidou, Makrina | Hodges, Angela | Galimberti, Daniela | Scarpini, Elio | Scherer, Martin | Peters, Oliver | Ramirez, Alfredo | Leber, Markus | Pichler, Sabrina | Mayhaus, Manuel | Gu, Wei | Riemenschneider, Matthias | Wiltfang, Jens | Heun, Reinhard | Kölsch, Heike | Kornhuber, Johannes | Heuser, Isabella | Rujescu, Dan | Hartmann, Annette M | Giegling, Ina | Hüll, Michael | Lovestone, Simon | Cruchaga, Carlos | Morris, John | Mayo, Kevin | Feulner, Thomas | Sussams, Rebecca | Holmes, Clive | Mann, David | Pickering-Brown, Stuart | Hooper, Nigel M | Mcquillin, Andrew | Livingston, Gill | Bass, Nicholas J | Vronskaya, Maria | Morgan, Taniesha | Denning, Nicola | Cushion, Thomas D | Jones, Lesley | Marshall, Rachel | Meggy, Alun | Menzies, Georgina | Grozeva, Detelina | O'Donovan, Michael C | Owen, Michael J | Holmans, Peter A | Salani, Francesca | Russo, Giancarlo | Maier, Wolfgang | Jessen, Frank | Wichmann, H-Erich | Morgan, Kevin | Goate, Alison M | Vellas, Bruno | Vardy, Emma | Moebus, Susanne | Jöckel, Karl-Heinz | Dichgans, Martin | Klopp, Norman | Turton, James | Lord, Jenny | Brown, Kristelle | Medway, Christopher | Nöthen, Markus M | Hoffmann, Per | Daniele, Antonio | Bayer, Anthony | Gallacher, John | Bussche, Hendrik van Den | Brayne, Carol | Riedel-Heller, Steffi | Powell, John F | Al-Chalabi, Ammar | Shaw, Christopher E | Kloszewska, Iwona | Pastor, Pau | Diez-Fairen, Monica | Lynch, Aoibhinn | Lawlor, Brian | Gill, Michael | Coto, Eliecer | Alvarez, Victoria | Singleton, Andrew B | Collinge, John | Mead, Simon | Ryan, Natalie | Nacmias, Benedetta | Ortega-Cubero, Sara | Rodriguez-Rodriguez, Eloy | Sanchez-Juan, Pascual | Shofany, Jacob | Banaj, Nerisa | Ciullo, Valentina | Sacchinelli, Eleonora | Clarke, Robert | Smith, a David | Warden, Donald | Ben-Shlomo, Yoav | Cupidi, Chiara | Maletta, Raffaele Giovanni | Bruni, Amalia Cecilia | Gallo, Maura | Harold, Denise | Cecchetti, Roberta | Mecocci, Patrizia | Boccardi, Virginia | Warner, Nick | Wilcock, Gordon | Deloukas, Panagiotis | Gwilliam, Rhian | Corcoran, Chris | Tschanz, Joann | Munger, Ron
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CCSD ; Wiley -
International audience.
Abstract Objective Genome‐wide association studies ( GWAS ) have identified over 30 susceptibility loci associated with Alzheimer's disease ( AD ). Using AD GWAS data from the International Genomics of Alzheimer's Project ( IGAP ), Polygenic Risk Score ( PRS ) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score ( PHS ) is able to quantify individuals with age‐specific genetic risk for AD . The aim of this study was to quantify the age‐specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS . Methods Quantification of individual differences in age‐specific genetic risk for AD identified by the PRS , was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium ( GERAD ). Polygenic Hazard Scores were generated for the same individuals. The age‐specific genetic risk for AD identified by the PRS was compared with that generated by the PHS . This was repeated using varying SNP s P ‐value thresholds for disease association. Results Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNP s were preselected for association to AD at P ≤ 0.001. The strongest effect ( B = 0.28, SE = 0.04, P = 2.5 × 10 −12 ) was observed for PRS based upon genome‐wide significant SNP s ( P ≤ 5 × 10 −8 ). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation Both PRS and PHS can be used to predict an age‐specific risk for developing AD . The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNP s were selected based upon AD GWAS case/control P ≤ 10 −3 , we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNP s are selected for association with AD risk at P > 10 −3 , the age‐specific risk prediction results are not significant for either PRS or PHS . However PHS could be more advantageous than PRS of age specific AD risk predictions when SNP s are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).
