Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes
Max Tamlander, Nina Mars, Matti Pirinen, FinnGen, Aarno Palotie, Mark J. Daly, Pharmaceutical companies, Bridget Riley-Gills, Howard J. Jacob, Dirk S. Paul, Heiko Runz, Sally John, Robert M. Plenge, Joseph Maranville, George Okafo, Nathan Lawless, Heli Salminen‐Mankonen, Mark I. McCarthy, Julie Hunkapiller, Meg Ehm, Kirsi Auro, Simonne Longerich, Caroline S. Fox, Anders Mälarstig, Katherine Klinger, Deepak Raipal, Eric Green, Robert Graham, Robert Yang, Chris O’Donnell, Tomi P. Mäkelä, Jaakko Kaprio, Petri Virolainen, Antti Hakanen, Terhi Kilpi, Markus Perola, Jukka Partanen, Anne Pitkäranta, Juhani Junttila, Raisa Serpi, Tarja Laitinen, Veli‐Matti Kosma, Arto Mannermaa, Jari Laukkanen, Marco Hautalahti, Other Experts/Non-Voting Members, Outi Tuovila, Raimo Pakkanen, Pharmaceutical companies, Jeffrey Waring, Ioanna Tachmazidou, Chia-Yen Chen, Shameek Biswas, Zhihao Ding, Marc Jung, Rion Pendergrass, David Pulford, Neha Raghavan, Adriana Huertas‐Vázquez, Jae-Hoon Sul, Xinli Hu, Sahar V. Mozaffari, Dawn Waterworth, Nicole Renaud, Ma ́en Obeidat, Samuli Ripatti, Johanna Schleutker, Mikko Arvas, Olli Carpén, Reetta Hinttala, Johannes Kettunen, Katriina Aalto‐Setälä, Mika Kähönen, Johanna Mäkelä, Neurology Group, Reetta Kälviäinen, Valtteri Julkunen, Hilkka Soininen, Anne M. Remes, Mikko Hiltunen, Jukka Peltola, Pentti J. Tienari, Juha O. Rinne, Roosa Kallionpää, Ali Abbasi, Adam Ziemann, Sahar Esmaeeli, Nizar Smaoui, Anne Lehtonen, Susan Eaton, Sanni Lahdenperä, Janet van Adelsberg, Natalie Bowers, Edmond Teng, Sarah A. Pendergrass, Onuralp Söylemez, Kari Linden, Fanli Xu, Laura Addis, John D. Eicher
Abstract
Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8-4.6] up to 6.2 [4.6-7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.