S Rostami1, M Hoff2,3,4, M A Brown5, K Hveem6, V Videm1,7. 1. Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. 2. Department of Rheumatology, St. Olavs University Hospital, Trondheim, Norway. 3. Department of Neuromedicine and Movement Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. 4. Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. 5. Institute of Health and Biomedical Innovation, Translational Research Institute, Princess Alexandra Hospital, Queensland University of Technology, Brisbane, Queensland, Australia. 6. KG Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. 7. Department of Immunology and Transfusion Medicine, St. Olavs University Hospital, Trondheim, Norway.
Abstract
OBJECTIVES: To evaluate selection methods among published single-nucleotide polymorphisms (SNPs) associated with RA to construct predictive genetic risk scores (GRSs) in a population-based setting. METHODS: The Nord-Trøndelag Health (HUNT) Study is a prospective cohort study among the whole adult population of northern Trøndelag, Norway. Participants in HUNT2 (1995-1997) and HUNT3 (2006-2008) were included (489 RA cases, 61 584 controls). The initial SNP selection from relevant genome-wide studies included 269 SNPs from 30 studies. Following different selection criteria, SNPs were weighted by published odds ratios. The sum of each person's carriage of all weighted susceptibility variants was calculated for each GRS. RESULTS: The best-fitting risk score included 27 SNPs [weighted genetic risk score 27 (wGRS27)] and was identified using P-value selection criterion ≤5 × 10-8, the largest possible SNP selection without high linkage disequilibrium (r2 < 0.8), and lasso regression to select for positive coefficients. In a logistic regression model adjusted for gender, age and ever smoking, wGRS27 was associated with RA [odds ratio 1.86 (95% CI 1.71, 2.04) for each s.d. increase, P < 0.001]. The AUC was 0.76 (95% CI 0.74, 0.78). The positive and negative predictive values were 1.6% and 99.7%, respectively, and the positive predictive value was not improved in sensitivity analyses subselecting participants to illustrate settings with increased RA prevalences. Other schemes selected more SNPs but resulted in GRSs with lower predictive ability. CONCLUSION: Constructing a wGRS based on a smaller selection of informative SNPs improved predictive ability. Even with a relatively high AUC, the low PPV illustrates that there was a large overlap in risk variants among RA patients and controls, precluding clinical usefulness.
OBJECTIVES: To evaluate selection methods among published single-nucleotide polymorphisms (SNPs) associated with RA to construct predictive genetic risk scores (GRSs) in a population-based setting. METHODS: The Nord-Trøndelag Health (HUNT) Study is a prospective cohort study among the whole adult population of northern Trøndelag, Norway. Participants in HUNT2 (1995-1997) and HUNT3 (2006-2008) were included (489 RA cases, 61 584 controls). The initial SNP selection from relevant genome-wide studies included 269 SNPs from 30 studies. Following different selection criteria, SNPs were weighted by published odds ratios. The sum of each person's carriage of all weighted susceptibility variants was calculated for each GRS. RESULTS: The best-fitting risk score included 27 SNPs [weighted genetic risk score 27 (wGRS27)] and was identified using P-value selection criterion ≤5 × 10-8, the largest possible SNP selection without high linkage disequilibrium (r2 < 0.8), and lasso regression to select for positive coefficients. In a logistic regression model adjusted for gender, age and ever smoking, wGRS27 was associated with RA [odds ratio 1.86 (95% CI 1.71, 2.04) for each s.d. increase, P < 0.001]. The AUC was 0.76 (95% CI 0.74, 0.78). The positive and negative predictive values were 1.6% and 99.7%, respectively, and the positive predictive value was not improved in sensitivity analyses subselecting participants to illustrate settings with increased RA prevalences. Other schemes selected more SNPs but resulted in GRSs with lower predictive ability. CONCLUSION: Constructing a wGRS based on a smaller selection of informative SNPs improved predictive ability. Even with a relatively high AUC, the low PPV illustrates that there was a large overlap in risk variants among RApatients and controls, precluding clinical usefulness.
Authors: Bhuwan Khatri; Kandice L Tessneer; Astrid Rasmussen; Farhang Aghakhanian; Tove Ragna Reksten; Adam Adler; Ilias Alevizos; Juan-Manuel Anaya; Lara A Aqrawi; Eva Baecklund; Johan G Brun; Sara Magnusson Bucher; Maija-Leena Eloranta; Fiona Engelke; Helena Forsblad-d'Elia; Stuart B Glenn; Daniel Hammenfors; Juliana Imgenberg-Kreuz; Janicke Liaaen Jensen; Svein Joar Auglænd Johnsen; Malin V Jonsson; Marika Kvarnström; Jennifer A Kelly; He Li; Thomas Mandl; Javier Martín; Gaétane Nocturne; Katrine Brække Norheim; Øyvind Palm; Kathrine Skarstein; Anna M Stolarczyk; Kimberly E Taylor; Maria Teruel; Elke Theander; Swamy Venuturupalli; Daniel J Wallace; Kiely M Grundahl; Kimberly S Hefner; Lida Radfar; David M Lewis; Donald U Stone; C Erick Kaufman; Michael T Brennan; Joel M Guthridge; Judith A James; R Hal Scofield; Patrick M Gaffney; Lindsey A Criswell; Roland Jonsson; Per Eriksson; Simon J Bowman; Roald Omdal; Lars Rönnblom; Blake Warner; Maureen Rischmueller; Torsten Witte; A Darise Farris; Xavier Mariette; Marta E Alarcon-Riquelme; Caroline H Shiboski; Marie Wahren-Herlenius; Wan-Fai Ng; Kathy L Sivils; Indra Adrianto; Gunnel Nordmark; Christopher J Lessard Journal: Nat Commun Date: 2022-07-27 Impact factor: 17.694