Genome-wide Association Study of Response to Methotrexate in Early Rheumatoid Arthritis Patients

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10‾⁷ for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Changing perspectives on the internationalization of R&D and innovation by multinational enterprises: a review of the literature

Internationalization of R&D and innovation by Multinational Enterprises (MNEs) has undergone a gradual and comprehensive change in perspective over the past 50 years. From sporadic works in the late 1950s and in the 1960s, it became a systematically analysed topic in the 1970s, starting with pioneering reports and “foundation texts”. Our review unfolds the theoretical and empirical evolution of the literature from dyadic interpretations of centralization versus decentralization of R&D by MNEs to more comprehensive frameworks, wherein established MNEs from Advanced Economies still play a pivotal role, but new players and places also emerge in the global generation and diffusion of knowledge. Hence views of R&D internationalization increasingly rely on concepts, ideas and methods from IB and other related disciplines such as industrial organization, international economics and economic geography. Two main findings are highlighted. First, scholarly research pays an increasing attention to the network-like characteristics of international R&D activities. Second, different streams of literature have emphasized the role of location- specific factors in R&D internationalization. The increasing emphasis on these aspects has created new research opportunities in some key areas, including inter alia: cross-border knowledge sourcing strategies, changes in the geography of R&D and innovation, and the international fragmentation of production and R&D activities