MIS research directions: A survey of researchers' views

Several studies have addressed important issues for MIS Research, but until now no empirical studies have been conducted to asses how MIS researchers themselves view the relatively new field. This survey of 397 MIS researchers revealed preferences for research methods and current areas of concentration, current studies being conducted, publication history, and other factors of interest. It found the subjects' assessment of the quality of MIS research relatively low and that they feel there is an overemphasis on transient topics, rather than on topics of lasting significance. There is continuing evidence of fragmentation in the field. Few MIS researchers appear to rely on research frameworks. However, there is overall optimism that the quality of MIS research has been improving, and will continue to improve in the future. Implications for the future of MIS research are then discussed. © 1991, ACM. All rights reserved

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background