A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

Archiving Primary Data: Solutions for Long-Term Studies.

The recent trend for journals to require open access to primary data included in publications has been embraced by many biologists, but has caused apprehension amongst researchers engaged in long-term ecological and evolutionary studies. A worldwide survey of 73 principal investigators (Pls) with long-term studies revealed positive attitudes towards sharing data with the agreement or involvement of the PI, and 93% of PIs have historically shared data. Only 8% were in favor of uncontrolled, open access to primary data while 63% expressed serious concern. We present here their viewpoint on an issue that can have non-trivial scientific consequences. We discuss potential costs of public data archiving and provide possible solutions to meet the needs of journals and researchers.