Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe

Social Networks of Researchers and Educators on nanoHUB.org

The science gateway nanoHUB.org is the world’s largest nanotechnology user facility, serving 167,196 users in 2010 with over 2,300 resources including 189 simulation programs. Surveys of nanoHUB users and automated usage analysis find widespread simulation use in formal classroom education, thereby connecting recent research more rapidly and closely to education. Analysis of 719 citations in the scientific literature by over 1,300 authors to nanoHUB.org resources documents use of simulation programs by new research collaborations, by researchers outside of the community originating the program, and by experimentalists. The publication and author networks reveal research collaborations and capacity building through knowledge transfer. Analysis of secondary citations documents the quality of the conducted research with an h-index of 30 after just 10 years of operation. Our analysis proves with quantitative metrics that impactful research can be conducted by an ever growing research community. We argue that HUBzero technology and the user- focused design and operation of nanoHUB.org are success criteria that can be transferred to other science gateways

Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals

<div><p>Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of <i>in vitro</i> data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.</p></div