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

Listen to Your Customers: Insights into Brand Image Using Online Consumer-Generated Product Reviews

Online consumer-generated product reviews are a growing phenomenon and have led to the posting of colossal amounts of data by consumers on the Web. These data include consumers' thoughts, opinions, and feelings about brands and offer firms the opportunity to listen in on consumers to get a better understanding of the topics discussed about their brands. Using the human associative memory model as the theoretical framework, the authors introduce an approach to convert online product reviews into meaningful information about brand images using a novel combination of text mining and network analysis methodologies. Following a network-based understanding of brand image, the authors use online product reviews to extract consumers' brand associations and their interconnections as well as to depict and characterize the network of brand associations. In an empirical study, the authors test the approach and illustrate its managerial usefulness. The suggested approach allows managers to effectively monitor and detect strengths and weaknesses of brand image. Moreover, the proposed approach is one of the first attempts to measure brand image using consumer-generated content by applying text mining and network analysis