Tissue-specific alternative splicing of TCF7L2.

Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r(2) = 0.84-0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164-FJ010174.

Screening of 134 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes replicates association with 12 SNPs in nine genes.

More than 120 published reports have described associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. However, multiple studies of the same variant have often been discordant. From a literature search, we identified previously reported type 2 diabetes-associated SNPs. We initially genotyped 134 SNPs on 786 index case subjects from type 2 diabetes families and 617 control subjects with normal glucose tolerance from Finland and excluded from analysis 20 SNPs in strong linkage disequilibrium (r(2) > 0.8) with another typed SNP. Of the 114 SNPs examined, we followed up the 20 most significant SNPs (P < 0.10) on an additional 384 case subjects and 366 control subjects from a population-based study in Finland. In the combined data, we replicated association (P < 0.05) for 12 SNPs: PPARG Pro12Ala and His447, KCNJ11 Glu23Lys and rs5210, TNF -857, SLC2A2 Ile110Thr, HNF1A/TCF1 rs2701175 and GE117881_360, PCK1 -232, NEUROD1 Thr45Ala, IL6 -598, and ENPP1 Lys121Gln. The replication of 12 SNPs of 114 tested was significantly greater than expected by chance under the null hypothesis of no association (P = 0.012). We observed that SNPs from genes that had three or more previous reports of association were significantly more likely to be replicated in our sample (P = 0.03), although we also replicated 4 of 58 SNPs from genes that had only one previous report of association.

Evaluating a Novel Summary Visualization for Clinical Trial Reports: A Usability Study.

Contributions of clinical trials are captured in published reports that are unstructured and often require extensive manual review to gain a deeper understanding of the study itself. Our goal is to increase comprehension and decrease the time necessary to understand these reports through the use of visualization tools. In this paper, we specify and evaluate the visualization of a previously developed representation as well as gain insight from user input for further development. The usability experiment consisted of a two-arm study with users either having or not having access to the visualization. A user questionnaire was used to measure time spent and accuracy in comprehension; intuitiveness and reproducibility of the visualization; and preferences. We found that having the visualization required on average 28.1% less time (25.8 min vs. 35.8 min, p=0.01) while maintaining similar accuracy (73.7% vs. 67.0%). Users were then asked to create their own visualizations, with their visualizations averaging 86.1% similar to the gold standard. All participants either preferred the visualization over the status quo or preferred both equally. These results demonstrate that novel visualizations for trial reports could provide time savings and achieve similar accuracy as reviewing the paper itself. Understanding the strength and quality of clinical trials can be alleviated with a visualization that makes content explicit.

A formal representation for numerical data presented in published clinical trial reports.

Assessing the quality of and integrating clinical trial reports are necessary to practice evidence-based medicine. In particular, the numerical data is essential to understanding the strength and quality of the clinical trial study. In this paper, we present a formal representation for standardizing numerical data in published clinical trial reports, and our efforts towards developing computational tools to capture and visualize this representation. The approach includes two aspects: a process model used to precisely define experimental context behind the numerical value; and a spreadsheet, an intuitive and familiar tool used to organize numerical data. We demonstrated this representation using clinical trial reports on non-small cell lung cancer (NSCLC). We performed a preliminary evaluation to determine the usefulness of this formalism for identifying the characteristics, quality and significance of a clinical trial. Our initial results demonstrate that the representation is sufficiently expressive to capture reported numerical information in published papers.

Improving the accuracy of therapy descriptions in clinical trials using a bottom-up approach.

Randomized clinical trial (RCT) reports commonly have complicated therapy descriptions that are written in free-text. Drug therapy is difficult to describe due to the dynamic nature of how protocols change and the many ways drugs can be administered. Details regarding protocol changes and drug administration must be explained clearly for reproducibility and reliability. A process model supplemented with concept ontologies can clarify the dynamics of how therapies change and make knowledge more explicit. We demonstrated the process to develop a representation model to reveal specific context concerning drug therapies within clinical trial report literature. A PubMed search was conducted to identify RCTs on non-small-cell lung cancer (NSCLC) pertaining to epithelial growth factor receptor (EGFR) mutations. Twenty-seven clinical trials were used to develop the model using a bottom-up approach. This representation describes drug dosage, administration details, and drug cycles within different experimental arms and control groups. We then presented preliminary evaluation of the clarity and understandability of the representation.

Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes.

OBJECTIVE: Type 2 diabetes is a common complex disorder with environmental and genetic components. We used a candidate gene-based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to type 2 diabetes. RESEARCH DESIGN AND METHODS: In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland. RESULTS: Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG, KCNJ11, and SLC2A2; identified significant SNPs in genes with previously reported associations (ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility. CONCLUSIONS: Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility.

A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.

Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk. This brings the number of T2D loci now confidently identified to at least 10.