Summary: We describe a tool that produces summary statistics and basic quality assessments for gene-mapping data, accommodating either pedigree or case-control datasets. Our tool can also produce graphic output in the PDF format. Availability:http://www.sph.umich.edu/csg/abecasis/Pedstats/download/ Contact:wiggie@umich.edu Supplementary information:http://www.sph.umich.edu/csg/abecasis/Pedstats/

As the incidence of obesity and type 2 diabetes (T2D) is occurring at a younger age, studying adolescent nutrient metabolism can provide insights on the development of T2D. Metabolic challenges, including an oral glucose tolerance test (OGTT) can assess the effects of perturbations in nutrient metabolism. Here, we present alterations in the global metabolome in response to an OGTT, classifying the influence of obesity and insulin resistance (IR) in adolescents that arrived at the clinic fasted and in a random-fed state. Participants were recruited as lean (n = 55, aged 8–17 years, BMI percentile 5–85%) and overweight and obese (OVOB, n = 228, aged 8–17 years, BMI percentile ≥ 85%). Untargeted metabolomics profiled 246 annotated metabolites in plasma at t0 and t60 min during the OGTT. Our results suggest that obesity and IR influence the switch from fatty acid (FA) to glucose oxidation in response to the OGTT. Obesity was associated with a blunted decline of acylcarnitines and fatty acid oxidation intermediates. In females, metabolites from the Fasted and Random-Fed OGTT were associated with HOMA-IR, including diacylglycerols, leucine/isoleucine, acylcarnitines, and phosphocholines. Our results indicate that at an early age, obesity and IR may influence the metabolome dynamics in response to a glucose challenge.

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.