Search

We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women.

Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma.

BACKGROUND Colorectal cancer is highly prevalent, and the vast majority of cases are thought to be sporadic, although few risk factors have been identified. Using metabolomics technology, our aim was to identify biomarkers prospectively associated with colorectal cancer. METHODS This study included 254 incident colorectal cancers and 254 matched controls nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Serum samples were collected at baseline, and the mean length of follow-up was 8 years. Serum metabolites were analyzed by ultra-high performance liquid-phase chromatography with tandem mass spectrometry, and gas chromatography coupled with mass spectrometry. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) for metabolites above the limit of detection and present in at least 80% of participants. RESULTS A total of 676 serum metabolites were measured; of these, 447 were of known identity and 278 of these were present in >80% of individuals. Overall, there was no association between serum metabolites and colorectal cancer; however, some suggestive associations were observed between individual metabolites and colorectal cancer but none reached statistical significance after Bonferroni correction for multiple comparisons. For example, leucyl-leucine was inversely associated (OR comparing the 90th to the 10th percentile = 0.50; 95% CI = 0.32-0.80; P = .003). In sex-stratified analyses, serum glycochenodeoxycholate was positively associated with colorectal cancer among women (OR90th vs.10th percentile = 5.34; 95% CI = 2.09-13.68; P = .0001). CONCLUSIONS No overall associations were observed between serum metabolites and colorectal cancer, but serum glycochenodeoxycholate, a bile acid metabolite, was positively associated with colorectal cancer among women. Cancer 2014;120:3049–3057. © 2014 American Cancer Society.

The protein world has a hierarchical and redundant organization that can be specified in terms of evolutionary units of molecular structure, the protein domains. The Structural Classification of Proteins (SCOP) has unified domains into a comparatively small set of folding architectures, the protein fold families and superfamilies, and these have been further grouped into protein folds. In this study, we reconstruct the evolution of the protein world using information embedded in a structural genomic census of fold architectures defined by a phylogenomic analysis of 185 completely sequenced genomes using advanced hidden Markov models and 776 folds described in SCOP release 1.67. Our study confirms the existence of defined evolutionary patterns of architectural diversification and explores how phylogenomic trees generated from folds relate to those reconstructed from fold superfamilies. Evolutionary patterns help us propose a general conceptual model that describes the growth of architectures in the protein world. © 2006 Wiley Periodicals, Inc. Complexity 12: 27– 40, 2006