Long term outcomes in men screened for abdominal aortic aneurysm : prospective cohort study

PMID: 22563092 [PubMed - indexed for MEDLINE] PMCID: PMC3344734 Free PMC ArticlePeer reviewedPublisher PD

Report on assessment and management advice for 2004 of the anchovy fishery in the Yellow Sea

The Bei Dou Fisheries Management Project 2001-2005; Institute of Marine Research, Bergen, April 24-30, 200

Bayesian model search and multilevel inference for SNP association studies

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally ``validated'' in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS322 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Long-term carbon and nitrogen dynamics at SPRUCE revealed through stable isotopes in peat profiles

Peatlands encode information about past vegetation dynamics, climate, and microbial processes. Here, we used δ15N and δ13C patterns from 16 peat profiles to deduce how the biogeochemistry of the Marcell S1 forested bog in northern Minnesota responded to environmental and vegetation change over the past  ∼ 10000 years. In multiple regression analyses, δ15N and δ13C correlated strongly with depth, plot location, C∕N, %N, and each other. Correlations with %N, %C, C∕N, and the other isotope accounted for 80% of variance for δ15N and 38% of variance for δ13C, reflecting N and C losses. In contrast, correlations with depth and topography (hummock or hollow) reflected peatland successional history and climate. Higher δ15N in plots closer to uplands may reflect upland-derived DON inputs and accompanying shifts in N dynamics in the lagg drainage area surrounding the bog. The Suess effect (declining δ13CO2 since the Industrial Revolution) lowered δ13C in recent surficial samples. High δ15N from −35 to −55cm probably indicated the depth of ectomycorrhizal activity after tree colonization of the peatland over the last 400 years, as confirmed by the occasional presence of wood down to −35cm depth. High δ13C at  ∼ 4000 years BP (−65 to −105cm) could reflect a transition at that time to slower rates of peat accumulation, when 13C discrimination during peat decomposition may increase in importance. Low δ13C and high δ15N at −213 and −225cm ( ∼ 8500 years BP) corresponded to a warm period during a sedge-dominated rich fen stage. The above processes appear to be the primary drivers of the observed isotopic patterns, whereas there was no clear evidence for methane dynamics influencing δ13C patterns