Swedish University of Agricultural Sciences

About: Swedish University of Agricultural Sciences is a education organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Population & Soil water. The organization has 13510 authors who have published 35241 publications receiving 1414458 citations. The organization is also known as: Sveriges Lantbruksuniversitet & SLU.

Polymorphisms in the Tyrosine Kinase 2 and Interferon Regulatory Factor 5 Genes Are Associated with Systemic Lupus Erythematosus

Abstract: Systemic lupus erythematosus (SLE) is a complex systemic autoimmune disease caused by both genetic and environmental factors. Genome scans in families with SLE point to multiple potential chromosomal regions that harbor SLE susceptibility genes, and association studies in different populations have suggested several susceptibility alleles for SLE. Increased production of type I interferon (IFN) and expression of IFN-inducible genes is commonly observed in SLE and may be pivotal in the molecular pathogenesis of the disease. We analyzed 44 single-nucleotide polymorphisms (SNPs) in 13 genes from the type I IFN pathway in 679 Swedish, Finnish, and Icelandic patients with SLE, in 798 unaffected family members, and in 438 unrelated control individuals for joint linkage and association with SLE. In two of the genes—the tyrosine kinase 2 (TYK2) and IFN regulatory factor 5 (IRF5) genes—we identified SNPs that displayed strong signals in joint analysis of linkage and association (unadjusted P

How to make more out of community data? A conceptual framework and its implementation as models and software.

Abstract: Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.