Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers.

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions, finding major pathways contributing to risk, with some loci shared across immune disorders. To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new T1D-associated regions (P < 5 × 10(-8)). A comparative analysis with 15 immune diseases showed that T1D is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new T1D risk loci. Using a Bayesian approach, we defined credible sets for the T1D-associated SNPs. The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34(+) stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.This research uses resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the National Institute of Allergy and Infectious Diseases (NIAID), the National Human Genome Research Institute (NHGRI), the National Institute of Child Health and Human Development (NICHD) and JDRF and supported by grant U01 DK062418 from the US National Institutes of Health. Further support was provided by grants from the NIDDK (DK046635 and DK085678) to P.C. and by a joint JDRF and Wellcome Trust grant (WT061858/09115) to the Diabetes and Inflammation Laboratory at Cambridge University, which also received support from the NIHR Cambridge Biomedical Research Centre. ImmunoBase receives support from Eli Lilly and Company. C.W. and H.G. are funded by the Wellcome Trust (089989). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). We gratefully acknowledge the following groups and individuals who provided biological samples or data for this study. We obtained DNA samples from the British 1958 Birth Cohort collection, funded by the UK Medical Research Council and the Wellcome Trust. We acknowledge use of DNA samples from the NIHR Cambridge BioResource. We thank volunteers for their support and participation in the Cambridge BioResource and members of the Cambridge BioResource Scientific Advisory Board (SAB) and Management Committee for their support of our study. We acknowledge the NIHR Cambridge Biomedical Research Centre for funding. Access to Cambridge BioResource volunteers and to their data and samples are governed by the Cambridge BioResource SAB. Documents describing access arrangements and contact details are available at http://www.cambridgebioresource.org.uk/. We thank the Avon Longitudinal Study of Parents and Children laboratory in Bristol, UK, and the British 1958 Birth Cohort team, including S. Ring, R. Jones, M. Pembrey, W. McArdle, D. Strachan and P. Burton, for preparing and providing the control DNA samples. This study makes use of data generated by the Wellcome Trust Case Control Consortium, funded by Wellcome Trust award 076113; a full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk/.This is the author accepted manuscript. The final version is available via NPG at http://www.nature.com/ng/journal/v47/n4/full/ng.3245.html

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Management of Orthopedic Blast Injuries

Over the past two decades, the complexity and severity of orthopedic blast injuries have increased significantly. This has been the case not only with the combat operations undertaken in Iraq and Afghanistan but also with casualties sustained in terrorist attacks around the globe. Multiple, high-energy extremity injuries are the predominate injury patterns in these scenarios. With greater experience and understanding of these injury patterns, orthopedic management of the blast injury patient has significantly evolved over this time frame. From the initial resuscitation of the patient in the emergency department (ED), to the advent of damage control orthopedics, to the definitive reconstructive care of these patients’ injuries, the evolution in understanding how to properly treat these injuries has led to substantially improved outcomes. As knowledge of how to treat these complex injuries continues to evolve, there has been dramatic improvement in the management of the long-term sequela of these injuries such as infection, heterotopic ossification, chronic pain, PTSD, and functional outcome

A Novel Test for Recessive Contributions to Complex Diseases Implicates Bardet-Biedl Syndrome Gene BBS10 in Idiopathic Type 2 Diabetes and Obesity.

Rare-variant association studies in common, complex diseases are customarily conducted under an additive risk model in both single-variant and burden testing. Here, we describe a method to improve detection of rare recessive variants in complex diseases termed RAFT (recessive-allele-frequency-based test). We found that RAFT outperforms existing approaches when the variant influences disease risk in a recessive manner on simulated data. We then applied our method to 1,791 Finnish individuals with type 2 diabetes (T2D) and 2,657 matched control subjects. In BBS10, we discovered a rare variant (c.1189A>G [p.Ile397Val]; rs202042386) that confers risk of T2D in a recessive state (p = 1.38 x 10(-6)) and would be missed by conventional methods. Testing of this variant in an established in vivo zebrafish model confirmed the variant to be pathogenic. Taken together, these data suggest that RAFT can effectively reveal rare recessive contributions to complex diseases overlooked by conventional association tests

Taxonomy based on science is necessary for global conservation

International audienc