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Abstract
This article studies how the presence of concealable hard evidence affects the timing of agreement and the size and distribution of surplus in bargaining. A buyer and a seller receive randomly arriving, verifiable, but concealable evidence about the value of a tradable good. Each party discloses individually favourable information but conceals signals that benefit the other side, giving rise to mutual suspicion; the seller repeatedly posts prices valid for one period. In the leading case of interest with a finite horizon and sufficiently patient players, the equilibrium is characterised by an interval of skimming (a sequence of prices acceptable only to a buyer who has learned that the good's value is high) concluded by a single settlement period in which agreement is reached for sure. The length of delay until agreement and the corresponding efficiency loss are decreasing in the time horizon and in the abilities of the trading parties to identify the good's value, but increasing in impatience. An arbitrarily long time horizon leads to immediate agreement if the parties are even slightly impatient and interaction is frequent enough; if the parties are perfectly patient, there is no trade without evidence disclosure.

Methods for analysis of GWAS summary statistics have encouraged data sharing and democratised the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some "truth" is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, available as an open source R package, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. ; MF and CW are funded by the Wellcome Trust (WT099772, WT107881) and CW by the MRC (MC_UU_00002/4). MF is currently funded by Dementia Platforms UK.

MOTIVATION: Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some 'truth' is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. RESULTS: We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. AVAILABILITY AND IMPLEMENTATION: Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions. We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1). ; We thank Frank Dudbridge for providing the R scripts to analyse the interaction data. We would like to acknowledge the Faculty of Life Sciences Genomics Facility, the assistance given by IT Services and the use of the Computational Shared Facility at The University of Manchester. This work was funded by Arthritis Research UK (grant numbers 20385, 20571 (K.D.)); Wellcome Trust Research Career Development Fellowship (G.O., AM 095684); Wellcome Trust (097820/Z/11/B); S.E. is supported through the European Union's FP7 Health Programme, under the grant agreement FP7-HEALTH-F2-2012-305549 (Euro-TEAM). A.Y. is supported by the Innovative Medicines Initiative (BeTheCure project 115142); C.W. by the Wellcome Trust (089989); C.W. and N.C. by the Wellcome Trust (091157), JDRF (9-2011-253) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). The research leading to ...

Seasonal variations are rarely considered a contributing component to human tissue function or health, although many diseases and physiological process display annual periodicities. Here we find more than 4,000 protein-coding mRNAs in white blood cells and adipose tissue to have seasonal expression profiles, with inverted patterns observed between Europe and Oceania. We also find the cellular composition of blood to vary by season, and these changes, which differ between the United Kingdom and The Gambia, could explain the gene expression periodicity. With regards to tissue function, the immune system has a profound pro-inflammatory transcriptomic profile during European winter, with increased levels of soluble IL-6 receptor and C-reactive protein, risk biomarkers for cardiovascular, psychiatric and autoimmune diseases that have peak incidences in winter. Circannual rhythms thus require further exploration as contributors to various aspects of human physiology and disease. ; The Gambian study providing data for analysis was supported by core funding MC-A760-5QX00 to the International Nutrition Group by the UK Medical Research Council (MRC) and the UK Department for the International Development (DFID) under the MRC/DFID Concordat agreement. This work was supported by the JDRF UK Centre for Diabetes-Genes, Autoimmunity and Prevention (D-GAP; 4-2007-1003), the JDRF (9-2011-253), the Wellcome Trust (WT061858/091157), the National Institute for Health Research Cambridge Biomedical Research Centre (CBRC) and the Medical Research Council (MRC) Cusrow Wadia Fund. The research leading to these results has received funding from the European Union's 7th Framework Programme (FP7/2007–2013) under grant agreement no.241447 (NAIMIT). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (WT100140). X.C.D. was a University of Cambridge/Wellcome Trust Infection and Immunity PhD student. R.C.F. is funded by a JDRF post-doctoral fellowship (3-2011-374). C.W. and H.G are funded by ...

BACKGROUND: Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes. RESULTS: Within four hours, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C (PCHi-C). By integrating PCHi-C data with genetic associations for five autoimmune diseases we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach. CONCLUSIONS: Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes. ; This work was funded by the JDRF (9-2011-253), the Wellcome Trust (089989, 091157, 107881), the UK Medical Research Council (MR/L007150/1, MC_UP_1302/5), the UK Biotechnology and Biological Sciences Research Council (BB/J004480/1) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The research leading to these results has received funding from the European Union's 7th Framework Programme (FP7/2007-2013) under grant agreement no. 241447 (NAIMIT). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140).