Toll-like receptor 4 and CD14 polymorphisms in ankylosing spondylitis: Evidence of a weak association in finns

Objective To investigate the association of CD14 and Toll-like receptor (TLR4) with ankylosing spondylitis (AS). Methods A promoter variant in CD14 and 2 coding polymorphisms in TLR4 were investigated in UK and Finnish families with AS and in a UK case-control study. A metaanalysis of published TLR4 and CD14 studies was performed. Results In the Finnish study the CD74-260bp T variant showed an association (p = 0.006), and the common 2-marker TLR4 haplotype showed a weak association (global p = 0.03), with AS. No associations were seen in the UK based studies or in the metaanalyses. Conclusion CD14 and TLR4 showed an association with AS in the Finns only.

Fine mapping of the MHC Class III region demonstrates association of AIF1 and rheumatoid arthritis

Objective. The heritability of RA has been estimated to be ∼55%, of which the MHC contributes about one-third. HLA-DRB1 alleles are strongly associated with RA, but it is likely that significant non-DRB1 MHC genetic susceptibility factors are involved. Previously, we identified two three-marker haplotypes in a 106-kb region in the MHC class III region immediately centromeric to TNF, which are strongly associated with RA on HLA-DRB1*0404 haplotypes. In the present study, we aimed to refine these associations further using a combination of genotyping and gene expression studies. Methods. Thirty-nine nucleotide polymorphisms (SNPs) were genotyped in 95 DRB1*0404 carrying unrelated RA cases, 125 DRB1*0404 - carrying healthy controls and 87 parent-case trio RA families in which the affected child carried HLA-DRB1*04. Quantitative RT-PCR was used to assess the expression of the positional candidate MHC class III genes APOM, BAT2, BAT3, BAT4, BAT5, AIF1, C6orf47, CSNK2β and LY6G5C, and the housekeeper genes, hypoxanthine-guanine phosphoribosyltransferase (HPRT) and β2-microglobulin (B2M) in 31 RA cases and 21 ethnically, age- and sex-matched healthy controls. Synovial membrane specimens from RA, PsA and OA cases were stained by an indirect immunoperoxidase technique using a mouse-anti-human AIF1 monoclonal antibody. Results. Association was observed between RA and single markers or two marker haplotypes involving AIF1, BAT3 and CSNK. AIF1 was also significantly overexpressed in RA mononuclear cells (1.5- to 1.9-fold difference, P = 0.02 vs HPRT, P = 0.002 vs B2M). AIF1 protein was clearly expressed by synovial macrophages in all the inflammatory synovial samples in contrast to the non-inflammatory OA samples. Conclusions. The results of the genotyping and expression studies presented here suggest a role for AIF1 in both the aetiology and pathogenesis of RA.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Disease mapping using Bayesian hierarchical models

description and analysis of geographically indexed health data with respect to demographic, environmental, behavioural, socioeconomic, genetic, and infectious risk factors (Elliott andWartenberg 2004). Disease maps can be useful for estimating relative risk; ecological analyses, incorporating area and/or individual-level covariates; or cluster analyses (Lawson 2009). As aggregated data are often more readily available, one common method of mapping disease is to aggregate the counts of disease at some geographical areal level, and present them as choropleth maps (Devesa et al. 1999; Population Health Division 2006). Therefore, this chapter will focus exclusively on methods appropriate for areal data...

Design studio terrains: Mapping the learning landscapes of Australian architectural education

It has been nearly 25 years since the problems associated with passive learning in large undergraduate classes were first established by McDermott (1991). STEM education, for example North Carolina State University’s SCALE-UP project, has subsequently been influenced by some unique aspects of design studio education. While there are now many institutions applying SCALE-UP or similar approaches to enable lively interaction, enhanced learning, increased student engagement, and to teach many different content areas to classes of all sizes, nearly all of these have remained in the STEM fields (Beichner, 2008). Architectural education, although originally at the forefront of this field, has arguably been left behind. Architectural practice is undergoing significant change, globally. Access to new technology and the development of specialised architectural documentation software has scaffolded new building procurement methods and allowed consultant teams to work more collaboratively, efficiently and even across different time zones. Up until recently, the spatial arrangements, pedagogical approaches, and project work outcomes in the architectural design studio, have not been dissimilar to its inception. It is not possible to keep operating architectural design studios the same way that they have for the past two hundred years, with this new injection of high-end technology and personal mobile Wi-Fi enabled devices. Employing a grounded theory methodology, this study reviews the current provision of architectural design learning terrains across a range of tertiary institutions, in Australia. Some suggestions are provided for how these spaces could be modified to address the changing nature of the profession, and implications for how these changes may impact the design of future SCALE-UP type spaces outside of the discipline of architecture, are also explored.

Delineating environmental envelopes to improve mapping of species distributions, via a hurdle model with CART &/or MaxEnT

Species distribution modelling (SDM) typically analyses species’ presence together with some form of absence information. Ideally absences comprise observations or are inferred from comprehensive sampling. When such information is not available, then pseudo-absences are often generated from the background locations within the study region of interest containing the presences, or else absence is implied through the comparison of presences to the whole study region, e.g. as is the case in Maximum Entropy (MaxEnt) or Poisson point process modelling. However, the choice of which absence information to include can be both challenging and highly influential on SDM predictions (e.g. Oksanen and Minchin, 2002). In practice, the use of pseudo- or implied absences often leads to an imbalance where absences far outnumber presences. This leaves analysis highly susceptible to ‘naughty-noughts’: absences that occur beyond the envelope of the species, which can exert strong influence on the model and its predictions (Austin and Meyers, 1996). Also known as ‘excess zeros’, naughty noughts can be estimated via an overall proportion in simple hurdle or mixture models (Martin et al., 2005). However, absences, especially those that occur beyond the species envelope, can often be more diverse than presences. Here we consider an extension to excess zero models. The two-staged approach first exploits the compartmentalisation provided by classification trees (CTs) (as in O’Leary, 2008) to identify multiple sources of naughty noughts and simultaneously delineate several species envelopes. Then SDMs can be fit separately within each envelope, and for this stage, we examine both CTs (as in Falk et al., 2014) and the popular MaxEnt (Elith et al., 2006). We introduce a wider range of model performance measures to improve treatment of naughty noughts in SDM. We retain an overall measure of model performance, the area under the curve (AUC) of the Receiver-Operating Curve (ROC), but focus on its constituent measures of false negative rate (FNR) and false positive rate (FPR), and how these relate to the threshold in the predicted probability of presence that delimits predicted presence from absence. We also propose error rates more relevant to users of predictions: false omission rate (FOR), the chance that a predicted absence corresponds to (and hence wastes) an observed presence, and the false discovery rate (FDR), reflecting those predicted (or potential) presences that correspond to absence. A high FDR may be desirable since it could help target future search efforts, whereas zero or low FOR is desirable since it indicates none of the (often valuable) presences have been ignored in the SDM. For illustration, we chose Bradypus variegatus, a species that has previously been published as an exemplar species for MaxEnt, proposed by Phillips et al. (2006). We used CTs to increasingly refine the species envelope, starting with the whole study region (E0), eliminating more and more potential naughty noughts (E1–E3). When combined with an SDM fit within the species envelope, the best CT SDM had similar AUC and FPR to the best MaxEnt SDM, but otherwise performed better. The FNR and FOR were greatly reduced, suggesting that CTs handle absences better. Interestingly, MaxEnt predictions showed low discriminatory performance, with the most common predicted probability of presence being in the same range (0.00-0.20) for both true absences and presences. In summary, this example shows that SDMs can be improved by introducing an initial hurdle to identify naughty noughts and partition the envelope before applying SDMs. This improvement was barely detectable via AUC and FPR yet visible in FOR, FNR, and the comparison of predicted probability of presence distribution for pres/absence.

Genome-wide mapping of cystitis due to Streptococcus agalactiae and Escherichia coli in mice identifies a unique bladder transcriptome that signifies pathogen-specific antimicrobial defense against urinary tract infection

The most common causes of urinary tract infections (UTIs) are Gram-negative pathogens such as Escherichia coli; however, Gram-positive organisms including Streptococcus agalactiae, or group B streptococcus (GBS), also cause UTI. In GBS infection, UTI progresses to cystitis once the bacteria colonize bladder, but the host responses triggered in the bladder immediately following infection are largely unknown. Here, we used genome-wide expression profiling to map the bladder transcriptome of GBS UTI in mice infected transurethrally with uropathogenic GBS that was cultured from a 35 year-old women with cystitis. RNA from bladders was applied to Affymetrix Gene-1.0ST microarrays; qRT-PCR was used to analyze selected gene responses identified in array datasets. A surprisingly small significant gene list of 172 genes was identified at 24h; this compared to 2507 genes identified in a side-by-side comparison with uropathogenic E. coli (UPEC). No genes exhibited significantly altered expression at 2h in GBS-infected mice according to arrays despite high bladder bacterial loads at this early time point. The absence of a marked early host response to GBS juxtaposed with broad-based bladder responses activated by UPEC at 2h. Bioinformatics analyses including integrative systems-level network mapping revealed multiple activated biological pathways in the GBS cystitis transcriptome that regulate leukocyte activation, inflammation, apoptosis, and cytokine-chemokine biosynthesis. These findings define a novel, minimalistic type of bladder host response triggered by GBS UTI, which comprises collective antimicrobial pathways that differ dramatically from those activated by UPEC. Overall, this study emphasizes the unique nature of bladder immune activation mechanisms triggered by distinct uropathogens.

Mapping the integrity of differential obligations within the United Nations framework convention on climate change

This chapter unpacks public institutional integrity concepts through an examination of differential obligations within the global climate regime.

Fine mapping of variants associated with endometriosis in the WNT4 region on chromosome 1p36

Genome-wide association studies show strong evidence of association with endometriosis for markers on chromosome 1p36 spanning the potential candidate genes WNT4, CDC42 and LINC00339. WNT4 is involved in development of the uterus, and the expression of CDC42 and LINC00339 are altered in women with endometriosis. We conducted fine mapping to examine the role of coding variants in WNT4 and CDC42 and determine the key SNPs with strongest evidence of association in this region. We identified rare coding variants in WNT4 and CDC42 present only in endometriosis cases. The frequencies were low and cannot account for the common signal associated with increased risk of endometriosis. Genotypes for five common SNPs in the region of chromosome 1p36 show stronger association signals when compared with rs7521902 reported in published genome scans. Of these, three SNPs rs12404660, rs3820282, and rs55938609 were located in DNA sequences with potential functional roles including overlap with transcription factor binding sites for FOXA1, FOXA2, ESR1, and ESR2. Functional studies will be required to identify the gene or genes implicated in endometriosis risk.

High-density fine-mapping of a chromosome 10q26 linkage peak suggests association between endometriosis and variants close to CYP2C19

OBJECTIVE To refine a previously reported linkage peak for endometriosis on chromosome 10q26, and conduct follow-up analyses and a fine-mapping association study across the region to identify new candidate genes for endometriosis. DESIGN Case-control study. SETTING Academic research. PATIENT(S) Cases=3,223 women with surgically confirmed endometriosis; controls=1,190 women without endometriosis and 7,060 population samples. INTERVENTION(S) Analysis of 11,984 single nucleotide polymorphisms on chromosome 10. MAIN OUTCOME MEASURE(S) Allele frequency differences between cases and controls. RESULT(S) Linkage analyses on families grouped by endometriosis symptoms (primarily subfertility) provided increased evidence for linkage (logarithm of odds score=3.62) near a previously reported linkage peak. Three independent association signals were found at 96.59 Mb (rs11592737), 105.63 Mb (rs1253130), and 124.25 Mb (rs2250804). Analyses including only samples from linkage families supported the association at all three regions. However, only rs11592737 in the cytochrome P450 subfamily C (CYP2C19) gene was replicated in an independent sample of 2,079 cases and 7,060 population controls. CONCLUSION(S) The role of the CYP2C19 gene in conferring risk for endometriosis warrants further investigation.

Association mapping

Association mapping seeks to identify marker alleles present at significantly different frequencies in cases carrying a particular disease or trait compared with controls. Genome-wide association studies are increasingly replacing candidate gene-based association studies for complex diseases, where a number of loci are likely to contribute to disease risk and the effect size of each particular risk allele is typically modest or low. Good study design is essential to the success of an association study, and factors such as the heritability of the disease under investigation, the choice of controls, statistical power, multiple testing and whether the association can be replicated need to be considered before beginning. Likewise, thorough quality control of the genotype data needs to be undertaken prior to running any association analyses. Finally, it should be kept in mind that a significant genetic association is not proof positive that a particular genetic locus causes a disease, but rather an important first step in discovering the genetic variants underlying a complex disease.

Dark-energy constraints and correlations with systematics from CFHTLS weak lensing, SNLS supernovae Ia and WMAP5

Aims We combine measurements of weak gravitational lensing from the CFHTLS-Wide survey, supernovae Ia from CFHT SNLS and CMB anisotropies from WMAP5 to obtain joint constraints on cosmological parameters, in particular, the dark-energy equation-of-state parameter w. We assess the influence of systematics in the data on the results and look for possible correlations with cosmological parameters. Methods We implemented an MCMC algorithm to sample the parameter space of a flat CDM model with a dark-energy component of constant w. Systematics in the data are parametrised and included in the analysis. We determine the influence of photometric calibration of SNIa data on cosmological results by calculating the response of the distance modulus to photometric zero-point variations. The weak lensing data set is tested for anomalous field-to-field variations and a systematic shape measurement bias for high-redshift galaxies. Results Ignoring photometric uncertainties for SNLS biases cosmological parameters by at most 20% of the statistical errors, using supernovae alone; the parameter uncertainties are underestimated by 10%. The weak-lensing field-to-field variance between 1 deg2-MegaCam pointings is 5-15% higher than predicted from N-body simulations. We find no bias in the lensing signal at high redshift, within the framework of a simple model, and marginalising over cosmological parameters. Assuming a systematic underestimation of the lensing signal, the normalisation increases by up to 8%. Combining all three probes we obtain -0.10 < 1 + w < 0.06 at 68% confidence ( -0.18 < 1 + w < 0.12 at 95%), including systematic errors. Our results are therefore consistent with the cosmological constant . Systematics in the data increase the error bars by up to 35%; the best-fit values change by less than 0.15.

Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

Using relationship mapping to understand sustainable housing stakeholders’ actions

The production of sustainable housing requires the cooperation of a variety of participants with different goals, needs, levels of commitment and cultures. To achieve mainstream net zero energy housing objectives, there is arguably a need for a non-linear network of collaboration between all the stakeholders. In order to create and improve such collaborative networks between stakeholders, we first need to map stakeholders’ relationships, processes, and practices. This paper discusses compares and contrasts maps of the sustainable housing production life-cycle in Australia, developed from different perspectives. The paper highlights the strengths and weaknesses of each visualization, clarifying where gaps in connectivity exist within existing industry networks. Understanding these gaps will help researchers and practitioners identify how to improve the collaboration between participants in the housing industry. This in turn may improve decision making across all stakeholder groups, leading to mainstream implementation of sustainability into the housing industry.