We conducted genomewide linkage analyses on 1,152 individuals from 250 families segregating for bipolar disorder and related affective illnesses. These pedigrees were ascertained at 10 sites in the United States, through a proband with bipolar I affective disorder and a sibling with bipolar I or schizoaffective disorder, bipolar type. Uniform methods of ascertainment and assessment were used at all sites. A 9-cM screen was performed by use of 391 markers, with an average heterozygosity of 0.76. Multipoint, nonparametric linkage analyses were conducted in affected relative pairs. Additionally, simulation analyses were performed to determine genomewide significance levels for this study. Three hierarchical models of affection were analyzed. Significant evidence for linkage (genomewide P<.05) was found on chromosome 17q, with a peak maximum LOD score of 3.63, at the marker D17S928, and on chromosome 6q, with a peak maximum LOD score of 3.61, near the marker D6S1021. These loci met both standard and simulation-based criteria for genomewide significance. Suggestive evidence of linkage was observed in three other regions (genomewide P<.10), on chromosomes 2p, 3q, and 8q. This study, which is based on the largest linkage sample for bipolar disorder analyzed to date, indicates that several genes contribute to bipolar disorder.

The results suggest that a gene or genes on chromosome 1 may predispose some individuals to alcoholism and others to depression (which may be alcohol induced). Loci on other chromosomes may also be of interest.

While talk of “Big Data” is now prevalent in many sectors, there are still relatively few examples of Big Data being used to shape public policy. This article reports an international study of Big Data for policy initiatives to understand the role played by data‐driven approaches in the policy process. Drawing on evidence (including policy analysis and interviews with stakeholders) from 58 initiatives, we find that some policy areas, notably efforts to improve government transparency, are far more represented than others, such as use of social media data for policy evaluation. We also find Big Data used more often in the policy cycle for foresight and agenda setting, or interim evaluation and monitoring, rather than for policy implementation and ex post evaluation. Many different types of data are used in the policy process, with traditional sources such as government statistics still favored over new and emerging sources. We find that use of Big Data for public policy is therefore at an early stage, with expectations far outstripping the current reality.

The risk of alcohol dependence in relatives of probands compared with controls is increased about 2-fold. The aggregation of antisocial personality disorder, drug dependence, anxiety disorders, and mood disorders suggests common mechanisms for these disorders and alcohol dependence within some families. These data suggest new phenotypes for molecular genetic studies and alternative strategies for studying the heterogeneity of alcohol dependence.

Researchers in the humanities adopt a wide variety of approaches to their research. Their work tends to focus on texts and images, but they use and also create a wide range of information resources, in print, manuscript and digital forms. Like other researchers, they face multiple demands on their time, and so they find the ease and speed of access to digital resources very attractive: some of them note that they are reluctant on occasion to consult Communication uses (e-mail lists & noti cations) Electronic journals Journals Grid and cloud resources Text mining Web 2.0 tools Virtual Research Environment (VRE) Image and data processing Arti cial Intelligence Case Studies

Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses to this opportunity amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines.

Objective-Brain-derived neurotrophic factor (BDNF) plays an important role in the survival, differentiation, and outgrowth of select peripheral and central neurons throughout adulthood. There is growing evidence suggesting that BDNF is involved in the pathophysiology of mood disorders. Methods-TenSNPs across the BDNF gene were genotyped in a sample of 1,749 Caucasian Americans from 250 multiplex bipolar families. Family-based association analysis was employed with three hierarchical bipolar disorder models to test for association between SNPs in BDNF and the risk for bipolar disorder. In addition, an exploratory analysis was performed to test for an association of the SNPs in BDNF and the phenotypes of rapid cycling and episode frequency.Results-Evidence of association (p<0.05) was found with several of the SNPs using multiple models of bipolar disorder; one of these SNPs also showed evidence of association (p<0.05) with rapid cycling. Conclusion-These results provide further evidence that variation in BDNF affects the risk for bipolar disorder.

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