Two non-synonymous markers in PTPN21, identified by genome-wide association study data-mining and replication, are associated with schizophrenia

We conducted data-mining analyses of genome wide association (GWA) studies of the CATIE and MGS-GAIN datasets, and found 13 markers in the two physically linked genes, PTPN21 and EML5, showing nominally significant association with schizophrenia. Linkage disequilibrium (LD) analysis indicated that all 7 markers from PTPN21 shared high LD (r(2)>0.8), including rs2274736 and rs2401751, the two non-synonymous markers with the most significant association signals (rs2401751, P=1.10 × 10(-3) and rs2274736, P=1.21 × 10(-3)). In a meta-analysis of all 13 replication datasets with a total of 13,940 subjects, we found that the two non-synonymous markers are significantly associated with schizophrenia (rs2274736, OR=0.92, 95% CI: 0.86-0.97, P=5.45 × 10(-3) and rs2401751, OR=0.92, 95% CI: 0.86-0.97, P=5.29 × 10(-3)). One SNP (rs7147796) in EML5 is also significantly associated with the disease (OR=1.08, 95% CI: 1.02-1.14, P=6.43 × 10(-3)). These 3 markers remain significant after Bonferroni correction. Furthermore, haplotype conditioned analyses indicated that the association signals observed between rs2274736/rs2401751 and rs7147796 are statistically independent. Given the results that 2 non-synonymous markers in PTPN21 are associated with schizophrenia, further investigation of this locus is warranted.

'Mining money': science, legal technologies & working the borderlands of global finance

This paper explores the roles of science and market devices in the commodification of ‘nature’ and the configuration of flows of speculative capital. It focuses on mineral prospecting and the market for shares in ‘junior’ mining companies. In recent years these companies have expanded the reach of their exploration activities overseas, taking advantage of innovations in exploration methodologies and the liberalisation of fiscal and property regimes in ‘emerging’ mineral rich developing countries. Recent literature has explored how the reconfiguration of notions of ‘risk’ has structured the uneven distribution of rents. It is increasingly evident that neoliberal framing of environmental, political, social and economic risks has set in motion overflows that multinational mining capital had not bargained for (e.g. nationalisation, violence and political resistance). However, the role of ‘geological risk’ in animating flows of mining finance is often assumed as a ‘technical’ given. Yet geological knowledge claims, translated locally, designed to travel globally, assemble heterogeneous elements within distanciated regimes of metrology, valuation and commodity production. This paper explores how knowledge of nature is enrolled within systems of property relations, focusing on the genealogy of the knowledge practices that animate contemporary circuits of speculative mining finance. It argues that the financing of mineral prospecting mobilises pragmatic and situated forms of knowledge rather than actuarially driven calculations that promise predictability. A Canadian public enquiry struck in the wake of scandal associated with Bre-X’s prospecting activities in Indonesia is used to glean insights into the ways in which the construction of a system of public warrant to underpin financial speculation is predicated upon particular subjectivities and the outworking of everyday practices and struggles over ‘value’. Reflection on practical investments in processes of standardisation, rituals of verification and systems of accreditation reveal much about how the materiality of things shape the ways in which regional and global financial circuits are integrated, selectively transforming existing social relations and forms of knowledge production.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage