15 research outputs found

    Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

    Get PDF
    We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

    Distributed Decision Making in Checkers

    No full text
    Proceeding of: First International Conference, CG'98, Tsukuba (Japan), November 1998The game of checkers can be played by machines running either heuristic search algorithms or complex decision making programs trained using machine learning techniques. The first approach has been used with remarkable success. The latter approach yielded encouraging results in the past, but later results were not so useful, partly because of the limitations of current machine learning algorithms. The focus of this work is the study of techniques for distributed decision making and learning by Multi-Agent DEcision Systems (MADES), by means of their application to the development of a checkers playing program. In this paper, we propose a new architecture for knowledge based systems dedicated to checkers playing. Our aim is to show how the combination of several known models for checkers playing can be integrated into a MADES, that learns how to combine individual decisions, so that the MADES plays better than any of them, without “a priori” knowledge of the quality or area of expertise of each model. In our MADES, we integrate well known search algorithms along standard machine learning algorithms. We present results that clearly show that the team as a single entity plays better than any of its components working in isolation.Publicad

    Smarca4 ATPase mutations disrupt direct eviction of PRC1 from chromatin

    No full text
    Trithorax-group genes and mammalian homologues, including BAF (mSWI/SNF) complexes, have been known for nearly 30 years to oppose Polycomb repressive activity(1–5). This opposition underlies the tumor-suppression role of BAF(3,5–7) and is expected to contribute to neurodevelopmental disorders, as evidenced by frequent driving mutations(8,9). However, the mechanisms underlying opposition to Polycomb silencing are poorly understood. Here we report that recurrent disease mutations of BAF subunits induce genome-wide increases in Polycomb complex deposition and activity. We show that point mutations of the Smarca4 (Brg) ATPase domain cause loss of direct binding between BAF and PRC1 that occurs independently of chromatin. Release of this direct interaction occurs via an ATP-dependent mechanism, consistent with a role as a transient intermediate of eviction. Using a new in vivo assay, we find that BAF directly evicts Polycomb factors within minutes of its occupancy, together establishing a new mechanism for the widespread opposition underlying development and disease
    corecore