Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry

The Functions of Grainy Head-Like Proteins in Animals and Fungi and the Evolution of Apical Extracellular Barriers

The Grainy head (GRH) family of transcription factors are crucial for the development and repair of epidermal barriers in all animals in which they have been studied. This is a high-level functional conservation, as the known structural and enzymatic genes regulated by GRH proteins differ between species depending on the type of epidermal barrier being formed. Interestingly, members of the CP2 superfamily of transcription factors, which encompasses the GRH and LSF families in animals, are also found in fungi – organisms that lack epidermal tissues. To shed light on CP2 protein function in fungi, we characterized a Neurospora crassa mutant lacking the CP2 member we refer to as grainy head-like (grhl). We show that Neurospora GRHL has a DNA-binding specificity similar to that of animal GRH proteins and dissimilar to that of animal LSF proteins. Neurospora grhl mutants are defective in conidial-spore dispersal due to an inability to remodel the cell wall, and we show that grhl mutants and the long-known conidial separation-2 (csp-2) mutants are allelic. We then characterized the transcriptomes of both Neurospora grhl mutants and Drosophila grh mutant embryos to look for similarities in the affected genes. Neurospora grhl appears to play a role in the development and remodeling of the cell wall, as well as in the activation of genes involved in defense and virulence. Drosophila GRH is required to activate the expression of many genes involved in cuticular/epidermal-barrier formation. We also present evidence that GRH plays a role in adult antimicrobial defense. These results, along with previous studies of animal GRH proteins, suggest the fascinating possibility that the apical extracellular barriers of some animals and fungi might share an evolutionary connection, and that the formation of physical barriers in the last common ancestor was under the control of a transcriptional code that included GRH-like proteins

Fast retrieval of electronic messages that contain mistyped words or spelling errors

Abstract—This paper presents an index structure for retrieving electronic messages that contain mistyped words or spelling errors. Given a query string (e.g., a search key), we want to find those messages that approximately contain the query, i.e., certain inserts, deletes and mismatches are allowed when matching the query with a word (or phrase) in the messages. Our approach is to store the messages sequentially in a database and hash their “fingerprints ” into a number of “fingerprint files. ” When the query is given, its fingerprints are also hashed into the files and a histogram of votes is constructed on the messages. We derive a lower bound, based on which one can prune a large number of nonqualifying messages (i.e., those whose votes are below the lower bound) during searching. The paper presents some experimental results, which demonstrate the effectiveness of the index structure and the lower bound. I

Abstract Evaluating A Class of Distance-Mapping Algorithms for Data Mining and Clustering*

A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuris-tics that combine the two algorithms in different ways. Ex-perimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary in-formation about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.

Exact and Approximate Algorithms for Unordered 'he Matching

Abstract-We consider the problem of comparison between unordered trees, i.e., trees for which the order among siblings is unimportant. The criterion for comparison is the distance as measured by a weighted sum of the costs of deletion, insertion and relabel operations on tree nodes. Such comparisons may contribute to pattern recognition efforts in any field (e.g., genetics) where data can naturally be characterized by unordered trees. In companion work, we have shown this problem to be NP-complete. This paper presents an efflcient enumerative algorithm and several heuristics leading to approximate solutions. The algorithms are based on probabilistic hill climbing and bipartite matching techniques. The paper evaluates the accuracy and time efficiency of the heuristics by applying them to a set of trees transformed from industrial parts based on a previously proposed morphological model. I

Evaluating A Class of Distance-Mapping Algorithms for Data Mining and Clustering

A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudo-Euclidean space in such a way that the distances among objects are approximately preserved. Distancemapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore ca..

Structural Matching and Discovery in Document Databases

This memo is being written to ... the SGML markup minimization ... .................................................. Although only one tag is visible in ... "carbon copy" recipient), the SGML ... Yours truly, ................................................ <!doctype memo SYSTEM> Fig. 2. An SGML memo document A. Charles F. Goldfar