Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Comparison of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments

The objective of this study is a comparison of two models of the genetic algorithm, the generational and incremental/steady state genetic algorithms, for use in nonstationary/dynamic environments. It is experimentally shown that choice of a suitable version of the genetic algorithm can improve its performance in such environments. This can extend ability of the genetic algorithm to track environmental changes which are relatively small and occur with low frequency without need to implement an additional technique for tracking changing optima. 1 Introduction The genetic algorithm is a proven search/optimisation technique [Holland 1975] based on an adaptive mechanism of biological systems. In our previous work we showed that the genetic algorithm is a suitable on-line optimization method to balance the load of the presses in a sugar beet pressing station [Fogarty,Vavak,Cheng 1995] and in load balancing of multiple burner boiler [Vavak,Fogarty,Jukes 1995]. Because both mentioned applica..

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Abstract. This paper addresses the problem of investment optimization using genetic control. Time series for stock values are obtained from data available on the www and asset prices are predicted using adaptive algorithms. A portfolio is optimized with the genetic algorithm based on a recursive model of portfolio composition obtained on-the-fly using genetic programming. These two steps are integrated into an automatic system- the final result is a real-time system for updating portfolio composition for each asset. Introduction. IBM and other companies are undertaking massive studies on the application of advanced computing technologies to stock brokerage and obtaining better results than the New York stock market’s sharpest traders [1]. Every investor knows that there is a trade off between risk and reward: to obtain