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Full-Text Articles in Genetics and Genomics
Meta-Analysis Of Five Genome-Wide Association Studies Identifies Multiple New Loci Associated With Testicular Germ Cell Tumor., Zhaoming Wang, Katherine A Mcglynn, Ewa Rajpert-De Meyts, D Timothy Bishop, Charles C Chung, Marlene D Dalgaard, Mark H Greene, Ramneek Gupta, Tom Grotmol, Trine B Haugen, Robert Karlsson, Kevin Litchfield, Nandita Mitra, Kasper Nielsen, Louise C Pyle, Stephen M Schwartz, Vésteinn Thorsson, Saran Vardhanabhuti, Fredrik Wiklund, Clare Turnbull, Stephen J Chanock, Peter A Kanetsky, Katherine L Nathanson
Meta-Analysis Of Five Genome-Wide Association Studies Identifies Multiple New Loci Associated With Testicular Germ Cell Tumor., Zhaoming Wang, Katherine A Mcglynn, Ewa Rajpert-De Meyts, D Timothy Bishop, Charles C Chung, Marlene D Dalgaard, Mark H Greene, Ramneek Gupta, Tom Grotmol, Trine B Haugen, Robert Karlsson, Kevin Litchfield, Nandita Mitra, Kasper Nielsen, Louise C Pyle, Stephen M Schwartz, Vésteinn Thorsson, Saran Vardhanabhuti, Fredrik Wiklund, Clare Turnbull, Stephen J Chanock, Peter A Kanetsky, Katherine L Nathanson
Articles, Abstracts, and Reports
The international Testicular Cancer Consortium (TECAC) combined five published genome-wide association studies of testicular germ cell tumor (TGCT; 3,558 cases and 13,970 controls) to identify new susceptibility loci. We conducted a fixed-effects meta-analysis, including, to our knowledge, the first analysis of the X chromosome. Eight new loci mapping to 2q14.2, 3q26.2, 4q35.2, 7q36.3, 10q26.13, 15q21.3, 15q22.31, and Xq28 achieved genome-wide significance (P < 5 × 10
Solving The Influence Maximization Problem Reveals Regulatory Organization Of The Yeast Cell Cycle., David L Gibbs, Ilya Shmulevich
Solving The Influence Maximization Problem Reveals Regulatory Organization Of The Yeast Cell Cycle., David L Gibbs, Ilya Shmulevich
Articles, Abstracts, and Reports
The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. …