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Full-Text Articles in Computational Biology
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Dartmouth Scholarship
BackgroundA goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.
Multifactor Dimensionality Reduction Analysis Identifies Specific Nucleotide Patterns Promoting Genetic Polymorphisms, Eric Arehart, Scott Gleim, Bill White, John Hwa, Jason H. Moore
Multifactor Dimensionality Reduction Analysis Identifies Specific Nucleotide Patterns Promoting Genetic Polymorphisms, Eric Arehart, Scott Gleim, Bill White, John Hwa, Jason H. Moore
Dartmouth Scholarship
The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation.