English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cancer susceptibility may be controlled not only by host genes and mutated genes in cancer cells, but also by the epistatic interactions between genes from the host and cancer genomes. We derive a novel statistical model for cancer gene identification by integrating the gene mutation hypothesis of cancer formation into the mixture- model framework. Within this framework, genetic interactions of DNA sequences (or haplotypes) between host and cancer genes responsible for cancer risk are defined in terms of quantitative genetic principle. Our model was founded on a commonly used genetic association design in which a random sample of patients is drawn from a natural human population. Each patient is typed for single nucleotide polymorphisms (SNPs) on normal and cancer cells and measured for cancer susceptibility. The model is formulated within the maximum likelihood context and implemented with the EM algorithm, allowing the estimation of both population and quantitative genetic parameters. The model provides a general procedure for testing the distribution of haplotypes con- structed by SNPs from host and cancer genes and the linkage disequilibria of different orders among the SNPs. The model also formulates a series of testable hypotheses about the effects of host genes, cancer genes, and their interactions on cancer susceptibility. We carried out simulation studies to examine the statistical proper- ties of the model. The implications of this model for cancer gene identification are discussed.

Modeling Host-Cancer Genetic Interactions with Multilocus Sequence Data