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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Background Independence models among variables is one of the most relevant topics in epidemiology, particularly in molecular epidemiology for the study of gene-gene and gene-environment interactions. They have been studied using three main kinds of analysis: regression analysis, data mining approaches and Bayesian model selection. Recently, methods of algebraic statistics have been extensively used for applications to biology. In this paper we present a synthetic, but complete description of independence models in algebraic statistics and a new method of analyzing interactions, that is equivalent to the correction by Markov bases of the Fisher’s exact test. Methods We identified the suitable algebraic independence model for describing the dependence of two genetic variables from the occurrence of cancer and exploited the theory of toric varieties and Grobner basis for developing an exact independence test based on the Diaconis-Sturmfels algorithm. We implemented it in a Maple routine and we applied it to the study of gene-gene interaction in Gen-Air, an European case-control study. We computed the p-value for each pair of genetic variables interacting with disease status and we compared our results with the standard asymptotic chi-square test. Results We found an association among COMT Val158Met, APE1 Asp148Glu and bladder cancer (p-value: 0.009). We also found the interaction among TP53 Arg72Pro, GSTP1 Ile105Val and lung cancer (p-value: 0.00035). Leukaemia was observed to significantly interact with the pairs ERCC2 Lys751Gln and RAD51 172 G > T (p-value 0.0072), ERCC2 Lys751Gln and LIG4 Thr9Ile (p-value: 0.0095) and APE1 Asp148Glu and GSTP1 Ala114Val (p-value: 0.0036). Conclusion Taking advantage of results from theoretical and computational algebra, the method we propose was more selective than other methods in detecting new interactions, and nevertheless its results were consistent with previous epidemiological and functional findings. It also helped us in controlling the multiple comparison problem. In the light of our results, we believe that the epidemiologic study of interactions can benefit of algebraic methods based on properties of toric varieties and Grobner bases.

Algebraic Methods for Studying Interactions Between Epidemiological Variables