From GWAS to transcriptomics in prospective cancer design - new statistical challenges

TL;DR

This paper discusses a new prospective study design integrating transcriptomics into cancer research, highlighting statistical challenges and proposing a trajectory-based analytical approach to understand carcinogenic processes.

Contribution

It introduces a novel post-genome study design for transcriptomics in epidemiology and proposes a trajectory-based analysis method for carcinogenic processes.

Findings

Proposed a new study design for transcriptomics in cancer epidemiology.

Highlighted limitations of traditional survival analysis models.

Suggested a trajectory-based approach for analyzing gene changes during carcinogenesis.

Abstract

Background. With the increasing interest in post-GWAS research which represents a transition from genome-wide association discovery to analysis of functional mechanisms, attention has been lately focused on the potential of including various biological material in epidemiological studies. In particular, exploration of the carcinogenic process through transcriptional analysis at the epidemiological level opens up new horizons in functional analysis and causal inference, and requires a new design together with adequate analysis procedures. Results. In this article, we present the post-genome design implemented in the NOWAC cohort as an example of a prospective nested case-control study built for transcriptomics use, and discuss analytical strategies to explore the changes occurring in transcriptomics during the carcinogenic process in association with questionnaire information. We emphasize the inadequacy of survival analysis models usually considered in GWAS for post-genome design, and propose instead to parameterize the gene trajectories during the carcinogenic process. Conclusions. This novel approach, in which transcriptomics are considered as potential intermediate biomarkers of cancer and exposures, offers a flexible framework which can include various biological assumptions.