Polygenic prediction and gene regulation networks

TL;DR

This paper explores how well statistical models can predict complex traits by combining gene regulation networks with polygenic prediction methods.

Contribution

The study introduces a novel framework linking gene regulation networks with polygenic prediction models to understand complex trait prediction.

Findings

Regulatory connections in gene networks significantly influence phenotypic prediction accuracy.

The study connects findings to core and peripheral causal determinants in the omnigenic model.

Results relate to global sensitivity and sloppy parameters in biological systems.

Abstract

Exploring the degree to which phenotypic variation, influenced by intrinsic nonlinear biological mechanisms, can be accurately captured using statistical methods is essential for advancing our comprehension of complex biological systems and predicting their functionality. Here, we examine this issue by combining a computational model of gene regulation networks with a linear additive prediction model, akin to polygenic scores utilized in genetic analyses. Inspired by the variational framework of quantitative genetics, we create a population of individual networks possessing identical topology yet showcasing diversity in regulatory strengths. By discerning which regulatory connections determine the prediction of phenotypes, we contextualize our findings within the framework of core and peripheral causal determinants, as proposed by the omnigenic model of complex traits. We establish connections between our results and concepts such as global sensitivity and local stability in dynamical systems, alongside the notion of sloppy parameters in biological models. Furthermore, we explore the implications of our investigation for the broader discourse surrounding the role of epistatic interactions in the prediction of complex phenotypes.

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