Sviluppo e applicazione di metodi statistici per l'identificazione in-silico di meccanismi causali alla base di tratti complessi e malattie mendeliane (DSB.AD005.076)

Thematic area

Biomedical sciences

Project area

Structure responsible for the research project

Institute for Genetic and Biomedical Research (IRGB)

Project manager

SERENA SANNA
Phone number: 070/6754618
Email: serena.sanna@cnr.it

Abstract

In the past years, large scale genome-wide association studies have drastically increased our knowledge of the genetic component of complex traits, with >60,000 variants known today to be significantly associated to a trait. However, little is known about the mechanisms that link such variants to the associated complex trait. Large scale omics studies that investigate the genetic component of molecular layers such as changes in transcripts levels or metabolites, can be used to infer the role of such genetic variants in a specific tissue, but it remains challenging to infer if such role is ultimately linked to the complex traits associated to the variants, as the variant may act through different tissues or cell developmental stages. Fine Understanding of mechanisms that lead a variant to the associated complex traits is essential to develop the appropriate therapeutic intervention.
Likewise, large scale environmental studies have correlated hundreds of environmental changes to complex traits, but such correlations cannot distinguish between cause, consequence and pure statistical correlation. Understanding causality is crucial to develop appropriate prevention actions.

Goals

In this project, we aim to:

1. develop and apply statistical causal inference methods that can efficiently integrate knowledge derived from large-scale genome-wide association studies and large-scale omics studies, to understand the causal relationship between variants, molecular layers, and complex traits.

2. develop and apply statistical causal inference methods that can efficiently discriminate the causal relationships between environmental factors and complex traits.

3. Apply the previous models to understand expression and phenotypic variability of Mendelian traits

We aim to develop and test such methods using statistical simulations, and apply these to real data including publicly available data sets as well as using individual level data available through national and international collaborations.

Start date of activity

01/11/2019

Keywords

causal inference, statistical models, complex traits

Last update: 01/08/2025