In medicine or agronomy, the understanding of dynamic processes, such as disease progress or growth, is crucial. Such processes are controlled by complex biological mechanisms that involve markers from different molecular levels and by environmental factors. Disentangling these mechanisms may help in the development of more effective therapeutic strategies in human disease and may contribute to gaining insights into the adaptation of plants in the face of climate change.
During the last decades, advances of high-throughput technologies have led to the acquisition of various types of -omic data, including whole-genome sequencing, methylation, transcriptomic, glycomic, proteomic, and metabolomic. Such data offer the opportunity of gaining insights into complex biological mechanisms by making possible the study of relationships between features from various molecular levels and the study of their implication in biological processes. The first approaches have analyzed each dataset separately providing a snapshot of the molecular processes involved. Over the last years, integrative approaches that combine complementary information from each data type have been proposed. While they have led to significant results, they have also highlighted that the integration of several biological layers of information is still challenging. Indeed, statistical models need to efficiently deal with high-dimension while considering dependence between/within -omic data. In addition, there has been a growing interest in identifying biological mechanisms involved in the evolution of traits along time, for example by identifying active genes along plant growth, or biological pathways involved in fetus development. The associated data are characterized by measures along time on the same individuals and are called longitudinal data. The dependence among measurements on a same individual needs to be considered in the statistical models.
The joint analysis of traits evolving over time in relation to high-dimensional -omic data from various biological levels is a recent active area that gives new opportunities for an enhanced understanding of dynamic biological processes. But this also raises new statistical challenges related to, among others, high-dimensionality and dependence structure between variables. To this end, innovative statistical methods need to be developed. The ISULO project’s main objective was to propose innovative methodologies to simultaneously analyze longitudinal data and high-dimensional -omic data. The research methodology has been subdivided into two major parts, one focusing on the analysis of one longitudinal outcome in relation to one -omic dataset, and a second part focusing on the integration of multiple high-dimensional -omic datasets for explaining non-longitudinal and/or longitudinal outcomes.
In the two parts, the proposed methods aim at dealing with high-dimensional data for which prior knowledge about relationships between predictors and/or outcomes may be available.
The ISULO project has provided innovative statistical approaches addressing biological questions that arise both in medicine and agronomy. The results have highlighted the advantage of integrating known structures between covariates, between responses or between covariates and responses into the statistical models. They have also pointed out the difficulties/limitations that may be encountered when jointly modelling multivariate outcomes with multiple -omic datasets. The interdisciplinary nature of the project has allowed exploration of various biological and statistical areas and high-collaboration with researchers in statistics, genetics, epidemiology, molecular biology, and oncology. The wide applicability of the proposed approaches as well as the expertise acquired in the analysis of data generated by cutting-edge technologies have led to new collaborations with national and international researchers working in different domains. By addressing challenging methodological questions, this action has also generated new interdisciplinary questions that will be investigated in future projects.
