Thibault Laurent

The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

About predictions in spatial autoregressive models: optimal and almost optimal strategies

ABSTRACT About predictions in spatial autoregressive models: optimal and almost optimal strategies. Spatial Economic Analysis. This paper addresses the problem of prediction in the spatial autoregressive (SAR) model for areal data, which is classically used in spatial econometrics. With kriging theory, prediction using the best linear unbiased predictors (BLUPs) is at the heart of the geostatistical literature. From a methodological point of view, we explore the limits of the extension of BLUP formulas in the context of SAR models for out-of-sample prediction simultaneously at several sites. We propose a more tractable ‘almost best’ alternative and clarify the relationship between the BLUP and a proper expectation–maximization (EM) algorithm predictor. From an empirical perspective, we present data-based simulations to compare the efficiency of classical formulas with the best and almost best predictions.

Indirect and feedback effects as measure of knowledge spillovers in French regions

The aim of this article is to provide a precise measure of the role of geographical proximity in the innovation process on the French metropolitan NUTS (Nomenclature of Territorial Units for Statistics) 3 regions over the period 1995 to 2008. We study the relationship between patents applications and internal R&D, and we propose a spatial decomposition coefficient of the independent variables to measure more explicitly the spatial extent of knowledge spillovers (LeSage and Pace, 2009). Our estimation result shows that the internal R&D expenditures have a positive direct and indirect effect on the patents applications, but only for the regions with a strong innovation activity. For these regions, the spillover effect is observed in the first-order neighbourhood, but the spatial lag coefficient is not significant enough to get a positive feedback effect.