The preceding analyses revealed that each risk factor was associated with a large number of expression traits, thus emphasizing the multiple inter-relations existing between the transcriptomic and risk factor profiles of an individual. While it is important from a biological and mechanistic perspective to characterize at best all the genes that are influenced by a given condition, from a clinical perspective, it might be more relevant to identify a limited set of gene expressions that could efficiently discriminate individuals with different risk profiles. Indeed, because of the tight co-regulation of genes within biological systems, numerous gene expressions are inter-correlated and consequently, their association with risk factors are not independent. To account for this inter-dependency, we conducted a multivariate analysis to identify expression traits that were independently associated with each risk factor.
