Deciphering the biological mechanisms driving the phenotype of interest

Abstract

The two key concepts of Neo-Darwinian evolution theory are genotype and phenotype. Genotype is defined as the genetic constitution of an organism and phenotype refers to the observable characteristics of that organism. Schematically the relationship between genotype and phenotype can be settled as Genotype + Environment + Random Variation [special characters omitted] Phenotype.This schematic representation has led to the fundamental problem of given the interactions of the genes and environment, up to what extent is possible to establish a relationship between gene structure and function to the phenotype (Weatherall, D. J., et al., (2001)). Since R. A. Fisher establishing the basis of quantitative trait loci up to the work of Subramanian, et al., (1995) gene set enrichment analysis, several statistical methods have been devoted to answer this question, some with more success and scientific repercussion than others.

In this work we attempt to answer to this question by delineating the biological mechanisms driven by the genes that are characterize the differences and actions of the phenotypes of interest. Our contribution resides on two pillars: we present an alternative way to conceive gene expression measurements and the use of functional gene set annotation systems as guided prior knowledge of the biological mechanisms that drive the phenotype of interest. Based on these two pillars we propose a method to infer the Functional Network Inference and an alternative method to perform expression Quantitative Trait Loci analysis. (eQTL) From the Functional Network Inference method we are able to identify what mechanisms describe the behavior of most of the, there fore establishing its importance. The alternative method to perform eQTL analysis that we present, is more direct way to associated variations at a sequence level and the biological mechanisms it affects. With this proposal we attempt to address two important issues of traditional eQTL analysis: statistical power and biological implications.