title: Association weight matrix: a network-based approach towards functional genome-wide association studies.
abstract: In this chapter we describe the Association Weight Matrix (AWM), a novel procedure to exploit the results from genome-wide association studies (GWAS) and, in combination with network inference algorithms, generate gene networks with regulatory and functional significance. In simple terms, the AWM is a matrix with rows represented by genes and columns represented by phenotypes. Individual {i, j}th elements in the AWM correspond to the association of the SNP in the ith gene to the jth phenotype. While our main objective is to provide a recipe-like tutorial on how to build and use AWM, we also take the opportunity to briefly reason the logic behind each step in the process. To conclude, we discuss the impact on AWM of issues like the number of phenotypes under scrutiny, the density of the SNP chip and the choice of contrast upon which to infer the cause-effect regulatory interactions.
authors.affiliation: CSIRO Livestock Industries, Queensland Bioscience Precinct, Brisbane, QLD, Australia.
authors.forename: Antonio
authors.initials: A
authors.lastname: Reverter
authors.forename: Marina R S
authors.initials: MR
authors.lastname: Fortes
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D015894:Genome, Human; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D015999:Multivariate Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D014157:Transcription Factors / Q000235:genetics
chemical_list: D014157:Transcription Factors