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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fortes MRS et al (2010) Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci USA 107:13642–13647
Fortes MRS et al (2010) A new method for exploring genome-wide associations applied to cattle puberty. 9th World Congress on genetics applied to livestock production. German Society for Animal Science, Leipzig, Germany, pp 4–166. ISBN 978-3-00-031608-1.
Fortes MRS et al (2011) A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. J Anim Sci 89:1669–1683
Hudson NJ, Reverter A, Dalrymple BP (2009) A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput Biol 5:e1000382
Reverter A et al (2010) Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics 26:896–904
Reverter A, Chan EKF (2008) Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics 24:2491–2497
Cookson W et al (2009) Mapping complex disease traits with global gene expression. Nat Rev Genet 10:184–194
Caraux G, Pinloche S (2005) Permutmatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21:1280–1281
Watson-Haigh NS, Kadarmideen HN, Reverter A (2010) PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches. Bioinformatics 26:411–413
Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2
Snelling WM et al (2012) How SNP chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females. J Anim Sci 90(4):1152–1165. doi:10.2527/jas.2011-4581
Weller JI, Ron M (2011) Invited review: quantitative trait nucleotide determination in the era of genomic selection. J Dairy Sci 94:1082–1090
Acknowledgments
We are grateful to Yuliaxis Ramayo-Caldas for assistance composing the R scripts included in this work.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Reverter, A., Fortes, M.R.S. (2013). Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies. In: Gondro, C., van der Werf, J., Hayes, B. (eds) Genome-Wide Association Studies and Genomic Prediction. Methods in Molecular Biology, vol 1019. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-447-0_20
Download citation
DOI: https://doi.org/10.1007/978-1-62703-447-0_20
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-446-3
Online ISBN: 978-1-62703-447-0
eBook Packages: Springer Protocols