Genetical genomics : use all data
Perez-Enciso, Miguel (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Quevedo, José R. (Universidad de Oviedo. Centro de Inteligencia Artificial)
Bahamonde, Antonio (Universidad de Oviedo. Centro de Inteligencia Artificial)

Data:	2007
Resum:	Background: Genetical genomics is a very powerful tool to elucidate the basis of complex traits and disease susceptibility. Despite its relevance, however, statistical modeling of expression quantitative trait loci (eQTL) has not received the attention it deserves. Based on two reasonable assertions (i) a good model should consider all available variables as potential effects, and (ii) gene expressions are highly interconnected, we suggest that an eQTL model should consider the rest of expression levels as potential regressors, in addition to the markers. Results: It is shown that power can be increased with this strategy. We also show, using classical statistical and support vector machines techniques in a reanalysis of public data, that the external transcripts, i. e. , transcripts other than the one being analysed, explain on average much more variability than the markers themselves. The presence of eQTL hotspots is reassessed in the light of these results. Conclusion: Model choice is a critical yet neglected issue in genetical genomics studies. Although we are far from having a general strategy for model choice in this area, we can at least propose that any transcript level is scanned not only for the markers genotyped but also for the rest of gene expression levels. Some sort of stepwise regression strategy can be used to select the final model.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	BMC genomics, Vol. 8, Núm. 69 (March 2007), p. 1-8, ISSN 1471-2164

DOI: 10.1186/1471-2164-8-69
PMID: 17352813

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