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Next generation modeling in GWAS: comparing different genetic architectures

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Next generation modeling in GWAS: comparing different genetic architectures

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dc.contributor.author López de Maturana, Evangelina es_ES
dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.contributor.author González-Recio, Oscar es_ES
dc.contributor.author Marenne, Gaëlle es_ES
dc.contributor.author Mehrban, Hossein es_ES
dc.contributor.author Chanock, Stephen J. es_ES
dc.contributor.author Goddard, Michael E. es_ES
dc.contributor.author Malats, Núria es_ES
dc.date.accessioned 2020-01-31T21:02:48Z
dc.date.available 2020-01-31T21:02:48Z
dc.date.issued 2014 es_ES
dc.identifier.issn 0340-6717 es_ES
dc.identifier.uri http://hdl.handle.net/10251/136134
dc.description.abstract [EN] The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Human Genetics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title Next generation modeling in GWAS: comparing different genetic architectures es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00439-014-1461-1 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.description.bibliographicCitation López De Maturana, E.; Ibáñez-Escriche, N.; González-Recio, O.; Marenne, G.; Mehrban, H.; Chanock, SJ.; Goddard, ME.... (2014). Next generation modeling in GWAS: comparing different genetic architectures. Human Genetics. 133(10):1235-1253. https://doi.org/10.1007/s00439-014-1461-1 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00439-014-1461-1 es_ES
dc.description.upvformatpinicio 1235 es_ES
dc.description.upvformatpfin 1253 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 133 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\343535 es_ES
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