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Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0003-4390-1979
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-1057-5401
2011 (engelsk)Inngår i: BMC Proceedings, ISSN 1753-6561, E-ISSN 1753-6561, Proc. 14th European Workshop on QTL Mapping and Marker Assisted Selection (QTL-MAS), nr 5(Suppl 3)Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background Genome-wide dense markers have been used to detect genes and estimate relative genetic values. Among many methods, Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. However, computation is known to be intensive under the Bayesian framework, and specifying a prior distribution for each parameter is always required for Bayesian computation. We propose the use of hierarchical likelihood to solve such problems. Results Using double hierarchical generalized linear models, we analyzed the simulated dataset provided by the QTLMAS 2010 workshop. Marker-specific variances estimated by double hierarchical generalized linear models identified the QTL with large effects for both the quantitative and binary traits. The QTL positions were detected with very high accuracy. For young individuals without phenotypic records, the true and estimated breeding values had Pearson correlation of 0.60 for the quantitative trait and 0.72 for the binary trait, where the quantitative trait had a more complicated genetic architecture involving imprinting and epistatic QTL. Conclusions Hierarchical likelihood enables estimation of marker-specific variances under the likelihoodist framework. Double hierarchical generalized linear models are powerful in localizing major QTL and computationally fast.

sted, utgiver, år, opplag, sider
2011. nr 5(Suppl 3)
Emneord [en]
hierarchical likelihood, quantitative trait loci, genomic selection, double hierarchical generalized linear model
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-4841DOI: 10.1186/1753-6561-5-S3-S14OAI: oai:dalea.du.se:4841DiVA, id: diva2:520214
Tilgjengelig fra: 2010-06-18 Laget: 2010-06-18 Sist oppdatert: 2017-12-07bibliografisk kontrollert

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