Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps

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.

Rönnegård, LarsSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Högskolan Dalarna,Statistik,Institutionen för husdjursgenetik (HGEN),Department of Animal Breeding and Genetics,Dalarna University(Swepub:slu)47773 (author)
Carlborg, ÖrjanSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för husdjursgenetik (HGEN),Department of Animal Breeding and Genetics,Uppsala University,SLU(Swepub:slu)48083 (author)
Högskolan DalarnaStatistik (creator_code:org_t)
Sveriges lantbruksuniversitet

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Probability Theo ...

By the university: Högskolan Dalarna; Uppsala University; Swedish University of Agricultural Sciences

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.