du.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Genetic heterogeneity of residual variance: estimation of variance components using double hierarchical generalized linear models
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-1057-5401
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
Visa övriga samt affilieringar
2010 (Engelska)Ingår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 42, artikel-id 8Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms.

Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model.

Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Ort, förlag, år, upplaga, sidor
2010. Vol. 42, artikel-id 8
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
Komplexa system - mikrodataanalys, Statistisk modellering är grunden till en ökad förståelse inom genetik!
Identifikatorer
URN: urn:nbn:se:du-10497DOI: 10.1186/1297-9686-42-8ISI: 000277118500001PubMedID: 20302616OAI: oai:DiVA.org:du-10497DiVA, id: diva2:542773
Tillgänglig från: 2012-08-03 Skapad: 2012-08-03 Senast uppdaterad: 2017-12-07Bibliografiskt granskad
Ingår i avhandling
1. Genetic Heteroscedasticity for Domestic Animal Traits
Öppna denna publikation i ny flik eller fönster >>Genetic Heteroscedasticity for Domestic Animal Traits
2014 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Animal traits differ not only in mean, but also in variation around the mean. For instance, one sire’s daughter group may be very homogeneous, while another sire’s daughters are much more heterogeneous in performance. The difference in residual variance can partially be explained by genetic differences. Models for such genetic heterogeneity of environmental variance include genetic effects for the mean and residual variance, and a correlation between the genetic effects for the mean and residual variance to measure how the residual variance might vary with the mean.

The aim of this thesis was to develop a method based on double hierarchical generalized linear models for estimating genetic heteroscedasticity, and to apply it on four traits in two domestic animal species; teat count and litter size in pigs, and milk production and somatic cell count in dairy cows.

The method developed is fast and has been implemented in software that is widely used in animal breeding, which makes it convenient to use. It is based on an approximation of double hierarchical generalized linear models by normal distributions. When having repeated observations on individuals or genetic groups, the estimates were found to be unbiased.

For the traits studied, the estimated heritability values for the mean and the residual variance, and the genetic coefficients of variation, were found in the usual ranges reported. The genetic correlation between mean and residual variance was estimated for the pig traits only, and was found to be favorable for litter size, but unfavorable for teat count.

Ort, förlag, år, upplaga, sidor
Uppsala: Sveriges Lantbruksuniversitet, 2014. s. 54
Serie
Acta Universitatis agriculturae Sueciae, ISSN 1652-6880 ; 2014:43
Nyckelord
Quantitative genetics, genetic heteroscedasticity of residuals, genetic heterogeneity of environmental variation, genetic heterogeneity of residual variance, double hierarchical generalized linear models, teat count in pigs, litter size in pigs, milk yield in cows, somatic cell count in cows
Nationell ämneskategori
Husdjursvetenskap Sannolikhetsteori och statistik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-14310 (URN)978-91-576-8035-8 (ISBN)978-91-576-8034-1 (ISBN)
Disputation
2014-06-11, Room L, Undervisningsplan 8, Uppsala, 09:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2014-06-16 Skapad: 2014-06-16 Senast uppdaterad: 2015-06-08Bibliografiskt granskad

Open Access i DiVA

fulltext(411 kB)260 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 411 kBChecksumma SHA-512
56ce816acc94f42764b64d8bf3c41d3c31fc6ddbcc8fd0b7bcbb6716b0834122b8bfd1eae7d408683b140456e1bea9368cb540c037d021ebec94f8197b5103d8
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextPubMed

Personposter BETA

Rönnegård, Lars

Sök vidare i DiVA

Av författaren/redaktören
Rönnegård, LarsFelleki, Majbritt
Av organisationen
Statistik
I samma tidskrift
Genetics Selection Evolution
Sannolikhetsteori och statistik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 260 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
pubmed
urn-nbn

Altmetricpoäng

doi
pubmed
urn-nbn
Totalt: 752 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf