Online algorithm for variance components estimation

2021 ◽  
Vol 97 ◽  
pp. 105722
Author(s):  
Xinggang Zhang ◽  
Xiaochun Lu
Keyword(s):  
Variance Components ◽  
Online Algorithm ◽  
Variance Components Estimation

scholarly journals Repeatability and Heritability of Behavioural Types in a Social Cichlid

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Noémie Chervet ◽  
Markus Zöttl ◽  
Roger Schürch ◽  
Michael Taborsky ◽  
Dik Heg
Keyword(s):  
Environmental Factors ◽  
Quantitative Genetics ◽  
Variance Components ◽  
Significant Variation ◽  
Cichlid Fish ◽  
Animal Personality ◽  
Cichlid Species ◽  
Behavioural Type ◽  
Variance Components Estimation ◽  
Behavioural Types

Aim. The quantitative genetics underlying correlated behavioural traits (‘‘animal personality’’) have hitherto been studied mainly in domesticated animals. Here we report the repeatability () and heritability () of behavioural types in the highly social cichlid fish Neolamprologus pulcher. Methods. We tested 1779 individuals repeatedly and calculated the of behavioural types by variance components estimation (GLMM REML), using 1327 offspring from 162 broods from 74 pairs. Results. Repeatability of behavioural types was significant and considerable (0.546), but declined from 0.83 between tests conducted on the same day, to 0.19 on tests conducted up to 1201 days apart. All estimates were significant but low (e.g., pair identity SE). Additionally, we found significant variation between broods nested within the parent(s), but these were not related to several environmental factors tested. Conclusions. We conclude that despite a considerable , in this cichlid species is low, and variability in behavioural type appears to be strongly affected by other (non)genetic effects.

scholarly journals Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

2000 ◽  
Vol 76 (2) ◽  
pp. 187-198 ◽  
Author(s):  
F.-X. DU ◽  
I. HOESCHELE
Keyword(s):  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Effects ◽  
Single Site ◽  
Phenotypic Data ◽  
Variance Components Estimation ◽  
Locus Number ◽  
Variance Estimates ◽  
Finite Locus

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

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