A Bayesian generalized random regression model for estimating heritability using overdispersed count data

Mair, C. , Stear, M., Johnson, P. , Denwood, M., Jimenez de Cisneros, J. P., Stefan, T. and Matthews, L. (2015) A Bayesian generalized random regression model for estimating heritability using overdispersed count data. Genetics Selection Evolution, 47, 51. (doi: 10.1186/s12711-015-0125-5) (PMID:26092676) (PMCID:PMC4473853)

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Background: Faecal egg counts are a common indicator of nematode infection and since it is a heritable trait, it provides a marker for selective breeding. However, since resistance to disease changes as the adaptive immune system develops, quantifying temporal changes in heritability could help improve selective breeding programs. Faecal egg counts can be extremely skewed and difficult to handle statistically. Therefore, previous heritability analyses have log transformed faecal egg counts to estimate heritability on a latent scale. However, such transformations may not always be appropriate. In addition, analyses of faecal egg counts have typically used univariate rather than multivariate analyses such as random regression that are appropriate when traits are correlated. We present a method for estimating the heritability of untransformed faecal egg counts over the grazing season using random regression. Results: Replicating standard univariate analyses, we showed the dependence of heritability estimates on choice of transformation. Then, using a multitrait model, we exposed temporal correlations, highlighting the need for a random regression approach. Since random regression can sometimes involve the estimation of more parameters than observations or result in computationally intractable problems, we chose to investigate reduced rank random regression. Using standard software (WOMBAT), we discuss the estimation of variance components for log transformed data using both full and reduced rank analyses. Then, we modelled the untransformed data assuming it to be negative binomially distributed and used Metropolis Hastings to fit a generalized reduced rank random regression model with an additive genetic, permanent environmental and maternal effect. These three variance components explained more than 80 % of the total phenotypic variation, whereas the variance components for the log transformed data accounted for considerably less. The heritability, on a link scale, increased from around 0.25 at the beginning of the grazing season to around 0.4 at the end. Conclusions: Random regressions are a useful tool for quantifying sources of variation across time. Our MCMC (Markov chain Monte Carlo) algorithm provides a flexible approach to fitting random regression models to non-normal data. Here we applied the algorithm to negative binomially distributed faecal egg count data, but this method is readily applicable to other types of overdispersed data.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Mair, Dr Colette and Stefan, Mr Thorsten and Denwood, Dr Matthew and Johnson, Dr Paul and Stear, Professor Mike and Matthews, Professor Louise and Prada Jimenez de Cisneros, Dr Joaquin
Authors: Mair, C., Stear, M., Johnson, P., Denwood, M., Jimenez de Cisneros, J. P., Stefan, T., and Matthews, L.
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Genetics Selection Evolution
Publisher:BioMed Central
ISSN (Online):1297-9686
Copyright Holders:Copyright © 2015 The Authors
First Published:First Published in Genetics Selection Evolution 47:51
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
468351The effect of selection for nematode resistance on mhc class II diversiityMichael StearBiotechnology and Biological Sciences Research Council (BBSRC)BB/F015313/1III - PARASITOLOGY
553701A Systems Biology Approach to controlling Nematode Infections of LivestockMichael StearEuropean Commission (EC)PITN-GA-2010-26III - PARASITOLOGY