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      Genetic analysis of infectious diseases: estimating gene effects for susceptibility and infectivity

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      Genetics, Selection, Evolution : GSE

      BioMed Central

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          Abstract

          Background

          Genetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases. Most genetic approaches that aim at reducing disease prevalence assume that individual disease status (infected/not-infected) is solely a function of its susceptibility to a particular pathogen. However, individual infectivity also affects the risk and prevalence of an infection in a population. Variation in susceptibility and infectivity between hosts affects transmission of an infection in the population, which is usually measured by the value of the basic reproduction ratio R 0 . R 0 is an important epidemiological parameter that determines the risk and prevalence of infectious diseases. An individual’s breeding value for R 0 is a function of its genes that influence both susceptibility and infectivity. Thus, to estimate the effects of genes on R 0 , we need to estimate the effects of genes on individual susceptibility and infectivity. To that end, we developed a generalized linear model (GLM) to estimate relative effects of genes for susceptibility and infectivity. A simulation was performed to investigate bias and precision of the estimates, the effect of R 0 , the size of the effects of genes for susceptibility and infectivity, and relatedness among group mates on bias and precision. We considered two bi-allelic loci that affect, respectively, the individuals’ susceptibility only and individuals’ infectivity only.

          Results

          A GLM with complementary log–log link function can be used to estimate the relative effects of genes on the individual’s susceptibility and infectivity. The model was developed from an equation that describes the probability of an individual to become infected as a function of its own susceptibility genotype and infectivity genotypes of all its infected group mates. Results show that bias is smaller when R 0 ranges approximately from 1.8 to 3.1 and relatedness among group mates is higher. With larger effects, both absolute and relative standard deviations become clearly smaller, but the relative bias remains the same.

          Conclusions

          We developed a GLM to estimate the relative effect of genes that affect individual susceptibility and infectivity. This model can be used in genome-wide association studies that aim at identifying genes that influence the prevalence of infectious diseases.

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          Most cited references 17

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          Exact stochastic simulation of coupled chemical reactions

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            Prediction of total genetic value using genome-wide dense marker maps.

            Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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              A Contribution to the Mathematical Theory of Epidemics

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                Author and article information

                Affiliations
                [ ]Animal Breeding and Genomics Centre, Wageningen University, 6700 AH Wageningen, The Netherlands
                [ ]Quantitative Veterinary Epidemiology Group, Wageningen University, 6700 AH Wageningen, The Netherlands
                Contributors
                mahlet.anche@wur.nl , mahlet.teka@gmail.com
                piter.bijma@wur.nl
                mart.dejong@wur.nl
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                4 November 2015
                4 November 2015
                2015
                : 47
                163
                10.1186/s12711-015-0163-z
                4634202
                26537023
                © Anche et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Research Article
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                © The Author(s) 2015

                Genetics

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