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      On the origin of the genetic variation in infectious disease prevalence: Genetic analysis of disease status versus infections for Digital Dermatitis in Dutch dairy cattle

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          Abstract

          The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not). The analyses of the two traits were compared using cross‐validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious.

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          Efficient methods to compute genomic predictions.

          P VanRaden (2008)
          Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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            The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

            The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
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              Approximate Inference in Generalized Linear Mixed Models

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                Author and article information

                Contributors
                pranav.kulkarni@wur.nl
                Journal
                J Anim Breed Genet
                J Anim Breed Genet
                10.1111/(ISSN)1439-0388
                JBG
                Journal of Animal Breeding and Genetics
                John Wiley and Sons Inc. (Hoboken )
                0931-2668
                1439-0388
                09 June 2021
                November 2021
                : 138
                : 6 ( doiID: 10.1111/jbg.v138.6 )
                : 629-642
                Affiliations
                [ 1 ] Animal Breeding and Genomics Wageningen University & Research Wageningen The Netherlands
                [ 2 ] Wageningen Business Economics Group Wageningen University & Research Wageningen The Netherlands
                [ 3 ] Farm Animal Health Faculty of Vet. Med Department of Population Health Sciences Utrecht University Utrecht The Netherlands
                [ 4 ] Quantitative Veterinary Epidemiology Wageningen University & Research Wageningen The Netherlands
                [ 5 ] Centre for Veterinary Epidemiology and Risk Analysis UCD School of Veterinary Medicine University College Dublin Belfield, Dublin Ireland
                [ 6 ] INRAE BIOEPAR Nantes France
                Author notes
                [*] [* ] Correspondence

                Pranav Shrikant Kulkarni, Animal Breeding and Genomics, Wageningen University & Research, 6700AH Wageningen, The Netherlands.

                Email: pranav.kulkarni@ 123456wur.nl

                Author information
                https://orcid.org/0000-0003-4105-7559
                Article
                JBG12635
                10.1111/jbg.12635
                8518086
                34105197
                75b39502-3f49-43e7-aa28-a2cb7a36c99d
                © 2021 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 April 2021
                : 20 December 2020
                : 17 May 2021
                Page count
                Figures: 4, Tables: 10, Pages: 14, Words: 10461
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                November 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.8 mode:remove_FC converted:15.10.2021

                mortellaro,heritability,susceptibility,recovery,disease transmission

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