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

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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.
Original languageEnglish
Article number85
Number of pages15
JournalGenetics, Selection, Evolution
Volume47
DOIs
Publication statusPublished - 2015

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infectivity
genetic analysis
infectious disease
infectious diseases
Communicable Diseases
genetic techniques and protocols
pathogenicity
gene
Genes
genes
Linear Models
linear models
Basic Reproduction Number
Genotype
relatedness
vpr Genes
Infectious Disease Transmission
Genetic Selection
Genome-Wide Association Study
genotype

Cite this

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title = "Genetic analysis of infectious diseases: Estimating gene effects for susceptibility and infectivity",
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.",
author = "M.T. Anche and P. Bijma and {de Jong}, M.C.M.",
year = "2015",
doi = "10.1186/s12711-015-0163-z",
language = "English",
volume = "47",
journal = "Genetics, Selection, Evolution",
issn = "0999-193X",
publisher = "Springer Verlag",

}

Genetic analysis of infectious diseases: Estimating gene effects for susceptibility and infectivity. / Anche, M.T.; Bijma, P.; de Jong, M.C.M.

In: Genetics, Selection, Evolution, Vol. 47, 85, 2015.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Genetic analysis of infectious diseases: Estimating gene effects for susceptibility and infectivity

AU - Anche, M.T.

AU - Bijma, P.

AU - de Jong, M.C.M.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1186/s12711-015-0163-z

DO - 10.1186/s12711-015-0163-z

M3 - Article

VL - 47

JO - Genetics, Selection, Evolution

JF - Genetics, Selection, Evolution

SN - 0999-193X

M1 - 85

ER -