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On the genetic interpretation of disease data.

Bishop SC, Woolliams JA - PLoS ONE (2010)

Bottom Line: We show that these factors all reduce the estimable heritabilities.For prevalences less than 0.5, imperfect diagnostic test sensitivity results in a small underestimation of heritability, whereas imperfect specificity leads to a much greater underestimation, with the impact increasing as prevalence declines.These results help to explain the often low disease resistance heritabilities observed under field conditions.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, United Kingdom. Stephen.Bishop@roslin.ed.ac.uk

ABSTRACT

Background: The understanding of host genetic variation in disease resistance increasingly requires the use of field data to obtain sufficient numbers of phenotypes. We introduce concepts necessary for a genetic interpretation of field disease data, for diseases caused by microparasites such as bacteria or viruses. Our focus is on variance component estimation and we introduce epidemiological concepts to quantitative genetics.

Methodology/principal findings: We have derived simple deterministic formulae to predict the impacts of incomplete exposure to infection, or imperfect diagnostic test sensitivity and specificity on heritabilities for disease resistance. We show that these factors all reduce the estimable heritabilities. The impacts of incomplete exposure depend on disease prevalence but are relatively linear with the exposure probability. For prevalences less than 0.5, imperfect diagnostic test sensitivity results in a small underestimation of heritability, whereas imperfect specificity leads to a much greater underestimation, with the impact increasing as prevalence declines. These impacts are reversed for prevalences greater than 0.5. Incomplete data recording in which infected or diseased individuals are not observed, e.g. data recording for too short a period, has impacts analogous to imperfect sensitivity.

Conclusions/significance: These results help to explain the often low disease resistance heritabilities observed under field conditions. They also demonstrate that incomplete exposure to infection, or suboptimal diagnoses, are not fatal flaws for demonstrating host genetic differences in resistance, they merely reduce the power of datasets. Lastly, they provide a tool for inferring the true extent of genetic variation in disease resistance given knowledge of the disease biology.

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Model for transmission of bacterial or viral infections.
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pone-0008940-g001: Model for transmission of bacterial or viral infections.

Mentions: Consider a generic microparasitic disease in which individuals may move between infection states as illustrated in Figure 1. Upon exposure to infection a susceptible (i.e. not yet infected) individual may become infected and infectious, after which it may either recover or die. For simplicity, the states of diseased and infectious are considered equivalent in this study. The term susceptible does not indicate an individual's liability to infection; rather, it denotes that it is not immunologically resistant and can become infected. If susceptible individuals are replenished, either through loss of immunity of recovered individuals or through immigration of new individuals, then an endemic equilibrium may be reached in which the expected disease prevalence is constant. Otherwise, under assumptions of homogeneous random mixing the number of infected individuals will ultimately go to zero, and the epidemic will die out with the expected proportion of individuals ever infected during the course of the epidemic (I*) satisfying the equation [12], where R0 is the basic reproductive ratio of the disease. Therefore, assuming no disease-independent mortality, the expected proportion of susceptible individuals remaining in the population at the completion of the epidemic is .


On the genetic interpretation of disease data.

Bishop SC, Woolliams JA - PLoS ONE (2010)

Model for transmission of bacterial or viral infections.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2812510&req=5

pone-0008940-g001: Model for transmission of bacterial or viral infections.
Mentions: Consider a generic microparasitic disease in which individuals may move between infection states as illustrated in Figure 1. Upon exposure to infection a susceptible (i.e. not yet infected) individual may become infected and infectious, after which it may either recover or die. For simplicity, the states of diseased and infectious are considered equivalent in this study. The term susceptible does not indicate an individual's liability to infection; rather, it denotes that it is not immunologically resistant and can become infected. If susceptible individuals are replenished, either through loss of immunity of recovered individuals or through immigration of new individuals, then an endemic equilibrium may be reached in which the expected disease prevalence is constant. Otherwise, under assumptions of homogeneous random mixing the number of infected individuals will ultimately go to zero, and the epidemic will die out with the expected proportion of individuals ever infected during the course of the epidemic (I*) satisfying the equation [12], where R0 is the basic reproductive ratio of the disease. Therefore, assuming no disease-independent mortality, the expected proportion of susceptible individuals remaining in the population at the completion of the epidemic is .

Bottom Line: We show that these factors all reduce the estimable heritabilities.For prevalences less than 0.5, imperfect diagnostic test sensitivity results in a small underestimation of heritability, whereas imperfect specificity leads to a much greater underestimation, with the impact increasing as prevalence declines.These results help to explain the often low disease resistance heritabilities observed under field conditions.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, United Kingdom. Stephen.Bishop@roslin.ed.ac.uk

ABSTRACT

Background: The understanding of host genetic variation in disease resistance increasingly requires the use of field data to obtain sufficient numbers of phenotypes. We introduce concepts necessary for a genetic interpretation of field disease data, for diseases caused by microparasites such as bacteria or viruses. Our focus is on variance component estimation and we introduce epidemiological concepts to quantitative genetics.

Methodology/principal findings: We have derived simple deterministic formulae to predict the impacts of incomplete exposure to infection, or imperfect diagnostic test sensitivity and specificity on heritabilities for disease resistance. We show that these factors all reduce the estimable heritabilities. The impacts of incomplete exposure depend on disease prevalence but are relatively linear with the exposure probability. For prevalences less than 0.5, imperfect diagnostic test sensitivity results in a small underestimation of heritability, whereas imperfect specificity leads to a much greater underestimation, with the impact increasing as prevalence declines. These impacts are reversed for prevalences greater than 0.5. Incomplete data recording in which infected or diseased individuals are not observed, e.g. data recording for too short a period, has impacts analogous to imperfect sensitivity.

Conclusions/significance: These results help to explain the often low disease resistance heritabilities observed under field conditions. They also demonstrate that incomplete exposure to infection, or suboptimal diagnoses, are not fatal flaws for demonstrating host genetic differences in resistance, they merely reduce the power of datasets. Lastly, they provide a tool for inferring the true extent of genetic variation in disease resistance given knowledge of the disease biology.

Show MeSH
Related in: MedlinePlus