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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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Related in: MedlinePlus

An example of the proportion of individuals recorded as infectious/diseased relative to those ever infectious/diseased during an SIR epidemic, as a function of recording period.Two cases are shown, with only I individuals observable or with both I and R observable. In this example, recording is triggered when prevalence reaches 5%. Parameters in this model are: β = 0.00015, γ = 0.1 and R0 = 1.5.
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pone-0008940-g004: An example of the proportion of individuals recorded as infectious/diseased relative to those ever infectious/diseased during an SIR epidemic, as a function of recording period.Two cases are shown, with only I individuals observable or with both I and R observable. In this example, recording is triggered when prevalence reaches 5%. Parameters in this model are: β = 0.00015, γ = 0.1 and R0 = 1.5.

Mentions: As an illustration, consider an SIR model with parameters β = 0.00015, γ = 0.1, where γ is the recovery rate, R0 = 1.5 and hence I* = 0.59. For this parameterization, and starting with one infected individual, it will take approximately 180 days for 95% of all individuals potentially infected during an epidemic to become diseased. It is assumed that recording starts when the disease prevalence reaches 5% and that the diagnostic test is perfect, i.e. sensitivity and specificity are both unity. Two scenarios are considered, (i) where only infectious/diseased individuals are observed, and (ii) where recovered/removed, e.g. dead, individuals are also observed. Plotted in Figure 4 are the proportions of individuals ever diseased during the course of the epidemic that are observed during the observation period, i.e. the epidemic sensitivity . Observations taken only at one time point will result in a low epidemic sensitivity, hence underestimated heritabilities, and observations taken at different start points will also vary. If both diseased and recovered/removed individuals are observable, then the epidemic sensitivity becomes high with an extended observation period, since individuals that are infected and recover or removed prior to recording are also observed. However, if recovered individuals are not observable, i.e. they are healthy and no longer show any symptoms or clinical signs, then the epidemic sensitivity remains low and heritabilities remain underestimated.


On the genetic interpretation of disease data.

Bishop SC, Woolliams JA - PLoS ONE (2010)

An example of the proportion of individuals recorded as infectious/diseased relative to those ever infectious/diseased during an SIR epidemic, as a function of recording period.Two cases are shown, with only I individuals observable or with both I and R observable. In this example, recording is triggered when prevalence reaches 5%. Parameters in this model are: β = 0.00015, γ = 0.1 and R0 = 1.5.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0008940-g004: An example of the proportion of individuals recorded as infectious/diseased relative to those ever infectious/diseased during an SIR epidemic, as a function of recording period.Two cases are shown, with only I individuals observable or with both I and R observable. In this example, recording is triggered when prevalence reaches 5%. Parameters in this model are: β = 0.00015, γ = 0.1 and R0 = 1.5.
Mentions: As an illustration, consider an SIR model with parameters β = 0.00015, γ = 0.1, where γ is the recovery rate, R0 = 1.5 and hence I* = 0.59. For this parameterization, and starting with one infected individual, it will take approximately 180 days for 95% of all individuals potentially infected during an epidemic to become diseased. It is assumed that recording starts when the disease prevalence reaches 5% and that the diagnostic test is perfect, i.e. sensitivity and specificity are both unity. Two scenarios are considered, (i) where only infectious/diseased individuals are observed, and (ii) where recovered/removed, e.g. dead, individuals are also observed. Plotted in Figure 4 are the proportions of individuals ever diseased during the course of the epidemic that are observed during the observation period, i.e. the epidemic sensitivity . Observations taken only at one time point will result in a low epidemic sensitivity, hence underestimated heritabilities, and observations taken at different start points will also vary. If both diseased and recovered/removed individuals are observable, then the epidemic sensitivity becomes high with an extended observation period, since individuals that are infected and recover or removed prior to recording are also observed. However, if recovered individuals are not observable, i.e. they are healthy and no longer show any symptoms or clinical signs, then the epidemic sensitivity remains low and heritabilities remain underestimated.

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