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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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Ratio of estimated to true heritability on the liability scale for differing true prevalences.Results are shown for (A) incomplete sensitivity, where specificity = 1, (B) incomplete specificity, where sensitivity = 1 or (C) for incomplete specificity and sensitivity, where the two parameters equal.
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pone-0008940-g003: Ratio of estimated to true heritability on the liability scale for differing true prevalences.Results are shown for (A) incomplete sensitivity, where specificity = 1, (B) incomplete specificity, where sensitivity = 1 or (C) for incomplete specificity and sensitivity, where the two parameters equal.

Mentions: Impacts of various specificities and sensitivities on estimated heritability values are illustrated in Figures 3a and 3b, where only sensitivity and specificity, respectively, are varied and 3c, in which they are varied jointly. For all prevalences, imperfect sensitivity and specificity both result in underestimated heritabilities on the liability scale. However the impact of poor specificities is much greater, for true prevalence less than 0.5. The reason for this difference is that when decreasing Se, the term decreases, and the observed prevalence p′ decreases also, so although , this is partially compensated by . In contrast, when Sp decreases, the observed prevalence p′ increases, and so both and . When both sensitivity and specificity are imperfect, then liability-scale heritabilities are considerably underestimated. This is likely to be the case in many practical situations, indicating that true genetic variation in disease resistance is likely to be much greater than indicated by analyses of field data.


On the genetic interpretation of disease data.

Bishop SC, Woolliams JA - PLoS ONE (2010)

Ratio of estimated to true heritability on the liability scale for differing true prevalences.Results are shown for (A) incomplete sensitivity, where specificity = 1, (B) incomplete specificity, where sensitivity = 1 or (C) for incomplete specificity and sensitivity, where the two parameters equal.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0008940-g003: Ratio of estimated to true heritability on the liability scale for differing true prevalences.Results are shown for (A) incomplete sensitivity, where specificity = 1, (B) incomplete specificity, where sensitivity = 1 or (C) for incomplete specificity and sensitivity, where the two parameters equal.
Mentions: Impacts of various specificities and sensitivities on estimated heritability values are illustrated in Figures 3a and 3b, where only sensitivity and specificity, respectively, are varied and 3c, in which they are varied jointly. For all prevalences, imperfect sensitivity and specificity both result in underestimated heritabilities on the liability scale. However the impact of poor specificities is much greater, for true prevalence less than 0.5. The reason for this difference is that when decreasing Se, the term decreases, and the observed prevalence p′ decreases also, so although , this is partially compensated by . In contrast, when Sp decreases, the observed prevalence p′ increases, and so both and . When both sensitivity and specificity are imperfect, then liability-scale heritabilities are considerably underestimated. This is likely to be the case in many practical situations, indicating that true genetic variation in disease resistance is likely to be much greater than indicated by analyses of field data.

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