On the genetic interpretation of disease data

Area of Science:

• Quantitative genetics
• Epidemiology
• Host-pathogen interactions

Background:

• Understanding host genetic variation in disease resistance necessitates field data with sufficient phenotypes.
• Field data interpretation for genetic resistance requires integrating epidemiological concepts into quantitative genetics.
• Focus on variance component estimation for microparasitic diseases (bacteria, viruses).

Purpose of the Study:

• To develop methods for genetically interpreting field disease data.
• To quantify the impact of imperfect data on heritability estimates for disease resistance.
• To explain low heritability observations in field studies.

Main Methods:

• Derived deterministic formulae to predict impacts of imperfect exposure and diagnostics on heritability.
• Analyzed effects of incomplete exposure, diagnostic sensitivity, and specificity.
• Investigated impacts of incomplete data recording.

Main Results:

• Incomplete exposure, imperfect sensitivity, and imperfect specificity all reduce estimable heritabilities.
• Impact of incomplete exposure is linear with exposure probability and depends on disease prevalence.
• Imperfect specificity significantly underestimates heritability, especially at low prevalences (<0.5).

Conclusions:

• Findings explain low disease resistance heritabilities observed in field conditions.
• Incomplete exposure and suboptimal diagnoses reduce dataset power but do not preclude demonstrating host genetic differences.
• Provides a framework for inferring true genetic variation in disease resistance using disease biology knowledge.