Prevalence estimation for monogenic autosomal recessive diseases using population-based genetic data.

Area of Science:

• Genetics
• Biostatistics
• Public Health

Background:

• Epidemiological surveys and clinical registries are traditional methods for disease prevalence estimation.
• Large population-based genetic databases are increasingly available, offering new avenues for research.
• Estimating prevalence of monogenic autosomal recessive diseases is crucial for public health initiatives.

Purpose of the Study:

• To develop and apply a Bayesian estimation procedure for determining the prevalence of monogenic autosomal recessive phenotypes.
• To derive explicit equations for credible intervals of disease prevalence estimates.
• To address challenges in prevalence calculation, including variant pathogenicity, population structure, allelic heterogeneity, and reduced penetrance.

Main Methods:

• A Bayesian framework was developed to calculate the posterior probability density of autosomal recessive phenotypes.
• Hardy-Weinberg equilibrium and known penetrance were assumed for allele frequency calculations.
• The method was applied to genetic data from the Schmiedeleut Hutterites and a central European study of hereditary fructose intolerance.

Main Results:

• Prevalence and 95% credible intervals were estimated for 13 autosomal recessive diseases in the Hutterite population.
• The Bayesian procedure was successfully applied to estimate hereditary fructose intolerance prevalence.
• The study demonstrates a robust method for estimating recessive disease prevalence using population genetic data.

Conclusions:

• The developed Bayesian approach offers a viable method for robustly estimating autosomal recessive disease prevalence and credible intervals.
• This approach can leverage increasingly available population-based genetic databases for public health and economic analyses.
• The methods are anticipated to be valuable for the management and treatment of rare genetic diseases.