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Benchmarking genetic birth prevalence estimates against newborn screening data.

Michael C Sierant1, Nicholas Knoblauch1, Evan Witt1

  • 1BioMarin Pharmaceutical, San Rafael, CA 94901, USA.

American Journal of Human Genetics
|April 25, 2026
PubMed
Summary

Estimating rare autosomal-recessive (AR) disease prevalence using genetic data is feasible for 89% of newborn screening (NBS) conditions. Refinements improved accuracy, but genetic estimates generally underestimated actual birth prevalence.

Keywords:
ancestry-informed prevalenceautosomal-recessive diseasedemographic weightinggenetic birth prevalencegenetic epidemiologymethod benchmarkingnewborn screeningrare-disease epidemiology

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Area of Science:

  • Genetics
  • Epidemiology
  • Public Health

Background:

  • Accurate prevalence estimates for rare congenital conditions are vital for epidemiology and drug development.
  • Genetics-based models using biobanks can estimate disease prevalence, particularly for autosomal-recessive (AR) diseases.
  • Newborn screening (NBS) offers a robust comparator for birth prevalence due to its large sample size and low diagnostic variability.

Purpose of the Study:

  • To benchmark a genetics-first approach for estimating AR disease birth prevalence against large-scale clinical data.
  • To compare genetic prevalence estimates with reported birth prevalence from 23 million newborns in the United States.
  • To identify factors influencing the concordance between genetic and NBS-derived prevalence estimates.

Main Methods:

  • Applied a genetic model to estimate birth prevalence for 28 AR diseases included in NBS panels.
  • Compared genetic estimates with reported birth prevalence data from 23 million US newborns.
  • Analyzed the impact of allele frequency sources, ancestry matching, and variant inclusion criteria on concordance.

Main Results:

  • A refined genetics-first approach provided first-order birth prevalence estimates for 89% (25 of 28) of NBS diseases.
  • Concordance was influenced by allele frequency data, ancestry matching, and pathogenic variant selection.
  • Genetic prevalence estimates generally underestimated NBS-derived prevalence, indicating a need for more comprehensive pathogenicity data.

Conclusions:

  • A genetics-first approach is a viable method for estimating birth prevalence of AR diseases, particularly when refined.
  • Further identification of pathogenic variants is needed to improve the accuracy of genetic prevalence estimates.
  • Understanding epidemiological and genetic variables is crucial for accurate disease prevalence assessment.