Mia J Gruzin | PubMed Health

PubMed2025

A dataset of estimated heterozygous individual and carrier couple frequencies for pan-ancestry carrier screening.

The data described in this publication supported the development and evaluation of pan-ancestry reproductive carrier screening panels for autosomal recessive (AR) and X-linked (XL) conditions. Raw data included combined sets of DNA variants in 1,350 AR/XL genes obtained from the ClinVar and gnomAD databases. The dataset enabled calculations of positive yield for individuals and couples across both ancestry-specific and pan-ancestry, optimised "Goldilocks"-ranked gene panels, addressing population-specific variations in the frequencies of heterozygous individuals and carrier couples. The positive yield analysis offered a performance metric for carrier screening panels, facilitating the modeling of screening performance for panels of varying sizes and composition and providing resources for optimizing panel content to ensure equity across underrepresented genetic ancestries The dataset can support ongoing research into the equitable application of carrier screening and offers significant reuse potential for refining population genetic screening practices, validating computational models, and developing frameworks to update carrier screening panels in alignment with evolving genomic data, including in underrepresented and minority populations.

Carrier screening↗

PubMed2025

Optimizing gene panels for equitable reproductive carrier screening: The Goldilocks approach.

PURPOSE: Professional organizations recommend pan-ancestry carrier screening for autosomal recessive and X-linked conditions. Advances in DNA sequencing have allowed the analysis of hundreds of genes; however, the optimal number of genes for carrier screening remains unclear. The American College of Medical Genetics and Genomics (ACMG) has proposed a tiered approach recommending screening for 113 genes. METHODS: We analyzed ClinVar and gnomAD v4.1.0, for genes associated with serious autosomal recessive and X-linked conditions and modeled screening performance across panels of varying compositions and sizes in diverse genetic ancestries. We also reevaluated the ACMG gene list using the updated gnomAD data. RESULTS: We identified potential inconsistencies in the ACMG gene lists, particularly in the carrier test performance (defined as a positive yield) for underrepresented genetic ancestry groups. Modeling of the population data for 1310 genes revealed that the screening of 152, 248, 531, and 725 genes achieved 90%, 95%, 99%, and 99.7% positive yields, respectively, in couples. Real-world data from the screening of more than 60,000 couples were used to validate the model. CONCLUSION: Our methodology optimizes the gene content of carrier screening panels for diverse ancestry groups, provides a mechanism for continually updating guidelines, ensures consistency with genomic population data, and improves equity across populations.

Humans↗