Biomedical subjects

Alessandra Ferlini

Publications and source records attributed to Alessandra Ferlini.

2 recordsLinked to original sources

PubMed2025

Operationalizing the Wilson-Jungner principles for the genomics era: Consensus recommendations from the International Consortium on Newborn Sequencing.

PURPOSE: For decades, the selection of disorders included in newborn screening (NBS) programs has been guided by principles published by Wilson and Jungner in 1968. As research explores the expansion of conditions included in NBS through genomic sequencing, there is a critical need for updated recommendations to address the opportunities and complexities of genomic data. METHODS: The International Consortium on Newborn Sequencing includes leaders from over 16 research projects investigating genomic NBS across the United Kingdom, Europe, United States, and Oceania. Consortium members were invited to participate in a modified Delphi study, aggregating opinion on the selection of conditions for genomic NBS through 3 rounds of online questionnaires, with feedback provided to participants between rounds. RESULTS: In round 1, 94 participants completed the questionnaire, and 10 of 43 statements reached consensus. In round 2, 81 participants completed the questionnaire, and 14 of 27 statements reached consensus. In round 3, 68 participants completed the questionnaire, and all 10 statements reached 72% or more consensus. CONCLUSION: The 10 consensus recommendations developed in this study can guide future research and public health programs performing genomic NBS. This process also identified key areas of participant discordance, highlighting important topics for future research.

Humans↗

PubMed2025

Data-driven consideration of genetic disorders for global genomic newborn screening programs.

PURPOSE: Over 30 international studies are exploring newborn sequencing (NBSeq) to expand the range of genetic disorders included in newborn screening. Substantial variability in gene selection across programs exists, highlighting the need for a systematic approach to prioritize genes. METHODS: We assembled a data set comprising 25 characteristics about each of the 4390 genes included in 27 NBSeq programs. We used regression analysis to identify several predictors of inclusion and developed a machine learning model to rank genes for public health consideration. RESULTS: Among 27 NBSeq programs, the number of genes analyzed ranged from 134 to 4299, with only 74 (1.7%) genes included by over 80% of programs. The most significant associations with gene inclusion across programs were presence on the US Recommended Uniform Screening Panel (inclusion increase of 74.7%, CI: 71.0%-78.4%), robust evidence on the natural history (29.5%, CI: 24.6%-34.4%), and treatment efficacy (17.0%, CI: 12.3%-21.7%) of the associated genetic disease. A boosted trees machine learning model using 13 predictors achieved high accuracy in predicting gene inclusion across programs (area under the curve = 0.915, R2 = 84%). CONCLUSION: The machine learning model developed here provides a ranked list of genes that can adapt to emerging evidence and regional needs, enabling more consistent and informed gene selection in NBSeq initiatives.

Humans↗