Evidence map›Paper›PMID 40084622›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2025

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

Mia J Gruzin, Matthew Hobbs, Rachel E Ellsworth, Sarah Poll, Sienna Aguilar, Jaysen Knezovich, Nicole Faulkner, Nick Olsen, Swaroop Aradhya, Leslie Burnett

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Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. The clinical utility of carrier screening.NPJ genomic medicine · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Mia J GruzinGarvan Institute of Medical Research, Darlinghurst, NSW, Australia; School of Clinical Medicine, UNSW Medicine and Health, St Vincent's Clinical Healthcare Campus, Darlinghurst, NSW, Australia.
Matthew HobbsGarvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Rachel E EllsworthInvitae Corporation, San Francisco, CA.
Sarah PollInvitae Corporation, San Francisco, CA.
Sienna AguilarInvitae Corporation, San Francisco, CA.
Jaysen KnezovichBlue Jay Consulting Services, Brisbane QLD, Australia.
Nicole FaulknerInvitae Corporation, San Francisco, CA.
Nick OlsenStats Central, Mark Wainwright Analytical Centre, UNSW Sydney, NSW, Australia.
Swaroop AradhyaInvitae Corporation, San Francisco, CA; Stanford University School of Medicine, Department of Pathology, Stanford, CA.
Leslie BurnettGarvan Institute of Medical Research, Darlinghurst, NSW, Australia; School of Clinical Medicine, UNSW Medicine and Health, St Vincent's Clinical Healthcare Campus, Darlinghurst, NSW, Australia; Northern Clinical School, Faculty of Medicine and Health, University of Sydney, St Leonards, NSW, Australia. Electronic address: leslie.burnett@garvan.org.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeProfessional 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.

methodsWe 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.

resultsWe 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.

conclusionOur 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.

Indexed as

Genetic Carrier ScreeningGenetic TestingFemaleGenomicsHeterozygoteHumansMaleSequence Analysis, DNAACMGCarrier screeningEquityPan-ancestryReproductive genetics

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.