Evidence map›Paper›PMID 42078353›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Determinants of DNA-sequence-based Diagnostic Yield in the CSER Consortium.

Yusuph Mavura, David Crosslin, Kathleen Ferar, James Mj Lawlor, John M Greally, Lucia Hindorff, Gail P Jarvik, Sara Kalla, Barbara A Koenig, Mark Kvale and 9 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Yusuph MavuraDepartment of Biomedical Data Science, Stanford University, Palo Alto, CA.ORCID 0000-0001-8527-0736
David CrosslinBioMed. Informatics and Genomics, John W. Deming Dept. of Med., Tulane Univ. Sch. of Med., New Orleans, LA.
Kathleen FerarDepartment of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA.
James Mj LawlorHudsonAlpha Inst. for Biotechnology, Huntsville, AL.ORCID 0000-0003-1735-1766
John M GreallyDepartments of Genetics and Pediatrics, Albert Einstein College of Medicine, Bronx, NY.ORCID 0000-0001-6069-7960
Lucia HindorffNHGRI, NIH, Bethesda, MD.
Gail P JarvikMedicine (Medical Genetics) and Genome Sciences, Univ. of Washington Med. Ctr., Seattle, WA.
Sara KallaHGSC - Baylor Coll. of Med., Houston, TX.
Barbara A KoenigInstitute for Human Genetics, University of California San Francisco, San Francisco, CA.
Mark KvaleDepartment of Biomedical Data Science, Stanford University, Palo Alto, CA.
Pui-Yan KwokInstitute for Human Genetics, University of California San Francisco, San Francisco, CA.ORCID 0000-0002-5087-3059
Mary NortonInstitute for Human Genetics, University of California San Francisco, San Francisco, CA.
Sharon E PlonMolecular and Human Genetics Dept., Baylor Coll. Med., Houston, TX.ORCID 0000-0002-9626-0936
Bradford C PowellDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Anne SlavotinekInstitute for Human Genetics, University of California San Francisco, San Francisco, CA.
Michelle L ThompsonDepartments of Genetics and Pediatrics, Albert Einstein College of Medicine, Bronx, NY.
Alice B PopejoyDivision of Epidemiology, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, CA.
Eimear E KennyThe Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY.
Neil RischInstitute for Human Genetics, University of California San Francisco, San Francisco, CA.

Funding

North Carolina Clinical Genomic Evaluation by Next-gen Exome Sequencing 2U01HG006487 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BERG, JONATHAN S, POWELL, BRADFORD C. · 2012 to 2021
$22.7M
Measuring Perceptions of Utility of Clinical Genome Sequencing: Instrument Testing and ValidationU01HG006485 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI MCGUIRE, AMY L, PARSONS, DONALD W. · 2012 to 2021
$22.1M
Incorporating genomics into the clinical care of diverse NYC childrenU01HG009610 · NHGRI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GELB, BRUCE D, HOROWITZ, CAROL R · 2017 to 2021
$17.0M
Sharing Aggregate Genomics Research Findings with Diverse Participants: A Pilot Study of an Innovative Design StrategyU01HG009599 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KOENIG, BARBARA A., KWOK, PUI-YAN · 2017 to 2021
$16.3M
South-seq: DNA sequencing for newborn nurseries in the SouthU01HG007301 · NHGRI · HUDSON-ALPHA INSTITUTE FOR BIOTECHNOLOGY · PI BARSH, GREGORY STEFAN, COOPER, GREGORY MICHAEL · 2017 to 2021
$11.9M
Evolving Our Partnership: The CSER2 Centralized Support Coordinating CenterU24HG007307 · NHGRI · UNIVERSITY OF WASHINGTON · PI FULLERTON, STEPHANIE MALIA, JARVIK, GAIL PAIRITZ · 2017 to 2021
$5.8M
NHGRI NIH HHS U01 HG006485NHGRI NIH HHS U01 HG006487NHGRI NIH HHS U01 HG007301NHGRI NIH HHS U01 HG009599NHGRI NIH HHS U01 HG009610NHGRI NIH HHS U24 HG007307
6 · The paper itself

Abstract

Purpose: Diagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. Methods: We performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. Results: The overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes.. A model without fixed effects attributed ~10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. Conclusion: Across five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome- and genome-sequencing diagnostic workflows may help reduce inter-site differences.

Identifiers

PMID42078353
PMCPMC13131712

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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.