Evidence map›Paper›PMID 30209273›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019

Combining newborn metabolic and DNA analysis for second-tier testing of methylmalonic acidemia.

Gang Peng, Peidong Shen, Neeru Gandotra, Anthony Le, Eula Fung, Laura Jelliffe-Pawlowski, Ronald W Davis, Gregory M Enns, Hongyu Zhao, Tina M Cowan and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 9% of its field
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

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  19. The Editor's Choice for Issue 3, Volume 7.International journal of neonatal screening · 2021
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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

11 authors at 3 institutions in 1 country.

Gang PengDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Peidong ShenStanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
Neeru GandotraDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Anthony LeDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Eula FungDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Laura Jelliffe-PawlowskiDepartment of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, USA.
Ronald W DavisStanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
Gregory M EnnsDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Hongyu ZhaoDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Tina M CowanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Curt ScharfeDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA. curt.scharfe@yale.edu.
Stanford University · USYale University · USUniversity of California, San Francisco · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Technology development for rapid detection and diagnosis of metabolic disordersR01HD081355 · NICHD · YALE UNIVERSITY · PI SCHARFE, CURT · 2014 to 2016
$866k
NCATS NIH HHS UL1 TR001863NICHD NIH HHS R01 HD081355
6 · The paper itself

Abstract

purposeImproved second-tier tools are needed to reduce false-positive outcomes in newborn screening (NBS) for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP).

methodsWe designed an assay for multiplex sequencing of 72 metabolic genes (RUSPseq) from newborn dried blood spots. Analytical and clinical performance was evaluated in 60 screen-positive newborns for methylmalonic acidemia (MMA) reported by the California Department of Public Health NBS program. Additionally, we trained a Random Forest machine learning classifier on NBS data to improve prediction of true and false-positive MMA cases.

resultsOf 28 MMA patients sequenced, we found two pathogenic or likely pathogenic (P/LP) variants in a MMA-related gene in 24 patients, and one pathogenic variant and a variant of unknown significance (VUS) in 1 patient. No such variant combinations were detected in MMA false positives and healthy controls. Random Forest-based analysis of the entire NBS metabolic profile correctly identified the MMA patients and reduced MMA false-positive cases by 51%. MMA screen-positive newborns were more likely of Hispanic ethnicity.

conclusionOur two-pronged approach reduced false positives by half and provided a reportable molecular finding for 89% of MMA patients. Challenges remain in newborn metabolic screening and DNA variant interpretation in diverse multiethnic populations.

Indexed as

Genetic VariationNeonatal ScreeningAmino Acid Metabolism, Inborn ErrorsDried Blood Spot TestingFemaleHumansInfant, NewbornMachine LearningMaleMetabolism, Inborn ErrorsDNA diagnosticsinborn metabolic disordersmachine learningnewborn screeningnext-generation sequencing

Identifiers

PMID30209273
PMCPMC6416784
OpenAlexW2891139232

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LicenceCC BY
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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.