Evidence map›Paper›PMID 28851938›Full record

ArticleScientific reports2017

Prevalence of Rare Genetic Variations and Their Implications in NGS-data Interpretation.

Yangrae Cho, Chul-Ho Lee, Eun-Goo Jeong, Min-Ho Kim, Jong Hui Hong, Younhee Ko, Bomnun Lee, Gilly Yun, Byong Joon Kim, Jongcheol Jung and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
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  4. Dispatches from Biotech beginning BeginNGS: Rapid newborn genome sequencing to end the diagnostic and therapeutic odyssey.American journal of medical genetics. Part C, Seminars in medical genetics · 2022
    Article
  5. Review
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  8. Article
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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

12 authors.

Yangrae ChoSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Chul-Ho LeeDepartment of Clinical Genetics, Department of Pediatrics, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
Eun-Goo JeongSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Min-Ho KimSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Jong Hui HongSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Younhee KoDepartment of Clinical Genetics, Department of Pediatrics, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
Bomnun LeeSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Gilly YunSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Byong Joon KimSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Jongcheol JungSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea.
Jongsun JungSyntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, 34025, Republic of Korea. jung@syntekabio.com.
Jin-Sung LeeDepartment of Clinical Genetics, Department of Pediatrics, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea. jinsunglee@yuhs.ac.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation sequencing (NGS) technology has improved enough to discover mutations associated with genetic diseases. Our study evaluated the feasibility of targeted NGS as a primary screening tool to detect causal variants and subsequently predict genetic diseases. We performed parallel computations on 3.7-megabase-targeted regions to detect disease-causing mutations in 103 participants consisting of 81 patients and 22 controls. Data analysis of the participants took about 6 hours using local databases and 200 nodes of a supercomputer. All variants in the selected genes led on average to 3.6 putative diseases for each patient while variants restricted to disease-causing genes identified the correct disease. Notably, only 12% of predicted causal variants were recorded as causal mutations in public databases: 88% had no or insufficient records. In this study, most genetic diseases were caused by rare mutations and public records were inadequate. Most rare variants, however, were not associated with genetic diseases. These data implied that novel, rare variants should not be ignored but interpreted in conjunction with additional clinical data. This step is needed so appropriate advice can be given to primary doctors and parents, thus fulfilling the purpose of this method as a primary screen for rare genetic diseases.

Indexed as

Genetic Association StudiesGenetic Predisposition to DiseaseGenetic VariationHigh-Throughput Nucleotide SequencingSequence Analysis, DNAAllelesHumansMutationPolymorphism, Single NucleotideWorkflow

Identifiers

PMID28851938
PMCPMC5574920

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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.