Evidence map›Paper›PMID 32345347›Full record

ArticleOrphanet journal of rare diseases2020

A scoping review and proposed workflow for multi-omic rare disease research.

Katie Kerr, Helen McAneney, Laura J Smyth, Caitlin Bailie, Shane McKee, Amy Jayne McKnight

Abstract readScoping Review
In one paragraph

Article in Orphanet journal of rare diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Review
  2. RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Hereditary spastic paraplegia: from decades of therapy to future innovations.Therapeutic advances in neurological disorders · 2026
    Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. WGS Data Collections: How Do Genomic Databases Transform Medicine?International journal of molecular sciences · 2023
    Review
  19. Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023
    Review
  20. Article
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

6 authors.

Katie KerrCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland.
Helen McAneneyCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland.
Laura J SmythCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland.
Caitlin BailieCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland.
Shane McKeeRegional Genetics Centre, Belfast City Hospital, Level A, Tower Block, Lisburn Road, Belfast, BT9 7AB, Northern Ireland.
Amy Jayne McKnightCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland. a.j.mcknight@qub.ac.uk.

Funding

Medical Research Council MC_PC_16018
6 · The paper itself

Abstract

backgroundPatients with rare diseases face unique challenges in obtaining a diagnosis, appropriate medical care and access to support services. Whole genome and exome sequencing have increased identification of causal variants compared to single gene testing alone, with diagnostic rates of approximately 50% for inherited diseases, however integrated multi-omic analysis may further increase diagnostic yield. Additionally, multi-omic analysis can aid the explanation of genotypic and phenotypic heterogeneity, which may not be evident from single omic analyses. MAIN BODY: This scoping review took a systematic approach to comprehensively search the electronic databases MEDLINE, EMBASE, PubMed, Web of Science, Scopus, Google Scholar, and the grey literature databases OpenGrey / GreyLit for journal articles pertaining to multi-omics and rare disease, written in English and published prior to the 30th December 2018. Additionally, The Cancer Genome Atlas publications were searched for relevant studies and forward citation searching / screening of reference lists was performed to identify further eligible articles. Following title, abstract and full text screening, 66 articles were found to be eligible for inclusion in this review. Of these 42 (64%) were studies of multi-omics and rare cancer, two (3%) were studies of multi-omics and a pre-cancerous condition, and 22 (33.3%) were studies of non-cancerous rare diseases. The average age of participants (where known) across studies was 39.4 years. There has been a significant increase in the number of multi-omic studies in recent years, with 66.7% of included studies conducted since 2016 and 33% since 2018. Fourteen combinations of multi-omic analyses for rare disease research were returned spanning genomics, epigenomics, transcriptomics, proteomics, phenomics and metabolomics.

conclusionsThis scoping review emphasises the value of multi-omic analysis for rare disease research in several ways compared to single omic analysis, ranging from the provision of a diagnosis, identification of prognostic biomarkers, distinct molecular subtypes (particularly for rare cancers), and identification of novel therapeutic targets. Moving forward there is a critical need for collaboration of multi-omic rare disease studies to increase the potential to generate robust outcomes and development of standardised biorepository collection and reporting structures for multi-omic studies.

Indexed as

GenomicsRare DiseasesAdultEpigenomicsHumansMetabolomicsWorkflowEpigenomicsExomicsGenomicsMethylomicsMulti-omicsRare diseaseTranscriptomicsWhole exome sequencingWhole genome sequencing

Identifiers

PMID32345347
PMCPMC7189570

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.