Evidence map›Paper›PMID 29522190›Full record

ReviewHuman molecular genetics2018

Society and personal genome data.

Anna Middleton

Abstract readReview
In one paragraph

Review in Human molecular genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Optimizing Precision Medicine for Public Health.Frontiers in public health · 2019
    Review
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

1 author.

Anna MiddletonSociety and Ethics Research Group, Connecting Science, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Funding

Wellcome TrustWellcome Trust 206194
6 · The paper itself

Abstract

Genomic data offer a goldmine of information for understanding the contribution of genetic variation makes to health and disease. The potential of genomic medicine, to predict, diagnose, manage and treat genetic disease, is underpinned by accurate variant interpretation. This in itself hinges on the ability to access large and varied genomic databases. There is now recognition that international collaboration between research and healthcare systems are paramount to delivering the scale of genomic data required. No single research group, institute or country will liberate our understanding, it is only through global cooperation, together with super computing power, will we truly make sense of how genotype and phenotype correlate. Whilst it is logistically possible to create computing systems that talk to each other and aggregate datasets ready to reveal novel correlations, the bottom line is that this will only happen if people (whether they be scientists, clinicians, patients, research participants, policy makers, politicians, law makers) support the principle that we should be donating, accessing and sharing our DNA data in this way. And in order to make the most sense of genomics, given the geographical and ancestral variation between us, such people are likely to be the majority of society. Within this review, a perspective is proffered on the human story that underpins genomic 'big data' access and how we are at a tipping point as a society-we need to decide collectively, are we in? and if so, what needs to be in place to protect us? or are we out?

Indexed as

Big DataComputing MethodologiesDatabases, GeneticGenetic Association StudiesGenome, HumanGenomicsHumansInformation Dissemination

Identifiers

PMID29522190
PMCPMC5946868

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.