Evidence map›Paper›PMID 31161416›Full record

ReviewHuman genetics2019

Mind the gap: resources required to receive, process and interpret research-returned whole genome data.

Dana C Crawford, Jessica N Cooke Bailey, Farren B S Briggs

Abstract readReview
In one paragraph

Review in Human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Research ethics · 2025
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

3 authors.

Dana C CrawfordDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA. dana.crawford@case.edu.ORCID http://orcid.org/0000-0002-6437-6248
Jessica N Cooke BaileyDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.
Farren B S BriggsDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.

Funding

Clinical and Translational Science Collaborative of ClevelandUL1TR000439 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI KONSTAN, MICHAEL W. · 2012 to 2016
$50.0M
Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Institutional Career Development CoreKL2TR002547 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI DWEIK, RAED A. · 2018 to 2022
$7.7M
Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University KL2TR002547Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University UL1TR000439NCATS NIH HHS KL2 TR002547NCATS NIH HHS UL1 TR000439NCATS NIH HHS UL1 TR002548
6 · The paper itself

Abstract

Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most well known of these new cohort efforts is arguably All of Us, formerly known as the Precision Medicine Cohort Initiative Program. All of Us intends to ascertain at least one million participants in the United States representative of the multiple dimensions of diversity. As an incentive to participate, All of Us is offering the return of research results, including whole genome sequencing data, as well as the opportunity to contribute to the scientific process as non-scientists. The scale and scope of the proposed return of research results are unprecedented. Here, we briefly review possible return of genetic data models, including the likely data file formats and modes of data transfer or access. We also review the resources required to access and interpret the genetic or genomic data once received by the average participant, highlighting the nuanced anticipated barriers that will challenge both the digitally, computationally literate and illiterate participant alike. This inventory of resources required to receive, process, and interpret return of research results exposes the potential for access disparities and warns the scientific community to mind the gap so that all participants have equal access and understanding of the benefits of human genetic research.

Indexed as

Data Interpretation, StatisticalGenetic ResearchGenome, HumanPrecision MedicineData MiningGenomicsHumansUnited States

Identifiers

PMID31161416
PMCPMC6767905

What OpenQuestion holds

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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.