Evidence map›Paper›PMID 22797899›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2012

A public resource facilitating clinical use of genomes.

Madeleine P Ball, Joseph V Thakuria, Alexander Wait Zaranek, Tom Clegg, Abraham M Rosenbaum, Xiaodi Wu, Misha Angrist, Jong Bhak, Jason Bobe, Matthew J Callow and 35 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 1 pooled it
–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

116 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Personalized genomic disease risk of volunteers.Proceedings of the National Academy of Sciences of the United States of America · 2013
    Trial
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  9. A complete diploid human genome benchmark for personalized genomics.bioRxiv : the preprint server for biology · 2025
    Article
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  15. Review
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56 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

45 authors.

Madeleine P BallDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Joseph V Thakuria
Alexander Wait Zaranek
Tom Clegg
Abraham M Rosenbaum
Xiaodi Wu
Misha Angrist
Jong Bhak
Jason Bobe
Matthew J Callow
Carlos Cano
Michael F Chou
Wendy K Chung
Shawn M Douglas
Preston W Estep
Athurva Gore
Peter Hulick
Alberto Labarga
Je-Hyuk Lee
Jeantine E Lunshof
Byung Chul Kim
Jong-Il Kim
Zhe Li
Michael F Murray
Geoffrey B Nilsen
Brock A Peters
Anugraha M Raman
Hugh Y Rienhoff
Kimberly Robasky
Matthew T Wheeler
Ward Vandewege
Daniel B Vorhaus
Joyce L Yang
Luhan Yang
John Aach
Euan A Ashley
Radoje Drmanac
Seong-Jin Kim
Jin Billy Li
Leonid Peshkin
Christine E Seidman
Jeong-Sun Seo
Kun Zhang
Heidi L Rehm
George M Church

Funding

Causal Transcriptional Consequences of Human Genetic VariationP50HG005550 · NHGRI · HARVARD MEDICAL SCHOOL · PI CHURCH, GEORGE M · 2010 to 2014
$19.0M
The Management and Organization Core will be administered by the Center for GenomP50HG003391 · NHGRI · DUKE UNIVERSITY · PI BESKOW, LAURA M. · 2004 to 2014
$9.9M
Targeted 2nd generation sequencing in phenotyped Framingham & PGP populations.R01HL094963 · NHLBI · HARVARD MEDICAL SCHOOL · PI CHURCH, GEORGE M · 2008 to 2010
$4.3M
NHGRI NIH HHS P50 HG003391NHGRI NIH HHS P50 HG005550NHGRI NIH HHS P50HG005550NHLBI NIH HHS R01 HL094963NHLBI NIH HHS R01HL094963
6 · The paper itself

Abstract

Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain-we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.

Indexed as

Databases, GeneticGenetic VariationPhenotypeSoftwareCell LineData CollectionGenome, HumanHumansPrecision MedicineSequence Analysis, DNA

Identifiers

PMID22797899
PMCPMC3409785

What OpenQuestion holds

Textmetadata
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.