Evidence map›Paper›PMID 26146598›Full record

ArticleCurrent genetic medicine reports2015

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery.

S A Pendergrass, M D Ritchie

Abstract read
In one paragraph

Article in Current genetic medicine reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Review
  7. Phenomic Studies on Diseases: Potential and Challenges.Phenomics (Cham, Switzerland) · 2023
    Review
  8. Review
  9. Review
  10. New insights into pathogenesis of IgA nephropathy.International urology and nephrology · 2022
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. A regression framework to uncover pleiotropy in large-scale electronic health record data.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  17. Review
  18. Review
  19. Article
  20. 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

2 authors.

S A Pendergrass
M D Ritchie

Funding

Pharmacogenomics of Arrhythmia TherapyU19HL065962 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RODEN, DAN M · 2010 to 2014
$17.4M
NHLBI NIH HHS U19 HL065962
6 · The paper itself

Abstract

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

Indexed as

Electronic Health Record (EHR)genotype-phenotype networksInternational Classification of Disease Codes (ICD)phenomicspleiotropy

Identifiers

PMID26146598
PMCPMC4489156

What OpenQuestion holds

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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.