Evidence map›Paper›PMID 19209715›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2009

Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies.

William S Bush, Scott M Dudek, Marylyn D Ritchie

Abstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.

0numbers the graph read from it
0cells of the map it votes in
82citing 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

82 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  16. Article
  17. Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  18. Article
  19. Article
  20. Article

22 more citing papers are in PubMed but not listed here.

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.

William S BushCenter for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA.
Scott M Dudek
Marylyn D Ritchie

Funding

PHARMACOGENOMICS OF ARRHYTHMIA THERAPYU01HL065962 · NHLBI · VANDERBILT UNIVERSITY · PI RODEN, DAN M · 2001 to 2009
$24.8M
Pharmacogenomics of Arrhythmia TherapyU19HL065962 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RODEN, DAN M · 2010 to 2014
$17.4M
Revealing Epistasis in Alzheimer DiseaseR01AG020135 · NIA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MARTIN, EDEN R. · 2002 to 2006
$2.7M
NHLBI NIH HHS HL65962NHLBI NIH HHS U01 HL065962NHLBI NIH HHS U19 HL065962NIA NIH HHS AG20135NIA NIH HHS R01 AG020135
6 · The paper itself

Abstract

Genome-wide association studies provide an unprecedented opportunity to identify combinations of genetic variants that contribute to disease susceptibility. The combinatorial problem of jointly analyzing the millions of genetic variations accessible by high-throughput genotyping technologies is a difficult challenge. One approach to reducing the search space of this variable selection problem is to assess specific combinations of genetic variations based on prior statistical and biological knowledge. In this work, we provide a systematic approach to integrate multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. This approach yields a collection of models which can be tested statistically in genome-wide data, along with an ordinal quantity describing the number of data sources that support any given model. Using this knowledge-driven approach reduces the computational and statistical burden of large-scale interaction analysis while simultaneously providing a biological foundation for the relevance of any significant statistical result that is found.

Indexed as

Knowledge BasesBiometryComputer SimulationDatabases, GeneticEpistasis, GeneticGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansModels, GeneticPolymorphism, Single Nucleotide

Identifiers

PMID19209715
PMCPMC2859610

What OpenQuestion holds

Textmetadata
LicenceTDM
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.