Evidence map›Paper›PMID 19737788›Full record

ReviewQJM : monthly journal of the Association of Physicians2009

Genetics and the general physician: insights, applications and future challenges.

J C Knight

Abstract readReview
In one paragraph

Review in QJM : monthly journal of the Association of Physicians, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Evolution and medicine in undergraduate education: a prescription for all biology students.Evolution; international journal of organic evolution · 2012
    Article
  3. 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

1 author.

J C KnightWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK. julian@well.ox.ac.uk

Funding

Wellcome Trust 074318
6 · The paper itself

Abstract

Scientific and technological advances in our understanding of the nature and consequences of human genetic variation are now allowing genetic determinants of susceptibility to common multifactorial diseases to be defined, as well as our individual response to therapy. I review how genome-wide association studies are robustly identifying new disease susceptibility loci, providing insights into disease pathogenesis and potential targets for drug therapy. Some of the remarkable advances being made using current genetic approaches in Crohn's disease, coronary artery disease and atrial fibrillation are described, together with examples from malaria, HIV/AIDS, asthma, prostate cancer and venous thrombosis which illustrate important principles underpinning this field of research. The limitations of current approaches are also noted, highlighting how much of the genetic risk remains unexplained and resolving specific functional variants difficult. There is a need to more clearly understand the significance of rare variants and structural genomic variation in common disease, as well as epigenetic mechanisms. Specific examples from pharmacogenomics are described including warfarin dosage and prediction of abacavir hypersensitivity that illustrate how in some cases such knowledge is already impacting on clinical practice, while in others prospective evaluation of clinical utility and cost-effectiveness is required to define opportunities for personalized medicine. There is also a need for a broader debate about the ethical implications of current advances in genetics for medicine and society.

Indexed as

AsthmaCoronary Artery DiseaseCrohn DiseaseFemaleGenetic Predisposition to DiseaseGenetic VariationGenome-Wide Association StudyHIV-1HumansMalariaMalePharmacogeneticsProstatic NeoplasmsVenous Thrombosis

Identifiers

PMID19737788
PMCPMC2766102

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.