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"Are We There Yet?": Deciding When One Has Demonstrated Specific Genetic Causation in Complex Diseases and Quantitative Traits

  • Grier P. Page(corresponding author)
    ,
  • Varghese George
    ,
  • Rodney C. Go
    ,
  • Patricia Z. Page
    ,
  • David B. Allison
*Corresponding author for this work
  • University of Alabama at Birmingham
Scholary Output:
Contribution to journal
Editorial
Peer-review

Open access

Abstract

Although mathematical relationships can be proven by deductive logic, biological relationships can only be inferred from empirical observations. This is a distinct disadvantage for those of us who strive to identify the genes involved in complex diseases and quantitative traits. If causation cannot be proven, however, what does constitute sufficient evidence for causation? The philosopher Karl Popper said, "Our belief in a hypothesis can have no stronger basis than our repeated unsuccessful critical attempts to refute it." We believe that to establish causation, as scientists, we must make a serious attempt to refute our own hypotheses and to eliminate all known sources of bias before association becomes causation. In addition, we suggest that investigators must provide sufficient data and evidence of their unsuccessful efforts to find any confounding biases. In this editorial, we discuss what "causation" means in the context of complex diseases and quantitative traits, and we suggest guidelines for steps that may be taken to address possible confounders of association before polymorphisms may be called "causative.".

Publication Information

Output type

Scholary Output:
Contribution to journal
Editorial
Peer-review

Original language

English (US)

Pages from-to (Number of pages)

Pages 711-719 (9 pages)

Journal (Volume, Issue Number)

American journal of human genetics (Volume 73, Issue 4)

Publication milestones

  • Published - 10/01/2003

Publication status

Published - 10/01/2003

ISSN

0002-9297

Publication IDs

  • Scopus: 0142059667
  • PubMed: 13680525

Publication metrics

Metrics

Scopus
citations
SciVal
FWCI
41.42
SciVal
Author count
5
SciVal
citations
165
SciVal
Paper percentile
97
SciVal
Top percentile
5
Fractional count
1
Fractional count
0.20
Fractional count
4
Fractional count
0.80
Fractional count
1
Fractional count
1

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Captures
116
Citation count
180

Funding Details

This work was supported, in part, by NIH grants DK056366, P30DK056336, R01ES09912, P20RR016430, and P01AR049084.
FundersFunding numbers
NIH
P20RR016430, R01ES09912, P30DK056336, DK056366
NIAMS
P01AR049084