- Statistical evidence
- p values
In human genetics, we measure the strength of statistical evidence using a variety of maximized likelihood ratios, LODs, and empirical p values. I argue here that these statistics have highly undesirable properties as evidence measures when applied to complex disorders. Among other deficiencies, I show that when following up on an interesting finding, they will tend to erroneously indicate diminished evidence as more data are considered (e.g., the LOD will tend to go down at a linked locus as the sample size increases). This violates a fundamental assumption underlying standard linkage and association designs in which we first scan the genome for our best signals, and then follow up at those genomic positions with additional data. I argue here for a coherent theoretical approach to formalizing statistical evidence measures, and derive a set of minimal requirements that any evidence measure should meet, drawing heavily on an analogy with the thermometer. I speculate that measures of evidence that come closer to meeting these requirements will do a better job of finding and characterizing genes, and I propose an alternative evidence metric as a step in this direction.
Copyright © 2006 S. Karger AG, Basel
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Prof. Veronica J. Vieland
Center for Statistical Genetics Research
2190 Westlawn Bldg., University of Iowa
Iowa City IA 52242-1008 (USA)
Tel. +1 319 353 4782, Fax +1 319 353 3038, E-Mail email@example.com
Received: February 27, 2006
Accepted after revision: April 12, 2006
Published online: June 12, 2006
Number of Print Pages : 13
Number of Figures : 2, Number of Tables : 1, Number of References : 20
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 61, No. 3, Year 2006 (Cover Date: August 2006)
Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (print), 1423–0062 (Online)
For additional information: http://www.karger.com/HHE
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