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. 2009 Nov;25(11):486-8.
doi: 10.1016/j.tig.2009.09.008. Epub 2009 Oct 12.

Detecting new neurodegenerative disease genes: does phenotype accuracy limit the horizon?

David C Samuels, David J Burn, Patrick F Chinnery

Detecting new neurodegenerative disease genes: does phenotype accuracy limit the horizon?

David C Samuels et al. Trends Genet. 2009 Nov.
No abstract available

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Figures

Figure 1
Figure 1
Power to detect a genetic association in the context of diagnostic errors. In each example, the probability of affected individuals being classified as controls is 1 × 10−5. Varying this parameter has negligible impact on power and/or optimal sample size for diseases that are present in <10% of the population . (a) Power to detect an association between a common allele (allele frequency = 0.5; GRR = 1.1– 1.3 under a multiplicative model) and disease in 20 000 cases and 20 000 controls with varying degrees of diagnostic error at P < 5 × 10−7. Disease frequency = 0.01. (b) Power to detect an association between alleles of different frequency (0.5, 0.25, 0.1) and disease in 20 000 cases and 20 000 controls with varying degrees of diagnostic error at P < 5 × 10−7. GRR = 1.3, disease frequency = 0.01. (c) Power to detect an association between an allele (frequency = 0.125, GRR = 1.3) and diseases of different prevalence (0.01, 0.001, 0.0001) in 20 000 cases and 20 000 controls with varying degrees of diagnostic error at P < 5 × 10−7. (d) Ratio of the number of inaccurately phenotyped cases (nerror) to the number of accurately phenotyped cases (nnoerror) required to detect an association between an allele (frequency = 0.1, varying GRR from 1.1 to 1.3) and a disease (frequency = 0.01) with 95% power at varying degrees of diagnostic error at P < 5 × 10−7. All calculations used PAWE-PH Phenotype Edition .
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