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Comment
. 2010 Jun;26(6):241-2; author reply 242-3.
doi: 10.1016/j.tig.2010.03.003. Epub 2010 Apr 22.

Detecting genes in complex disease: does phenotype accuracy limit the horizon?

Comment

Detecting genes in complex disease: does phenotype accuracy limit the horizon?

Koon Lee et al. Trends Genet. 2010 Jun.
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 less than 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 20000 cases and 20000 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 20000 cases and 20000 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 20000 cases and 20000 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 (nno error) 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 [2].

Comment on

References

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    1. Sawcer S. The complex genetics of multiple sclerosis: pitfalls and prospects. Brain. 2008;131:3118–3131. - PMC - PubMed