PubMed · 15558821
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John C Ruckdeschel. 2005-01-15. Fatigue is becoming an exhausting problem.. https://doi.org/10.1002/cncr.20770
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The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.
The aim of this article was to derive an approximation of the distribution of Pearson's statistic for 2 x m contingency tables to calculate power for case-control studies accurately and efficiently in terms of computer time. We first prove that, rather than a non-central chi-square distribution, Pearson's statistic is asymptotically equivalent with the sum of squares of m independent normal random variables whose variances are typically different under the alternative hypothesis. Based on this asymptotically equivalent (AE) we derive a cost-effective approximation (CE). Numerical results show that CE was almost as precise as AE, but computationally more efficient. Although somewhat less costly in terms of CPU time, we show that a commonly used approximation using the non-central chi-square distribution can be very inaccurate and overestimate power in some scenarios and underestimate it in others. Simulations and exact distributions, on the other hand, are accurate but computationally very intensive compared to our CE. The CE reached its lowest accuracy under the null hypothesis where it has a chi-square distribution with m - 1 degree of freedom. This suggests that CE can be used to calculate power for tables where researchers would feel comfortable using Pearson's chi-square test.
Little is known of the predictive value of the levels of DNA adducts in terms of cancer risk. We examined the association between bulky DNA adducts and risk of lung cancer in a population-based cohort, comprising of 25,717 men and 27,972 women aged 50-64 years at entry. We included 245 cases (137 men and 108 women) with lung cancer and a comparison group of 255 individuals (137 men and 118 women), matched on sex, age and smoking duration. Bulky adducts in white blood cells collected at enrollment and stored at -150 degrees C were analyzed by (32)P-postlabeling method, using the butanol enrichment procedure. The median level of bulky DNA adducts was 0.196 adduct/10(8) nucleotides (5-95 percentiles: 0.094-0.595) among current smokers who were later diagnosed with lung cancer and 0.163 adduct/10(8) nucleotides (5-95 percentiles: 0.091-0.455) among current smokers in the comparison group. The smoking adjusted incidence rate ratios (IRR) for lung cancer in relation to one log unit (natural logarithm) difference in adduct levels were 1.22 (95% CI 0.85-1.74), 1.33 (95% CI 0.89-1.98) and 0.76 (95% CI 0.39-1.47) among all, current and former smokers, respectively. Current smokers with bulky DNA adduct levels above the median had a significant higher lung cancer rate than those with adduct levels below the median (IRR = 1.61; 95% CI 1.04-2.49). The results are compatible with previous studies, suggesting a slightly higher risk of lung cancer with higher levels of adducts among smokers. Our results indicate that bulky DNA adducts may have a weak association with lung cancer risk.