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As pooling variance increases, the ability of a pool-based GWAS to detect odds ratios similar to those detectable by conventional GWAS decreases.
With as few as 269 cases and 267 controls, we could detect odds ratios as small as 1.8 in SNPs with allele frequencies of 0.10 or higher (alpha = 0.05, beta = 0.10).
The power to detect odds ratios as low as 1.6 for mtSNPs and considering an average population minimum allele frequency (MAF) of 20% (lower than the average MAF in CG1) was 92%.
Genome-wide association studies published to-date have had mostly modest sample sizes, and even the ongoing efforts sponsored by Wellcome Trust and the GAIN initiative [8], [9] may still be underpowered to detect odds ratios in the range of 1.0 1.3, especially if the genetic variants of interest are not very common.
The power to detect odds ratios >1.5 was found to be >80% under all models.
We had 99% and 63% power to detect odds ratios of 2.1 and 1.5 in our study.
Similar(30)
There was sufficient power to detect odds-ratios of 1.7, 1.7, 1.6, and 1.6 for the four most common traits glaucoma, PVD, AMD, and VMT, respectively.
For example, an analysis restricted to lung adenocarcinomas will have 80% power to detect odds-ratios greater than 1.4, 1.3 and 1.2 if the exposure prevalence is 5%, 10%and20%0% respectively.
In the present study, the FPRP value was calculated for each genetic variant using the assigned prior probability range, the statistical power to detect an odds ratio of 1.5, and detected odds ratios and p values.
At an unadjusted per-test significance level of 5%, the experiment had 80% power to detect an odds ratio of 1.6, and 33% power to detect an odds ratio of 1.3, assuming that 20% of the controls were carriers of the risk factor (Figure S2).
Nonetheless, with MAF as low as 5%, even this study was not sufficiently powered to detect allelic odds ratios of ∼1.2 (probability of detection ∼44%).
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