ArticleBMC genomics2014
Cohen's h for detection of disease association with rare genetic variants.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Article
- Robust sex differences in functional brain connectivity are present in utero.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Racial and ethnic disparities in a real-world precision oncology data registry.NPJ precision oncology · 2023Article
- Genetic heterogeneity of Alzheimer's disease in subjects with and without hypertension.GeroScience · 2019Article
- A non-threshold region-specific method for detecting rare variants in complex diseases.PloS one · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundThe power of the genome wide association studies starts to go down when the minor allele frequency (MAF) is below 0.05. Here, we proposed the use of Cohen's h in detecting disease associated rare variants. The variance stabilizing effect based on the arcsine square root transformation of MAFs to generate Cohen's h contributed to the statistical power for rare variants analysis. We re-analyzed published datasets, one microarray and one sequencing based, and used simulation to compare the performance of Cohen's h with the risk difference (RD) and odds ratio (OR).
resultsThe analysis showed that the type 1 error rate of Cohen's h was as expected and Cohen's h and RD were both less biased and had higher power than OR. The advantage of Cohen's h was more obvious when MAF was less than 0.01.
conclusionsCohen's h can increase the power to find genetic association of rare variants and diseases, especially when MAF is less than 0.01.
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