Abstract
Beware of risk for increased false positive rates in genome-wide association studies for phenotypic variability
Highlights
Performing genome-wide association studies (GWAS) to identify genes regulating the between-genotype variability, rather than the mean, is a new promising approach for dissecting the genetics of complex traits
As it is likely that this paper will increase the interest for applying this methodology in other human and experimental populations, we think that it is important to make prospective users aware that one need to be careful when applying similar methodology to smaller datasets than those used by Yang et al Yang et al (2012) noticed that the mapping of variance-controlling loci is prone to inflated test statistics when the minor allele frequency (MAF) is small, but provided no further explanation for this
We will briefly explain why such observation is only half true and why GWAS analyses to detect variance heterogeneity is inherently sensitive to unbalanced data, and why researchers aiming to perform similar analyses need to be careful to avoid reporting false positive signals
Summary
Performing genome-wide association studies (GWAS) to identify genes regulating the between-genotype variability, rather than the mean, is a new promising approach for dissecting the genetics of complex traits. Using this strategy, Yang et al (2012) successfully identified and replicated the FTO locus and showed that it has a role in regulating the between-genotype variance heterogeneity of human body mass index using a parametric regression model. To illustrate this inherent problem in the statistical methodology used to test for variance heterogeneity, we used simple simulations in two populations: one with two genotypes: AA and BB and one with three genotypes: AA, AB, and BB.
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