Correspondence
Recently, Sun et al.1 raised an interesting suggestion concerning the use of variance-stabilization transformations in genome-wide association studies (GWAS) for phenotypic variability. Specifically, Sun et al. revisited Yang et al.’s2 results on the variability-controlling locus FTO for human body mass index (BMI) and claimed that the underlying variability across genotypes might not be as large as Yang et al. had seen. Although it was an important point that Sun et al. discussed, especially when quantitatively studying phenotypic variability has become such a hot topic, it is our opinion that there are some issues with the transformation approach that Sun et al. proposed.
First of all, if we take Sun et al.’s transformation according to Yang et al.’s phenotypic mean and variance per FTO genotype class, i.e. a one-to-one map through an inverse hyperbolic sine function, the BMI scale will become rather different compared with the ordinary measurement that we normally use (Figure 1). On the transformed scale of BMI, the difference between two persons who have a BMI of 24 and 25 kg/m2 is much larger than that between two BMIs of 20 and 21 kg/m2, which is strange in reality since the original BMI scale is what we commonly use and also what we care about. Sun et al.’s main argument here is that nearly all the measurement units are manmade. However, considering one of the traits of most interest, e.g. height, why should we regard the difference between 160cm and 170cm different from 170cm and 180cm? Although the definitions of most units can be arbitrary, some measurement scales do have meaning in real life.
Secondly, a key problem with Sun et al.’s transformation in practice is that such a transformation is marker-specific. Namely, when performing a GWAS, one needs to transform the phenotypic records differently for different markers, according to the phenotypic distribution across the genotypes per marker. This does not make much sense in practical analyses, because if there is a "best" scale of the phenotype, it should be used for all the markers across the genome, before testing the association between the phenotype and the markers. Using the tested marker to determine the transformation of the phenotype is strange. If a marker-specific transformation can be estimated, one should estimate a genome-specific transformation for GWAS, instead of doing different transformations marker-by-marker.
Thirdly, if the transformation of the phenotype is determined by one marker showing a significant effect on the phenotypic variability before testing the other markers, another significant effect on the phenotypic variability might be created due to such a transformation. In such a situation, it is problematic to decide which phenotypic scale we should choose.
Fourthly, several recent studies discussed that gene-gene or gene-environment interactions could cause significant variance heterogeneity across genotypes3–6, which makes testing variance-controlling loci a powerful tool to reveal potential interaction effects. Reducing the difference in variance across genotypes using a marker-specific variance-stabilization transformation would dramatically reduce such power. Regarding the biological sense of genetically regulated variance heterogeneity, empirical evidence has shown that a single causal locus could show a much higher significant effect on variance compared with the mean6. In a particular population, such a locus may only be mappable through testing the variability rather than the magnitude of the phenotype.
The above issues cause us to question Sun et al.’s transformation in practice. The scale of the phenotype is certainly an important concern when interpreting an effect on phenotypic variability7. However, one needs to be careful for the points above before applying any transformation on the data. In particular, the statistical power in detecting functional loci and the real meaning of the scale used should be emphasized.
Comments on this article Comments (0)