Missing Heritability in the Tails of Quantitative Traits? A Simulation Study on the Impact of Slightly Altered True Genetic Models

Objective: Genome-wide association studies have identified robust
associations between single nucleotide polymorphisms and complex
traits. As the proportion of phenotypic variance explained is still
limited for most of the traits, larger and larger meta-analyses are
being conducted to detect additional associations. Here we
investigate the impact of the study design and the underlying
assumption about the true genetic effect in a bimodal mixture
situation on the power to detect associations. Methods: We
performed simulations of quantitative phenotypes analysed by
standard linear regression and dichotomized case-control data sets
from the extremes of the quantitative trait analysed by standard
logistic regression. Results: Using linear regression, markers with
an effect in the extremes of the traits were almost undetectable,
whereas analysing extremes by case-control design had superior
power even for much smaller sample sizes. Two real data examples
are provided to support our theoretical findings and to explore our
mixture and parameter assumption. Conclusions: Our findings support
the idea to re-analyse the available meta-analysis data sets to
detect new loci in the extremes. Moreover, our investigation offers
an explanation for discrepant findings when analysing quantitative
traits in the general population and in the extremes. Copyright (C)
2011 S. Karger AG, Basel