  Imaging genetic studies aim to find associations between genetic variants and
imaging quantitative traits. Traditional genome-wide association studies (GWAS)
are based on univariate statistical tests, but when multiple traits are
analyzed together they suffer from a multiple-testing problem and from not
taking into account correlations among the traits. An alternative approach to
multi-trait GWAS is to reverse the functional relation between genotypes and
traits, by fitting a multivariate regression model to predict genotypes from
multiple traits simultaneously. However, current reverse genotype prediction
approaches are mostly based on linear models. Here, we evaluated random forest
regression (RFR) as a method to predict SNPs from imaging QTs and identify
biologically relevant associations. We learned machine learning models to
predict 518,484 SNPs using 56 brain imaging QTs. We observed that genotype
regression error is a better indicator of permutation p-value significance than
genotype classification accuracy. SNPs within the known Alzheimer disease (AD)
risk gene APOE had lowest RMSE for lasso and random forest, but not ridge
regression. Moreover, random forests identified additional SNPs that were not
prioritized by the linear models but are known to be associated with
brain-related disorders. Feature selection identified well-known brain regions
associated with AD,like the hippocampus and amygdala, as important predictors
of the most significant SNPs. In summary, our results indicate that non-linear
methods like random forests may offer additional insights into
phenotype-genotype associations compared to traditional linear multi-variate
GWAS methods.
