rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes

TL;DR

This study uses machine learning, specifically random forest regression, to predict genotypes from brain imaging traits, revealing novel associations and providing a non-linear alternative to traditional GWAS methods.

Contribution

It introduces the application of random forest regression for genotype prediction from imaging phenotypes, highlighting its advantages over linear models in uncovering new associations.

Findings

Random forest regression outperformed linear models in SNP prediction.

Identified SNPs associated with brain regions linked to Alzheimer's disease.

Non-linear models reveal additional genotype-phenotype associations.

Abstract

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.