43: Comparison of Linear Mixed-Effect and Deep Learning Models for Predicting Phenotypes using GWAS

Music City Center 

Large biobank studies, such as the UK Biobank, provide us with unprecedented opportunities to predict various phenotypes with their rich genome-wide association studies (GWAS) data collected from massive populations. The adoption of linear mixed models (LMMs) to predict phenotypes was a significant milestone and a major success in the history of GWAS. Nevertheless, the classic LMM-based methods for GWAS data often fail to account for the dependence structure between single nucleotide polymorphisms (SNPs). Meanwhile, recently, deep learning has demonstrated remarkable success in computer vision, protein structure prediction and functional genomics. Deep learning is able to model complex non-linear relationships and can exploit dependent structure among features. Therefore, it is of great interest to compare the predictive capabilities between classic LMM-based methods and deep learning models for GWAS data. Here, we systematically compare the performance of LMM-based methods and deep learning models in predicting a dozen phenotypes using the UK Biobank data and discuss the strengths and limitations of both approaches.

Genome-Wide Association Studies (GWAS)

Linear Mixed-Effect Models

Deep Learning

Biobank 

Section on Statistics in Genomics and Genetics