  Background: Missing data is a common challenge in mass spectrometry-based
metabolomics, which can lead to biased and incomplete analyses. The integration
of whole-genome sequencing (WGS) data with metabolomics data has emerged as a
promising approach to enhance the accuracy of data imputation in metabolomics
studies. Method: In this study, we propose a novel method that leverages the
information from WGS data and reference metabolites to impute unknown
metabolites. Our approach utilizes a multi-view variational autoencoder to
jointly model the burden score, polygenetic risk score (PGS), and linkage
disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature
extraction and missing metabolomics data imputation. By learning the latent
representations of both omics data, our method can effectively impute missing
metabolomics values based on genomic information. Results: We evaluate the
performance of our method on empirical metabolomics datasets with missing
values and demonstrate its superiority compared to conventional imputation
techniques. Using 35 template metabolites derived burden scores, PGS and
LD-pruned SNPs, the proposed methods achieved R^2-scores > 0.01 for 71.55% of
metabolites. Conclusion: The integration of WGS data in metabolomics imputation
not only improves data completeness but also enhances downstream analyses,
paving the way for more comprehensive and accurate investigations of metabolic
pathways and disease associations. Our findings offer valuable insights into
the potential benefits of utilizing WGS data for metabolomics data imputation
and underscore the importance of leveraging multi-modal data integration in
precision medicine research.
