Adventures in Multi-Omics I: Combining heterogeneous data sets via relationships matrices

TL;DR

This paper introduces a covariance-based method using an expectation-maximization algorithm to combine partial, heterogeneous genomic relationship matrices, enhancing genomic prediction and phenomics analysis.

Contribution

The paper presents a novel EM algorithm for integrating overlapping relationship matrices, offering an alternative to feature imputation in multi-omics data integration.

Findings

Accurate combination of genomic relationship matrices demonstrated.

Improved genomic prediction using combined data sets.

Method applicable to various heterogeneous genotype-phenotype data.

Abstract

In this article, we propose a covariance based method for combining partial data sets in the genotype to phenotype spectrum. In particular, an expectation-maximization algorithm that can be used to combine partially overlapping relationship/covariance matrices is introduced. Combining data this way, based on relationship matrices, can be contrasted with a feature imputation based approach. We used several public genomic data sets to explore the accuracy of combining genomic relationship matrices. We have also used the heterogeneous genotype/phenotype data sets in the https://triticeaetoolbox.org/ to illustrate how this new method can be used in genomic prediction, phenomics, and graphical modeling.