Integration of multi-omics data for prediction of phenotypic traits using random forest

Abstract

Background: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest.

Results: We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine.

Conclusions: In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.

Keywords: Data integration; Genetical genomics; Networks; Random forest.

Fig. 1

(a) A partial correlation network of the phenotypic trait tuber flesh colour (yellow) with gene expression features (red), metabolites from LC-MS (black), metabolites from GC-MS (purple) and proteins (green). The dotted lines represent negative partial correlation coefficients, solid lines represent positive partial correlation coefficients. Bch = beta-carotene hydroxylase, LC_X represents metabolites derived from LC-MS with centrotype number X, GC_X represents metabolites derived from GC-MS with centrotype X, Gene_X = Gene with gene ID X. Pro_X represents a protein with protein ID X. (b) Shows the existing published part of the carotenoid pathway [37], and some of the genes: Bch and Zep are identified by our data

Fig. 2

A partial correlation network of tuber shape (yellow) with gene expression features (red). The dotted lines represent negative partial correlation coefficients, solid lines represent positive partial correlation coefficients. Gene_X = Gene with gene ID X

Fig. 3

A partial correlation network of DSC onset (yellow) with gene expression features (red), metabolites from GC-MS (purple) and proteins (green). The dotted lines represent negative partial correlation coefficients, solid lines represent positive partial correlation coefficients. GC_X represents metabolites derived from GC-MS with centrotype X, Gene_X = Gene with gene ID X. Pro_X represents proteins with protein ID X

Fig. 4

A partial correlation network of enzymatic discoloration (yellow) with gene expression features (red), metabolites from LC-MS (black) and proteins (green). The dotted lines represent negative partial correlation coefficients, solid lines represent positive partial correlation coefficients. LC_X represents metabolites derived from LC-MS with centrotype number X, Gene_X = Gene with gene ID X. Pro_X represents proteins with protein ID X