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

BMC Bioinformatics

Research

Chris Maliepaard¹  Richard G. F. Visser¹  Bjorn Kloosterman²  Animesh Acharjee³ 

[1]Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands

[2]Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands

[3]Keygene NV, PO Box 216, 6700 AE, Wageningen, The Netherlands

[4]Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands

[5]MRC Human Nutrition Research, 120 Fulbourn Road, CB1 9NL, Cambridge, UK

关键词: Data integration;    Genetical genomics;    Networks;    Random forest;   

DOI  :  10.1186/s12859-016-1043-4

来源: Springer

【 摘 要 】

BackgroundIn 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.ResultsWe 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.ConclusionsIn 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.

【 授权许可】

CC BY   
© Acharjee et al. 2016