Integration of multi-omics data for prediction of phenotypic traits using random forest
Open accessAbstract
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.
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Multi-omics studies have been successfully conducted in plants and animals.
“Multi-omics studies have also been successfully conducted in plants (potato (Acharjee et al., 2016(Acharjee et al., , 2011))) and animals (such as canine heart disease (Li et al., 2015)).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Acharjee et al., 2016
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Using machine learning approaches for multi-omics data analysis: A review
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- Authors
- Acharjee, A., Kloosterman, B., Visser, R. G. F., & Maliepaard, C.
- Affiliations
- MRC Elsie Widdowson Laboratory (GB)
- KeyGene (Netherlands) (NL)
- Wageningen University & Research (NL)
- Corresponding author
- Maliepaard, Chris
- Venue
- BMC Bioinformatics
- Published
- 2016-06
- Volume
- 17
- Issue
- S5
- Pages
- 180-180
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4905610
- OpenAlex
- W2418706671
- Cited by
- 109
- References
- 42
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 10 · 2023: 13 · 2022: 10 · 2021: 15 · 2020: 13 · 2019: 10 · 2018: 5 · 2017: 7 · 2016: 3
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- multi-omics integration, potato tuber quality, phenotype prediction, tuber shape, gene expression, metabolomics, proteomics, expression QTL, β-carotene hydroxylase, zeaxanthin epoxidase, tyrosine, random forest regression, network analysis
- MeSH terms
- Chromatography, High Pressure Liquid, Gas Chromatography-Mass Spectrometry, Phenotype, Plant Proteins, Solanum tuberosum, Mass Spectrometry, Gene Expression Regulation, Plant, Genomics, Chromosomes, Plant, Plant Tubers, Quantitative Trait Loci, Proteomics, Metabolomics
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