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  • Review Article
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Methods of integrating data to uncover genotype–phenotype interactions

Nature Reviews Genetics volume 16, pages 85–97 (2015)Cite this article

Subjects

Key Points

  • Technological advances have vastly expanded the amount of omic data currently available. Historically, each type of data was analysed separately, although approaches to integrate omic data sets to predict complex phenotypic traits are now emerging.

  • Such systems genomics approaches to combine multiple data types provide a more comprehensive understanding of complex genotype–phenotype associations than analysis of one data set.

  • Data from multiple sources that point to the association of the same gene or pathway are less likely to result in false positives.

  • There are various strengths and weaknesses of the available strategies. The approach used needs to be selected according to specific types of data, different types of scientific questions or different types of underlying genomic models.

Abstract

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration — including meta-dimensional and multi-staged analyses — which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.

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Figure 1: Biological systems multi-omics from the genome, epigenome, transcriptome, proteome and metabolome to the phenome.
Figure 2: Alternative hypothesis of complex-trait aetiology.
Figure 3: Categorization of multi-staged analysis.
Figure 4: Categorization of meta-dimensional analysis.

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Acknowledgements

Support for the authors was provided by the US National Institutes of Health grants LM010040 (ATHENA) and HL065962 (the P-STAR Network Resource of the PGRN). E.R.H. was funded by grant Z01 HG00153-08-IDRB. R.L. was funded by the US National Science Foundation under Grant number DGE1255832. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation.

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  1. Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA

    Marylyn D. Ritchie, Ruowang Li, Sarah A. Pendergrass & Dokyoon Kim

  2. National Human Genome Research Institute, Inherited Disease Research Branch, Baltimore, 21224, Maryland, USA

    Emily R. Holzinger

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PowerPoint slides

Glossary

Complex traits

Characteristics that arise from interactions among multiple molecular factors, with the potential influence of environmental and behavioural factors. Complex traits do not conform to the inheritance pattern of Mendelian traits.

Meta-dimensional analysis

An approach whereby all scales of data are combined simultaneously to produce complex models defined as multiple variables from multiple scales of data.

Multi-staged analysis

A stepwise or hierarchical analysis method that reduces the search space through different stages of analysis.

Systems genomics

An analysis approach that models the complex inter- and intra-individual variations of traits and diseases using data from next-generation omic data.

Data integration

The incorporation of multi-omic information in a meaningful way to provide a more comprehensive analysis of a biological point of interest.

Quality control

Various techniques used to remove noise and confounding factors from the data.

Factor analysis

A statistical method used to describe variability among observed, correlated variables in terms of a smaller number of unobserved (latent) variables.

Multi-omics data

Multiple types of genome-scale data sets that emerged from high-throughput technologies, including genome sequencing data (genomics), genome-wide RNA-sequencing data (transcriptomics), methylation and histone modification data (epigenomics), and mass spectrometry protein data (proteomics).

Population stratification

A situation in which different subpopulations exist within a data set owing to different allele frequencies because of underlying genetic ancestry that leads to different strata being present in the data set. This can lead to spurious associations if not adjusted for appropriately.

Multivariate Cox LASSO (least absolute shrinkage and selection operator) model

A method that performs variable selection via LASSO, followed by a multivariate Cox regression analysis.

Kernel-based integration

The use of a valid kernel to perform a data matrix transformation before the integration of multiple data types.

Graph-based integration

The use of graphs to perform a data matrix transformation before integration. A graph is a natural method for analysing relationships between samples, as the nodes depict individual samples and the edges represent their possible relationships.

Majority voting

A method in which multiple models are constructed and subsequently evaluated to determine which performs best.

Ensemble classifiers

Classifiers constructed through the use of multiple learning methods to obtain better predictive performance than could be obtained from any of the individual learning algorithms.

Bayesian network

A type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.

Overfitting

Building a statistical model that explains the training data set that but does not generalize to independent data.

Type I errors

(Also known as false positives). The acceptance of the alternative hypothesis when the null hypothesis is true.

Genome-wide association studies

Studies that aim to identify disease- or trait-related genetic variations from the whole genome.

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Ritchie, M., Holzinger, E., Li, R. et al. Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet 16, 85–97 (2015). https://doi.org/10.1038/nrg3868

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