Methods of integrating data to uncover genotype–phenotype interactions

Journal name:
Nature Reviews Genetics
Year published:
Published online


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.

At a glance


  1. Biological systems multi-omics from the genome, epigenome, transcriptome, proteome and metabolome to the phenome.
    Figure 1: Biological systems multi-omics from the genome, epigenome, transcriptome, proteome and metabolome to the phenome.

    Heterogeneous genomic data exist within and between levels, for example, single-nucleotide polymorphism (SNP), copy number variation (CNV), loss of heterozygosity (LOH) and genomic rearrangement, such as translocation, at the genome level; DNA methylation, histone modification, chromatin accessibility, transcription factor (TF) binding and micro RNA (miRNA) at the epigenome level; gene expression and alternative splicing at the transcriptome level; protein expression and post-translational modification at the proteome level; and metabolite profiling at the metabolome level. Arrows indicate the flow of genetic information from the genome level to the metabolome level and, ultimately, to the phenome level. The red crosses indicate inactivation of transcription or translation. CSF, cerebrospinal fluid; Me, methylation; TFBS, transcription factor-binding site.

  2. Alternative hypothesis of complex-trait aetiology.
    Figure 2: Alternative hypothesis of complex-trait aetiology.

    Hypothesis A (grey arrow) is the theory that variation is hierarchical, such that variation in DNA leads to variation in RNA and so on in a linear manner. Hypothesis B (black arrow) is the idea that it is the combination of variation across all possible omic levels in concert that leads to phenotype.

  3. Categorization of multi-staged analysis.
    Figure 3: Categorization of multi-staged analysis.

    Multi-staged analysis can be divided into three categories. a | Analysis of expression quantitative trait loci (eQTLs) analysis involves the identification of genetic variation associated with measures of quantitative gene expression. b | Allele-specific expression involves the analysis of whether the maternal or paternal allele is preferentially expressed, followed by the association of this allele with cis-element variations and epigenetic modifications. c | Domain knowledge overlap involves a two-step analysis in which an initial association analysis is performed at the single-nucleotide polymorphism (SNP) or gene expression variable followed by the annotation of the significant associations with knowledge generated by other biological experiments. This approach enables the selection of association results with functional data to corroborate the association. CTCF, CCCTC-binding factor; Pol II, RNA polymerase II.

  4. Categorization of meta-dimensional analysis.
    Figure 4: Categorization of meta-dimensional analysis.

    Meta-dimensional analysis can be divided into three categories. a | Concatenation-based integration involves combining data sets from different data types at the raw or processed data level before modelling and analysis. b | Transformation-based integration involves performing mapping or data transformation of the underlying data sets before analysis, and the modelling approach is applied at the level of transformed matrices. c | Model-based integration is the process of performing analysis on each data type independently, followed by integration of the resultant models to generate knowledge about the trait of interest. miRNA, microRNA; SNP, single-nucleotide polymorphism.


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

    • Marylyn D. Ritchie,
    • Ruowang Li,
    • Sarah A. Pendergrass &
    • Dokyoon Kim
  2. National Human Genome Research Institute, Inherited Disease Research Branch, Baltimore, Maryland 21224, USA.

    • Emily R. Holzinger

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The authors declare no competing interests.

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  • Marylyn D. Ritchie

    Marylyn D. Ritchie is a professor in the Department of Biochemistry and Molecular Biology, and Director of the Center for Systems Genomics at The Pennsylvania State University, State College, USA. She is a statistical and computational geneticist who focuses on understanding the genetic architecture of complex human diseases. She has expertise in developing new bioinformatic tools for complex analysis of large data sets in genetics, genomics and clinical databases, in particular in the area of pharmacogenomics. She has received several awards and honours, including selection as a Genome Technology, Rising Young Investigator in 2006, an Alfred P. Sloan Research Fellow in 2010 and a KAVLI Frontiers of Science fellow by the US National Academy of Science for each of the past four consecutive years. She has extensive experience in all aspects of genetic epidemiology and bioinformatics related to human genomics, including study design, genotyping platform selection, statistical analysis and interpretation of results. She also has wide knowledge of dealing with large data sets and complex analysis, including genome-wide association studies, next-generation sequencing, copy number variations and data integration of meta-dimensional omic data. Marylyn D. Ritchie's homepage.

  • Emily R. Holzinger

    Emily R. Holzinger is a postdoctoral fellow with Joan Bailey-Wilson at the Computational and Statistical Genomics Research Branch of the National Human Genome Research Institute, Baltimore, Maryland, USA. She completed her Ph.D. work in Human Genetics at Vanderbilt University, Nashville, Tennessee, USA. Her research interests focus on developing novel computational methods to improve the analysis of complex human traits using high-throughput data.

  • Ruowang Li

    Ruowang Li is pursuing a Ph.D. in bioinformatics and genomics at The Pennsylvania State University, State College, USA. He was fascinated by the complexity of molecular biology, so he studied biology and computer science at Worcester Polytechnic Institute, Massachusetts, USA, from 2007 to 2011. He has been developing and applying computational methods to identify the molecular factors affecting the varied responses of different individuals to chemotherapeutic drugs, as well as the survival status of patients with cancer. He is currently a National Science Foundation graduate fellow in the laboratory of Marylyn D. Ritchie.

  • Sarah A. Pendergrass

    Sarah A. Pendergrass is a research faculty member in the Department of Biochemistry and Molecular Biology at the Center for Systems Genomics and the laboratory of Marylyn D. Ritchie at The Pennsylvania State University, State College, USA. She is a genetic bioinformaticist and focuses on high-throughput data analysis and data-mining projects for uncovering the genetic architecture of complex traits. She has extensive experience in developing novel methodologies, such as those for phenome-wide association studies. She has developed unique software tools to enable researchers to access and analyse data in new ways, including several tools for data visualization. She obtained her Ph.D. in genetics from Dartmouth College, Hanover, New Hampshire, USA, focusing on gene expression analyses and bioinformatics for biomarker and biological discovery for systemic sclerosis, and obtained an M.S. in biomedical engineering from Thayer School of Engineering at Dartmouth College.

  • Dokyoon Kim

    Dokyoon Kim obtained his Ph.D. in biomedical informatics from Seoul National University College of Medicine, Korea. His research entails the development and application of data integration approaches, mostly using data from The Cancer Genome Atlas to improve the ability to diagnose, treat and prevent cancer. His primary focus lies in integrating multi-omic data and biological knowledge to better translate genomic and biomedical data into clinical products. He is currently a postdoctoral fellow in the laboratory of Marylyn D. Ritchie at The Pennsylvania State University, State College, USA.

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