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  • Review Article
  • Published:

Phenomics: the next challenge

Nature Reviews Genetics volume 11, pages 855–866 (2010)Cite this article

Subjects

Key Points

  • The area of phenomics is the acquisition of high-dimensional phenotypic data on an organism-wide scale.

  • Phenotypes are the characteristics of organisms that are of the most interest. Useful explanations of important outcomes such as disease can be obtained by studying phenotypes.

  • Phenomic data allow a better understanding of the genotype–phenotype map, that is, of the pathways that connect genotypes to phenotypes. Phenomics is also a necessary complement to genomics.

  • The dimensionality of phenomes is high and so analyses of phenomic data call for new concepts and techniques.

  • Nonlinear models that integrate information across the phenotypic hierarchy are necessary to integrate phenomic information.

  • Gathering phenomic data is currently expensive and time consuming; technical advances can increase phenomic throughput and lower costs.

  • The development of phenomic capabilities requires collaborations between scientists with diverse expertise.

  • Accelerating our ability to gather phenomic data should be a priority for funding.

Abstract

A key goal of biology is to understand phenotypic characteristics, such as health, disease and evolutionary fitness. Phenotypic variation is produced through a complex web of interactions between genotype and environment, and such a 'genotype–phenotype' map is inaccessible without the detailed phenotypic data that allow these interactions to be studied. Despite this need, our ability to characterize phenomes — the full set of phenotypes of an individual — lags behind our ability to characterize genomes. Phenomics should be recognized and pursued as an independent discipline to enable the development and adoption of high-throughput and high-dimensional phenotyping.

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Figure 1: Causation scenarios.
Figure 2: Quantitation of eye colour.

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Acknowledgements

We acknowledge support from the National Institutes of Health and National Science Foundation, USA, and the Research Council of Norway's eVITA programme. We thank H. Martens, E. Marquez and S. Schwinn for comments. D.R.G. is indebted to C. Lee for support.

Author information

Authors and Affiliations

  1. Department of Biological Science, Florida State University, Tallahassee, 32306-4295, Florida, USA

    David Houle

  2. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, 02115, Massachusetts, USA

    Diddahally R. Govindaraju

  3. Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics (CIGENE), Norwegian University of Life Sciences, N-1432 Ås, Norway

    Stig Omholt

  4. Centre for Ecological and Evolutionary Synthesis, University of Oslo, NO-0316, Oslo, Norway

    Stig Omholt

Authors
  1. David Houle
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  2. Diddahally R. Govindaraju
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  3. Stig Omholt
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The authors declare no competing financial interests.

Related links

Related links

DATABASES

Database of Genotypes and Phenotypes (dbGaP)

The Mouse Phenome Database (MPD)

Online Mendelian Inheritance in Man (OMIM)

FURTHER INFORMATION

Australian Plant Phenomics Facility

Canine Phenome Project

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

Consortium for Neuropsychiatric Phenomics (CNP)

Drosophila Genetic Reference Panel (DGRP)

Drosophila Population Genomics Project (DPGP)

European Mouse Disease Clinic (EUMODIC)

European Mouse Phenotyping Resource of Standardised Screens (EMPRESS)

Europhenome Mouse Phenotyping Resource

International Plant Phenomics Network (IPPN)

Jülich Plant Phenotyping Centre (JPPC)

National BioResource Project for the Rat in Japan (NBRP)

National Institutes of Health Clinical and Translational Science Awards

Personal Genome Project

The Virtual Physiological Human Initiative (VPH)

UK Biobank

USC Nordborg Laboratory GWA studies in Arabidopsis thaliana

Glossary

Pleiotropy

The ability of a single genetic change to affect more than one phenotype.

Metabolic syndrome

The tendency for obesity, diabetes, increased blood pressure, triglycerides and cholesterol to co-occur.

Observational study

A study in which conclusions are drawn from differences between subjects that are not under the control of the investigator.

Odds ratio

The ratio of the probability that an event will occur in one group to the probability that it will occur in another, for example, diseased versus healthy groups. It is a measure of effect size for binary variables.

Heritability

The proportion of the observed phenotypic variation that is attributable to genetic variation.

Effect size

The magnitude of the inferred effect of one variable on another. The effect size of a SNP is the difference in phenotype between genotypes with and without one of the nucleotides.

Prospective study

An observational study in which phenotypes are measured at the beginning of the study and the fate of individuals is tracked over subsequent time intervals.

Stabilizing selection

A type of natural selection that favours intermediate phenotypes.

Directional selection

A type of natural selection in which fitness increases monotonically with increasing or decreasing phenotype.

Endophenotype

A phenotype correlated with or possibly causally related to a disease state. In psychiatric research, endophenotype is synonymous with biomarker.

Biomarker

A phenotype that is objectively measured and used as an indicator of other biological processes.

Function-valued trait

A phenotype that is a continuous function, such as a surface or a time course. It is also known as an infinite dimensional trait.

Over-fitting

The prediction by a statistical model of error instead of the relationship of interest. An over-fitted model has poor predictive power.

Ridge and LASSO regression

Regression techniques that choose models that both fit well and minimize the number of predictor variables (LASSO) or their total effects (Ridge).

Cross-validation

The process of choosing a statistical model based on its ability to predict data that are not used to fit the model. It is commonly accomplished by splitting one data set into two, with one part used for training and the other for validation.

Dimensionality

The number of orthogonal directions in a space defined by multiple phenotypic measurements that have independent variation.

Partial least-squares regression

A statistical technique that identifies the combinations of variables in one set that best predict the variables in another set.

Random forest

An algorithm that classifies observations into categories using a family of hierarchical rules randomly chosen from a large family of such rules.

Support vector machine

A set of machine-learning algorithms for finding the polynomial functions of predictors that best separate a data set into two categories.

Positron emission tomography

This produces three-dimensional images through time of the concentration of a biologically interesting molecule such as glucose labelled with a radionuclide.

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Houle, D., Govindaraju, D. & Omholt, S. Phenomics: the next challenge. Nat Rev Genet 11, 855–866 (2010). https://doi.org/10.1038/nrg2897

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