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  • Review Article
  • Published:

Genotype to phenotype: lessons from model organisms for human genetics

Nature Reviews Genetics volume 14, pages 168–178 (2013)Cite this article

Subjects

Key Points

  • Forward- and reverse-genetic screens in model organisms have revealed that phenotypic traits are typically influenced by the activities of hundreds or thousands of genes and that, conversely, individual genes typically influence many different traits. 'Phenologue' relationships allow these gene–trait connections to be systematically transferred between species.

  • The results of these systematic screens can be used to evaluate and to refine genome-scale methods for linking genes to phenotypes such as integrated 'functional' networks.

  • One reason why the outcome of a particular mutation can vary across individuals is epistasis or genetic interactions between mutations. The systematic mapping of genetic interaction networks in model organisms is providing basic insights into how mutations interact and is leading to the development of computational methods to predict epistasis.

  • The environment can influence the outcome of mutations not only in specific ways, but also in promiscuous ways, such as by altering the activity of molecular chaperones.

  • Whole-genome reverse genetics is the challenge of predicting how individuals vary from their complete genome sequences. Making and experimentally evaluating these predictions in model organisms will lead to the development of improved computational methods for predicting phenotypic variation from genetic variation.

  • In model organisms, mutations often have variable outcomes even in the absence of genetic variation and in a controlled environment. One cause of this is inter-individual variation in the expression or activity of genetic interaction partners, which is termed epigenetic epistasis and occurs, for example, during early embryonic development.

  • Transgenerational genetic and environmental influences can also underlie phenotypic variation. These influences are now being dissected at a molecular level in model organisms.

  • Genetic predictions have both practical and fundamental limitations. More effort should be focused on building clinically useful personalized predictions that incorporate genetic markers and intermediate biomarkers that capture both genetic and non-genetic sources of variance.

Abstract

To what extent can variation in phenotypic traits such as disease risk be accurately predicted in individuals? In this Review, I highlight recent studies in model organisms that are relevant both to the challenge of accurately predicting phenotypic variation from individual genome sequences ('whole-genome reverse genetics') and for understanding why, in many cases, this may be impossible. These studies argue that only by combining genetic knowledge with in vivo measurements of biological states will it be possible to make accurate genetic predictions for individual humans.

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Figure 1: Phenologues: mapping phenotypes between organisms.
Figure 2: Guilt-by-association: integrating data into genome-scale networks that can be used to link genes to phenotypes.
Figure 3: Systematic analysis of genetic interactions (epistasis): disease specifiers and disease modifiers.
Figure 4: Whole-genome reverse genetics: making and evaluating phenotypic predictions from the genome sequences of individuals.
Figure 5: Epistasis with only a single mutation: inter-individual variation in the expression of genetic interaction partners contributes to incomplete penetrance.
Figure 6: Sources of phenotypic variance in individuals.

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Acknowledgements

Our research is funded by the European Research Council (ERC), MINECO Plan Nacional grants BFU2008-00365 and BFU2011-26206, ERASysBio+ ERANET project EUI2009-04059 GRAPPLE, the European Molecular Biology Organization (EMBO) Young Investigator Program, EU Framework 7 project 277899 4DCellFate and the EMBL/CRG Systems Biology Program.

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  1. EMBL–CRG Systems Biology, Centre for Genomic Regulation (CRG) and UPF, Carrer Dr. Aiguader 88, Barcelona, 08003, Spain

    Ben Lehner

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain

    Ben Lehner

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Glossary

Modules

Groups of genes or proteins in a network that have strong interactions among themselves and that carry out particular functions largely independently of other genes or proteins. Mutations in genes from a module often have similar phenotypic consequences.

Orthologous

A gene in one species is orthologous to a gene in another species if they are derived from a common ancestor.

Disordered regions

Regions of proteins that are intrinsically unfolded; that is, they are without a well-defined tertiary structure under physiological conditions.

Expression quantitative trait loci

(eQTLs). Regions of the genome containing genetic polymorphisms that alter how genes are regulated, influencing how much RNA or protein they produce.

Major- and minor-effect loci

Regions of the genome containing genetic polymorphisms that account for a large or small proportion of variance in a particular phenotype, respectively.

Isogenic

Lacking genetic variation. Some laboratory animals, such as Caenorhabditis elegans and mice, are inbred and so siblings have identical genome sequences except for de novo mutations arising in each generation.

Haploinsufficiency

A gene is haploinsufficient if removal of one of the two copies in a diploid organism has a detectable effect on fitness or a phenotype.

Dominance

The extent to which one allele of a gene exerts its effects irrespective of a second allele in diploid organisms. Complete dominance implies that the heterozgygote has a phenotype that is indistinguishable from that of the dominant homozygote. Overdominance implies that the phenotype of the heterozygote lies outside the range of both homozygote parents.

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Lehner, B. Genotype to phenotype: lessons from model organisms for human genetics. Nat Rev Genet 14, 168–178 (2013). https://doi.org/10.1038/nrg3404

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