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Reverse engineering the genotype–phenotype map with natural genetic variation

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  • Matthew V. Rockman

    (Center for Genomics and Systems Biology, New York University)

Abstract

The genetic variation that occurs naturally in a population is a powerful resource for studying how genotype affects phenotype. Each allele is a perturbation of the biological system, and genetic crosses, through the processes of recombination and segregation, randomize the distribution of these alleles among the progeny of a cross. The randomized genetic perturbations affect traits directly and indirectly, and the similarities and differences between traits in their responses to common perturbations allow inferences about whether variation in a trait is a cause of a phenotype (such as disease) or whether the trait variation is, instead, an effect of that phenotype. It is then possible to use this information about causes and effects to build models of probabilistic 'causal networks'. These networks are beginning to define the outlines of the 'genotype–phenotype map'.

Suggested Citation

  • Matthew V. Rockman, 2008. "Reverse engineering the genotype–phenotype map with natural genetic variation," Nature, Nature, vol. 456(7223), pages 738-744, December.
  • Handle: RePEc:nat:nature:v:456:y:2008:i:7223:d:10.1038_nature07633
    DOI: 10.1038/nature07633
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    Cited by:

    1. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    2. Yunpeng Wang & Jon Olav Vik & Stig W Omholt & Arne B Gjuvsland, 2013. "Effect of Regulatory Architecture on Broad versus Narrow Sense Heritability," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-12, May.
    3. Lingfei Wang & Tom Michoel, 2017. "Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-26, August.
    4. Marika Plöthner & Martin Frank & J.-Matthias Graf Schulenburg, 2017. "Cost analysis of whole genome sequencing in German clinical practice," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(5), pages 623-633, June.

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