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Parameters in Dynamic Models of Complex Traits are Containers of Missing Heritability

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  • Yunpeng Wang
  • Arne B Gjuvsland
  • Jon Olav Vik
  • Nicolas P Smith
  • Peter J Hunter
  • Stig W Omholt

Abstract

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology. Author Summary: Despite an ever-increasing number of genome locations reported to be associated with complex human diseases or quantitative traits, only a small proportion of phenotypic variations in a typical quantitative trait can be explained by the discovered variants. We argue that this problem can partly be resolved by combining the statistical methods of quantitative genetics with computational biology. We demonstrate this for the in silico genotype-to-phenotype map of a model heart cell in conjunction with publically accessible genomic data. We show that genome wide association studies (GWAS) on model parameters identify more causal variants and can build better prediction models for the higher-level phenotypes than by performing GWAS on the higher-level phenotypes themselves. Since model parameters are in principle measurable physiological phenotypes, our findings suggest that development of future phenotyping technologies could be guided by mathematical models of the biological systems being targeted.

Suggested Citation

  • Yunpeng Wang & Arne B Gjuvsland & Jon Olav Vik & Nicolas P Smith & Peter J Hunter & Stig W Omholt, 2012. "Parameters in Dynamic Models of Complex Traits are Containers of Missing Heritability," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-9, April.
    • Handle: RePEc:plo:pcbi00:1002459
    DOI: 10.1371/journal.pcbi.1002459
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    1. Isaac Salazar-Ciudad & Jukka Jernvall, 2010. "A computational model of teeth and the developmental origins of morphological variation," Nature, Nature, vol. 464(7288), pages 583-586, March.
    2. Robert Makowsky & Nicholas M Pajewski & Yann C Klimentidis & Ana I Vazquez & Christine W Duarte & David B Allison & Gustavo de los Campos, 2011. "Beyond Missing Heritability: Prediction of Complex Traits," PLOS Genetics, Public Library of Science, vol. 7(4), pages 1-9, April.
    3. Gertz, Jason & Gerke, Justin P. & Cohen, Barak A., 2010. "Epistasis in a quantitative trait captured by a molecular model of transcription factor interactions," Theoretical Population Biology, Elsevier, vol. 77(1), pages 1-5.
    4. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
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    Cited by:

    1. 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.
    2. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.

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