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Effect of Regulatory Architecture on Broad versus Narrow Sense Heritability

Overview of attention for article published in PLoS Computational Biology, May 2013
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Title
Effect of Regulatory Architecture on Broad versus Narrow Sense Heritability
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003053
Pubmed ID
Authors

Yunpeng Wang, Jon Olav Vik, Stig W. Omholt, Arne B. Gjuvsland

Abstract

Additive genetic variance (VA ) and total genetic variance (VG ) are core concepts in biomedical, evolutionary and production-biology genetics. What determines the large variation in reported VA /VG ratios from line-cross experiments is not well understood. Here we report how the VA /VG ratio, and thus the ratio between narrow and broad sense heritability (h(2) /H(2) ), varies as a function of the regulatory architecture underlying genotype-to-phenotype (GP) maps. We studied five dynamic models (of the cAMP pathway, the glycolysis, the circadian rhythms, the cell cycle, and heart cell dynamics). We assumed genetic variation to be reflected in model parameters and extracted phenotypes summarizing the system dynamics. Even when imposing purely linear genotype to parameter maps and no environmental variation, we observed quite low VA /VG ratios. In particular, systems with positive feedback and cyclic dynamics gave more non-monotone genotype-phenotype maps and much lower VA /VG ratios than those without. The results show that some regulatory architectures consistently maintain a transparent genotype-to-phenotype relationship, whereas other architectures generate more subtle patterns. Our approach can be used to elucidate these relationships across a whole range of biological systems in a systematic fashion.

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Geographical breakdown

Country Count As %
United States 4 8%
Norway 3 6%
Portugal 1 2%
Austria 1 2%
Unknown 44 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 13 25%
Student > Master 5 9%
Student > Bachelor 4 8%
Other 4 8%
Other 6 11%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 45%
Biochemistry, Genetics and Molecular Biology 5 9%
Medicine and Dentistry 3 6%
Mathematics 2 4%
Engineering 2 4%
Other 6 11%
Unknown 11 21%