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Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework

Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways wi...

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Autores principales: Zhang, Fan, Zhai, Hu-Qu, Paterson, Andrew H., Xu, Jian-Long, Gao, Yong-Ming, Zheng, Tian-Qing, Wu, Rong-Ling, Fu, Bin-Ying, Ali, Jauhar, Li, Zhi-Kang
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024316/
https://www.ncbi.nlm.nih.gov/pubmed/21283795
http://dx.doi.org/10.1371/journal.pone.0014541
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author Zhang, Fan
Zhai, Hu-Qu
Paterson, Andrew H.
Xu, Jian-Long
Gao, Yong-Ming
Zheng, Tian-Qing
Wu, Rong-Ling
Fu, Bin-Ying
Ali, Jauhar
Li, Zhi-Kang
author_facet Zhang, Fan
Zhai, Hu-Qu
Paterson, Andrew H.
Xu, Jian-Long
Gao, Yong-Ming
Zheng, Tian-Qing
Wu, Rong-Ling
Fu, Bin-Ying
Ali, Jauhar
Li, Zhi-Kang
author_sort Zhang, Fan
collection PubMed
description Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many ‘downstream’ loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and “co-adapted” gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.
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spelling pubmed-30243162011-01-31 Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework Zhang, Fan Zhai, Hu-Qu Paterson, Andrew H. Xu, Jian-Long Gao, Yong-Ming Zheng, Tian-Qing Wu, Rong-Ling Fu, Bin-Ying Ali, Jauhar Li, Zhi-Kang PLoS One Research Article Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many ‘downstream’ loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and “co-adapted” gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis. Public Library of Science 2011-01-20 /pmc/articles/PMC3024316/ /pubmed/21283795 http://dx.doi.org/10.1371/journal.pone.0014541 Text en Zhang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhang, Fan
Zhai, Hu-Qu
Paterson, Andrew H.
Xu, Jian-Long
Gao, Yong-Ming
Zheng, Tian-Qing
Wu, Rong-Ling
Fu, Bin-Ying
Ali, Jauhar
Li, Zhi-Kang
Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title_full Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title_fullStr Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title_full_unstemmed Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title_short Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
title_sort dissecting genetic networks underlying complex phenotypes: the theoretical framework
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024316/
https://www.ncbi.nlm.nih.gov/pubmed/21283795
http://dx.doi.org/10.1371/journal.pone.0014541
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