TY - JOUR
T1 - A Drive to Driven Model of Mapping Intraspecific Interaction Networks
AU - Jiang, Libo
AU - Xu, Jian
AU - Sang, Mengmeng
AU - Zhang, Yan
AU - Ye, Meixia
AU - Zhang, Hanyuan
AU - Wu, Biyin
AU - Zhu, Youxiu
AU - Xu, Peng
AU - Tai, Ruyu
AU - Zhao, Zixia
AU - Jiang, Yanliang
AU - Dong, Chuanju
AU - Sun, Lidan
AU - Griffin, Christopher H.
AU - Gragnoli, Claudia
AU - Wu, Rongling
N1 - Funding Information:
We thank Jennifer Wilson for her editorial modification to this manuscript. This work is supported by Fundamental Research Funds for the Central Universities 2015ZCQ-SW-06 and BLX2015-23 (R.W. and M.Y.), grant 31700576 from National Natural Science Foundation of China (L.J.), grant 31600536 from National Natural Science Foundation of China (M.Y.), grant NICHD 5R01HD086911-02 from the National Institute of Health (C.G.), grant CMMI-1463482 from National Science Foundation (C.H.G.), The National Key Research and Development Program (2018YFD0900102), The National Science Foundation of China (31502151), Central Public-interest Scientific Institute Basal Research Fund, CAFS (NO. 2016GH02 and NO.2015C007), and The State Administration of Forestry of China (201404102). L.J. derived the model and conducted data analysis and computer simulation. J.X. performed the experiment, collected the data, conducted gene annotation analysis, and contributed to the writing of the Results and Material part. M.S. M.Y. and L.S. participated in model derivations, data analysis, and result interpretation. Y.Z. H.Z. B.W. Y.Z. P.X. R.T. Z.Z. Y.J. and C.D. participated in fish data collection. C.H.G. interpreted game theory and formulated its mathematical analysis. C.G. critically read and revised the manuscript. R.W. conceived the idea, supervised the overall study, and wrote the paper. The authors have no competing interests.
Funding Information:
We thank Jennifer Wilson for her editorial modification to this manuscript. This work is supported by Fundamental Research Funds for the Central Universities 2015ZCQ-SW-06 and BLX2015-23 (R.W. and M.Y.), grant 31700576 from National Natural Science Foundation of China (L.J.), grant 31600536 from National Natural Science Foundation of China (M.Y.), grant NICHD 5R01HD086911-02 from the National Institute of Health (C.G.), grant CMMI-1463482 from National Science Foundation (C.H.G.), The National Key Research and Development Program ( 2018YFD0900102 ), The National Science Foundation of China ( 31502151 ), Central Public-interest Scientific Institute Basal Research Fund , CAFS (NO. 2016GH02 and NO. 2015C007 ), and The State Administration of Forestry of China ( 201404102 ).
Publisher Copyright:
© 2019 The Author(s)
PY - 2019/12/20
Y1 - 2019/12/20
N2 - Community ecology theory suggests that an individual's phenotype is determined by the phenotypes of its coexisting members to the extent at which this process can shape community evolution. Here, we develop a mapping theory to identify interaction quantitative trait loci (QTL) governing inter-individual dependence. We mathematically formulate the decision-making strategy of interacting individuals. We integrate these mathematical descriptors into a statistical procedure, enabling the joint characterization of how QTL drive the strengths of ecological interactions and how the genetic architecture of QTL is driven by ecological networks. In three fish full-sib mapping experiments, we identify a set of genome-wide QTL that control a range of societal behaviors, including mutualism, altruism, aggression, and antagonism, and find that these intraspecific interactions increase the genetic variation of body mass by about 50%. We showcase how the interaction QTL can be used as editors to reconstruct and engineer new social networks for ecological communities.
AB - Community ecology theory suggests that an individual's phenotype is determined by the phenotypes of its coexisting members to the extent at which this process can shape community evolution. Here, we develop a mapping theory to identify interaction quantitative trait loci (QTL) governing inter-individual dependence. We mathematically formulate the decision-making strategy of interacting individuals. We integrate these mathematical descriptors into a statistical procedure, enabling the joint characterization of how QTL drive the strengths of ecological interactions and how the genetic architecture of QTL is driven by ecological networks. In three fish full-sib mapping experiments, we identify a set of genome-wide QTL that control a range of societal behaviors, including mutualism, altruism, aggression, and antagonism, and find that these intraspecific interactions increase the genetic variation of body mass by about 50%. We showcase how the interaction QTL can be used as editors to reconstruct and engineer new social networks for ecological communities.
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U2 - 10.1016/j.isci.2019.11.002
DO - 10.1016/j.isci.2019.11.002
M3 - Article
C2 - 31765992
AN - SCOPUS:85075202223
SN - 2589-0042
VL - 22
SP - 109
EP - 122
JO - iScience
JF - iScience
ER -