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, 22, 109-122

A Drive to Driven Model of Mapping Intraspecific Interaction Networks

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A Drive to Driven Model of Mapping Intraspecific Interaction Networks

Libo Jiang et al. iScience.

Abstract

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.

Keywords: Biological Sciences; Evolutionary Ecology; Mathematical Biosciences.

Conflict of interest statement

The authors have no competing interests.

Figures

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Figure 1
Figure 1
Mathematical Descriptors of Four Types of Ecological Interactions, Mutualism (zmu), Antagonism (zan), Aggression (zag), and Altruism (zal) We use wL and wS to denote phenotypic values of a larger animal L and a smaller animal S, respectively, constituting a pair in a mapping population. The product of phenotypic values between two animals is used as a descriptor for the strength of mutualism, i.e., how much the two animals benefit from one another through cooperation (Zhu et al., 2016). The strength of antagonism is described by the inverse of the product of phenotypic values, reflecting how much one animal grew reciprocally at a cost of the other. To adjust the scale effect, these two descriptors are normalized by dividing them by the phenotypic difference of the larger from the smaller animal. The ratio of phenotypic values of the larger over the smaller animal is used to measure the strength of aggression, by which the former grows by harming the latter. The strength of altruism is calculated as one minus the ratio of phenotypic values of the smaller over the larger animal.
Figure 2
Figure 2
Biological Validation of Interaction Measures in a Fish Experiment Scatterplots of mathematical descriptors given in Figure 1 against the strength of ecological interactions across five different pairs of fish (dots) with relative body mass 0.10, 0.38, 0.61, 0.80, and 1.00. (A) Aggression descriptor (zag) versus the strength of aggression. (B) Mutualism descriptor (zmu) versus the strength of mutualism. (C) Altruism descriptor (zal) versus the strength of altruism. The relationship between two variables is roughly fitted by a curve, with correlation coefficient (r) given within each plot.
Figure 3
Figure 3
Biological Validation of Interaction Measures in a Bacterial Experiment Scatterplots of mathematical descriptors given in Figure 1 against the strength of ecological interactions across 45 interspecific pairs of strains from E. coli strains and S. aureus at three distinct phases of microbial growth (lag, linear, and asymptotic). (A) Aggression descriptor (zag) versus the strength of aggression. (B) Mutualism descriptor (zmu) versus the strength of mutualism. The strength of mutualism is measured by the average of the ratio of abundance of each bacterial species in co-culture to monoculture. Thus, this ratio average quantifies the strength of cooperation if it is above 1 and the strength of competition if it is below 1. (C) Altruism descriptor (zal) versus the strength of altruism. Dots represent observations of different interspecific strain pairs at each time point. The relationship between two variables is roughly fitted by a curve, with correlation coefficient (r) given within each plot.
Figure 4
Figure 4
Quantitative Genetic Dissection of Genotype Combination Values For Fish Body Mass Upper panel: Genotypic values of combinations CC × CC, CC × TC, and TC × TC at pdlim3 (testcross QTL) for the strength of mutualism; combinations AA × AA, AA × GA, and GA × GA at thraa (testcross QTL) for the strength of antagonism; combinations GG × GG, GG × CG, CG × GG, and CG × CG at bmp1 (testcross QTL) for the strength of aggression; and combinations CC × CC, CC × CT, CC × TT, CT × CC, CT × CT, CT × TT, TT × CC, TT × CT, and TT × TT at notch2 (intercross QTL) for the strength of altruism. Lower panel: Direct genetic effects that describe how the alleles of a fish in a pair affects its own body mass; indirect genetic effects that specify how each fish gene affects its conspecific's phenotypes; and genome-genome epistatic effects that quantify how the interactions between genes of two fish affect the phenotype of each fish. For the intercross QTL, both the direct and indirect effects include additive (blue) and dominant (green) effects and genome-genome epistatic effects include additive × additive, additive × dominant, dominant × additive, and dominant × dominant effects (in order from left to right). Standard errors for each value are given.
Figure 5
Figure 5
A Bidirectional, Signed, and Weighted Social Network of All Fish Driven by Various Types of QTL Constructed from Ordinary Differential Equations (A) Social network of family H1 constructed from all QTL with edges representing how one fish interacts with others through mutualism (doubly arrowed), antagonism (doubly T-shaped), aggression (singly T-shaped), or altruism (singly arrowed). Hubs of the network are highlighted in red. (B) The network is characterized by the difference in body mass between groups of hubs (red) and non-hubs (blue), the percentages of mutualistic and antagonistic edges among hubs (red), among hubs and non-hubs (purple), and among non-hubs, and the percentages of aggressive and altruistic edges from one fish to the second both from the hub group (red), from one fish from the hub group to the second from the non-hub group (purple), from one fish from the non-hub group to the second from the hub group (gray), and from one fish to the second both from the non-hub group (blue). (C) The numbers of mutualistic, antagonistic, aggressive, or altruistic edges with the social networks constructed from all QTL as well as from all QTL, except for, respectively, mutualism, antagonism, aggression, and altruism QTL. Comparisons of edge numbers are given not only for family H1, but also for the two family replicates G1 and Z22.
Figure 6
Figure 6
Dynamic Bayesian Genetic Network of All Detected QTL The entire network is dissolved into two distinct modules: one composed of mutualism QTL (green circle), aggression QTL (yellow circle), and altruism QTL (purple circle) and the other composed of antagonism QTL (red circle). The first module contains a proportion of QTL (mix-colored circle) that pleiotropically affect mutualistic, aggressive, and altruistic behaviors. In each module, hub QTL are highlighted in dark colors. Of all significant detected SNPs, 41 (each labeled by a number) were identified as uniquely segregating in the mapped population, which was used for QTL network construction. It is possible that different uniquely segregating SNPs may correspond to the same candidate gene if they are physically close enough on the common carp genome. Candidate genes adjacent to significant SNPs are listed below. Aggression bmp1, mutualism hmcn1, and antagonism rarab are annotated, respectively, by SNPs #37 and #38 and SNPs #35 and #24. The arrow denotes the direction by which one gene regulates the other.

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References

    1. Barraclough T.G. How do species interactions affect evolutionary dynamics across whole communities? Ann. Rev. Ecol. Evol. Syst. 2015;46:25–48.
    1. Beckers A., Lodish M.B., Trivellin G., Rostomyan L., Lee M., Faucz F.R., Yuan B., Choong C.S., Caberg J.H., Verrua E. X-linked acrogigantism syndrome: clinical profile and therapeutic responses. Endocr. Relat. Cancer. 2015;22:353–367. - PMC - PubMed
    1. Biscarini F., Bovenhuis H., van Arendonk J.A., Parmentier H.K., Jungerius A.P., van der Poel J.J. Across-line SNP association study of innate and adaptive immune response in laying hens. Anim. Genet. 2010;41:26–38. - PubMed
    1. Bohn T., Amundsen P.-A. Ecological interactions and evolution: forgotten parts of biodiversity? BioScience. 2004;54:804–805.
    1. Camazine S., Deneubourg J.-L., Franks N., Sneyd J., Theraulaz G., Bonabeau E. Princeton University Press; 2001. Self-Organization in Biological Systems.

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