Many animals, including humans, have evolved to live and move in groups. In humans, disrupted social interactions are a fundamental feature of many psychiatric disorders. However, we know little about how genes regulate social behavior. Zebrafish may serve as a powerful model to explore this question. By comparing the behavior of wild-type fish with 90 mutant lines, we show that mutations of genes associated with human psychiatric disorders can alter the collective behavior of adult zebrafish. We identify three categories of behavioral variation across mutants: “scattered,” in which fish show reduced cohesion; “coordinated,” in which fish swim more in aligned schools; and “huddled,” in which fish form dense but disordered groups. Changes in individual interaction rules can explain these differences. This work demonstrates how emergent patterns in animal groups can be altered by genetic changes in individuals and establishes a framework for understanding the fundamentals of social information processing.

Across many species, survival depends on coordination of individuals in a group, such as in a school of fish, flock of birds, or colony of ants. Group dynamics serve, for example, to enhance foraging abilities and confuse predators (). Social interactions are also essential to humans, as shown by the profound dysfunction caused by social processing deficits in psychiatric disorders such as autism and schizophrenia (). Work across several species has shown that motion patterns formed by individuals in animal groups can emerge as a consequence of relatively local interactions (). Although previous work has provided evidence that schooling behavior in stickleback fish is under genetic control (), it remains unclear what attributes of collective behavior might be genetically regulated, and by which specific genes. The genetic, neural, and behavioral techniques developed for zebrafish make them a valuable model for beginning to address these questions (). Zebrafish are highly social animals and exhibit a range of complex behavior (). Several recent studies have investigated a single mutant line in comparison with wild-type (WT) fish, providing evidence that individual genes can alter the interactions and collective behavior of zebrafish ().

For WT fish, the effective “force” of attraction to neighbors increases with distance, whereas alignment decreases with distance ( Figure 4 C). These trends agree with previous work on fish (). Compared with WT, scn1lab+/− fish exhibit a weaker distance-dependence for attraction and a more rapid decrease in alignment as a function of distance ( Figure 4 C). This suggests that scn1lab+/− fish interact predominantly with close neighbors and tend to ignore those at a distance, leading to the group instability and scattered behavior we observe in these lines. Model fits to ctnnd2b−/− mutants show a similar trend ( Figure S5 B). Notably, the model predicts turning decisions of scn1lab+/− fish with lower accuracy than WT fish, indicating that the motor decisions of scn1lab+/− fish are less consistently affected by neighboring fish ( Figure 4 B). In contrast, chrna2a−/− mutants move more predictably than do WT and exhibit a relatively strong attraction to distant neighbors ( Figure 4 C), which accounts for the cohesive nature of groups formed by these lines. The interaction functions for disc1−/− are similar to WT overall but yield a higher ratio of attraction to alignment ( Figure 4 C), and fits to immp2l−/− mutants show a similar trend ( Figure S5 B). This change in social responsiveness results in individuals that stay close to one another but do not move together as a coherent group. Overall, our results suggest that differences in how individual fish respond to their neighbors can lead to the different observed group-level patterns.

(C) Fits to WT and mutant lines scn1lab+/-, disc1−/−, and chrna2a−/−. The attraction/alignment ratio α is shown along with the distance dependence of attraction G a t t ( r j ) and the distance dependence of alignment G a l i ( r j ) . The bold line shows the fit to all trials together, and the thin lines show fits to individual trials. For fits to the mutant lines, the overall fit to WT is shown in solid black for comparison. Compared with WT, scn1lab+/− fish exhibit a weaker distance-dependence for attraction and a more rapid decrease in alignment as a function of distance. In contrast, chrna2a−/− mutants exhibit a relatively strong attraction to distant neighbors. The interaction functions for disc1−/− are similar to WT overall but yield a higher ratio of attraction to alignment.

(B) Predictive ability of the model, showing boxplots for the distribution of fraction of turns predicted correctly for different trials with each line.

(A) Schematic of one focal fish (black) at a given point in time, with five neighbors (red) at different locations and around it. Arrows indicate heading. For each fish in the group, the model takes relative neighbor positions and headings as input to predict whether the individual will turn left or right after a specified time delay of 1 s for WT (delay time adjusted for different speeds of the mutant lines—see Transparent Methods ). The fit yields the interaction functions for effective attraction and alignment forces that best predict movement decisions across all fish in a trial.

Evidence suggests that collective motion dynamics can be explained by relatively simple rules of interaction between individuals, such as how an individual fish alters its trajectory depending on the locations of its neighbors (). We use a model to ask if salient observed differences in collective motion (i.e., the scattered, huddled, and coherent collective motion patterns) can be explained by differences in how individuals turn in response to neighboring fish. The model uses neighbor positions and velocities to determine the effective attraction and alignment forces that best predict whether a fish will turn left or right after a specified time delay (see Transparent Methods for details). We examined differences in the distance-dependence of attraction (), the distance-dependence of alignment (), and the relative overall strength of attraction to alignment (α) for WT fish and for mutant lines that exemplified the three distinctive group patterns: scattered (scn1lab+/-), coordinated (chrna2a−/−), and huddled (disc1−/−).

We use principal component analysis (PCA) on the median values of the behavioral metrics for each line after controlling for speed ( Figure 2 B) to describe and quantify the different patterns of group behavior ( Figure 3 ). We find that the first two components reveal much of the relative behavioral differences between lines, showing differences between the scattered, coordinated, or huddled phenotypes. Note that, although Figure 3 highlights five mutant lines that exhibit clear differences along the PCA dimensions, other lines also exhibit differences from WT ( Figures 3 C and S3 ). Accounting for trial variability and limited sampling using a bootstrap procedure shows that some lines are separated from WT in one or both of the first two PCA components, whereas other lines overlap with WT ( Figure S3 ). Although the first two components reveal the largest fraction of variation, some lines show distinct differences in the third PCA component. For example, although both display the huddled phenotype, unlike disc1−/− fish, immp2l−/− fish spend time without moving, or are “frozen.” Because of this these two lines have opposite signs for PC3 ( Figure 3 B) and are separated in the “freezing component” by using a modified PCA procedure ( Figure S4 ; see Transparent Methods ).

(B) Center: projection of the data for all genetic lines onto the first two components; projection onto the third component shown separately on the right-hand side. Edges: example trajectories for WT and five highlighted lines (1,000 frames,17 s). The “scattered” phenotype is described by positive values of the first PCA component (PC1) and negative values of the second component (PC2). The “coordinated” phenotype is described by positive values of PC2. The “huddled” phenotype is described by negative values of PC1 and PC2. Table S2 lists each line in order of position along each PC.

(A) Left: After accounting for speed, 90.8% of the remaining variation across lines is described by three orthogonal components. The first component reflects a change in group spacing. The second and third components reflect combinations of polarization and fraction of time moving: positive values of PC2 correspond to high polarization and increased time moving, whereas high values of PC3 correspond to high polarization and frequent freezing. Right: Values of the input metrics for five highlighted lines, relative to WT.

We find that three general patterns, scattered, coordinated, and huddled, describe the most distinct differences in movement of the mutant fish. The scattered phenotype has high spacing and low polarization among individuals in the group, exemplified by the mutant lines scn1lab+/− (encoding the Nav1.1 protein []) and ctnnd2b−/− (encoding δ-catenin []). These mutants have high inter-individual spacing, and although they occasionally form groups, they tend to dissociate from each other more frequently and show less collective coordination. The coordinated phenotype describes individuals that exhibit an increased tendency to align their direction of travel and to move coherently as a group, exemplified by chrna2a−/− (encoding the α2-nicotinic acetylcholine receptor []). The huddled phenotype is characterized by groups of individuals exhibiting low polarization and tight spacing. These groups are generally dense but disordered, and fish spend more time swimming in a relatively local area, as exemplified by disc1−/− (encoding disrupted-in-schizophrenia []) and immp2l−/− (encoding the inner mitochondrial membrane peptidase2-like protein []).

Previous work in several fish species has shown that individual speed directly affects motion dynamics, with higher speed being associated with both wider spacing and increased alignment between the individuals (). Consistent with this work, most mutant lines with higher swimming speed have larger inter-individual spacing and group polarization ( Figures 1 D–1F, 2 A, and S2 ). Linear and exponential regression reveals that speed can explain approximately 30% of the total variance in inter-individual spacing, polarization, speed inter-quartile range (IQR), time moving, nearest neighbor distance, and group centroid speed ( Figure S2 ; see Transparent Methods ). However, after subtracting the effects of speed and accounting for trial variability, multiple lines continue to show differences from WT ( Figure 2 B).

Differences between mutant lines and WT in the median values of seven behavioral metrics before (A) and after (B) correcting for speed differences. Lines are listed in order of increasing speed. Colors of five highlighted lines have the same conventions as in Figure 1 . Bold outlines indicate statistically significant differences from WT, determined by considering the variability of each quantity across trials for a given line (Dunnett's test, p < 0.05). Units refer to the standard deviation of median values across lines.

Zebrafish tend to swim in groups, sometimes aligning and moving together with others ( Figure 1 B) and at other times swimming closely and in a disordered configuration ( Figure 1 C). We found that mutant fish vary in their swimming speed, group spacing, and polarization. Although there was considerable variation between trials performed with a given line, many lines showed consistent trends ( Figures 1 D–1F).

We used CRISPR-Cas9 to generate mutations in 90 genes associated with psychiatric disorders ( Data S1 ) and performed experiments to ask how these mutations affect the collective behavior of freely swimming zebrafish. We evaluated mutant fish as adults, after the development of the full range of social interactions has matured (), comparing their collective behavior as they swam in an open circular arena ( Figure 1 A, Video S1 ). All mutants were tested as homozygous adults, except for scn1lab and slc18a2, which could not be raised to adulthood as homozygous and thus were tested as heterozygous fish. We performed multiple trials, with each trial featuring different fish, for each line ( Table S1 ).

(D–F) Box plots of median speed (D), group spacing (E), and polarization (F) for all genetic lines. Each point shows data from one trial. Lines are ordered from lowest to highest speed. Colored points highlight examples of lines that differ from WT in certain aspects of behavior. Figure S1 contains analogous plots for other behavioral metrics.

(B and C) Example trajectories from WT fish, showing groups in aligned (B) and disordered (C) configurations. Each trace shows 1 s of swimming.

(A) In each trial, six adult fish were filmed from above as they swam in a circular arena.

Discussion

Couzin et al., 2002 Couzin I.D.

Krause J.

James R.

Ruxton G.M.

Franks N.R. Collective memory and spatial sorting in animal groups. Tunstrøm et al., 2013 Tunstrøm K.

Katz Y.

Ioannou C.C.

Huepe C.

Lutz M.J.

Couzin I.D. Collective states, multistability and transitional behavior in schooling fish. Jolles et al., 2017 Jolles J.W.

Boogert N.J.

Sridhar V.H.

Couzin I.D.

Manica A. Consistent individual differences drive collective behavior and group functioning of schooling fish. This work uses quantitative behavioral metrics to show how genetics may direct patterns of collective behavior. The patterns that arise in groups—their structure, cohesion, leadership, and dynamics—contribute to species fitness and adaptation to environmental changes and hence to their evolution. This study establishes a fundamental framework for understanding the relationship between genes, social interaction, and sensorimotor transformations. Prior work has demonstrated that swimming speed alone can drive changes in the shoal structure and dynamics (), and we find here that mutations that alter speed do, in general, change the behavior of the group in predictable ways. However, we also discovered mutations with effects on the group pattern and dynamics dissociated from the effects of speed. These fall into three patterns, coordinated, scattered, and huddled, which each describe the behavior of several mutant zebrafish lines and can arise from differences in interaction rules among individuals.

Hoffman et al., 2016 Hoffman E.J.

Turner K.J.

Fernandez J.M.

Cifuentes D.

Ghosh M.

Ijaz S.

Jain R.A.

Kubo F.

Bill B.R.

Baier H.

et al. Estrogens suppress a behavioral phenotype in zebrafish mutants of the autism risk gene, CNTNAP2. El-Brolosy et al., 2019 El-Brolosy M.A.

Kontarakis Z.

Rossi A.

Kuenne C.

Günther S.

Fukuda N.

Kikhi K.

Boezio G.L.M.

Takacs C.M.

Lai S.L.

et al. Genetic compensation triggered by mutant mRNA degradation. In our results we highlighted five lines with distinctive behavioral differences from WT: scn1lab+/−, ctnnd2b−/−, chrna2a−/−, immp2l−/−, and disc1−/−. However, we note that, in addition to these, other mutant lines also exhibited distinct differences from WT ( Figures 2 3 , and S3 ). We also note the possibility that some genes did not manifest a behavioral phenotype because of the activity of paralogs () or the transcriptional upregulation of paralogs or other genes triggered by the CRISPR-Cas9 ().

Schoonheim et al., 2010 Schoonheim P.J.

Arrenberg A.B.

Bene F.D.

Baier H. Optogenetic localization and genetic perturbation of saccade-generating neurons in zebrafish. Baraban et al., 2013 Baraban S.C.

Dinday M.T.

Hortopan G.A. Drug screening in scn1a zebrafish mutant identifies clemizole as a potential dravet syndrome treatment. Chen et al., 2018 Chen X.

Mu Y.

Hu Y.

Kuan A.T.

Nikitchenko M.

Randlett O.

Chen A.B.

Gavornik J.P.

Sompolinsky H.

Engert F.

Ahrens M.B. Brain-wide organization of neuronal activity and convergent sensorimotor transformations in larval zebrafish. Huang et al., 2019 Huang K.H.

Rupprecht P.

Schebesta M.

Serluca F.

Kitamura K.

Bouwmeester T.

Friedrich R.W. Predictive neural processing in adult zebrafish depends on shank3b. Stowers et al., 2017 Stowers J.R.

Hofbauer M.

Bastien R.

Griessner J.

Higgins P.

Farooqui S.

Fischer R.M.

Nowikovsky K.

Haubensak W.

Couzin I.D.

et al. Virtual reality for freely moving animals. The effect of the mutations upon the individual fish may be upon sensory, motor, or integrative processes, and we cannot suggest that the affected genes are specifically “social” in their roles, but rather that such effects upon individuals are manifest as changes in group behavior. For example, it is known that larvae of homozygous deficient scn1lab mutant fish (as opposed to the heterozygous fish studied here) partially lose the ability to maintain eye positions following saccades associated with the optokinetic reflex () and have increased levels of swim activity as individual larvae (). In future work it will be important to define the nature of the sensorimotor transformations and corresponding neural activity that underlie collective and social behavior. With transparent larval zebrafish, whole-brain imaging can examine neural activity from stimulus onset to the subsequent motor output (). New tools, including the use of fixed fish combined with virtual reality, may permit similar evaluations related to collective behavior in adult fish ().