Open Access


  • Robert E Drew1, 2,
  • Matthew L Settles3,
  • Erin J Churchill1,
  • Shayna M Williams1,
  • Soniya Balli2 and
  • Barrie D Robison1Email author
Contributed equally
BMC Genomics201213:323

DOI: 10.1186/1471-2164-13-323

Received: 9 February 2012

Accepted: 28 June 2012

Published: 20 July 2012









Zebrafish Transcriptome Microarray Behavior Anxiety Domestication


The process of animal domestication is a striking example of convergent behavioral evolution. During domestication, populations diverge from their wild progenitors as a result of genetic drift, artificial selection on desirable traits, relaxed selection on previously important characteristics, and adaptation to new environmental and ecological conditions [13]. Evolution in captivity is associated with a set of phenotypic changes, including enhanced growth rate [46], attenuated responsiveness to stress [7, 8], increased feeding behavior [9], and reduced predator avoidance behavior [6, 10, 11]. Reductions in predator avoidance behavior are the most consistent responses to domestication and have been observed across a broad range of taxa, including mammals, birds, and fish [1214]. However, it is not known whether the apparent convergent evolution of predator avoidance behavior during domestication is the result of similar genetic changes or of changes in a myriad of alternative molecular “routes” to the same phenotypic endpoint. In fact, little is known about the genetic polymorphisms associated with domestication in any vertebrate species.

Differences in gene regulation are an important source of genetic variation for phenotypic evolution [1517]. Transcriptome analyses have detected substantial variation among populations in numerous species, including yeast [18], Drosophila[19], fish [17, 2024], and humans [25, 26]. Much of this variation has a heritable component [25, 2730]. While the majority of variation in gene expression appears to evolve in a neutral fashion, some genes show patterns consistent with evolution under positive selection and may contribute to local adaptation [17, 29, 3133], including behavioral adaptations [20, 34]. In particular, microarray studies suggest that domestication may also be associated with changes in gene expression [22, 3537].

In the present study, we use the zebrafish (Danio rerio) as a model for examining the relationship between behavioral domestication and variation in gene expression. The utility of the zebrafish in developmental biology and genomics is widely known, and there has been increased usage of the zebrafish as a model for behavior genetics [3841]. Variation in behavior has been observed among populations of zebrafish, including variation among domesticated (laboratory) populations [38], among populations recently acquired from the wild [42], and between wild and domesticated populations [9, 4345]. In each of these studies, the populations were reared under identical conditions suggesting that the differences in behavior have a genetic component [9, 43, 44].

In our study, we used microarrays to perform a transcriptome-wide analysis of variation in brain gene expression among four behaviorally distinct strains of zebrafish with different histories of domestication. First, we quantified behavioral differences between wild and domesticated zebrafish strains using a high throughput behavior assay [46]. Second, using microarrays, we detected substantial variation in expression profiles among these behaviorally distinct populations, a portion of which was associated with domestication history. Finally we compared our results against other microarray studies [22, 3537, 47] and identified sixteen homologous genes that were associated with behavioral domestication and variation in fear-related behavior across multiple species.

Results and discussion

Behavioral variation among zebrafish strains


Although simple in design, the behaviors quantified by this assay can be used as indices of fear-related behavior in fish species, and are likely indicators of trade-offs between foraging and predator avoidance. In the wild, proximity to the surface is believed to increase the risk of predation by aerial predators [13, 48, 49] and observations of zebrafish and other fish species are consistent with this hypothesis [50, 51]. However, in captivity, fish are commonly offered food that floats on the surface, providing a contradictory stimulus for approaching the surface. Indeed, mean distance from the surface is negatively correlated with feeding behavior in zebrafish (M. Oswald and B.D.R., Unpublished data), masu salmon (O. masou) [52], and growth hormone transgenic coho salmon (O. kisutch) [53]. Also distance from the surface is positively correlated with freezing in response to a simulated predator by masu salmon [52]. Recent work has also shown that zebrafish spend less time in the upper half of aquaria in response to stressful stimuli and treatment with anxiogenic chemicals (alarm pheromone and caffeine), and more time following treatment with anxiolytic compounds (ethanol and fluoxetine) [54]. Therefore we conclude that these behaviors are valid indicators of fearfulness in various fish species.

Figure 1


The differences in behavior between wild and domesticated zebrafish populations were consistent with previous observations of these and other strains of zebrafish [9, 4345]. In particular, these four strains vary in their latency to feed from the surface of the water such that wild fish take much longer to commence feeding than domesticated fish [9]. Likewise, similar differences have been observed between wild and domesticated populations of other fish species (primarily salmonids) [13, 48, 5559]. In general, domesticated fish tend to be bolder, feed more frequently, and have reduced ability to avoid predation [13], characteristics that have been linked with decreased survival in a wild environment [60].

Transcriptome variation among zebrafish strains


A total of 7,958 probe sets were detected as expressed in our zebrafish brain samples, based on our filtering criteria for expression (see Methods). Of these, 3,199 probe sets displayed probe level variation indicative of sequence polymorphisms among individuals and populations, known as single feature polymorphisms (SFPs) [6163]. The presence of SFPs can confound comparisons of gene expression among different genotypes and therefore these features were removed from the analysis.

The remaining 4,759 probe sets were analyzed using linear models for microarrays (limma) [64, 65] to test for differences in transcriptome profiles among strains, between wild and domesticated strains, and between males and females. Our initial analysis revealed considerable variation in gene expression among the strains with 1,752 probe sets differentially expressed among strains, 259 with a 2-fold or greater difference in pairwise comparisons of mean expression levels [see Additional file 1. This level of brain transcriptome variation among populations is substantially higher that than observed between two populations of rainbow trout (201 genes, [37]) and among eight behaviorally diverse inbred mouse lines (188 genes, [47]); however these differences may reflect differences in statistical analyses or gene content of the array platforms.



Transcriptome variation between wild and domesticated strains


G protein-coupled receptor 177 (gpr177) showed the largest difference with a 5.59-fold down-regulation in domesticated zebrafish. gpr177, also known as wntless or evenness interrupted (evi), is necessary for the release of the WNT signaling peptide from WNT-secreting cells [69, 70]. The WNT signaling pathway is a major control pathway for many aspects of embryonic development, including patterning of the central nervous system, and is also involved in axon guidance and synapse formation in adult organisms [71, 72]. In addition, defects in WNT signaling are associated with neurological disorders in humans, including schizophrenia and Alzheimer’s disease [73]. Selenoprotein P (sepp1a) was also among the most differentially expressed genes with a 1.85-fold up-regulation in domesticated zebrafish, similar to previous observations of these strains [43]. The role of SEPP in the brain is not completely understood but the protein plays a vital role in selenium homeostasis [74]. Mouse SEPP knockout mutants have reduced ability to store selenium in the brain and other tissues, and show severe neurological impairment [7577]. In humans, selenium levels are associated with disease states including epilepsy, Parkinson’s disease, and Alzheimer’s disease [74]. Although it is unknown if SEPP plays a role in these disorders, sequence variation in sepp affects the ability to utilize selenium in humans [78].

DAVID identified seven functional categories that were overrepresented among all genes differentially expressed between wild and domesticated zebrafish. After accounting for overlap, the functional groups can be reduced to two categories: vesicle proteins and acyl-CoA binding domain containing proteins. Each of these groups was represented by at most two or three individual genes. The vesicle proteins included synaptophysin b and synaptoporin which are associated with synaptic vesicles and synaptic plasticity [79, 80], and SEC31 homolog, a subunit of the coat protein complex II which enables the formation of transport vesicles from the endoplasmic reticulum. Knockout mice lacking synaptophysin are viable but show increased exploratory behavior and reduced learning ability and memory [81], indicating that this gene can be directly involved in behavioral variation. Stress is also known to influence both mRNA and protein levels of synaptophysin in rats but the relationship is complex and influenced by factors including sex and the severity of the stressor [8284]. Reduced sensitivity to stress has been observed in domesticated fish [8, 85] and may be a factor in our study. The acyl-CoA binding domain containing proteins included acbd3 and acbd4. ACBD3 maintains the structure of the Golgi apparatus, influences protein transport between the Golgi apparatus and the endoplasmic reticulum, and is an important component of several cellular signaling pathways (reviewed by [86]). The function of ACBD4 has not been determined.

Transcriptome variation between zebrafish sexes

Although not a primary focus of this experiment, we also tested for sex differences in gene expression. We detected only eight sexually dimorphic genes in the brain: insulin-like growth factor 1 (igf1); deiodinase, iodothyronine, type II (dio2); inhibin, beta Aa (inhbaa); endothelial PAS domain protein 1 (epas1); and four unannotated genes (Dr.14275.1.A1_at, Dr.16113.1.S1_at, Dr.16580.1.A1_at, Dr.18139.1.S1_at, Dr.18392.1.A1_at, and Dr.6751.1.S1_at). Of these only dio2 was previously documented as sexually dimorphic in zebrafish brains [43, 87]. Interestingly, although 40 of the 42 sexually dimorphic genes identified by Santos et al.[87]were represented on the GeneChip® Zebrafish Genome microarray, only eleven were detected as expressed in our study. This lack of concordance with our study is surprising, but could result from differences in experimental procedures, microarray platform and chemistry, and statistical analysis. A higher number of sexually dimorphic genes (81 genes) were also detected in brain transcriptomes of immature Atlantic salmon (Salmo salar) [34]. Despite the differences between these studies and ours, all three studies showed substantially fewer sexually dimorphic genes in the brain relative to other tissues, such as liver (1249 genes [88]) and gonads (2940 genes [89]). This result is comparable to observations in mammals [90].

Validation using quantitative real-time PCR

We performed a technical validation using quantitative real-time PCR (qRT-PCR) to verify expression of six differentially expressed genes identified by the microarray analysis. The genes selected for qRT-PCR validation differed significantly among strains (gad2 gfap pomca), between wild and domesticated strains (sepp1a synpr), or between the sexes (dio2) in the microarray analysis. The qRT-PCR results were consistent with the microarray analysis for four of the six genes (Figure 2). qRT-PCR measures of gad2 expression did not agree with the microarray results, suggesting that there may be additional factors affecting hybridization to the microarray. Expression of sepp1a also differed between microarray and qRT-PCR in that the Gaighatta strain had higher levels than expected; however, we previously found agreement between our microarray findings (current study) and qRT-PCR measures of sepp1a in an independent sample of these strains [43]. This unexpected disagreement may reflect strain-specific regulation of sepp1a expression by dietary selenium levels [43].
Figure 2


Comparison with other studies

Examination of these findings in a comparative context can allow us to prioritize genes for future analyses. We therefore compared our results to other analyses in zebrafish and other species. Several microarray studies have examined transcriptome variation associated with domestication or fear-related behavioral variation in other vertebrates. These studies include a comparison between wild and domesticated rainbow trout [37], an analysis of the effect of selection for tame behavior in gray fox [35], a comparison between dogs and wolves [36], and an analysis of behavioral variation among inbred mouse lines [47]. Cross-species comparisons of microarray studies are complicated by differences in microarray platforms and chemistry, hybridization efficiencies, experimental design, and statistical analyses. The studies also differed at the scale at which gene expression was measured in the brain. The canid and mouse studies measured expression separately in multiple regions of the brain [35, 36, 47] while our study and the rainbow trout study [37] examined gene expression in the entire brain. Regional analyses of gene expression may be able to detect local changes in gene expression that are not detectable in whole brain analyses [91] but brain regions must be selected with care to ensure that relevant regions are included in the analysis.

Table 1

Related genes with brain expression patterns associated with domestication in multiple species


Zebrafish Strain

Zebrafish Domestication

Rainbow Trout Domestication[[37]]

Fox Tameness[[35]]

Dog-Wolf Comparison[[36]]

Mouse Behavior[[47]]


casp2, casp3a, caspb

casp3a (+)

casp8 (+)


Coiled-coil domain containing

ccdc47, ccdc53, ccdc93, Dr.18793.1.A1_at

Dr.18793.1.A1_at (+)

BU965755 (+)





col1a1 (+), col1a2 (+)


col1a1a, col3a1b

Cytochrome c oxidase


cox2 (–), cox3 (–)





dnajc3 (–)



Eukaryotic translation initiation factors

eif1ax, eif4enif1

eif1ax (+), eif4enif1 (–)

eif3s2 (+), eif3s7 (+)


Glutathione peroxidases

gpx1a, gpx4a


gpx3 (–)




hbaa1, ba1






NADH dehydrogenase (ubiquinone)


ndf1a4 (+)

ndufb8 (–)


Neurofilament, light peptide


CB514125 (+)



Potassium channel proteins

Dr.16785.1.A1_at (kcnk1)


kcnj4 (–)

AI835316 (kcnc2)a

Purkinje cell protein 4


pcp4 (*)

pcp4 (+)


Selenoprotein P


sepp1a (+)


sepp1 (+)



stx3a, stx6, syntaxin1b


stx1b2 (+)


Syntaxin binding proteins


stxbp3 (+)


stxbp2b, stxbp6e

Transthyretin (prealbumin, amyloidosis type I)


ttr (*)

ttr (+)


Studies included the present study on zebrafish (Danio rerio), an analysis of domestication in rainbow trout (Oncorhynchus mykiss) [37], silver fox (Vulpes vulpes) selected for tameness [35], a comparison between dog (Canis familiaris) and wolf (Canius lupus) [36], and an analysis of strain variation in laboratory mice (Mus musculus) [47]. Positive signs (+) indicated up-regulation in domesticated populations relative to wild, while negative signs (–) indicate down-regulation. An asterisk (*) indicates that the direction of differential expression differed among brain regions.


A few of these genes are associated with synapse function and long-term potentiation. Syntaxins are components of the SNARE complex which mediates the docking of vesicles with extracellular membranes, including the release of neurotransmitters from axon terminals [92], and appears to be regulated by several syntaxin-binding proteins [93, 94]. Protein levels of syntaxins increase during long-term potentiation in the rat hippocampus [95]. In studies related to domestication, mRNA expression of syntaxin1b2 was higher in foxes selected for tameness [35]; expression of syntaxin-binding proteins was higher in domesticated rainbow trout [37] and correlated with fear-related behaviors in mice [47]. In contrast, in our study of zebrafish, expression of three syntaxin genes differed among strains but was not associated with domestication history. Likewise, caspases, such as capase-3, are associated with synaptic plasticity and learning, in addition to their involvement in apoptosis [96, 97]. Caspase-3a and capase-8 were up-regulated in domesticated populations of zebrafish (current study) and rainbow trout [37]. These observations suggest that synapse development and function may be influenced by domestication.

The association of collagens and hemoglobins with domestication of multiple species was surprising. Collagens are rare in the brain and are usually associated with the meninges surrounding the brain [98]. This includes several of the collagen genes differentially expressed among zebrafish populations (present study), between wild and domesticated rainbow trout [37], and correlated with behavior in mice [47]. It is possible that the meninges may have contaminated the brain samples, although it is not clear why this contamination would differ in level between populations in multiple species. Alternatively a few collagen genes are expressed in the brain and appear to be involved in neurological development, including axon guidance and synaptogenesis in the CNS [98, 99].

Expression of hemoglobin genes was positively associated with domestication in rainbow trout [37], negatively associated with tameness in foxes [35], and positively associated with open field activity in mice [47]. Hemoglobin genes were also differentially expressed among zebrafish populations. It was recently discovered that hemoglobin genes are expressed in neurons, astrocytes, and oligodendrocytes of rats and humans, and may be involved in oxygen storage and mitochondrial function in the brain [100, 101].

Finally, SNP variation has been detected among zebrafish populations and may be associated with the variation in behavior and gene expression we observed here. Whiteley et al.[102] examined SNP variation among thirteen wild populations and three laboratory strains of zebrafish and detected 99 significantly divergent outlier SNP loci that may be signatures of natural selection. Eight of these outlier loci occurred in genes that were also differentially expressed in our analysis among populations (arg2 gpx1a pho pspc1, si:dkey-15j16.2, and zgc:77304) and between wild and domesticated populations (bhlhe40 and syntaxin1b), suggesting that variation in the expression of these genes may also be under selection. Their study populations included the laboratory TM1 strain and wild populations in geographic regions from which our wild strains were originally collected.




Zebrafish strains

We used zebrafish from four distinct strains for our experiments. Two of these strains have been reared in captivity for more than thirty generations each [9, 43, 44]. The Scientific Hatcheries strain (SH) was originally obtained from a commercial breeder (Scientific Hatcheries, Huntington, CA) and reared in our laboratory for three generations prior to the experiment. It is difficult to determine the total number of generations for which this strain has been in captivity, but it is known to be at least thirty. The TM1 strain was founded from a pet store population and reared for 24 generations at the University of Miami [66] and a further six generations prior to the experiment at the University of Idaho. The TM1 strain is GFP transgenic using a β-actin promoter [66]. Two additional strains, Nadia and Gaighata, were recently derived from wild populations in India. The Nadia strain was originally collected in India in 1999. At the time of this study, the strain had been reared in the laboratory for six generations, remaining qualitatively similar to the original wild collections [9, 43, 44]. The Gaighatta strain was collected in India in 2005, and was in its first generation of lab rearing at the time of this study.


Behavior assays




Tissue isolation and RNA extraction



Microarray hybridization


Statistical analysis of microarray data

CEL files containing raw data were then processed and analyzed using R software and Bioconductor packages [104, 105]. These CEL files have been deposited with the NCBI Gene Expression Omnibus (GEO, Accession: GSE38729, Microarray hybridization data were examined for physical anomalies on the chip by pseudochip and residual error visualizations. Quality assurance of microarray data was completed using the affyQAReport function from the Bioconductor package affyQCReport. Hybridization and housekeeping controls, RNA degradation, sample clustering, NUSE plots, LPE plots, and RLE plots all showed high quality data (not shown) and no chips were removed. The arrays were then pre-processed using the Robust Multi-array Average (RMA) procedure [106108] using the affy package [109]. Next, unexpressed and low variability genes were removed by unbiased filtering. Affymetrix present-marginal-absent (PMA) calls were determined using a P-value cut off for absent of greater than 0.04 and present less than 0.04; marginal calls were treated as absent. Unexpressed genes were then defined as having a signal less than the expression value at which 99 % of genes were called as absent across all samples. A filter on interquartile range was also applied to remove genes with low variability. Genes with an interquartile range of less than 0.5 across all chips in the experiment were excluded, reducing the dataset further to 7,958 genes.

Signal intensities were also examined at the probe level to identify single feature polymorphisms (SFPs), differences at the probe level due to genetic polymorphisms rather than expression differences, which may also impact computed expression values. Briefly, in R using previously described methods [61], the RMA normalized expression estimate for each probe set was subtracted from background corrected and normalized expression levels at individual probes within the probe set. Normalized residuals were analyzed using significance analysis of microarrays (SAM) [110] within the siggenes package to detect features with a significant effect for strain (FDR adjusted α < 0.01). A total of 3,199 genes with significant SFPs were then removed from the analysis.

The Linear Models for Microarray Data (limma) package was then used to perform differential expression analysis on the filtered gene list using a linear model on log2 signal values with an empirical Bayes correction to the variance [64, 65]. Comparisons of interest were extracted through contrasts, and P-values were corrected for multiple comparisons using the Benjamini and Hochberg method (FDR = 5 %) [111]. The data were initially analyzed with a fully crossed factorial model of Strain and Sex. However, because the Sex and interaction terms were not significant for the vast majority of genes (see Results), we also tested for differential expression among strains using a model that excluded sex. The effect of domestication history was tested using contrasts between wild and domesticated strains.

Functional analyses of the resulting gene lists were performed using DAVID version 6.7 [67, 68]. Functional categories were evaluated against the probability that they appeared in lists of differentially expressed genes at random based on their representation in the full list of genes that were classified as expressed in the zebrafish brain in our analysis. Functional classifications considered in the analysis included gene ontologies (GOs, [112]), COG ontologies, protein domains from PIR superfamilies, Interpro, and SMART databases, KEGG pathways, SP PIR keywords, and Up Seq features. Analysis of gene ontologies used the “FAT” option which filters the output, removing upper level GOs primarily from the top five levels of the hierarchy, in favor of more specific GOs that are often more informative. Categories were considered overrepresented if the EASE score was less than 0.05. We also report P-values adjusted for multiple tests using the Benjamini-Hochberg correction [111]. The Functional Annotation Clustering tool was used to identify categories with overlapping lists of differentially expressed genes.

qRT-PCR validation


qRT-PCR data were analyzed using mixed model analysis of covariance (ANCOVA) using the lm function in R. This approach analyzed expression of the target gene (CTs) while simultaneously normalizing expression by including expression of the reference gene (CTs) as a covariate in the model [88, 113]. We employed a fully crossed factorial model using the main effects of Strain and Sex. When the effect of Strain was significant, pairwise comparisons were performed using Tukey’s Studentized Range test.





Authors’ Affiliations



  1. Price EO: Behavioral genetics and the process of animal domestication. Genetics and the behavior of domestic animals. Edited by: Grandin T. 1998, San Diego, Academic Press, 41-65.Google Scholar
  2. Price EO: Behavioral development in animals undergoing domestication. Applied Animal Behaviour Science. 1999, 65 (3): 245-271.Google Scholar
  3. Waples RS: Dispelling some myths about hatcheries. Fisheries. 1999, 24 (2): 12-21.Google Scholar
  4. Devlin RH, Biagi CA, Yesaki TY, Smailus DE, Byatt JC: Growth of domesticated transgenic fish. Nature. 2001, 409 (6822): 781-782.PubMedGoogle Scholar
  5. Fleming IA, Agustsson T, Finstad B, Johnsson JI, Björnsson BT: Effects of domestication on growth physiology and endocrinology of Atlantic salmon (Salmo salar). Can J Fish Aquat Sci. 2002, 59 (8): 1323-1330.Google Scholar
  6. Johnsson JI, Petersson E, Jönsson E, Björnsson BT, Järvi T: Domestication and growth hormone alter antipredator behaviour and growth patterns in juvenile brown trout, Salmo trutta. Can J Fish Aquat Sci. 1996, 53 (7): 1546-1554.Google Scholar
  7. Künzl C, Sachser N: The behavioral endocrinology of domestication: a comparison between the domestic guinea pig (Cavia aperea f. porcellus) and its wild ancestor, the cavy (Cavia aperea). Horm Behav. 1999, 35 (1): 28-37.PubMedGoogle Scholar
  8. Woodward CC, Strange RJ: Physiological stress responses in wild and hatchery-reared rainbow trout. Trans Am Fish Soc. 1987, 116 (4): 574-579.Google Scholar
  9. Oswald M, Robison BD: Strain-specific alteration of zebrafish feeding behavior in response to aversive stimuli. Can J Zool. 2008, 86 (10): 1085-1094.PubMed CentralPubMedGoogle Scholar
  10. Berejikian BA, Mathews SB, Quinn TP: Effects of hatchery and wild ancestry and rearing environments on the development of agonistic behavior in steelhead trout (Oncorhynchus mykiss) fry. Can J Fish Aquat Sci. 1996, 53 (9): 2004-2014.Google Scholar
  11. Johnsson JI, Abrahams MV: Interbreeding with domestic strain increases foraging under threat of predation in juvenile steelhead trout (Oncorhynchus mykiss): an experimental study. Can J Fish Aquat Sci. 1991, 48: 243-247.Google Scholar
  12. Harri M, Mononen J, Ahola L, Plyusnina I, Rekila T: Behavioural and physiological differences between silver foxes selected and not selected for domestic behaviour. Animal Welfare. 2003, 12 (3): 305-314.Google Scholar
  13. Huntingford FA: Implications of domestication and rearing conditions for the behaviour of cultivated fishes. J Fish Biol. 2004, 65A (S1): 122-142.Google Scholar
  14. Jensen P, Andersson L: Genomics meets ethology: A new route to understanding domestication, behavior, and sustainability in animal breeding. Ambio. 2005, 34 (4–5): 320-324.PubMedGoogle Scholar
  16. Fay JC, Wittkopp PJ: Evaluating the role of natural selection in the evolution of gene regulation. Heredity. 2008, 100 (2): 191-199.PubMedGoogle Scholar
  18. Fay JC, McCullough HL, Sniegowski PD, Eisen MB: Population genetic variation in gene expression is associated with phenotypic variation in Saccharomyces cerevisiae. Genome Biol. 2004, 5 (4): R26-PubMed CentralPubMedGoogle Scholar
  19. Hutter S, Saminadin-Peter SS, Stephan W, Parsch J: Gene expression variation in African and European populations of Drosophila melanogaster. Genome Biol. 2008, 9 (1): R12-PubMed CentralPubMedGoogle Scholar
  20. Giger T, Excoffier L, Amstutz U, Day PJR, Champigneulle A, Hansen MM, Kelso J, Largiadèr CR: Population transcriptomics of life-history variation in the genus Salmo. Mol Ecol. 2008, 17 (13): 3095-3108.PubMedGoogle Scholar
  22. Roberge C, Einum S, Guderley H, Bernatchez L: Rapid parallel evolutionary changes of gene transcription profiles in farmed Atlantic salmon. Mol Ecol. 2006, 15 (1): 9-20.PubMedGoogle Scholar
  23. Townsend JP, Cavalieri D, Hartl DL: Population genetic variation in genome-wide gene expression. Mol Biol Evol. 2003, 20 (6): 955-963.PubMedGoogle Scholar
  24. Whitehead A, Crawford DL: Variation in tissue-specific gene expression among natural populations. Genome Biol. 2005, 6 (2): R13-PubMed CentralPubMedGoogle Scholar
  27. Brem RB, Kruglyak L: The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc Natl Acad Sci USA. 2005, 102 (5): 1572-1577.PubMed CentralPubMedGoogle Scholar
  28. Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature. 2004, 430 (7001): 743-747.PubMed CentralPubMedGoogle Scholar
  29. Roberge C, Guderley H, Bernatchez L: Genomewide identification of genes under directional selection: gene transcription QST scan in diverging Atlantic salmon subpopulations. Genetics. 2007, 177: 1011-1022.PubMed CentralPubMedGoogle Scholar
  30. Whiteley AR, Derome N, Rogers SM, St-Cyr J, Laroche J, Labbe A, Nolte A, Renaut S, Jeukens J, Bernatchez L: The phenomics and expression quantitative trait locus mapping of brain transcriptomes regulating adaptive divergence in lake whitefish species pairs (Coregonus sp.). Genetics. 2008, 180 (1): 147-164.PubMed CentralPubMedGoogle Scholar
  31. Landry CR, Townsend JP, Hartl DL, Cavalieri D: Ecological and evolutionary genomics of Saccharomyces cerevisiae. Mol Ecol. 2006, 15 (3): 575-591.PubMedGoogle Scholar
  32. Larsen PF, Nielsen EE, Williams TD, Hemmer-Hansen J, Chipman JK, Kruhøffer M, Grønkjær P, George SG, Dyrskjøt L, Loeschcke V: Adaptive differences in gene expression in European flounder (Platichthys flesus). Mol Ecol. 2007, 16 (22): 4674-4683.PubMedGoogle Scholar
  33. Whitehead A, Crawford DL: Neutral and adaptive variation in gene expression. Proc Natl Acad Sci USA. 2006, 103 (14): 5425-5430.PubMed CentralPubMedGoogle Scholar
  34. Aubin-Horth N, Landry CR, Letcher BH, Hofmann HA: Alternative life histories shape brain gene expression profiles in males of the same population. Proceedings of the Royal Society of London, Series B. 2005, 272 (1573): 1655-1662.Google Scholar
  35. Lindberg J, Bjornerfeldt S, Saetre P, Svartberg K, Seehuus B, Bakken M, Vila C, Jazin E: Selection for tameness has changed brain gene expression in silver foxes. Curr Biol. 2005, 15 (22): R915-R916.PubMedGoogle Scholar
  36. Saetre P, Lindberg J, Leonard JA, Olsson K, Pettersson U, Ellegren H, Bergström TF, Vilà C, Jazin E: From wild wolf to domestic dog: gene expression changes in the brain. Mol Brain Res. 2004, 126 (2): 198-206.PubMedGoogle Scholar
  37. Tymchuk W, Sakhrani D, Devlin R: Domestication causes large-scale effects on gene expression in rainbow trout: analysis of muscle, liver and brain transcriptomes. Gen Comp Endocrinol. 2009, 164 (2–3): 175-183.PubMedGoogle Scholar
  38. Gerlai R: Zebrafish: an uncharted behavior genetic model. Behav Genet. 2003, 33 (5): 461-468.PubMedGoogle Scholar
  39. Guo S: Linking genes to brain, behavior and neurological diseases: what can we learn from zebrafish?. Genes Brain Behav. 2004, 3 (2): 63-74.PubMedGoogle Scholar
  40. Sison M, Cawker J, Buske C, Gerlai R: Fishing for genes influencing vertebrate behavior: zebrafish making headway. Lab Anim. 2006, 35 (5): 33-39.Google Scholar
  41. Tropepe V, Sive HL: Can zebrafish be used as a model to study the neurodevelopmental causes of autism?. Genes Brain Behav. 2003, 2 (5): 268-281.PubMedGoogle Scholar
  42. Wright D, Rimmer LB, Pritchard VL, Krause J, Butlin RK: Inter and intra-population variation in shoaling and boldness in the zebrafish (Danio rerio). Naturwissenschaften. 2003, 90 (8): 374-377.PubMedGoogle Scholar
  43. Benner MJ, Drew RE, Hardy RW, Robison BD: Zebrafish (Danio rerio) vary by strain and sex in their behavioral and transcriptional responses to selenium supplementation. Comp Biochem Physiol A Mol Integr Physiol. 2010, 157 (4): 310-318.PubMed CentralPubMedGoogle Scholar
  44. Robison BD, Rowland W: A potential model system for studying the genetics of domestication: behavioral variation among wild and domesticated strains of zebra danio (Danio rerio). Can J Fish Aquat Sci. 2005, 62 (9): 2046-2054.Google Scholar
  45. Wright D, Nakamichi R, Krause J, Butlin RK: QTL analysis of behavioral and morphological differentiation between wild and laboratory zebrafish (Danio rerio). Behav Genet. 2006, 36 (2): 271-284.PubMedGoogle Scholar
  46. Robison BD, Singer M, Benner MJ, Oswald ME: A high throughput and inexpensive assay for anxiety related behaviors in the zebrafish, based on place preference and latency to feed. Zebrafish Protocols for Neurobehavioral Research. Edited by: Kalueff AV, Stewart AM. Humana Press, New York, New York, in press
  47. Letwin NE, Kafkafi N, Benjamini Y, Mayo C, Frank BC, Luu T, Lee NH, Elmer GI: Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene–behavior associations. J Neurosci. 2006, 26 (20): 5277-5287.PubMedGoogle Scholar
  48. Reinhardt UG: Selection for surface feeding in farmed and sea-ranched masu salmon juveniles. Trans Am Fish Soc. 2001, 130 (1): 155-158.Google Scholar
  49. Weber ED, Fausch KD: Interactions between hatchery and wild salmonids in streams: differences in biology and evidence for competition. Can J Fish Aquat Sci. 2003, 60 (8): 1018-1036.Google Scholar
  50. Hamilton IM, Dill LM: Monopolization of food by zebrafish increases in risky habitats. Can J Zool. 2002, 80 (12): 2164-2169.Google Scholar
  51. Rehnberg BG, Smith JF: The influence of alarm substance and shoal size on the behavior of zebra danios. J Fish Biol. 1988, 33 (1): 155-163.Google Scholar
  52. Iguchi K, Matsubara N, Hakoyama H: Behavioural individuality assessed from two strains of cloned fish. Anim Behav. 2001, 61 (2): 351-356.Google Scholar
  53. Sundström LF, Devlin RH, Johnsson JI, Biagi CA: Vertical position reflects increased feeding motivation in growth hormone transgenic coho salmon (Oncorhynchus kisutch). Ethology. 2003, 109 (8): 701-712.Google Scholar
  54. Egan RJ, Bergner CL, Hart PC, Cachat JM, Canavello PR, Elegante MF, Elkhayat SI, Bartels BK, Tien AK, Tien DH, et al: Understanding behavioral and physiological phenotypes of stress and anxiety in zebrafish. Behav Brain Res. 2009, 205 (1): 38-44.PubMed CentralPubMedGoogle Scholar
  55. Bachman RA: Foraging behaviour of free-ranging wild and hatchery brown trout in a stream. Trans Am Fish Soc. 1984, 113 (1): 1-31.Google Scholar
  56. Lucas MD, Drew RE, Wheeler PA, Verrell PA, Thorgaard GH: Behavioral differences among rainbow trout clonal lines. Behav Genet. 2004, 34 (3): 355-365.PubMedGoogle Scholar
  57. Moyle PB: Comparative behavior of young brook trout of domestic and wild origin. Progressive Fish-Culturist. 1969, 31 (1): 51-59.Google Scholar
  58. Vincent RE: Some influences of domestication upon three stocks of brook trout (Salvelinus fontinalis Mitchill). Trans Am Fish Soc. 1960, 89 (1): 35-52.Google Scholar
  59. Yamamoto T, Reinhardt UG: Dominance and predator avoidance in domesticated and wild masu salmon Oncorhynchus masou. Fisheries Science. 2003, 69 (1): 88-94.Google Scholar
  60. Biro PA, Abrahams MV, Post JR, Parkinson EA: Behavioural trade-offs between growth and mortality explain evolution of submaximal growth rates. J Anim Ecol. 2006, 75 (5): 1165-1171.PubMedGoogle Scholar
  61. Coram TE, Settles ML, Wang M, Chen X: Surveying expression level polymorphism and single-feature polymorphism in near-isogenic wheat lines differing for the Yr5 stripe rust resistance locus. Theor Appl Genet. 2008, 117 (3): 401-411.PubMedGoogle Scholar
  63. Xu WW, Cho S, Yang SS, Bolon Y-T, Bilgic H, Jia H, Xiong Y, Muehlbauer GJ: Single-feature polymorphism discovery by computing probe affinity shape powers. BMC Genomics. 2009, 10: 48-Google Scholar
  66. Gibbs PDL, Schmale MC: GFP as a genetic marker scorable throughout the life cycle of transgenic zebra fish. Marine Biotechnol. 2000, 2 (2): 107-125.Google Scholar
  67. Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (9): R60-PubMed CentralGoogle Scholar
  68. Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc. 2009, 4 (1): 44-57.Google Scholar
  69. Bänziger C, Soldini D, Schütt C, Zipperlen P, Hausmann G, Basler K: Wntless, a conserved membrane protein dedicated to the secretion of Wnt proteins from signaling cells. Cell. 2006, 125 (3): 509-522.PubMedGoogle Scholar
  71. Korkut C, Ataman B, Ramachandran P, Ashley J, Barria R, Gherbesi N, Budnik V: Trans-synaptic transmission of vesicular Wnt signals through Evi/Wntless. Cell. 2009, 139 (2): 393-404.PubMed CentralPubMedGoogle Scholar
  72. Salinas PC, Zou Y: Wnt signaling in neural circuit assembly. Annu Rev Neurosci. 2008, 31: 339-358.PubMedGoogle Scholar
  73. De Ferrari GV, Moon RT: The ups and downs of Wnt signaling in prevalent neurological disorders. Oncogene. 2006, 25 (7545): 7545-7553.PubMedGoogle Scholar
  74. Schweizer U, Schomburg L, Savaskan NE: The neurobiology of selenium: lessons from transgenic mice. J Nutr. 2004, 134 (4): 707-710.PubMedGoogle Scholar
  75. Hill KE, Zhou J, McMahan WJ, Motley AK, Atkins JF, Gesteland RF, Burk RF: Deletion of selenoprotein P alters distribution of selenium in the mouse. J Biol Chem. 2003, 278 (16): 13640-13646.PubMedGoogle Scholar
  76. Hill KE, Zhou J, McMahan WJ, Motley AK, Burk RF: Neurological dysfunction occurs in mice with targeted deletion of the selenoprotein P gene. J Nutr. 2004, 134 (1): 157-161.PubMedGoogle Scholar
  77. Schomburg L, Schweizer U, Holtmann B, Flohé L, Sendtner M, Köhrle J: Gene disruption discloses role of selenoprotein P in selenium delivery to target tissues. Biochem J. 2003, 370 (Pt. 2): 397-402.PubMed CentralPubMedGoogle Scholar
  78. Méplan C, Crosley LK, Nicol F, Beckett GJ, Howie AF, Hill KE, Horgan G, Mathers JC, Arthur JR, Hesketh JE: Genetic polymorphisms in the human selenoprotein P gene determine the response of selenoprotein markers to selenium supplementation in a gender-specific manner (the SELGEN study). FASEB J. 2007, 21 (12): 3063-3074.PubMedGoogle Scholar
  80. Janz R, Sudhof TC: Cellugyrin, a novel ubiquitous form of synaptogyrin that is phosphorylated by pp 60c-src. J Biol Chem. 1998, 273 (5): 2851-2857.PubMedGoogle Scholar
  81. Schmitt U, Tanimoto N, Seeliger M, Schaeffel F, Leube RE: Detection of behavioral alterations and learning deficits in mice lacking synaptophysin. Neuroscience. 2009, 162: 234-243.PubMedGoogle Scholar
  82. Thome J, Pesold B, Baader M, Hu M, Gewirtz JC, Duman RS, Henn FA: Stress differentially regulates synaptophysin and synaptotagmin expression in hippocampus. Biol Psychiatry. 2001, 50: 809-812.PubMedGoogle Scholar
  83. Gao Y, Bezchlibnyk YB, Sun X, Wang J-F, McEwen BS, Young LT: Effects of restraint stress on the expression of proteins involved in synaptic vesicle exocytosis in the hippocampus. Neuroscience. 2006, 141: 1139-1148.PubMedGoogle Scholar
  84. Carvalho-Netto EF, Myers B, Jones K, Solomon MB, Herman JP: Sex differences in synaptic plasticity in stress-responsive brain regions following chronic variable stress. Physiol Behav. 2011, 104: 242-247.PubMed CentralPubMedGoogle Scholar
  85. Salonius K, Iwama GK: Effects of early rearing environment on stress response, immune function, and disease resistance in juvenile coho (Oncorhynchus kisutch) and chinook salmon (O. tshawytscha). Can J Fish Aquat Sci. 1993, 50: 759-766.Google Scholar
  86. Fan J, Liu J, Culty M, Papadopoulos V: Acyl-coenzyme A binding domain containing 3 (ACBD3; PAP7; GCP60): An emerging signaling molecule. Prog Lipid Res. 2010, 49: 218-234.PubMed CentralPubMedGoogle Scholar
  87. Santos EM, Kille P, Workman VL, Paull GC, Tyler CR: Sexually dimorphic gene expression in the brains of mature zebrafish. Comp Biochem Physiol A Mol Integr Physiol. 2008, 149 (3): 314-324.PubMedGoogle Scholar
  88. Robison BD, Drew RE, Murdoch GK, Powell M, Rodnick KJ, Settles M, Stone D, Churchill E, Hill RA, Papasani MR, et al: Sexual dimorphism in hepatic gene expression and the response to dietary carbohydrate manipulation in the zebrafish (Danio rerio). Comp Biochem Physiol Part D Genomics Proteomics. 2008, 3 (2): 141-154.PubMed CentralPubMedGoogle Scholar
  89. Santos EM, Workman VL, Paull GC, Filby AL, Van Look KJW, Kille P, Tyler CR: Molecular basis of sex and reproductive status in breeding zebrafish. Physiol Genomics. 2007, 30 (2): 111-122.PubMedGoogle Scholar
  90. Rinn JL, Snyder M: Sexual dimorphism in mammalian gene expression. Trends Genet. 2005, 21 (5): 298-305.PubMedGoogle Scholar
  91. Reimers M, Heilig M, Sommer WH: Gene discovery in neuropharmacological and behavioral studies using Affymetrix microarray data. Methods. 2005, 37 (3): 219-228.PubMedGoogle Scholar
  92. Palfreyman MT, Jorgensen EM: Roles of SNARE proteins in synaptic vesicle fusion. Molecular Mechanisms of Neurotransmitter Release. Edited by: Wang Z-W. 2008, Humana Press, Totowa, NJ, 35-59.Google Scholar
  93. Fujita Y, Shirataki H, Sakisaka T, Asakura T, Ohya T, Kotani H, Yokoyama S, Nishioka H, Matsuura Y, Mizoguchi A, et al: Tomosyn: A syntaxin-1-binding protein that forms a novel complex in the neurotransmitter release process. Neuron. 1998, 20 (5): 905-915.PubMedGoogle Scholar
  94. Scales SJ, Hesser BA, Masuda ES, Scheller RH: Amisyn, a novel syntaxin-binding protein that may regulate SNARE complex assembly. J Biol Chem. 2002, 277 (31): 28271-28279.PubMedGoogle Scholar
  95. Smirnova T, Laroche S, Errington ML, Hicks AA, Bliss TVP, Mallet J: Transsynaptic expression of a presynaptic glutamate receptor during hippocampal long-term potentiation. Science. 1993, 262: 433-436.PubMedGoogle Scholar
  96. Kudryashova IV, Onufriev MV, Kudryashov IE, Gulyaeva NV: Regulation of cathepsin B and caspase-3 in long-term plasticity. Neurochemical Journal. 2009, 3 (4): 260-266.Google Scholar
  97. Huesmann GR, Clayton DF: Dynamic role of postsynaptic caspase-3 and BIRC4 in zebra finch song-response habituation. Neuron. 2006, 52: 1061-1072.PubMed CentralPubMedGoogle Scholar
  98. Hubert T, Grimal S, Carroll P, Fichard-Carroll A: Collagens in the developing and diseased nervous system. Cell Mol Life Sci. 2009, 66: 1223-1238.PubMedGoogle Scholar
  99. Su J, Gorse K, Ramirez F, Fox MA: Collagen XIX is expressed by interneurons and contributes to the formation of hippocampal synapses. J Comp Neurol. 2010, 518: 229-253.PubMed CentralPubMedGoogle Scholar
  100. Biagioli M, Pinto M, Cesselli D, Zaninello M, Lazarevic D, Roncaglia P, Simone R, Vlachouli C, Plessy C, Bertin N, et al: Unexpected expression of α- and β-globin in mesencephalic dopaminergic neurons and glial cells. Proc Natl Acad Sci USA. 2009, 106 (36): 15454-15459.PubMed CentralPubMedGoogle Scholar
  101. Richter F, Meurers BH, Zhu C, Medvedeva VP, Chesselet M-F: Neurons express hemoglobin a- and b-chains in rat and human brains. J Comp Neurol. 2009, 515: 538-547.PubMed CentralPubMedGoogle Scholar
  102. Whiteley AR, Bhat A, Martins EP, Mayden RL, Arunachalam M, Uusi-Heikkilä , Ahmed ATA, Shrestha J, Clark M, Stemple D, et al: Population genomics of wild and laboratory zebrafish (Danio rerio). Mol Ecol. 2011, 20 (20): 4259-4276.PubMed CentralPubMedGoogle Scholar
  103. Renn SCP, Aubin-Horth N, Hofmann HA: Fish and chips: functional genomics of social plasticity in an African cichlid fish. J Exp Biol. 2008, 211: 3041-3056.PubMed CentralPubMedGoogle Scholar
  105. R Development Core Team: R: A language and environment for statistical computing. 2006, R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  106. Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics. 2003, 19 (2): 185-193.PubMedGoogle Scholar
  111. Benjamini Y, Hochberg Y: On the adaptive control of the false discovery rate in multiple testing with independent statistics. Journal of Educational and Behavioral Statistics. 2000, 25 (1): 60-83.Google Scholar
  113. Drew RE, Rodnick KJ, Settles M, Wacyk J, Churchill E, Powell MS, Hardy RW, Murdoch GK, Hill RA, Robison BD: Effect of starvation on transcriptomes of brain and liver in adult female zebrafish (Danio rerio). Physiol Genomics. 2008, 35 (3): 283-295.PubMed CentralPubMedGoogle Scholar


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