- -

Evolutionary signals of selection on cognition from the great tit genome and methylome

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Evolutionary signals of selection on cognition from the great tit genome and methylome

Show full item record

Laine, V. N., Gossmann, T. I., Schachtschneider, K. M., Garroway, C. J., Madsen, O., Verhoeven, K. J. F., . . . Groenen, M. A. M. (2016). Evolutionary signals of selection on cognition from the great tit genome and methylome. Nature Communications, 710.1038/ncomms10474

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/62805

Files in this item

Item Metadata

Title: Evolutionary signals of selection on cognition from the great tit genome and methylome
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal
Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Issued date:
Abstract:
[EN] For over 50 years, the great tit (Parus major) has been a model species for research in evolutionary, ecological and behavioural research; in particular, learning and cognition have been intensively studied. Here, to ...[+]
Subjects: Wild bird population , Sequencing data , Phenotypic plasticity , Parus major , Clutch size , Alignment , Generation , History , Memory , Life
Copyrigths: Reconocimiento (by)
Source:
Nature Communications. (issn: 2041-1723 )
DOI: 10.1038/ncomms10474
Publisher:
Nature Publishing Group: Nature Communications
Publisher version: http://dx.doi.org/10.1038/ncomms10474
Thanks:
We thank Eveline Verhulst for help with the methylome data, Christa Mateman for lab assistance, Martijn Derks for calculating the sliding windows, Tieshan Xu for the help with the Trinity assembly, Louise Dittmar for the ...[+]
Type: Artículo

References

Rendell, L. et al. Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn. Sci. 15, 68–76 (2011) .

Leadbeater, E. What evolves in the evolution of social learning? J. Zool. 295, 4–11 (2015) .

Aplin, L. M. et al. Experimentally induced innovations lead to persistent culture via conformity in wild birds. Nature 518, 538–541 (2015) . [+]
Rendell, L. et al. Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn. Sci. 15, 68–76 (2011) .

Leadbeater, E. What evolves in the evolution of social learning? J. Zool. 295, 4–11 (2015) .

Aplin, L. M. et al. Experimentally induced innovations lead to persistent culture via conformity in wild birds. Nature 518, 538–541 (2015) .

Titulaer, M., van Oers, K. & Naguib, M. Personality affects learning performance in difficult tasks in a sex-dependent way. Anim. Behav. 83, 723–730 (2012) .

Cole, E. F., Morand-Ferron, J., Hinks, A. E. & Quinn, J. L. Cognitive ability influences reproductive life history variation in the wild. Curr. Biol. 22, 1808–1812 (2012) .

Overington, S. E., Morand-Ferron, J., Boogert, N. J. & Lefebvre, L. Technical innovations drive the relationship between innovativeness and residual brain size in birds. Anim. Behav. 78, 1001–1010 (2009) .

Boyce, M. S. & Perrins, C. M. Optimizing great tit clutch size in a fluctuating environment. Ecology 68, 142–153 (1987) .

Nussey, D. H., Postma, E., Gienapp, P. & Visser, M. E. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005) .

Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008) .

Pettifor, R. A., Perrins, C. M. & McCleery, R. H. Individual optimization of clutch size in great tits. Nature 336, 160–162 (1988) .

Knowles, S. C. L., Nakagawa, S. & Sheldon, B. C. Elevated reproductive effort increases blood parasitaemia and decreases immune function in birds: a meta-regression approach. Funct. Ecol. 23, 405–415 (2009) .

Bouwhuis, S., Sheldon, B. C., Verhulst, S. & Charmantier, A. Great tits growing old: selective disappearance and the partitioning of senescence to stages within the breeding cycle. Proc. R. Soc. B Biol. Sci. 276, 2769–2777 (2009) .

Van Noordwijk, A. J. & Scharloo, W. Inbreeding in an island population of the great tit. Evolution 35, 674–688 (1981) .

Greenwood, P. J., Harvey, P. H. & Perrins, C. M. Inbreeding and dispersal in the great tit. Nature 271, 52–54 (1978) .

Richner, H. Host-parasite interactions and life-history evolution. Zoology 101, 333–344 (1998) .

Krebs, J. R. Territory and breeding density in the great tit, Parus major L. Ecology 52, 2–22 (1971) .

Mappes, J. & Alatalo, R. V. Effects of novelty and gregariousness in survival of aposematic prey. Behav. Ecol. 8, 174–177 (1997) .

Van Oers, K. & Naguib, M. in Animal Personalities: Behavior, Physiology and Evolution eds Carere C., Maestripieri D. 520 Chicago Univ. Press (2013) .

Van Oers, K. et al. Replicated high-density genetic maps of two great tit populations reveal fine-scale genomic departures from sex-equal recombination rates. Heredity 112, 307–316 (2014) .

Warren, W. C. et al. The genome of a songbird. Nature 464, 757–762 (2010) .

ICGSC. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716 (2004) .

Kvist, L. et al. Evolution and genetic structure of the great tit (Parus major) complex. Proc. R. Soc. B Biol. Sci. 270, 1447–1454 (2003) .

Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011) .

Clayton, D. F. The genomics of memory and learning in songbirds. Annu. Rev. Genomics Hum. Genet. 14, 45–65 (2013) .

Hara, E., Kubikova, L., Hessler, N. A & Jarvis, E. D. Role of the midbrain dopaminergic system in modulation of vocal brain activation by social context. Eur. J. Neurosci. 25, 3406–3416 (2007) .

Dragunow, M. A role for immediate-early transcription factors in learning and memory. Behav. Genet. 26, 293–299 (1996) .

Bolhuis, J. J., Zijlstra, G. G. O., den Boer-Visser, A. M. & Van Der Zee, E. A. Localized neuronal activation in the zebra finch brain is related to the strength of song learning. Proc. Natl Acad. Sci. USA 97, 2282–2285 (2000) .

Enard, W. et al. Molecular evolution of FOXP2, a gene involved in speech and language. Nature 418, 869–872 (2002) .

Teramitsu, I. & White, S. A. FoxP2 regulation during undirected singing in adult songbirds. J. Neurosci. 26, 7390–7394 (2006) .

Haesler, S. et al. Incomplete and inaccurate vocal imitation after knockdown of FoxP2 in songbird basal ganglia nucleus area X. PLoS Biol. 5, e321 (2007) .

Goldberg, A. D., Allis, C. D. & Bernstein, E. Epigenetics: a landscape takes shape. Cell 128, 635–638 (2007) .

Ball, M. P., Li, J. B., Gao, Y., Lee, J. & Leproust, E. Targeted and genome-scale methylomics reveals gene body signatures in human cell lines. Nat. Biotechnol. 27, 361–368 (2009) .

Guo, J. U. et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17, 215–222 (2014) .

Shirane, K. et al. Mouse oocyte methylomes at base resolution reveal genome-wide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet. 9, e1003439 (2013) .

Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009) .

Nätt, D. et al. Heritable genome-wide variation of gene expression and promoter methylation between wild and domesticated chickens. BMC Genomics 13, 59 (2012) .

Jarvis, E. D. et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331 (2014) .

Zhang, G. et al. Comparative genomics reveals insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014) .

Drent, P. J., van Oers, K. & van Noordwijk, A. J. Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. B Biol. Sci. 270, 45–51 (2003) .

Gnerre, S. et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl. Acad. Sci. USA 108, 1513–1518 (2011) .

Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 7, 1–8 (2013) .

Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011) .

Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013) .

Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009) .

Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013) .

Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008) .

Ellegren, H. et al. The genomic landscape of species divergence in Ficedula flycatchers. Nature 491, 756–760 (2012) .

Qu, Y. et al. Ground tit genome reveals avian adaptation to living at high altitudes in the Tibetan plateau. Nat. Commun. 4, 2071 (2013) .

Stanke, M., Tzvetkova, A. & Morgenstern, B. AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome. Genome Biol. 7, (Suppl 1): S11.1–8 (2006) .

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009) .

DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011) .

Korneliussen, T., Albrechtsen, A. & Nielsen, R. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics 15, 356 (2014) .

Han, E., Sinsheimer, J. S. & Novembre, J. Characterizing bias in population genetic inferences from low-coverage sequencing data. Mol. Biol. Evol. 31, 723–735 (2014) .

Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014) .

Nielsen, R. et al. Genomic scans for selective sweeps using SNP data. Genome Res. 15, 1566–1575 (2005) .

Kim, S.Y. et al. Estimation of allele frequency and association mapping using next-generation sequencing data. BMC Bioinformatics 35, 231 (2011) .

Darling, A. E., Mau, B. & Perna, N. T. progressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS ONE 5, e11147 (2010) .

Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004) .

Wu, M., Chatterji, S. & Eisen, J. A. Accounting for alignment uncertainty in phylogenomics. PLoS ONE 7, e30288 (2012) .

Suyama, M., Torrents, D. & Bork, P. PAL2NAL: Robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006) .

Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007) .

Bindea, G. et al. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009) .

Guo, W. et al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics 14, 774 (2013) .

[-]

This item appears in the following Collection(s)

Show full item record