Dr Andrew Pocklington
Senior Lecturer, Division of Psychological Medicine and Clinical Neurosciences
- PocklingtonAJ@cardiff.ac.uk
- +44 29206 88428
- Hadyn Ellis Building, Room 2.10, Maindy Road, Cardiff, CF24 4HQ
Overview
I am interested in understanding how the human brain develops and functions and how this is disrupted in conditions such as schizophrenia, bipolar disorder, ADHD, autism and severe neurodevelopmental delay. My expertise lies in the analysis, integration and interpretation of molecular neuroscience and human genetic data. My group collaborates with neurobiologists and geneticists, analysing genomic, transcriptomic, proteomic and cellular & behavioural phenotypic data to generate insight into the functional organisation and regulation of cellular processes in health and disease. The high-level models developed through this work are used to guide further experimental studies and inform candidate target selection for drug development.
I pioneered the bioinformatic analysis of synapse function, shedding light on the organisation of postsynaptic signalling networks8,6 and the role of synapse molecular evolution in brain region specialisation7. My subsequent work has uncovered the first robust, genetic evidence for the disruption of excitatory and inhibitory synaptic signalling in schizophrenia2-5. Recently we have shown that cellular pathways active during early cortical development are highly enriched for genetic risk factors contributing to a wide spectrum of neuropsychiatric disorders1.
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1. Sanders B, D'Andrea D, Collins MO, Rees E, Steward TGJ, Zhu Y, Chapman G, Legge SE, Pardiñas AF, Harwood AJ, Gray WP, O'Donovan MC, Owen MJ, Errington AM, Blake DJ, Whitcomb DJ, Pocklington AJ§, Shin E§. DLG2 knockout reveals neurogenic transcriptional programs underlying neuropsychiatric disorders and cognition. Nat Commun 13:27 (2022)
2. Fernández E, Collins MO, Frank RAW, Zhu F, Kopanitsa MV, Nithianantharajah J, Lemprière SA, Fricker D, Elsegood KA, McLaughlin CL, Croning MDR, Mclean C, Armstrong JD, Hill WD, Deary IJ, Cencelli G, Bagni C, Fromer M, Purcell SM, Pocklington AJ, Choudhary JS, Komiyama NH, and Grant SGN. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence. Cell Rep 21:679-691 (2017)
3. Pocklington AJ§, Rees E, Walters JT, Han J, Kavanagh DH, Chambert KD, Holmans P, Moran JL, McCarroll SA, Kirov G, O'Donovan MC, and Owen MJ. Novel Findings from CNVs Implicate Inhibitory and Excitatory Signaling Complexes in Schizophrenia. Neuron 86:1203-1214 (2015)
4. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P, Georgieva L, Rees E, Palta P, Ruderfer DM, Carrera N, Humphreys I, Johnson JS, Roussos P, Barker DD, Banks E, Milanova V, Grant SG, Hannon E, Rose SA, Chambert K, Mahajan M, Scolnick EM, Moran JL, Kirov G, Palotie A, McCarroll SA, Holmans P, Sklar P, Owen MJ, Purcell SM, and O'Donovan MC. De novo mutations in schizophrenia implicate synaptic networks. Nature 506:179-184 (2014)
5. Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, Grozeva D, Fjodorova M, Wollerton R, Rees E, Nikolov I, van de Lagemaat LN, Bayés A, Fernandez E, Olason PI, Böttcher Y, Komiyama NH, Collins MO, Choudhary J, Stefansson K, Stefansson H, Grant SG, Purcell S, Sklar P, O'Donovan MC, and Owen MJ. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17:142-153 (2012)
6. Coba MP, Pocklington AJ, Collins MO, Kopanitsa MV, Uren RT, Swamy S, Croning MD, Choudhary JS, and Grant SG. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Sci Signal 2:ra19 (2009)
7. Emes RD*, Pocklington AJ*, Anderson CN*, Bayes A, Collins MO, Vickers CA, Croning MD, Malik BR, Choudhary JS, Armstrong JD, and Grant SG. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nat Neurosci 11:799-806 (2008)
8. Pocklington AJ, Cumiskey M, Armstrong JD, and Grant SG. The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour. Mol Syst Biol 2:2006.0023 (2006)
* joint first author, § corresponding author
Publication
2024
- Trastulla, L. et al. 2024. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. Nature Communications 15, article number: 5534. (10.1038/s41467-024-49338-2)
- Cabezas De La Fuente, D. et al. 2024. Impaired oxysterol-liver X receptor signaling underlies aberrant cortical neurogenesis in a human stem cell model of neurodevelopmental disorder. Cell Reports 43(3), article number: 113946. (10.1016/j.celrep.2024.113946)
2023
- Dimitriadis, S. I. et al. 2023. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cerebral Cortex 33(6), pp. 2997-3011. (10.1093/cercor/bhac256)
2022
- Trubetskoy, V. et al. 2022. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. Nature 604, pp. 502-508. (10.1038/s41586-022-04434-5)
- Sanders, B. et al. 2022. Transcriptional programs regulating neuronal differentiation are disrupted in DLG2 knockout human embryonic stem cells and enriched for schizophrenia and related disorders risk variants. Nature Communications 13(1), article number: 27. (10.1038/s41467-021-27601-0)
2021
- Clifton, N. E. et al. 2021. Genetic association of FMRP targets with psychiatric disorders. Molecular Psychiatry 26, pp. 2977-2990. (10.1038/s41380-020-00912-2)
- Hubbard, L. et al. 2021. Rare copy number variations are associated with poorer cognition in schizophrenia. Biological Psychiatry 90(1), pp. 28-34. (10.1016/j.biopsych.2020.11.025)
2020
- Grama, S. et al. 2020. Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort. Translational Psychiatry 10, article number: 309. (10.1038/s41398-020-00940-0)
- Rees, E. et al. 2020. De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia. Nature Neuroscience 23(2), pp. 179-184. (10.1038/s41593-019-0565-2)
- Hall, L. S. et al. 2020. A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. Human Molecular Genetics 29(1), pp. 159-167. (10.1093/hmg/ddz253)
2019
- Pain, O. et al. 2019. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics. Biological Psychiatry 86(4), pp. 265-273. (10.1016/j.biopsych.2019.04.034)
- Pardinas, A. F. et al. 2019. Pharmacogenomic variants and drug interactions identified through the genetic analysis of clozapine metabolism. American Journal of Psychiatry 176(6), pp. 477-486. (10.1176/appi.ajp.2019.18050589)
- Morgan, S. E. et al. 2019. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proceedings of the National Academy of Sciences 116(19), pp. 9604-9609. (10.1073/pnas.1820754116)
- Rees, E. et al. 2019. Targeted sequencing of 10,198 samples confirms abnormalities in neuronal activity and implicates voltage-gated sodium channels in schizophrenia pathogenesis. Biological Psychiatry 85(7), pp. 554-562. (10.1016/j.biopsych.2018.08.022)
- Lancaster, T. M. et al. 2019. Structural and functional neuroimaging of polygenic risk for schizophrenia: a recall-by-genotype-based approach. Schizophrenia Bulletin 45(2), pp. 405-414. (10.1093/schbul/sby037)
- Clifton, N. E. et al. 2019. Dynamic expression of genes associated with schizophrenia and bipolar disorder across development. Translational Psychiatry 9, article number: 74. (10.1038/s41398-019-0405-x)
- Vivian-Griffiths, T. et al. 2019. Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180(1), pp. 80-85. (10.1002/ajmg.b.32705)
2018
- Ruderfer, D. M. et al. 2018. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173(7), pp. 1705-1715.e16. (10.1016/j.cell.2018.05.046)
- Pardinas, A. F. et al. 2018. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics 50, pp. 381-389. (10.1038/s41588-018-0059-2)
2017
- Leonenko, G. et al. 2017. Mutation intolerant genes and targets of FMRP are enriched for nonsynonymous alleles in schizophrenia. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 174(7), pp. 724-731. (10.1002/ajmg.b.32560)
- Fernández, E. et al. 2017. Arc requires PSD95 for assembly into postsynaptic complexes involved with neural dysfunction and intelligence. Cell Reports 21(3), pp. 679-691. (10.1016/j.celrep.2017.09.045)
- Phillips, T. J. et al. 2017. Treating the placenta to prevent adverse effects of gestational hypoxia on fetal brain development. Scientific Reports 7, pp. -., article number: 9079. (10.1038/s41598-017-06300-1)
- McLaughlin, R. L. et al. 2017. Genetic correlation between amyotrophic lateral sclerosis and schizophrenia. Nature Communications 8, article number: 14774. (10.1038/ncomms14774)
- Clifton, N. E. et al. 2017. Schizophrenia copy number variants and associative learning. Molecular Psychiatry 22(2), pp. 178-182. (10.1038/mp.2016.227)
- Marshall, C. R. et al. 2017. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nature Genetics 49, pp. 27-35. (10.1038/ng.3725)
2016
- Rees, E. et al. 2016. Analysis of intellectual disability copy number variants for association with schizophrenia. JAMA Psychiatry 73(9), pp. 963-969. (10.1001/jamapsychiatry.2016.1831)
- Pardinas, A. et al. 2016. Common schizophrenia alleles are enriched in mutation-intolerant genes and maintained by background selection. [Online]. bioRxiv. (10.1101/068593) Available at: http://dx.doi.org/10.1101/068593
- Han, J. et al. 2016. Gender differences in CNV burden do not confound schizophrenia CNV associations. Scientific Reports 6, article number: 25986. (10.1038/srep25986)
- Franke, B. et al. 2016. Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nature Neuroscience 19(3), pp. 420-431. (10.1038/nn.4228)
- Richards, A. et al. 2016. Exome arrays capture polygenic rare variant contributions to schizophrenia. Human Molecular Genetics 25(5), pp. 1001-1007. (10.1093/hmg/ddv620)
2015
- Pocklington, A. et al. 2015. Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia. Neuron 86(5), pp. 1203-1214. (10.1016/j.neuron.2015.04.022)
- Vivian-Griffiths, T. et al. 2015. Utilising machine-learning algorithms to uncover complex genetic interactions in schizophrenia [Conference Abstract]. Human Heredity 79(1), pp. 48-48., article number: A46. (10.1159/000381109)
- Rees, E. et al. 2015. Analysis of exome sequence in 604 trios for recessive genotypes in schizophrenia. Translational Psychiatry 5(7), article number: e607. (10.1038/tp.2015.99)
2014
- Gusev, A. et al. 2014. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics 95(5), pp. 535-552. (10.1016/j.ajhg.2014.10.004)
- Martin, J. et al. 2014. Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants. Journal of the American Academy of Child and Adolescent Psychiatry 53(7), pp. 761-770.e26. (10.1016/j.jaac.2014.03.004)
- Ripke, S. et al. 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510), pp. 421-427. (10.1038/nature13595)
- Erk, S. et al. 2014. Replication of brain function effects of a genome-wide supported psychiatric risk variant in the CACNA1C gene and new multi-locus effects. NeuroImage 94, pp. 147-154. (10.1016/j.neuroimage.2014.03.007)
- Pocklington, A., O'Donovan, M. C. and Owen, M. J. 2014. The synapse in schizophrenia. European Journal of Neuroscience 39(7), pp. 1059-1067. (10.1111/ejn.12489)
- Fromer, M. et al. 2014. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, pp. 179-184. (10.1038/nature12929)
2012
- Kirov, G. et al. 2012. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Molecular Psychiatry 17(2), pp. 142-153. (10.1038/mp.2011.154)
2011
- Frank, R. A. et al. 2011. Clustered coding variants in the glutamate receptor complexes of individuals with schizophrenia and bipolar disorder. PLoS ONE 6(4), article number: e19011. (10.1371/journal.pone.0019011)
2010
- Jones, L. et al. 2010. Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease. PLoS ONE 5(11), article number: e13950. (10.1371/journal.pone.0013950)
2009
- Armstrong, J. D., Malik, B., Pocklington, A., Emes, R. and Grant, S. 2009. Evolution of the synapse proteome [Conference abstract]. Journal of Neurogenetics 23, pp. S34-S35.
- Coba, M. P. et al. 2009. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Science Signaling 2(68), article number: ra19. (10.1126/scisignal.2000102)
2008
- Emes, R. D. et al. 2008. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nature Neuroscience 11(7), pp. 799-806. (10.1038/nn.2135)
2006
- Armstrong, J. D., Pocklington, A., Cumiskey, M. A. and Grant, S. G. N. 2006. Reconstructing protein complexes: From proteomics to systems biology. Proteomics 6(17), pp. 4724-4731. (10.1002/pmic.200500895)
- Pocklington, A., Armstrong, J. D. and Grant, S. G. N. 2006. Organization of brain complexity--synapse proteome form and function. Briefings in Functional Genomics and Proteomics 5(1), pp. 66. (10.1093/bfgp/ell013)
- Pocklington, A., Cumiskey, M., Armstrong, J. D. and Grant, S. G. 2006. The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour. Molecular systems biology [electronic resource] 2(2006), pp. 23-23. (10.1038/msb4100041)
2003
- Dorey, P., Pocklington, A. and Tateo, R. 2003. Integrable aspects of the scaling q-state Potts models I: bound states and bootstrap closure. Nuclear Physics B 661(3), pp. 425-463. (10.1016/S0550-3213(03)00181-0)
- Dorey, P., Pocklington, A. and Tateo, R. 2003. Integrable aspects of the scaling q-state Potts models II: finite-size effects. Nuclear Physics B 661(3), pp. 464-513. (10.1016/S0550-3213(03)00182-2)
2000
- Khastgir, S. P., Pocklington, A. and Sasaki, R. 2000. Quantum Calogero-Moser models: integrability for all root systems. Journal of Physics A: Mathematical and General 33(49), pp. 9033-9064. (10.1088/0305-4470/33/49/303)
1998
- Dorey, P., Pocklington, A., Tateo, R. and Watts, G. 1998. TBA and TCSA with boundaries and excited states. Nuclear Physics B 525(3), pp. 641-663. (10.1016/S0550-3213(98)00339-3)
Articles
- Trastulla, L. et al. 2024. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. Nature Communications 15, article number: 5534. (10.1038/s41467-024-49338-2)
- Cabezas De La Fuente, D. et al. 2024. Impaired oxysterol-liver X receptor signaling underlies aberrant cortical neurogenesis in a human stem cell model of neurodevelopmental disorder. Cell Reports 43(3), article number: 113946. (10.1016/j.celrep.2024.113946)
- Dimitriadis, S. I. et al. 2023. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cerebral Cortex 33(6), pp. 2997-3011. (10.1093/cercor/bhac256)
- Trubetskoy, V. et al. 2022. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. Nature 604, pp. 502-508. (10.1038/s41586-022-04434-5)
- Sanders, B. et al. 2022. Transcriptional programs regulating neuronal differentiation are disrupted in DLG2 knockout human embryonic stem cells and enriched for schizophrenia and related disorders risk variants. Nature Communications 13(1), article number: 27. (10.1038/s41467-021-27601-0)
- Clifton, N. E. et al. 2021. Genetic association of FMRP targets with psychiatric disorders. Molecular Psychiatry 26, pp. 2977-2990. (10.1038/s41380-020-00912-2)
- Hubbard, L. et al. 2021. Rare copy number variations are associated with poorer cognition in schizophrenia. Biological Psychiatry 90(1), pp. 28-34. (10.1016/j.biopsych.2020.11.025)
- Grama, S. et al. 2020. Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort. Translational Psychiatry 10, article number: 309. (10.1038/s41398-020-00940-0)
- Rees, E. et al. 2020. De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia. Nature Neuroscience 23(2), pp. 179-184. (10.1038/s41593-019-0565-2)
- Hall, L. S. et al. 2020. A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. Human Molecular Genetics 29(1), pp. 159-167. (10.1093/hmg/ddz253)
- Pain, O. et al. 2019. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics. Biological Psychiatry 86(4), pp. 265-273. (10.1016/j.biopsych.2019.04.034)
- Pardinas, A. F. et al. 2019. Pharmacogenomic variants and drug interactions identified through the genetic analysis of clozapine metabolism. American Journal of Psychiatry 176(6), pp. 477-486. (10.1176/appi.ajp.2019.18050589)
- Morgan, S. E. et al. 2019. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proceedings of the National Academy of Sciences 116(19), pp. 9604-9609. (10.1073/pnas.1820754116)
- Rees, E. et al. 2019. Targeted sequencing of 10,198 samples confirms abnormalities in neuronal activity and implicates voltage-gated sodium channels in schizophrenia pathogenesis. Biological Psychiatry 85(7), pp. 554-562. (10.1016/j.biopsych.2018.08.022)
- Lancaster, T. M. et al. 2019. Structural and functional neuroimaging of polygenic risk for schizophrenia: a recall-by-genotype-based approach. Schizophrenia Bulletin 45(2), pp. 405-414. (10.1093/schbul/sby037)
- Clifton, N. E. et al. 2019. Dynamic expression of genes associated with schizophrenia and bipolar disorder across development. Translational Psychiatry 9, article number: 74. (10.1038/s41398-019-0405-x)
- Vivian-Griffiths, T. et al. 2019. Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180(1), pp. 80-85. (10.1002/ajmg.b.32705)
- Ruderfer, D. M. et al. 2018. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173(7), pp. 1705-1715.e16. (10.1016/j.cell.2018.05.046)
- Pardinas, A. F. et al. 2018. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics 50, pp. 381-389. (10.1038/s41588-018-0059-2)
- Leonenko, G. et al. 2017. Mutation intolerant genes and targets of FMRP are enriched for nonsynonymous alleles in schizophrenia. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 174(7), pp. 724-731. (10.1002/ajmg.b.32560)
- Fernández, E. et al. 2017. Arc requires PSD95 for assembly into postsynaptic complexes involved with neural dysfunction and intelligence. Cell Reports 21(3), pp. 679-691. (10.1016/j.celrep.2017.09.045)
- Phillips, T. J. et al. 2017. Treating the placenta to prevent adverse effects of gestational hypoxia on fetal brain development. Scientific Reports 7, pp. -., article number: 9079. (10.1038/s41598-017-06300-1)
- McLaughlin, R. L. et al. 2017. Genetic correlation between amyotrophic lateral sclerosis and schizophrenia. Nature Communications 8, article number: 14774. (10.1038/ncomms14774)
- Clifton, N. E. et al. 2017. Schizophrenia copy number variants and associative learning. Molecular Psychiatry 22(2), pp. 178-182. (10.1038/mp.2016.227)
- Marshall, C. R. et al. 2017. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nature Genetics 49, pp. 27-35. (10.1038/ng.3725)
- Rees, E. et al. 2016. Analysis of intellectual disability copy number variants for association with schizophrenia. JAMA Psychiatry 73(9), pp. 963-969. (10.1001/jamapsychiatry.2016.1831)
- Han, J. et al. 2016. Gender differences in CNV burden do not confound schizophrenia CNV associations. Scientific Reports 6, article number: 25986. (10.1038/srep25986)
- Franke, B. et al. 2016. Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nature Neuroscience 19(3), pp. 420-431. (10.1038/nn.4228)
- Richards, A. et al. 2016. Exome arrays capture polygenic rare variant contributions to schizophrenia. Human Molecular Genetics 25(5), pp. 1001-1007. (10.1093/hmg/ddv620)
- Pocklington, A. et al. 2015. Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia. Neuron 86(5), pp. 1203-1214. (10.1016/j.neuron.2015.04.022)
- Vivian-Griffiths, T. et al. 2015. Utilising machine-learning algorithms to uncover complex genetic interactions in schizophrenia [Conference Abstract]. Human Heredity 79(1), pp. 48-48., article number: A46. (10.1159/000381109)
- Rees, E. et al. 2015. Analysis of exome sequence in 604 trios for recessive genotypes in schizophrenia. Translational Psychiatry 5(7), article number: e607. (10.1038/tp.2015.99)
- Gusev, A. et al. 2014. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics 95(5), pp. 535-552. (10.1016/j.ajhg.2014.10.004)
- Martin, J. et al. 2014. Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants. Journal of the American Academy of Child and Adolescent Psychiatry 53(7), pp. 761-770.e26. (10.1016/j.jaac.2014.03.004)
- Ripke, S. et al. 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510), pp. 421-427. (10.1038/nature13595)
- Erk, S. et al. 2014. Replication of brain function effects of a genome-wide supported psychiatric risk variant in the CACNA1C gene and new multi-locus effects. NeuroImage 94, pp. 147-154. (10.1016/j.neuroimage.2014.03.007)
- Pocklington, A., O'Donovan, M. C. and Owen, M. J. 2014. The synapse in schizophrenia. European Journal of Neuroscience 39(7), pp. 1059-1067. (10.1111/ejn.12489)
- Fromer, M. et al. 2014. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, pp. 179-184. (10.1038/nature12929)
- Kirov, G. et al. 2012. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Molecular Psychiatry 17(2), pp. 142-153. (10.1038/mp.2011.154)
- Frank, R. A. et al. 2011. Clustered coding variants in the glutamate receptor complexes of individuals with schizophrenia and bipolar disorder. PLoS ONE 6(4), article number: e19011. (10.1371/journal.pone.0019011)
- Jones, L. et al. 2010. Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease. PLoS ONE 5(11), article number: e13950. (10.1371/journal.pone.0013950)
- Armstrong, J. D., Malik, B., Pocklington, A., Emes, R. and Grant, S. 2009. Evolution of the synapse proteome [Conference abstract]. Journal of Neurogenetics 23, pp. S34-S35.
- Coba, M. P. et al. 2009. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Science Signaling 2(68), article number: ra19. (10.1126/scisignal.2000102)
- Emes, R. D. et al. 2008. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nature Neuroscience 11(7), pp. 799-806. (10.1038/nn.2135)
- Armstrong, J. D., Pocklington, A., Cumiskey, M. A. and Grant, S. G. N. 2006. Reconstructing protein complexes: From proteomics to systems biology. Proteomics 6(17), pp. 4724-4731. (10.1002/pmic.200500895)
- Pocklington, A., Armstrong, J. D. and Grant, S. G. N. 2006. Organization of brain complexity--synapse proteome form and function. Briefings in Functional Genomics and Proteomics 5(1), pp. 66. (10.1093/bfgp/ell013)
- Pocklington, A., Cumiskey, M., Armstrong, J. D. and Grant, S. G. 2006. The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour. Molecular systems biology [electronic resource] 2(2006), pp. 23-23. (10.1038/msb4100041)
- Dorey, P., Pocklington, A. and Tateo, R. 2003. Integrable aspects of the scaling q-state Potts models I: bound states and bootstrap closure. Nuclear Physics B 661(3), pp. 425-463. (10.1016/S0550-3213(03)00181-0)
- Dorey, P., Pocklington, A. and Tateo, R. 2003. Integrable aspects of the scaling q-state Potts models II: finite-size effects. Nuclear Physics B 661(3), pp. 464-513. (10.1016/S0550-3213(03)00182-2)
- Khastgir, S. P., Pocklington, A. and Sasaki, R. 2000. Quantum Calogero-Moser models: integrability for all root systems. Journal of Physics A: Mathematical and General 33(49), pp. 9033-9064. (10.1088/0305-4470/33/49/303)
- Dorey, P., Pocklington, A., Tateo, R. and Watts, G. 1998. TBA and TCSA with boundaries and excited states. Nuclear Physics B 525(3), pp. 641-663. (10.1016/S0550-3213(98)00339-3)
Websites
- Pardinas, A. et al. 2016. Common schizophrenia alleles are enriched in mutation-intolerant genes and maintained by background selection. [Online]. bioRxiv. (10.1101/068593) Available at: http://dx.doi.org/10.1101/068593
Research
My work to date has identified neurodevelopmental pathways and components of the mature synaptic signalling machinery whose perturbation contributes to schizophrenia aetiology. Disruption of these same processes plays a role in other neuropsychiatric disorders. I am currently pursuing several lines of fundamental and translational research that build on these findings (below). Broadly speaking, my long-term research plans are centred around the following questions:
- What are the cellular pathways regulating neuronal development and diversification?
- Which developmental/mature cell-types are disrupted in neuropsychiatric disorders?
- How does disruption of cell-types/cellular pathways map onto behavioural symptoms?
- Can modulation of pathways in mature cell-types rescue cellular/circuit level deficits?
Fundamental research
Neurodevelopmental pathways in health and disease
I have become increasingly interested in unravelling the cellular processes regulating brain development and the diversification of excitatory and inhibitory neurons into specialised sub-types. The birth of neurons during CNS development is a sequential process involving the generation of progressively more specialised cell-types. Proliferating neuroectodermal cells in the neural tube give rise to neural precursors (NPCs): radial glia (RG) and intermediate progenitors (iPCs). Both RG and iPCs have a limited ability to proliferate, with RG giving rise to neurons either directly or via iPCs. Studies to date indicate that neuronal identity is determined by the internal state of NPCs immediately prior to their exit from the cell-cycle, with changes in this state over time leading to the progressive generation of different sub-types. However, the cellular pathways regulating this process remain unclear.
In collaboration with the stem-cell neurobiology group of Dr Jenny Shin we have begun to map out these pathways and investigate the extent to which they are disrupted across a wide spectrum of neuropsychiatric disorders; this work utilises the in vitro differentiation of human pluripotent stem cells to model neurodevelopment. Our recent study uncovered coordinated waves of gene expression regulating the growth and development of deep layer cortical excitatory neurons; it also revealed these transcriptional programs to be highly enriched for common and rare genetic variants conferring risk for neuropsychiatric disorders. Following on from this we are now analysing single-cell gene expression time-course data from the in vitro differentiation of multiple cortical inhibitory interneuron sub-types. In addition to this work I collaborate with the developmental neurobiology group of Prof Beatriz Rico, who utilise rodent model systems to investigate the role of interneuron development in schizophrenia with a particular focus on synaptogenesis.
Cell-type specificity in neuropsychiatric genetic disorders
Each aspect of behaviour arises from activity within a unique constellation of local neuronal circuits distributed across a network of brain regions. The computational properties of these circuits are determined by the neuronal sub-types of which they are composed: their abundance, connectivity and history of past activity as encoded in their internal state. In order to understand the role of genetic variants in generating the behavioural symptoms associated with a given neuropsychiatric disorder, we need to know which neuronal sub-types they affect; the functional properties they perturb in these sub-types; and the computational role(s) these properties play in the function of neuronal circuits and networks underlying specific behaviours.
My interest in the molecular basis of neuronal specificity and behaviour arose through an early study with the group of Prof Seth Grant; we showed that while mouse brain regions express a similar set of postsynaptic proteins, the levels of upstream signalling/structural components (e.g. receptors and closely associated scaffolding molecules) varied most between regions. This suggests that the precise expression of these molecules plays an important role in shaping the computational and cognitive properties of the brain. Genetic studies by ourselves and others robustly implicate the disruption of postsynaptic signalling (including individual channels and receptors) in disorders such as schizophrenia. This indicates that neuropsychiatric disorders are likely to involve the widespread disruption of brain function, with any one risk variant impacting multiple behavioural traits. My group is currently utilising single-cell expression data to explore the relative impact of genetic risk factors on different neuronal sub-types.
Refining disease-relevant biology via network analysis
Our ability to determine the precise cellular pathways disrupted in disease is limited by the resolution of existing biological data. For example, proteomic studies have revealed the molecular composition of a number of synaptic components: presynaptic neurotransmitter release vesicles, postsynaptic receptor-linked signal transduction complexes, etc. Using these data I was able to show that the constituents of postsynaptic NMDA receptor complexes (NRCs) are enriched for rare variants found in individuals with schizophrenia, implicating disruption of NRCs in the disorder. NRCs couple the NMDA receptor to multiple downstream signalling pathways whose activation plays a major role in regulating the induction of synaptic plasticity at excitatory synapses. This raises the question: does schizophrenia involve widespread disruption of NRC functioning, or is it associated with the perturbation of one or more specific signalling pathways within NRCs? We have previously shown that direct, physical interactions (protein-protein interactions, PPIs) organise NRC proteins into functionally distinct sub-units, shaping the computational properties of these complexes. Building on this, we can ask whether there are subsets of interacting proteins (functionally relevant subnetworks) that are more highly enriched for disease association than the NRC as a whole.
Such techniques are widely applicable. My group is using subnetwork identification methods to analyse both gene-regulatory and synaptic protein networks. The work on synaptic networks is being carried out in collaboration with the group of Prof Douglas Armstrong, who have curated a large body of PPI data for synapse proteins.
Translational research
Prioritising candidate genes for drug development
Existing medications are effective in controlling the psychotic symptoms of schizophrenia for many individuals. However, they do not treat the negative (e.g. anhedonia, social withdrawal, apathy) and cognitive symptoms of the disorder which have a major impact on quality of life. The development of more effective treatments has been extremely challenging, with relatively little progress in the past 60 years. A major factor contributing to this has been our limited understanding of disease mechanisms, which has made it virtually impossible to rationally select biological targets (molecules, pathways, cell-types) whose manipulation will impact one or more aspect of the disorder. Common and rare variant studies increasingly have power to generate insight into disease aetiology and are now starting to robustly identify schizophrenia risk genes. Drawing together the various strands of fundamental research outlined above, my group is seeking to uncover the cell types and biological pathways within which these risk genes operate and use this information to identify potential molecular targets for the development of novel therapeutics. This is being pursued through collaboration between Cardiff University and Takeda Pharmaceutical Company Ltd.
Biography
Following a degree in Mathematical Physics (BSc, Hons 1st class) at the University of Edinburgh and a PhD in Theoretical Physics at the University of Durham, I spent several years as a post-doctoral researcher in Japan and Brazil. Becoming increasingly interested in the emerging fields of bioinformatics and systems biology, I returned to the University of Edinburgh where I obtained an MSc in Informatics, specializing in bioinformatics and graduating with distinction. In 2003 I was awarded an MRC Special Research Training Fellowship in Bioinformatics to study the functional organisation of the synapse proteome and its role in behaviour and disease, analysing much of the molecular neuroscience data generated by the Genes to Cognition (G2C) research programme at the Wellcome Trust Sanger Institute. In 2009 I was appointed Senior Lecturer in Bioinformatics at the MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University.
Academic positions
- 2009-present Senior Lecturer in Bioinformatics
MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, UK - 2007-2009 Postdoctoral Research Fellow
University of Edinburgh and G2C (Wellcome Trust Sanger Institute), UK - 2003-2007 MRC Special Research Training Fellow in Bioinformatics
University of Edinburgh, UK - 2000-2001 Postdoctoral Research Fellow
Instituto de Física Teórica, UNESP, São Paulo, Brazil - 1998-2000 JSPS Research Fellow
Yukawa Institute of Theoretical Physics, Kyoto, Japan