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Using genetic data in cognitive neuroscience: from growing pains to genuine insights

Nature Reviews Neuroscience volume 9, pages 710–720 (2008)Cite this article

Abstract

Research that combines genetic and cognitive neuroscience data aims to elucidate the mechanisms that underlie human behaviour and experience by way of 'intermediate phenotypes': variations in brain function. Using neuroimaging and other methods, this approach is poised to make the transition from health-focused investigations to inquiries into cognitive, affective and social functions, including ones that do not readily lend themselves to animal models. The growing pains of this emerging field are evident, yet there are also reasons for a measured optimism.

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Figure 1: Integrating cognitive neuroscience and molecular genetics through the intermediate-phenotype approach.
Figure 2: Attention-related candidate genes validated using the Attention Network Test (ANT).
Figure 3: Connectivity analysis.
Figure 4: Molecular characterization of gene variations.

References

  1. Posner, M. I. & Rothbart, M. K. Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol. 58, 1–23 (2007).

    Article  PubMed  Google Scholar 

  2. Meyer-Lindenberg, A. & Weinberger, D. R. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Rev. Neurosci. 7, 818–827 (2006).

    Article  CAS  Google Scholar 

  3. Kosslyn, S. M. et al. Bridging psychology and biology. The analysis of individuals in groups. Am. Psychol. 57, 341–351 (2002).

    Article  PubMed  Google Scholar 

  4. Goldberg, T. E. & Weinberger, D. R. Genes and the parsing of cognitive processes. Trends Cogn. Sci. 8, 325–335 (2004).

    Article  PubMed  Google Scholar 

  5. Plomin, R. Psychology in a post-genomics world: it will be more important than ever. APS Obs. 13 (2000).

  6. Fan, J., McCandliss, B. D., Sommer, T., Raz, A. & Posner, M. I. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 14, 340–347 (2002).

    Article  PubMed  Google Scholar 

  7. Fan, J., Wu, Y., Fossella, J. A. & Posner, M. I. Assessing the heritability of attentional networks. BMC Neurosci. 2, 14 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fan, J. & Posner, M. Human attentional networks. Psychiatr. Prax 31 (Suppl. 2), S210–S214 (2004).

    Article  PubMed  Google Scholar 

  9. Posner, M. & Boies, S. Components of attention. Psychol. Rev. 78, 391–408 (1971).

    Article  Google Scholar 

  10. Raz, A. & Buhle, J. Typologies of attentional networks. Nature Rev. Neurosci. 7, 367–379 (2006).

    Article  CAS  Google Scholar 

  11. Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990).

    Article  CAS  PubMed  Google Scholar 

  12. Fossella, J. & Posner, M. I. in The Cognitive Neurosciences III (ed. Gazzaniga, M. S.) 1255–1266 (MIT Press, Cambridge, Massachusetts, 2004).

    Google Scholar 

  13. Fan, J., Fossella, J., Sommer, T., Wu, Y. & Posner, M. I. Mapping the genetic variation of executive attention onto brain activity. Proc. Natl Acad. Sci. USA 100, 7406–7411 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Meyer-Lindenberg, A. et al. Neural mechanisms of genetic risk for impulsivity and violence in humans. Proc. Natl Acad. Sci. USA 103, 6269–6274 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Engle, R. W. Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23 (2002).

    Article  Google Scholar 

  16. Baddeley, A. Working memory: looking back and looking forward. Nature Rev. Neurosci. 4, 829–839 (2003).

    Article  CAS  Google Scholar 

  17. Blasi, G. et al. Effect of catechol-O-methyltransferase val158met genotype on attentional control. J. Neurosci. 25, 5038–5045 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Winterer, G. et al. COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits. Neuroimage 32, 1722–1732 (2006).

    Article  PubMed  Google Scholar 

  19. Krabbendam, L. et al. Associations between COMTVal158Met polymorphism and cognition: direct or indirect effects? Eur. Psychiatry 21, 338–342 (2006).

    Article  PubMed  Google Scholar 

  20. Reuter, M., Ott, U., Vaitl, D. & Hennig, J. Impaired executive control is associated with a variation in the promoter region of the tryptophan hydroxylase 2 gene. J. Cogn. Neurosci. 19, 401–408 (2007).

    Article  PubMed  Google Scholar 

  21. Winterer, G. & Weinberger, D. R. Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends Neurosci. 27, 683–690 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. Fossella, J. et al. Assessing the molecular genetics of attention networks. BMC Neurosci. 3, 14 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Birkas, E. et al. Association between dopamine D4 receptor (DRD4) gene polymorphisms and novelty-elicited auditory event-related potentials in preschool children. Brain Res. 1103, 150–158 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L. & Posner, M. I. Training, maturation, and genetic influences on the development of executive attention. Proc. Natl Acad. Sci. USA 102, 14931–14936 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fossella, J., Green, A. E. & Fan, J. Evaluation of a structural polymorphism in the ankyrin repeat and kinase domain containing 1 (ANKK1) gene and the activation of executive attention networks. Cogn. Affect. Behav. Neurosci. 6, 71–78 (2006).

    Article  PubMed  Google Scholar 

  26. Passamonti, L. et al. Monoamine oxidase-a genetic variations influence brain activity associated with inhibitory control: new insight into the neural correlates of impulsivity. Biol. Psychiatry 59, 334–340 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Gallinat, J. et al. Genetic variations of the NR3A subunit of the NMDA receptor modulate prefrontal cerebral activity in humans. J. Cogn. Neurosci. 19, 59–68 (2007).

    Article  PubMed  Google Scholar 

  28. Porjesz, B. et al. Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proc. Natl Acad. Sci. USA 99, 3729–3733 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fallgatter, A. J. et al. Allelic variation of serotonin transporter function modulates the brain electrical response for error processing. Neuropsychopharmacology 29, 1506–1511 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Begleiter, H. & Porjesz, B. Genetics of human brain oscillations. Int. J. Psychophysiol. 60, 162–171 (2006).

    Article  PubMed  Google Scholar 

  31. Parasuraman, R., Greenwood, P. M., Kumar, R. & Fossella, J. Beyond heritability: neurotransmitter genes differentially modulate visuospatial attention and working memory. Psychol. Sci. 16, 200–207 (2005).

    Article  PubMed  Google Scholar 

  32. Winterer, G. et al. Association of attentional network function with exon 5 variations of the CHRNA4 gene. Hum. Mol. Genet. 16, 2165–2174 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Espeseth, T. et al. Interactive effects of APOE and CHRNA4 on attention and white matter volume in healthy middle-aged and older adults. Cogn. Affect. Behav. Neurosci. 6, 31–43 (2006).

    Article  PubMed  Google Scholar 

  34. Sarter, M. & Bruno, J. P. Cognitive functions of cortical acetylcholine: toward a unifying hypothesis. Brain Res. Brain Res. Rev. 23, 28–46 (1997).

    Article  CAS  PubMed  Google Scholar 

  35. Davidson, M. C. & Marrocco, R. T. Local infusion of scopolamine into intraparietal cortex slows covert orienting in rhesus monkeys. J. Neurophysiol. 83, 1536–1549 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Posner, M. I., Rothbart, M. K. & Sheese, B. E. Attention genes. Dev. Sci. 10, 24–29 (2007).

    Article  PubMed  Google Scholar 

  37. Tan, H. Y. et al. Catechol-O-methyltransferase Val158Met modulation of prefrontal-parietal-striatal brain systems during arithmetic and temporal transformations in working memory. J. Neurosci. 27, 13393–13401 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Frank, M. J., Moustafa, A. A., Haughey, H. M., Curran, T. & Hutchison, K. E. Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proc. Natl Acad. Sci. USA 104, 16311–16316 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Meyer-Lindenberg, A. et al. Genetic evidence implicating DARPP-32 in human frontostriatal structure, function, and cognition. J. Clin. Invest. 117, 672–682 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Diamond, A., Briand, L., Fossella, J. & Gehlbach, L. Genetic and neurochemical modulation of prefrontal cognitive functions in children. Am. J. Psychiatry 161, 125–132 (2004).

    Article  PubMed  Google Scholar 

  41. Petrides, M. & Milner, B. Deficits on subject-ordered tasks after frontal- and temporal-lobe lesions in man. Neuropsychologia 20, 249–262 (1982).

    Article  CAS  PubMed  Google Scholar 

  42. Wiegersma, S., van der Scheer, E. & Human, R. Subjective ordering, short-term memory, and the frontal lobes. Neuropsychologia 28, 95–98 (1990).

    Article  CAS  PubMed  Google Scholar 

  43. Collins, P., Roberts, A. C., Dias, R., Everitt, B. J. & Robbins, T. W. Perseveration and strategy in a novel spatial self-ordered sequencing task for nonhuman primates: effects of excitotoxic lesions and dopamine depletions of the prefrontal cortex. J. Cogn. Neurosci. 10, 332–354 (1998).

    Article  CAS  PubMed  Google Scholar 

  44. Diamond, A., Prevor, M. B., Callender, G. & Druin, D. P. Prefrontal cortex cognitive deficits in children treated early and continuously for PKU. Monogr. Soc. Res. Child. Dev. 62, i–v, 1–208 (1997).

    Article  CAS  PubMed  Google Scholar 

  45. Sheese, B. E., Voelker, P. M., Rothbart, M. K. & Posner, M. I. Parenting quality interacts with genetic variation in dopamine receptor D4 to influence temperament in early childhood. Dev. Psychopathol. 19, 1039–1046 (2007).

    Article  PubMed  Google Scholar 

  46. Caspi, A. & Moffitt, T. E. Gene–environment interactions in psychiatry: joining forces with neuroscience. Nature Rev. Neurosci. 7, 583–590 (2006).

    Article  CAS  Google Scholar 

  47. Meyer-Lindenberg, A. et al. Impact of complex genetic variation in COMT on human brain function. Mol. Psychiatry 11, 867–877, 797 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Egan, M. F. et al. Effect of COMT Val108/158Met genotype on frontal lobe function and risk for schizophrenia. Proc. Natl Acad. Sci. USA 98, 6917–6922 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hariri, A. R. & Holmes, A. Genetics of emotional regulation: the role of the serotonin transporter in neural function. Trends Cogn. Sci. 10, 182–191 (2006).

    Article  PubMed  Google Scholar 

  50. Neumeister, A. et al. Differential effects of 5-HTTLPR genotypes on the behavioral and neural responses to tryptophan depletion in patients with major depression and controls. Arch. Gen. Psychiatry 63, 978–986 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Spoont, M. R. Modulatory role of serotonin in neural information processing: implications for human psychopathology. Psychol. Bull. 112, 330–350 (1992).

    Article  CAS  PubMed  Google Scholar 

  52. Munafo, M. R., Clark, T. & Flint, J. Does measurement instrument moderate the association between the serotonin transporter gene and anxiety-related personality traits? A meta-analysis. Mol. Psychiatry 10, 415–419 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Schinka, J. A., Busch, R. M. & Robichaux-Keene, N. A meta-analysis of the association between the serotonin transporter gene polymorphism (5-HTTLPR) and trait anxiety. Mol. Psychiatry 9, 197–202 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Sen, S., Burmeister, M. & Ghosh, D. Meta-analysis of the association between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety-related personality traits. Am. J. Med. Genet. B Neuropsychiatr. Genet. 127, 85–89 (2004).

    Article  Google Scholar 

  55. Hariri, A. R. et al. Serotonin transporter genetic variation and the response of the human amygdala. Science 297, 400–403 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Hariri, A. R. et al. A susceptibility gene for affective disorders and the response of the human amygdala. Arch. Gen. Psychiatry 62, 146–152 (2005).

    Article  CAS  PubMed  Google Scholar 

  57. Munafo, M. R., Brown, S. A. & Hariri, A. R. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: a meta-analysis. Biol. Psychiatry 63, 852–857 (2008).

    Article  CAS  PubMed  Google Scholar 

  58. Johnstone, T. et al. Stability of amygdala BOLD response to fearful faces over multiple scan sessions. Neuroimage 25, 1112–1123 (2005).

    Article  PubMed  Google Scholar 

  59. Manuck, S. B., Brown, S. M., Forbes, E. E. & Hariri, A. R. Temporal stability of individual differences in amygdala reactivity. Am. J. Psychiatry 164, 1613–1614 (2007).

    Article  PubMed  Google Scholar 

  60. Pezawas, L. et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nature Neurosci. 8, 828–834 (2005).

    Article  CAS  PubMed  Google Scholar 

  61. Heinz, A. et al. Amygdala-prefrontal coupling depends on a genetic variation of the serotonin transporter. Nature Neurosci. 8, 20–21 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Gray, J. R. et al. Affective personality differences in neural processing efficiency confirmed using fMRI. Cogn. Affect. Behav. Neurosci. 5, 182–190 (2005).

    Article  PubMed  Google Scholar 

  63. Gray, J. R., Braver, T. S. & Raichle, M. E. Integration of emotion and cognition in the lateral prefrontal cortex. Proc. Natl Acad. Sci. USA 99, 4115–4120 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lewis, M. D. Bridging emotion theory and neurobiology through dynamic systems modeling. Behav. Brain Sci. 28, 169–194 (2005).

    Article  PubMed  Google Scholar 

  65. Enoch, M. A., Xu, K., Ferro, E., Harris, C. R. & Goldman, D. Genetic origins of anxiety in women: a role for a functional catechol-O-methyltransferase polymorphism. Psychiatr. Genet. 13, 33–41 (2003).

    Article  PubMed  Google Scholar 

  66. Karayiorgou, M. et al. Genotype determining low catechol-O-methyltransferase activity as a risk factor for obsessive-compulsive disorder. Proc. Natl Acad. Sci. USA 94, 4572–4575 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Olsson, C. A. et al. Association between the COMT Val158Met polymorphism and propensity to anxiety in an Australian population-based longitudinal study of adolescent health. Psychiatr. Genet. 15, 109–115 (2005).

    Article  PubMed  Google Scholar 

  68. Dunlop, B. W. & Nemeroff, C. B. The role of dopamine in the pathophysiology of depression. Arch. Gen. Psychiatry 64, 327–337 (2007).

    Article  CAS  PubMed  Google Scholar 

  69. Smolka, M. N. et al. Gene-gene effects on central processing of aversive stimuli. Mol. Psychiatry 12, 307–317 (2007).

    Article  CAS  PubMed  Google Scholar 

  70. Drabant, E. M. et al. Catechol O-methyltransferase Val158Met genotype and neural mechanisms related to affective arousal and regulation. Arch. Gen. Psychiatry 63, 1396–406 (2006).

    Article  CAS  PubMed  Google Scholar 

  71. Zubieta, J. K. et al. COMT val158met genotype affects mu-opioid neurotransmitter responses to a pain stressor. Science 299, 1240–1243 (2003).

    Article  CAS  PubMed  Google Scholar 

  72. Canli, T. et al. Beyond affect: a role for genetic variation of the serotonin transporter in neural activation during a cognitive attention task. Proc. Natl Acad. Sci. USA 102, 12224–12229 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Heinz, A. et al. Serotonin transporter genotype (5-HTTLPR): effects of neutral and undefined conditions on amygdala activation. Biol. Psychiatry 61, 1011–1014 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. Brown, S. M. et al. A regulatory variant of the human tryptophan hydroxylase-2 gene biases amygdala reactivity. Mol. Psychiatry 10, 884–888, 805 (2005).

    Article  CAS  PubMed  Google Scholar 

  75. Canli, T., Congdon, E., Gutknecht, L., Constable, R. T. & Lesch, K. P. Amygdala responsiveness is modulated by tryptophan hydroxylase-2 gene variation. J. Neural Transm. 112, 1479–1485 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Neumann, S. A. et al. Human choline transporter gene variation is associated with corticolimbic reactivity and autonomic-cholinergic function. Biol. Psychiatry 60, 1155–1162 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Pezawas, L. et al. Evidence of biologic epistasis between BDNF and SLC6A4 and implications for depression. Mol. Psychiatry 13, 654, 709–716 (2008).

    Article  Google Scholar 

  78. Jonsson, E. G., et al. Polymorphisms in the dopamine D2 receptor gene and their relationships to striatal dopamine receptor density of healthy volunteers. Mol. Psychiatry 4, 290–296 (1999).

    Article  CAS  PubMed  Google Scholar 

  79. Laakso, A. et al. The A1 allele of the human D2 dopamine receptor gene is associated with increased activity of striatal L-amino acid decarboxylase in healthy subjects. Pharmacogenet. Genomics 15, 387–391 (2005).

    Article  CAS  PubMed  Google Scholar 

  80. Cohen, M. X., Young, J., Baek, J. M., Kessler, C. & Ranganath, C. Individual differences in extraversion and dopamine genetics predict neural reward responses. Brain Res. Cogn. Brain Res. 25, 851–861 (2005).

    Article  CAS  PubMed  Google Scholar 

  81. Kirsch, P. et al. Imaging gene–substance interactions: the effect of the DRD2 TaqIA polymorphism and the dopamine agonist bromocriptine on the brain activation during the anticipation of reward. Neurosci. Lett. 405, 196–201 (2006).

    Article  CAS  PubMed  Google Scholar 

  82. Neville, M. J., Johnstone, E. C. & Walton, R. T. Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.1. Hum. Mutat. 23, 540–545 (2004).

    Article  CAS  PubMed  Google Scholar 

  83. Zhang, Y. et al. Polymorphisms in human dopamine D2 receptor gene affect gene expression, splicing, and neuronal activity during working memory. Proc. Natl Acad. Sci. USA 104, 20552–20557 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lerman, C. et al. Role of functional genetic variation in the dopamine D2 receptor (DRD2) in response to bupropion and nicotine replacement therapy for tobacco dependence: results of two randomized clinical trials. Neuropsychopharmacology 31, 231–242 (2006).

    Article  CAS  PubMed  Google Scholar 

  85. Dick, D. M. et al. Association of CHRM2 with IQ: converging evidence for a gene influencing intelligence. Behav. Genet. 37, 265–272 (2007).

    Article  PubMed  Google Scholar 

  86. Kane, M. J. & Engle, R. W. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon. Bull. Rev. 9, 637–671 (2002).

    Article  PubMed  Google Scholar 

  87. Bouchard, T. J. Jr & McGue, M. Familial studies of intelligence: a review. Science 212, 1055–1059 (1981).

    Article  PubMed  Google Scholar 

  88. Cools, R. & Robbins, T. W. Chemistry of the adaptive mind. Philos. Transact. A Math. Phys. Eng. Sci. 362, 2871–2888 (2004).

    Article  CAS  Google Scholar 

  89. Lotta, T. et al. Kinetics of human soluble and membrane-bound catechol O-methyltransferase: a revised mechanism and description of the thermolabile variant of the enzyme. Biochemistry 34, 4202–4210 (1995).

    Article  CAS  PubMed  Google Scholar 

  90. Goldberg, T. E. et al. Executive subprocesses in working memory: relationship to catechol-O-methyltransferase Val158Met genotype and schizophrenia. Arch. Gen. Psychiatry 60, 889–896 (2003).

    Article  CAS  PubMed  Google Scholar 

  91. Bertolino, A. et al. Additive effects of genetic variation in dopamine regulating genes on working memory cortical activity in human brain. J. Neurosci. 26, 3918–3922 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Bruder, G. E. et al. Catechol-O-methyltransferase (COMT) genotypes and working memory: associations with differing cognitive operations. Biol. Psychiatry 58, 901–907 (2005).

    Article  CAS  PubMed  Google Scholar 

  93. Tsai, S. J. et al. Association study of a functional catechol-O-methyltransferase-gene polymorphism and cognitive function in healthy females. Neurosci. Lett. 338, 123–126 (2003).

    Article  CAS  PubMed  Google Scholar 

  94. Kovas, Y. & Plomin, R. Generalist genes: implications for the cognitive sciences. Trends Cogn. Sci. 10, 198–203 (2006).

    Article  PubMed  Google Scholar 

  95. Fisher, S. E. Tangled webs: tracing the connections between genes and cognition. Cognition 101, 270–297 (2006).

    Article  PubMed  Google Scholar 

  96. Malhotra, A. K. et al. A functional polymorphism in the COMT gene and performance on a test of prefrontal cognition. Am. J. Psychiatry 159, 652–654 (2002).

    Article  PubMed  Google Scholar 

  97. Apud, J. A. et al. Tolcapone improves cognition and cortical information processing in normal human subjects. Neuropsychopharmacology 32, 1011–1020 (2007).

    Article  CAS  PubMed  Google Scholar 

  98. Meyer-Lindenberg, A. et al. Midbrain dopamine and prefrontal function in humans: interaction and modulation by COMT genotype. Nature Neurosci. 8, 594–596 (2005).

    Article  CAS  PubMed  Google Scholar 

  99. Bilder, R. M., Volavka, J., Lachman, H. M. & Grace, A. A. The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology 29, 1943–1961 (2004).

    Article  CAS  PubMed  Google Scholar 

  100. Ho, B. C. et al. Cognitive and magnetic resonance imaging brain morphometric correlates of brain-derived neurotrophic factor Val66Met gene polymorphism in patients with schizophrenia and healthy volunteers. Arch. Gen. Psychiatry 63, 731–740 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Mattay, V. S. et al. Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc. Natl Acad. Sci. USA 100, 6186–6191 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Savitz, J., Solms, M. & Ramesar, R. The molecular genetics of cognition: dopamine, COMT and BDNF. Genes Brain Behav. 5, 311–328 (2006).

    Article  CAS  PubMed  Google Scholar 

  103. Tan, H. Y. et al. Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes on working memory brain function. Proc. Natl Acad. Sci. USA 104, 12536–12541 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Rotaru, D. C., Lewis, D. A. & Gonzalez-Burgos, G. Dopamine D1 receptor activation regulates sodium channel-dependent EPSP amplification in rat prefrontal cortex pyramidal neurons. J. Physiol. 581, 981–1000 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Bertolino, A. et al. Prefrontal-hippocampal coupling during memory processing is modulated by COMT val158met genotype. Biol. Psychiatry 60, 1250–1258 (2006).

    Article  CAS  PubMed  Google Scholar 

  106. Pezawas, L. et al. The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J. Neurosci. 24, 10099–10102 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Szeszko, P. R. et al. Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Mol. Psychiatry 10, 631–636 (2005).

    Article  CAS  PubMed  Google Scholar 

  108. Bueller, J. A. et al. BDNF Val66Met allele is associated with reduced hippocampal volume in healthy subjects. Biol. Psychiatry 59, 812–815 (2006).

    Article  CAS  PubMed  Google Scholar 

  109. Bath, K. G. & Lee, F. S. Variant BDNF (Val66Met) impact on brain structure and function. Cogn. Affect. Behav. Neurosci. 6, 79–85 (2006).

    Article  PubMed  Google Scholar 

  110. Hariri, A. R. et al. Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J. Neurosci. 23, 6690–6694 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Durston, S. et al. Differential effects of DRD4 and DAT1 genotype on fronto-striatal gray matter volumes in a sample of subjects with attention deficit hyperactivity disorder, their unaffected siblings, and controls. Mol. Psychiatry 10, 678–685 (2005).

    Article  CAS  PubMed  Google Scholar 

  112. Rujescu, D. et al. Plexin B3 is genetically associated with verbal performance and white matter volume in human brain. Mol. Psychiatry 12, 190–194, 115 (2007).

    Article  CAS  PubMed  Google Scholar 

  113. de Quervain, D. J. & Papassotiropoulos, A. Identification of a genetic cluster influencing memory performance and hippocampal activity in humans. Proc. Natl Acad. Sci. USA 103, 4270–4274 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Papassotiropoulos, A. et al. Common Kibra alleles are associated with human memory performance. Science 314, 475–478 (2006).

    Article  CAS  PubMed  Google Scholar 

  115. Chanock, S. J. et al. Replicating genotype–phenotype associations. Nature 447, 655–660 (2007).

    Article  CAS  PubMed  Google Scholar 

  116. Blum, K. et al. Allelic association of human dopamine D2 receptor gene in alcoholism. JAMA 263, 2055–2060 (1990).

    Article  CAS  PubMed  Google Scholar 

  117. Lesch, K. P. et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274, 1527–1531 (1996).

    Article  CAS  PubMed  Google Scholar 

  118. Munafo, M. R. et al. 5-HTTLPR genotype and anxiety-related personality traits: a meta-analysis and new data. Am. J. Med. Genet. B Neuropsychiatr. Genet. (in the press).

  119. Munafo, M. R., Matheson, I. J. & Flint, J. Association of the DRD2 gene Taq1A polymorphism and alcoholism: a meta-analysis of case-control studies and evidence of publication bias. Mol. Psychiatry 12, 454–461 (2007).

    Article  CAS  PubMed  Google Scholar 

  120. Munafo, M. R., Attwood, A. S. & Flint, J. Neuregulin 1 genotype and schizophrenia. Schizophr. Bull. 34, 9–12 (2008).

    Article  PubMed  Google Scholar 

  121. Munafo, M. R., Thiselton, D. L., Clark, T. G. & Flint, J. Association of the NRG1 gene and schizophrenia: a meta-analysis. Mol. Psychiatry 11, 539–546 (2006).

    Article  CAS  PubMed  Google Scholar 

  122. Ioannidis, J. P. Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. JAMA 279, 281–286 (1998).

    Article  CAS  PubMed  Google Scholar 

  123. Ioannidis, J. P. Journals should publish all “null” results and should sparingly publish “positive” results. Cancer Epidemiol. Biomarkers Prev. 15, 186 (2006).

    Article  PubMed  Google Scholar 

  124. Patsopoulos, N. A., Tatsioni, A. & Ioannidis, J. P. Claims of sex differences: an empirical assessment in genetic associations. JAMA 298, 880–893 (2007).

    Article  CAS  PubMed  Google Scholar 

  125. Trikalinos, T. A., Ntzani, E. E., Contopoulos-Ioannidis, D. G. & Ioannidis, J. P. Establishment of genetic associations for complex diseases is independent of early study findings. Eur. J. Hum. Genet. 12, 762–769 (2004).

    Article  CAS  PubMed  Google Scholar 

  126. Ioannidis, J. P., Ntzani, E. E., Trikalinos, T. A. & Contopoulos-Ioannidis, D. G. Replication validity of genetic association studies. Nature Genet. 29, 306–309 (2001).

    Article  CAS  PubMed  Google Scholar 

  127. Ioannidis, J. P. & Trikalinos, T. A. An exploratory test for an excess of significant findings. Clin. Trials 4, 245–253 (2007).

    Article  PubMed  Google Scholar 

  128. Munafo, M. R., Attwood, A. S. & Flint, J. Bias in genetic association studies: effects of research location and resources. Psychol. Med. 38, 1213–1214 (2008).

    Article  PubMed  Google Scholar 

  129. Munafò, M. R. et al. 5-HTTLPR genotype and anxiety-related personality traits: a meta-analysis and new data. Am. J. Med. Genet. B Neuropsychiatr. Genet. 10 Jun 2008 (doi:10.1002/ajmg.b.30808).

    Article  Google Scholar 

  130. Flint, J. & Munafo, M. R. The endophenotype concept in psychiatric genetics. Psychol. Med. 37, 163–180 (2007).

    Article  PubMed  Google Scholar 

  131. Munafo, M. R., Brown, S. M. & Hariri, A. R. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: a meta-analysis. Biol. Psychiatry 63, 852–857 (2008).

    Article  CAS  PubMed  Google Scholar 

  132. Fan, J, McCandliss, B. D., Fossella, J., Flombaum, J. I. & Posner, M. I. The activation of attentional networks. Neuroimage 26, 471–479 (2005).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors wish to thank M. I. Posner, whose consultation contributed greatly to the conceptual direction and writing of the manuscript, and A. Gold, C. Juhasz and B. Stinson for thoughtful commentary on earlier versions of the manuscript. J.F. is presently funded by a K01 award sponsored by the National Institute of Mental Health. J.R.G. was funded by the National Science Foundation under awards REC-0634025 and DRL-0644131.

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Authors and Affiliations

  1. Colin G. DeYoung and Jeremy R. Gray are at the Department of Psychology, Adam E. Green, Yale University, New Haven, Connecticut 06520-8205, USA.,

    Adam E. Green, Colin G. DeYoung & Jeremy R. Gray

  2. Marcus R. Munafò is at the Department of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, UK.,

    Marcus R. Munafò

  3. John A. Fossella and Jin Fan are at the Department of Psychiatry, Mount Sinai School of Medicine, New York, 10029, New York, USA.,

    John A. Fossella & Jin Fan

  4. Jin Fan is also at the Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, 10029, USA.,

    Jin Fan

  5. Jeremy R. Gray is also at the Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06520-8205, USA.,

    Jeremy R. Gray

Authors
  1. Adam E. Green
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  2. Marcus R. Munafò
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  3. Colin G. DeYoung
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  4. John A. Fossella
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Corresponding author

Correspondence to Jeremy R. Gray.

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Glossary

Candidate gene

A candidate gene is a gene with a function that suggests that it might be involved in the variation observed for a particular trait. Polymorphisms in a gene that are known to alter its function (for example, through alterations in its expression) are used in candidate-gene association studies.

Intermediate phenotype

A heritable trait or characteristic that is not a direct symptom of the condition under investigation but that has been shown to be associated with the condition. It might reflect an intermediate step in the pathway between gene and psychological function (or dysfunction). In a brain-based intermediate-phenotype approach, brain function is assayed (for example, through neuroimaging technologies) in order to measure intermediate mechanisms at a systems level.

Linkage disequilibrium

The non-random association (that is, correlation) of alleles at two or more loci, so that certain combinations of alleles occur together more frequently than would be expected by chance. This means that a true causative locus might in fact be one that is in linkage disequilibrium with the one that is under investigation in a genetic-association study.

Odds ratio

A measure of effect size, defined as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group. In the context of a genetic-association study, this might be the odds of major depression occurring in one genotype group against the odds of it occurring in another genotype group.

Polymorphism

The presence of two or more variants (alleles) in a gene or other DNA sequence in a population. The most commonly investigated polymorphisms are single-nucleotide polymorphism (SNPs), in which a point mutation has occurred (for example, a C base is substituted for a T base in the DNA sequence).

Psychometrics

The design, administration and interpretation of quantitative tests for the valid and reliable measurement of psychological variables (phenotypes).

Publication bias

The greater tendency for statistically significant results to be published (relative to non-significant results). This might be due to the unwillingness of the author to submit non-significant results, to the unwillingness of the journal to accept them, or to both. Published studies might therefore not be representative of all the studies that have been conducted.

Trait

In relation to psychological variables such as mood, a trait is the dispositional level of a particular mood or emotion (for example, trait anxiety) and reflects the mean level of the mood or emotion over time. It is usually highly correlated with the current 'state' level. More generally, a trait is a dimension along which individuals can differ in behavioural dispositions.

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Green, A., Munafò, M., DeYoung, C. et al. Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nat Rev Neurosci 9, 710–720 (2008). https://doi.org/10.1038/nrn2461

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