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Genome-wide association study of positive emotion identifies a genetic variant and a role for microRNAs

Abstract

Positive affect denotes a state of pleasurable engagement with the environment eliciting positive emotion such as contentment, enthusiasm or happiness. Positive affect is associated with favorable psychological, physical and economic outcomes in many longitudinal studies. With a heritability of 64%, positive affect is substantially influenced by genetic factors; however, our understanding of genetic pathways underlying individual differences in positive affect is still limited. Here, through a genome-wide association study of positive affect in African-American participants, we identify a single-nucleotide polymorphism, rs322931, significantly associated with positive affect at P<5 × 10−8, and replicate this association in another cohort. Furthermore, we show that the minor allele of rs322931 predicts expression of microRNAs miR-181a and miR-181b in human brain and blood, greater nucleus accumbens reactivity to positive emotional stimuli and enhanced fear inhibition. Prior studies have suggested that miR-181a is part of the reward neurocircuitry. Taken together, we identify a novel genetic variant for further elucidation of genetic underpinning of positive affect that mediates positive emotionality potentially via the nucleus accumbens and miR-181.

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References

  1. Watson D, Clark LA . Manual for the Positive and Negative Affect Schedule - Expanded Form. The University of Iowa's Institutional Repository: IA, USA, 1999.

    Google Scholar 

  2. Mayer JD, Gaschke YN . The experience and meta-experience of mood. J Pers Soc Psychol 1988; 55: 102–111.

    Article  CAS  Google Scholar 

  3. Watson D, Clark LA, Tellegen A . Cross-cultural convergence in the structure of mood: a Japanese replication and a comparison with U.S. findings. J Pers Soc Psychol 1984; 47: 127–144.

    Article  Google Scholar 

  4. Watson D, Tellegen A . Toward a consensual structure of mood. Psychol Bull 1985; 98: 219–235.

    Article  CAS  Google Scholar 

  5. Chida Y, Steptoe A . Positive psychological well-being and mortality: a quantitative review of prospective observational studies. Psychosom Med 2008; 70: 741–756.

    Article  Google Scholar 

  6. De Neve JE, Oswald AJ . Estimating the influence of life satisfaction and positive affect on later income using sibling fixed effects. Proc Natl Acad Sci USA 2012; 109: 19953–19958.

    Article  CAS  Google Scholar 

  7. Zautra AJ, Johnson LM, Davis MC . Positive affect as a source of resilience for women in chronic pain. J Consult Clin Psychol 2005; 73: 212–220.

    Article  Google Scholar 

  8. Wichers MC, Myin-Germeys I, Jacobs N, Peeters F, Kenis G, Derom C et al. Evidence that moment-to-moment variation in positive emotions buffer genetic risk for depression: a momentary assessment twin study. Acta Psychiatr Scand 2007; 115: 451–457.

    Article  Google Scholar 

  9. Kujawa A, Hajcak G, Danzig AP, Black SR, Bromet EJ, Carlson GA et al. Neural reactivity to emotional stimuli prospectively predicts the impact of a natural disaster on psychiatric symptoms in children. Biol Psychiatry 2015; S0006-3223: 00768–4.

    Google Scholar 

  10. Boardman J, Blalock C, Button T . Sex differences in the heritability of resilience. Twin Res Hum Genet 2008; 11: 12–27.

    Article  Google Scholar 

  11. Gigantesco A, Stazi MA, Alessandri G, Medda E, Tarolla E, Fagnani C . Psychological well-being (PWB): a natural life outlook? An Italian twin study on heritability of PWB in young adults. Psychol Med 2011; 41: 2637–2649.

    Article  CAS  Google Scholar 

  12. Watson D, Clark LA, Tellegen A . Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol 1988; 54: 1063–1070.

    Article  CAS  Google Scholar 

  13. Watson D, Walker LM . The long-term stability and predictive validity of trait measures of affect. J Pers Soc Psychol 1996; 70: 567–577.

    Article  CAS  Google Scholar 

  14. Bernstein DP, Stein JA, Newcomb MD, Walker E, Pogge D, Ahluvalia T et al. Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse Neglect 2003; 27: 169–190.

    Article  Google Scholar 

  15. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  Google Scholar 

  16. LaLonde SM . Transforming variables for normality and linearity - when, how, why and why not's. In: SAS Global Forum (ed). Statistics and Data Analysis. SAS, 2012 pp 430–436.

    Google Scholar 

  17. Box GEP, Cox DR . An analysis of transformations. J R Stat Soc B 1964; 26: 211–252.

    Google Scholar 

  18. Barsh GS, Copenhaver GP, Gibson G, Williams SM . Guidelines for genome-wide association studies. PLoS Genet 2012; 8: e1002812.

    Article  CAS  Google Scholar 

  19. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381: 1371–1379.

  20. Trabzuni D, Ryten M, Walker R, Smith C, Imran S, Ramasamy A et al. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J Neurochem 2011; 119: 275–282.

    Article  CAS  Google Scholar 

  21. Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 2014; 17: 1418–1428.

    Article  CAS  Google Scholar 

  22. Bolger AM, Lohse M, Usadel B . Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30: 2114–2120.

    Article  CAS  Google Scholar 

  23. Kozomara A, Griffiths-Jones S . miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42: D68–D73.

    Article  CAS  Google Scholar 

  24. Rumble SM, Lacroute P, Dalca AV, Fiume M, Sidow A, Brudno M . SHRiMP: accurate mapping of short color-space reads. PLoS Comp Biol 2009; 5: e1000386.

    Article  Google Scholar 

  25. Love MI, Huber W, Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.

    Article  Google Scholar 

  26. Lang P, Bradley MM, Cuthbert BN . International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual. Technical Report A-8. University of Florida: Gainesville, FL, 2008.

    Google Scholar 

  27. Mazaika P, Whitfield-Gabrieli S, Reiss A . Artifact Repair for fMRI Data from High Motion Clinical Subjects. Human Brain Mapping 2007; http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html.

  28. Holmes A, Josephs O, Buchel C, Friston K . Statistical modelling of low-frequency confounds in fMRI. Neuroimage 1997; 5: S480.

    Google Scholar 

  29. Use of IBASPM atlas-based automatic segmentation toolbox in pathological brains: effect of template selection. Proceedings of the Nuclear Science Symposium Conference Record, 2008. NSS'08. IEEE2008. IEEE; http://www.thomaskoenig.ch/Lester/ibaspm.htm.

  30. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol 2005; 210: 343–352.

    Article  CAS  Google Scholar 

  31. Norrholm SD, Jovanovic T, Olin IW, Sands LA, Karapanou I, Bradley B et al. Fear extinction in traumatized civilians with posttraumatic stress disorder: relation to symptom severity. Biol Psychiatry 2011; 69: 556–563.

    Article  Google Scholar 

  32. Jovanovic T, Norrholm SD, Blanding NQ, Davis M, Duncan E, Bradley B et al. Impaired fear inhibition is a biomarker of PTSD but not depression. Depress Anxiety 2010; 27: 244–251.

    Article  Google Scholar 

  33. Ferranti EP, Dunbar SB, Higgins M, Dai J, Ziegler TR, Frediani JK et al. Psychosocial factors associated with diet quality in a working adult population. Res Nurs Health 2013; 36: 242–256.

    Article  Google Scholar 

  34. Bredle J, Salsman J, Debb S, Arnold B, Cella D . Spiritual well-being as a component of health-related quality of life: The Functional Assessment of Chronic Illness Therapy- Spiritual Well-Being Scale (FACIT-Sp). Religions 2011; 2: 77–94.

    Article  Google Scholar 

  35. Yang J, Lee SH, Goddard ME, Visscher PM . GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88: 76–82.

    Article  CAS  Google Scholar 

  36. Saba R, Storchel PH, Aksoy-Aksel A, Kepura F, Lippi G, Plant TD et al. Dopamine-regulated microRNA MiR-181a controls GluA2 surface expression in hippocampal neurons. Mol Cell Biol 2012; 32: 619–632.

    Article  CAS  Google Scholar 

  37. Chandrasekar V, Dreyer JL . Regulation of MiR-124, Let-7d, and MiR-181a in the accumbens affects the expression, extinction, and reinstatement of cocaine-induced conditioned place preference. Neuropsychopharmacology 2011; 36: 1149–1164.

    Article  CAS  Google Scholar 

  38. Martin SJ, Grimwood PD, Morris RG . Synaptic plasticity and memory: an evaluation of the hypothesis. Annu Rev Neurosci 2000; 23: 649–711.

    Article  CAS  Google Scholar 

  39. Henley JM, Wilkinson KA . AMPA receptor trafficking and the mechanisms underlying synaptic plasticity and cognitive aging. Dialogues Clin Neurosci 2013; 15: 11–27.

    PubMed  PubMed Central  Google Scholar 

  40. Li QJ, Chau J, Ebert PJ, Sylvester G, Min H, Liu G et al. miR-181a is an intrinsic modulator of T cell sensitivity and selection. Cell 2007; 129: 147–161.

    Article  CAS  Google Scholar 

  41. Zietara N, Lyszkiewicz M, Witzlau K, Naumann R, Hurwitz R, Langemeier J et al. Critical role for miR-181a/b-1 in agonist selection of invariant natural killer T cells. Proc Natl Acad Sci USA 2013; 110: 7407–7412.

    Article  CAS  Google Scholar 

  42. Beveridge NJ, Tooney PA, Carroll AP, Gardiner E, Bowden N, Scott RJ et al. Dysregulation of miRNA 181b in the temporal cortex in schizophrenia. Hum Mol Genet 2008; 17: 1156–1168.

    Article  CAS  Google Scholar 

  43. Beveridge NJ, Gardiner E, Carroll AP, Tooney PA, Cairns MJ . Schizophrenia is associated with an increase in cortical microRNA biogenesis. Mol Psychiatry 2010; 15: 1176–1189.

    Article  CAS  Google Scholar 

  44. Dwivedi Y, Roy B, Lugli G, Rizavi H, Zhang H, Smalheiser NR . Chronic corticosterone-mediated dysregulation of microRNA network in prefrontal cortex of rats: relevance to depression pathophysiology. Transl Psychiatry 2015; 5: e682.

    Article  CAS  Google Scholar 

  45. Liu Y, Zhao Z, Yang F, Gao Y, Song J, Wan Y . microRNA-181a is involved in insulin-like growth factor-1-mediated regulation of the transcription factor CREB1. J Neurochem 2013; 126: 771–780.

    Article  CAS  Google Scholar 

  46. Zhou J, Nagarkatti P, Zhong Y, Ginsberg JP, Singh NP, Zhang J et al. Dysregulation in microRNA expression is associated with alterations in immune functions in combat veterans with post-traumatic stress disorder. PLoS One 2014; 9: e94075.

    Article  Google Scholar 

  47. Bunzeck N, Düzel E . Absolute coding of stimulus novelty in the human substantia nigra/VTA. Neuron 2006; 51: 369–379.

    Article  CAS  Google Scholar 

  48. Buhle JT, Kober H, Ochsner KN, Mende-Siedlecki P, Weber J, Hughes BL et al. Common representation of pain and negative emotion in the midbrain periaqueductal gray. Soc Cogn Affect Neurosci 2013; 8: 609–616.

    Article  Google Scholar 

  49. Samuels E, Szabadi E . Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part II: physiological and pharmacological manipulations and pathological alterations of locus coeruleus activity in humans. Curr Neuropharmacol 2008; 6: 254.

    Article  CAS  Google Scholar 

  50. Leknes S, Lee M, Berna C, Andersson J, Tracey I . Relief as a reward: hedonic and neural responses to safety from pain. PLoS One 2011; 6: e17870.

    Article  CAS  Google Scholar 

  51. Ernst M, Nelson EE, Jazbec S, McClure EB, Monk CS, Leibenluft E et al. Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. Neuroimage 2005; 25: 1279–1291.

    Article  Google Scholar 

  52. Cauda F, Cavanna AE, D'Agata F, Sacco K, Duca S, Geminiani GC . Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis. J Cogn Neurosci 2011; 23: 2864–2877.

    Article  Google Scholar 

  53. Andersen SL, Teicher MH . Stress, sensitive periods and maturational events in adolescent depression. Trends Neurosci 2008; 31: 183–191.

    Article  CAS  Google Scholar 

  54. Young JC, Widom CS . Long-term effects of child abuse and neglect on emotion processing in adulthood. Child Abuse Negl 2014; 38: 1369–1381.

    Article  Google Scholar 

  55. Caspi A, McClay J, Moffitt TE, Mill J, Martin J, Craig IW et al. Role of genotype in the cycle of violence in maltreated children. Science 2002; 297: 851–854.

    Article  CAS  Google Scholar 

  56. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene [see comment]. Science 2003; 301: 386–389.

    Article  CAS  Google Scholar 

  57. Okbay A, Baselmans BM, De Neve JE, Turley P, Nivard MG, Fontana MA et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 2016;48: 624–633.

    Article  CAS  Google Scholar 

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Acknowledgements

We appreciate the technical support of all of the staff and volunteers of the Grady Trauma Project, particularly Kimberly Kerley, Jordan Laird, Allen W Graham, Angelo Brown and Rebecca Roffman. We thank CF Gillespie, A Schwartz and T Weiss for medical support, A Lott for maintaining the database and Biao Zeng and Jing Zhao for genetic analytical support. We thank the participants of the Grady Trauma Project for their time and effort. We gratefully acknowledge the support of the Center for Health Discovery and Well Being and the Atlanta Clinical and Translational Science Institute for the clinical profiling of the CHDWB and the Georgia Tech Research Institute for start-up support to GG for genomic profiling. This study was supported in part by the Department of Veterans Affairs Career Development Award IK2CX000601 and the NARSAD Young Investigator Award (to APW). This work was primarily supported by the National Institutes of Mental Health (MH096764 and MH071537 to KJR; F32-MH101976 to JSS). Support was also received from Emory and Grady Memorial Hospital General Clinical Research Center, National Institutes of Health (NIH). TSW was supported by the Veterans Health Administration (BX001820) and NIH/NIA AG025688. The contents do not represent the views of the Department of Veterans Affairs or the United States Government.

Author contributions

All authors edited and commented on the manuscript. KJR obtained funding for the GWAS of positive affect, oversaw the GTP study and revised the manuscript. APW, LMA and JSS wrote the first draft of the manuscript. APW and LMA performed the GWAS of positive affect. APW performed the eQTL analyses in brain and blood. JSS designed the fMRI study and analyzed the fMRI data. GG provided the genetic data for the CHDWB cohort. GG and APW performed the genetic replication study. TJ designed the study of fear conditioning and analyzed the neurophysiology data. APW and TSW performed the GCTA heritability analysis. GT contributed to the miRNA data analysis. YL and PJ contributed to the generation of miRNA data. EBB contributed to the generation of GWAS data. BB contributed to the generation of positive affect data.

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Correspondence to K J Ressler.

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Wingo, A., Almli, L., Stevens, J. et al. Genome-wide association study of positive emotion identifies a genetic variant and a role for microRNAs. Mol Psychiatry 22, 774–783 (2017). https://doi.org/10.1038/mp.2016.143

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