본문 바로가기
KERD
논문KCI 등재

Leveraging genetic overlap between irritability and psychiatric disorders to identify genetic variants of major psychiatric disorders

0

조회

0

다운로드

0

인용

0

참고문헌

주제분류생화학

초록

Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.

Abstract

Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.

참고문헌 (66)

  1. Barata, P. C., "Building a definition of irritability from academic definitions and lay descriptions", Emot. Rev, 8, pp.164~172 (2016)
  2. Akiskal, H. S., "TEMPS-A : Progress towards validation of a self-rated clinical version of the temperament evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire", J. Affect. Disord, 85, pp.3~16 (2005)
  3. Walsh, M. A., "The association of affective temperaments with impairment and psychopathology in a young adult sample", J. Affect. Disord, 141, pp.373~381 (2012)
  4. Stringaris, A., "Adult outcomes of youth irritability : A 20-year prospective community-based study", Am. J. Psychiat, 166, pp.1048~1054 (2009)
  5. Orri, M., "Association between irritability and suicide-related outcomes across the life-course. Systematic review of both community and clinical studies", J. Affect. Disord, 239, pp.220~233 (2018)
  6. Riglin, L., "Schizophrenia risk alleles and neurodevelopmental outcomes in childhood : a population-based cohort study", Lancet Psychiat, 4, pp.57~62 (2017)
  7. Biederman, J., "Can polygenic risk scores help identify pediatric bipolar spectrum and related disorders? : A systematic review", Psychiatry Res, 299, pp.113843~ (2021)
  8. Leppert, B., "A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank", PLoS Genet, 16, pp.e1008185~ (2020)
  9. Sparks, G. M., "Disruptive mood dysregulation disorder and chronic irritability in youth at familial risk for bipolar disorder", J. Am. Acad. Child. Psy, 53, pp.408~416 (2014)
  10. American Psychiatric Association, "Diagnostic and statistical manual of mental disorders" (2013), American Psychiatric Association Publishing
  11. Stringaris, A., "Adolescent irritability:Phenotypic associations and genetic links with depressed mood", Am. J. Psychiat, 169, pp.47~54 (2012)
  12. Coccaro, E. F., "Heritability of aggression and irritability: A twin study of the Buss-Durkee aggression scales in adult male subjects", Biol. Psychiat, 41, pp.273~284 (1997)
  13. Besteher, B., "Brain structural correlates of irritability : Findings in a large healthy cohort", Hum. Brain. Mapp, 38, pp.6230~6238 (2017)
  14. Deveney, C. M., "Multi-method assessment of irritability and differential linkages to neurophysiological indicators of attention allocation to emotional faces in young children", Dev. Psychobiol, 62, pp.600~616 (2020)
  15. Bycroft, C., "The UK Biobank resource with deep phenotyping and genomic data", Nature, 562, pp.203~209 (2018)
  16. O’Connell, J., "Haplotype estimation for biobank-scale data sets", Nat. Genet, 48, pp.817~820 (2016)
  17. Howie, B. N., "A flexible and accurate genotype imputation method for the next generation of genome-wide association studies", PLoS Genet, 5, pp.e1000529~ (2009)
  18. Ko, H., "Genome-wide association study of occupational attainment as a proxy for cognitive reserve", Brain, 145, pp.1436~1448 (2022)
  19. Zhou, W., "Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies", Nat. Genet, 50, pp.1335~1341 (2018)
  20. Lane, J. M., "Genome-wide association analysis identifies novel loci for chronotype in 100, 420 individuals from the UK Biobank", Nat. Commun, 7, pp.10889~ (2016)
  21. Purcell, S., "PLINK : A tool set for whole-genome association and populationbased linkage analyses", Am. J. Hum. Genet, 81, pp.559~575 (2007)
  22. Pruim, R. J., "LocusZoom : Regional visualization of genome-wide association scan results", Bioinformatics, 26, pp.2336~2337 (2010)
  23. Watanabe, K., "Genetic mapping of cell type specificity for complex traits", Nat. Commun, 10, pp.3222~ (2019)
  24. Wang, K., "ANNOVAR : Functional annotation of genetic variants from high-throughput sequencing data", Nucleic Acids Res, 38, pp.e164~ (2010)
  25. Wang, D., "Comprehensive functional genomic resource and integrative model for the human brain", Science, 362, pp.eaat8464~ (2018)
  26. Consortium, G. T., "The GTEx Consortium atlas of genetic regulatory effects across human tissues", Science, 369, pp.1318~1330 (2020)
  27. The Gene Ontology, C., "Expansion of the gene ontology knowledgebase and resources", Nucleic Acids Res, 45, pp.D331~D338 (2017)
  28. Finucane, H. K., "Partitioning heritability by functional annotation using genome-wide association summary statistics", Nat. Genet, 47, pp.1228~1235 (2015)
  29. Finucane, H. K., "Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types", Nat. Genet, 50, pp.621~629 (2018)
  30. Consortium, G. T., "Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans", Science, 348, pp.648~660 (2015)
  31. Fehrmann, R. S., "Gene expression analysis identifies global gene dosage sensitivity in cancer", Nat. Genet, 47, pp.115~125 (2015)
  32. Pers, T. H., "Biological interpretation of genome-wide association studies using predicted gene functions", Nat. Commun, 6, pp.5890~ (2015)
  33. Roadmap Epigenomics, C., "Integrative analysis of 111 reference human epigenomes", Nature, 518, pp.317~330 (2015)
  34. Consortium, E. P., "An integrated encyclopedia of DNA elements in the human genome", Nature, 489, pp.57~74 (2012)
  35. Cahoy, J. D., "A transcriptome database for astrocytes, neurons, and oligodendrocytes : A new resource for understanding brain development and function", J. Neurosci, 28, pp.264~278 (2008)
  36. Bulik-Sullivan, B., "An atlas of genetic correlations across human diseases and traits", Nat. Genet, 47, pp.1236~1241 (2015)
  37. Cross-Disorder Group of the Psychiatric Genomics Consortium, "Electronic address, p. m. h. e. & Cross-Disorder Group of the Psychiatric Genomics, C. Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders", Cell, 179, pp.1469~1482 (2019)
  38. Desikan, R. S., "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", Neuroimage, 31, pp.968~980 (2006)
  39. Mori, S., "Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template", Neuroimage, 40, pp.570~582 (2008)
  40. Xia, M., "BrainNet Viewer : a network visualization tool for human brain connectomics", PLoS One, 8, pp.e68910~ (2013)
  41. Mazziotta, J., "A probabilistic atlas and reference system for the human brain : International Consortium for Brain Mapping(ICBM)", Philos. s. T. R. Soc. Lon. B, 356, pp.1293~1322 (2001)
  42. Frei, O., "Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation", Nat. Commun, 10, pp.2417~ (2019)
  43. Holland, D., "Beyond SNP heritability : Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model", PLoS Genet, 16, pp.e1008612~ (2020)
  44. Andreassen, O. A., "Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate", PLoS Genet, 9, pp.e1003455~ (2013)
  45. Lindblad-Toh, K., "A high-resolution map of human evolutionary constraint using 29 mammals", Nature, 478, pp.476~482 (2011)
  46. Pardinas, A. F., "Common schizophrenia alleles are enriched in mutationintolerant genes and in regions under strong background selection", Nat. Genet, 50, pp.381~389 (2018)
  47. Mullins, N., "Genome-wide association study of more than 40, 000 bipolar disorder cases provides new insights into the underlying biology", Nat. Genet, 53, pp.817~829 (2021)
  48. Howard, D. M., "Genome-wide meta-analysis of depression identifies 102independent variants and highlights the importance of the prefrontal brain regions", Nat. Neurosci, 22, pp.343~352 (2019)
  49. Trubetskoy, V., "Mapping genomic loci implicates genes and synaptic biology in schizophrenia", Nature, 604, pp.502~508 (2022)
  50. Giannakopoulou, O., "The genetic architecture of depression in individuals of East Asian ancestry : A genome-wide association study", JAMA Psychiat, 78, pp.1258~1269 (2021)
  51. Tsukada, Y. -i, "KDM7 is a dual demethylase for histone H3 Lys 9 and Lys 27 and functions in brain development", Gen. Dev, 24, pp.432~437 (2010)
  52. Nagel, M., "Item-level analyses reveal genetic heterogeneity in neuroticism", Nat. Commun, 9, pp.905~ (2018)
  53. Stahl, E. A., "Genome-wide association study identifies 30 loci associated with bipolar disorder", Nat. Genet, 51, pp.793~803 (2019)
  54. Singh, T., "Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders", Nat. Neurosci, 19, pp.571~577 (2016)
  55. Hill, W. D., "Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life", Mol. Psychiatr, 25, pp.3034~3052 (2020)
  56. Savage, J. E., "Genome-wide association meta-analysis in 269, 867 individuals identifies new genetic and functional links to intelligence", Nat. Genet, 50, pp.912~919 (2018)
  57. Schoormans, D., "In search for the genetic basis of quality of life in healthy Swedish Women—A GWAS study using the iCOGS custom genotyping array", PLoS One, 10, pp.e0140563~ (2015)
  58. Sun, H., "Epigenetics of the depressed brain: role of histone acetylation and methylation", Neuropsychopharmacol, 38, pp.124~137 (2013)
  59. Dennis, E. L., "Irritability and brain volume in adolescents : cross-sectional and longitudinal associations", Soc. Cogn. Affect. Neur, 14, pp.687~698 (2019)
  60. Jirsaraie, R. J., "Accelerated cortical thinning within structural brain networks is associated with irritability in youth", Neuropsychopharmacology, 44, pp.2254~2262 (2019)
  61. Bilgi, M. M., "The relationship between childhood trauma, emotion recognition, and irritability in schizophrenia patients", Psychiat. Res, 251, pp.90~96 (2017)
  62. Dima, D., "The polygenic risk for bipolar disorder influences brain regional function relating to visual and default state processing of emotional information", Neuroimage–Clin, 12, pp.838~844 (2016)
  63. Strawbridge, R. J., "Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort", Transl. Psychiat, 8, pp.39~ (2018)
  64. Ward, J., "The genomic basis of mood instability : Identification of 46 loci in 363, 705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function", Mol. Psychiatr, 25, pp.3091~3099 (2020)
  65. Karlsson Linner, R., "Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences", Nat. Genet, 51, pp.245~257 (2019)
  66. Hindley, G., "The shared genetic basis of mood instability and psychiatric disorders : A cross-trait genome-wide association analysis", Am. J. Med. Genet. B, 189, pp.207~218 (2022)

관련 논문AI추천

Family Based Genome-Wide Association Analysis of Schizophrenia in a Korean Population: A Pilot Study김경원, 문은수, 박제민, 배소정, 서화규, 오하영, 이병대, 이영민, 정희정대한생물치료정신의학회 · 생물치료정신의학2023 · 제29권 제1호유럽인 자료로 산출된 양극성 장애 다유전자 위험 점수의 한국인 양극성 장애 환자에의 적용백지현, 이동빈, 조윤지, 조은영, 최민준, 홍경수대한신경정신의학회 · 신경정신의학2021 · 제60권 제3호Relationship between Polygenic Risk Score and the Hypnotics in Bipolar I DisorderBun-Hee Lee, Hyeon Woo Lee, John R. Kelsoe, Tatyana Shekhtman, Young-Min Park대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2024 · 제22권 제4호정신과 질환에서의 광범위 유전체 연합연구김세주, 홍경수대한신경정신의학회 · 신경정신의학2011 · 제50권 제1호다유전자 위험 점수를 이용한 한국인 조현병, 양극성 장애 및 정상 대조군 간의 유전적 조형 비교김성완, 명우재, 백지현, 안용민, 이동은, 이헌정, 이현주, 조은영, 최민준, 하규섭, 하태현, 홍경수대한조현병학회 · 대한조현병학회지2024 · 제27권 제2호ZNF804A Gene Variants Have a Cross-diagnostic Influence on Psychosis and Treatment Improvement in Mood DisordersAlessandro Serretti, Ashwin A Patkar, Changsu Han, Chi-Un Pae, Concetta Crisafulli, Laura Mandelli, Luigi Janiri, Marco Calabrò, Marco Di Nicola, Prakash S Masand, Roberto Colombo, Sheng Min Wang, Soo-Jung Lee, Tae-Youn Jun대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2020 · 제18권 제2호

이 간행물의 다른 논문 (225건)