다유전자 위험 점수를 이용한 한국인 조현병, 양극성 장애 및 정상 대조군 간의 유전적 조형 비교
Comparisons of Genetic Architecture Using Polygenic Risk Scores Derived From Large-Scale Genome-Wide Association Study Data Between Patients With Schizophrenia, Bipolar Disorder and Healthy Controls
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objectives: In this study, we aimed to compare the genetic architecture of schizophrenia (SCZ) and bipolar disorder (BD) in a Korean population by analyzing polygenic risk scores (PRS) derived from large-scale psychiatric disorder genome-wide association study data, based on genetic information collected from SCZ, BD, and healthy control groups. Methods: The study included 713 Korean patients with SCZ, 1,317 with BD, 526 healthy controls. Genotyping was performed using the Korean Biobank Array. PRS-continuous shrinkage method was used to calculate the PRS. Analysis of covariance (ANCOVA) was conducted to determine the association between SCZ or BD disorder and PRS after adjusting for sex. Results: ANCOVA revealed significant differences in PRS values by diagnosis for PRS for SCZ (F=215.281, p<0.001), PRS for BD (F=13.811, p<0.001), and PRS for major depressive disorder (F=6.042, p=0.002). Post-hoc analysis showed that PRS for SCZ was highest in SCZ, followed by BD, and healthy controls. PRS for BD was elevated in both BD and SCZ compared to healthy controls. Conclusion: Our study revealed quantitative differences in genetic architecture between SCZ and BD compared to healthy controls, while also suggesting a shared genetic background between the two disorders.
Abstract
Objectives: In this study, we aimed to compare the genetic architecture of schizophrenia (SCZ) and bipolar disorder (BD) in a Korean population by analyzing polygenic risk scores (PRS) derived from large-scale psychiatric disorder genome-wide association study data, based on genetic information collected from SCZ, BD, and healthy control groups. Methods: The study included 713 Korean patients with SCZ, 1,317 with BD, 526 healthy controls. Genotyping was performed using the Korean Biobank Array. PRS-continuous shrinkage method was used to calculate the PRS. Analysis of covariance (ANCOVA) was conducted to determine the association between SCZ or BD disorder and PRS after adjusting for sex. Results: ANCOVA revealed significant differences in PRS values by diagnosis for PRS for SCZ (F=215.281, p<0.001), PRS for BD (F=13.811, p<0.001), and PRS for major depressive disorder (F=6.042, p=0.002). Post-hoc analysis showed that PRS for SCZ was highest in SCZ, followed by BD, and healthy controls. PRS for BD was elevated in both BD and SCZ compared to healthy controls. Conclusion: Our study revealed quantitative differences in genetic architecture between SCZ and BD compared to healthy controls, while also suggesting a shared genetic background between the two disorders.
참고문헌 (31)
- Grande I, "Bipolar disorder", Lancet, 387, pp.1561~1572 (2016)
- Jauhar S, "Schizophrenia", Lancet, 399, pp.473~486 (2022)
- Craddock N, "Genetics of bipolar disorder", Lancet, 381, pp.1654~1662 (2013)
- Lichtenstein P, "Common genetic determinants of schizophrenia and bipolar disorder in Swedish families : a population-based study", Lancet, 373, pp.234~239 (2009)
- Sullivan PF, "Schizophrenia as a complex trait : evidence from a meta-analysis of twin studies", Arch Gen Psychiatry, 60, pp.1187~1192 (2003)
- Cardno AG, "Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder", Schizophr Bull, 40, pp.504~515 (2014)
- Sullivan PF, "Psychiatric genomics : an update and an agenda", Am J Psychiatry, 175, pp.15~27 (2018)
- Kraepelin E, "Manic-depressive insanity and paranoia" (1921), E & S Livingstone
- Baek JH, "Association between the zinc finger protein 804A(ZNF804A)gene and the risk of schizophrenia and bipolar I disorder across diagnostic boundaries", Bipolar Disord, 19, pp.305~313 (2017)
- Bush WS, "Chapter 11 : genome-wide association studies", PLoS Comput Biol, 8, pp.e1002822~ (2012)
- Dudbridge F, "Power and predictive accuracy of polygenic risk scores", PLoS Genet, 9, pp.e1003348~ (2013)
- Wray NR, "Prediction of individual genetic risk to disease from genome-wide association studies", Genome Res, 17, pp.1520~1528 (2007)
- Aminoff SR, "Polygenic risk scores in bipolar disorder subgroups", J Affect Disord, 183, pp.310~314 (2015)
- Hwang MY, "Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population", Front Genet, 13, pp.1008646~ (2022)
- Ge T, "Polygenic prediction via Bayesian regression and continuous shrinkage priors", Nat Commun, 10, pp.1776~ (2019)
- Zhou W, "Global biobank meta-analysis initiative : powering genetic discovery across human disease", Cell Genom, 2, pp.100192~ (2022)
- Rodriguez V, "Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study", Psychol Med, 53, pp.3396~3405 (2023)
- Liu J, "Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia", BMC Med, 19, pp.177~ (2021)
- Calafato MS, "Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders", Br J Psychiatry, 213, pp.535~541 (2018)
- Gatt JM, "Specific and common genes implicated across major mental disorders : a review of meta-analysis studies", J Psychiatr Res, 60, pp.1~13 (2015)
- Li HJ, "Novel risk loci associated with genetic risk for bipolar disorder among Han Chinese individuals : a genome-wide association study and metaanalysis", JAMA Psychiatry, 78, pp.320~330 (2021)
- Cross-Disorder Group of the Psychiatric Genomics Consortium, "Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders", Cell, 179, pp.1469~1482 (2019)
- Stahl EA, "Genome-wide association study identifies 30 loci associated with bipolar disorder", Nat Genet, 51, pp.793~803 (2019)
- Ahangari M, "Examining the source of increased bipolar disorder and major depressive disorder common risk variation burden in multiplex schizophrenia families", Schizophrenia(Heidelb), 8, pp.106~ (2022)
- Markota M, "Association of schizophrenia polygenic risk score with manic and depressive psychosis in bipolar disorder", Transl Psychiatry, 8, pp.188~ (2018)
- Coombes BJ, "Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores", Transl Psychiatry, 10, pp.314~ (2020)
- Jain PR, "Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation", BMC Genom Data, 24, pp.70~ (2023)
- Slunecka JL, "Implementation and implications for polygenic risk scores in healthcare", Hum Genomics, 15, pp.46~ (2021)
- Vassos E, "An examination of polygenic score risk prediction in individuals with first-episode psychosis", Biol Psychiatry, 81, pp.470~477 (2017)
- Mistry S, "The use of polygenic risk scores to identify phenotypes associated with genetic risk of bipolar disorder and depression : a systematic review", J Affect Disord, 234, pp.148~155 (2018)
- Curtis D, "Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia", Psychiatr Genet, 28, pp.85~89 (2018)