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EQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders.

ABSTRACT: BACKGROUND:Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders. METHODS:The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders. RESULTS:Without considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ. CONCLUSIONS:This study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.

SUBMITTER: Wen Y

PROVIDER: S-EPMC6483588 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders.

Wen Yan Y Zhang Feng F Zhang Feng F Ma Xiancang X Fan Qianrui Q Wang Wenyu W Xu Jiawen J Zhu Feng F Hao Jingcan J He Awen A Liu Li L Liang Xiao X Du Yanan Y Li Ping P Wu Cuiyan C Wang Sen S Wang Xi X Ning Yujie Y Guo Xiong X

Schizophrenia bulletin 20190401 3

<h4>Background</h4>Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders.<h4>Methods</h4>The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivit ...[more]

PMID: 29912442

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Project description:Background: Previous studies have revealed associations between psychiatric disorder diagnosis and shorter telomere length. Here, we attempt to discern whether genetic risk for psychiatric disorders, or use of pharmacological treatments (i.e., antidepressants), predict shorter telomere length and risk for aging-related disease in a United Kingdom population sample. Methods: DNA samples from blood were available from 351 participants who were recruited as part of the South East London Community Health (SELCoH) Study, and for which whole-genome genotype data was available. Leukocyte telomere length was characterized using quantitative polymerase chain reactions. Individualized polygenic risk scores for major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) were calculated using Psychiatric Genomics Consortium summary statistics. We subsequently performed linear models, to discern the impact polygenic risk for psychiatric disorders (an etiological risk factor) and antidepressant use (common pharmacological treatment) have on telomere length, whilst accounting for other lifestyle/health factors (e.g., BMI, smoking). Results: There were no significant associations between polygenic risk for any of the psychiatric disorders tested and telomere length (p > 0.05). Antidepressant use was significantly associated with shorter telomere length and this was independent from a depression diagnosis or current depression severity (p ? 0.01). Antidepressant use was also associated with a significantly higher risk of aging-related disease, which was independent from depression diagnosis (p ? 0.05). Conclusion: Genetic risk for psychiatric disorders is not associated with shorter telomere length. Further studies are now needed to prospectively characterize if antidepressant use increases risk for aging-related disease and telomere shortening, or whether people who age faster and have aging-related diseases are just more likely to be prescribed antidepressants.

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EQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders.

Publications

eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders.

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