An atlas of genetic correlations between psychiatric disorders and human blood plasma proteome

Abstract Background. Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins. Methods. The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins. Results. LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ). Conclusions. This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

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Linkage disequilibrium score regression distinguishes associations between lean body mass and blood plasma proteome

10.21203/rs.2.15375/v1 ◽

2019 ◽

Author(s):

Lu Zhang ◽

Yan Zhao ◽

Yan Wen ◽

Mei Ma ◽

Shiqiang Cheng ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Lean Body Mass ◽

Body Mass ◽

Plasma Proteins ◽

Genome Wide Association Study ◽

Genetic Relationships ◽

Genetic Correlations ◽

P Value ◽

Plasma Proteome ◽

Summary Data

Abstract Background: Lean body mass (LBM) is an essential component of body composition. Recent studies observed associations between altered plasma proteins and LBM. This study aims to explore the potential genetic relationships between LBM and plasma proteome. Methods: The genome-wide association study (GWAS) summary data of LBM were driven from ~85,000 individuals of European descent. The GWAS summary data of 3,622 plasma proteins were obtained from 3,301 healthy participants. Linkage disequilibrium score regression (LDSC) analysis were conducted to evaluate the potential genetic correlation between each plasma protein and LBM using the GWAS summary statistics. Results: LDSC scan identified multiple plasma proteins for LBM, such as stabilin-1(coefficient=-0.4583, P value=0.0253) and inter-alpha-trypsin inhibitor heavy chain H1 (coefficient=-0.1716, P value=0.0314) for append LBM, and glyceraldehyde-3-phosphate dehydrogenase (coefficient=0.5997, P value=0.0315) and immunoglobulin lambda-like polypeptide 1(coefficient=0.35, P value=0.0334) for whole LBM. We also detected two common plasma proteins shared by append and whole LBM, including stabilin-1 and glyceraldehyde-3-phosphate dehydrogenase. Conclusions: This study provided an atlas of genetic correlations between LBM and plasma proteome, which are helpful for understanding the biological mechanism of LBM.

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A Proteome-Wide Association Study Identified Candidate Human Brain Proteins Associated with Coffee Consumption

10.21203/rs.3.rs-910538/v1 ◽

2021 ◽

Author(s):

Chun'e Li ◽

Xiao Liang ◽

Yumeng Jia ◽

Yan Wen ◽

Huijie Zhang ◽

...

Keyword(s):

Human Brain ◽

Association Study ◽

Plasma Proteins ◽

Genome Wide Association Study ◽

Genetic Correlations ◽

Coffee Consumption ◽

Genetic Associations ◽

Brain Proteins ◽

Genome Wide ◽

Brain Proteome

Abstract Background Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites. Methods First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites. Results For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216). Conclusions We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.

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Genetic underpinnings of sociability in the UK Biobank

10.1101/781195 ◽

2019 ◽

Cited By ~ 2

Author(s):

J Bralten ◽

CJHM Klemann ◽

NR Mota ◽

W De Witte ◽

C Arango ◽

...

Keyword(s):

Social Anxiety ◽

Psychiatric Disorders ◽

Genetic Correlation ◽

Genome Wide Association Study ◽

Autism Spectrum ◽

Self Report ◽

Uk Biobank ◽

Genome Wide ◽

A Genome ◽

The Uk

ABSTRACTDifficulties with sociability include a tendency to avoid social contacts and activities, and to prefer being alone rather than being with others. While sociability is a continuously distributed trait in the population, decreased sociability represent a common early manifestation of multiple neuropsychiatric disorders such as Schizophrenia (SCZ), Bipolar Disorder (BP), Major Depressive Disorder (MDD), Autism Spectrum Disorders (ASDs), and Alzheimer’s disease (AD). We aimed to investigate the genetic underpinnings of sociability as a continuous trait in the general population. In this respect, we performed a genome-wide association study (GWAS) using a sociability score based on 4 social functioning-related self-report questions in the UK Biobank sample (n=342,461) to test the effect of individual genetic variants. This was followed by LD score analyses to investigate the genetic correlation with psychiatric disorders (SCZ, BP, MDD, ASDs) and a neurological disorder (AD) as well as related phenotypes (Loneliness and Social Anxiety). The phenotypic data indeed showed that the sociability score was decreased in individuals with ASD, (probable) MDD, BP and SCZ, but not in individuals with AD. Our GWAS showed 604 genome-wide significant SNPs, coming from 18 independent loci (SNP-based h2=0.06). Genetic correlation analyses showed significant correlations with SCZ (rg=0.15, p=9.8e-23), MDD (rg=0.68, p=6.6e-248) and ASDs (rg=0.27, p=4.5e-28), but no correlation with BP (rg=0.01, p=0.45) or AD (rg=0.04, p=0.55). Our sociability trait was also genetically correlated with Loneliness (rg=0.45, p=2.4e-8) and Social Anxiety (rg=0.48, p=0.002). Our study shows that there is a significant genetic component to variation in population levels of sociability, which is relevant to some psychiatric disorders (SCZ, MDD, ASDs), but not to BP and AD.

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Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

10.1101/177014 ◽

2017 ◽

Cited By ~ 1

Author(s):

Rona J. Strawbridge ◽

Joey Ward ◽

Breda Cullen ◽

Elizabeth M. Tunbridge ◽

Sarah Hartz ◽

...

Keyword(s):

Psychiatric Disorders ◽

Risk Taking ◽

Genome Wide Association Study ◽

Genetic Correlations ◽

Uk Biobank ◽

Physical And Mental Health ◽

Post Traumatic Stress ◽

Genome Wide ◽

A Genome ◽

The Uk

AbstractRisk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

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Characterization of genome-wide association study data reveals spatiotemporal heterogeneity of mental disorders

BMC Medical Genomics ◽

10.1186/s12920-020-00832-8 ◽

2020 ◽

Vol 13 (S11) ◽

Author(s):

Yulin Dai ◽

Timothy D. O’Brien ◽

Guangsheng Pei ◽

Zhongming Zhao ◽

Peilin Jia

Keyword(s):

Brain Development ◽

Psychiatric Disorders ◽

Genome Wide Association Study ◽

Developmental Stages ◽

Brain Regions ◽

Autism Spectrum ◽

Study Data ◽

Spatiotemporal Regulation ◽

Genome Wide ◽

Active Expression

Abstract Background Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders. Methods In this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders. Results We found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such as CTNNB1 and LNX1, and a hub gene, ATF2, in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively. Conclusion Our study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.

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Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Nature Communications ◽

10.1038/s41467-020-19366-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Vasiliki Lagou ◽

◽

Reedik Mägi ◽

Jouke- Jan Hottenga ◽

Harald Grallert ◽

...

Keyword(s):

Genome Wide Association Study ◽

Fasting Glucose ◽

Genetic Correlations ◽

Epidemiological Studies ◽

Genetic Effects ◽

Fasting Insulin ◽

Sex Dimorphism ◽

Waist To Hip Ratio ◽

Genome Wide ◽

Meta Analyses

AbstractDifferences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

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Genome-wide association study identifies 48 common genetic variants associated with handedness

10.1101/831321 ◽

2019 ◽

Author(s):

Gabriel Cuellar Partida ◽

Joyce Y Tung ◽

Nicholas Eriksson ◽

Eva Albrecht ◽

Fazil Aliev ◽

...

Keyword(s):

Association Study ◽

Genetic Variants ◽

Complex Traits ◽

Genome Wide Association Study ◽

Genetic Correlations ◽

Genome Wide Association ◽

Left Handedness ◽

Left Handed ◽

Genome Wide ◽

Common Genetic Variants

AbstractHandedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% – 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% – 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 – 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

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A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

10.1101/2021.01.28.428688 ◽

2021 ◽

Author(s):

Zachary F Gerring ◽

Jackson G Thorp ◽

Eric R Gamazon ◽

Eske M Derks

Keyword(s):

Gene Expression ◽

Mental Health ◽

Substance Use ◽

Prefrontal Cortex ◽

Psychiatric Disorders ◽

Developmental Disorders ◽

Association Studies ◽

Genetic Correlations ◽

Autism Spectrum ◽

Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

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