Linking electrophysiological brain activity to neurological and psychiatric liability genes: Large-scale collaborative studies by the ENIGMA-EEG group.

2020 ◽  
Author(s):  
Dirk Smit
Keyword(s):  
Psychiatric Disorders ◽  
Brain Function ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Meta Analysis ◽  
Genome Wide ◽  
Oscillatory Brain Activity ◽  
Eeg Features

The ENIGMA-EEG working group was established to enable large scale international collaborations among cohorts who investigate the genetics of brain function measured with electroencephalography (EEG). The collaboration resulted in the currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts’ results. Our endeavor has resulted in the first genome-wide significant hits for oscillatory brain function, and significant genes that were previously associated with psychiatric disorders. Our results have provided insight into the influence that psychitaric liability genes have on the functioning brain. In this overview, we also highlight how we have tackled methodological issues surrounding genetic meta-analysis of EEG features, and identify possible sources of heterogeneity across cohorts, which could affect the results of our meta-analysis. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features to be investigated in our future studies, e.g. temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. We argue that these represent some of the most important characteristics of the functioning brain. We conclude that disentangling the genetics of EEG will elucidate effects that genes have on brain function, as well as pathways from genes to neurological and psychiatric disorders.

F251. Psychiatric Liability Genes are Linked to Oscillatory Brain Activity: A Genome-Wide Association Study

2018 ◽  
Vol 83 (9) ◽  
pp. S336
Author(s):  
Dirk Smit ◽  
Margareth Wright ◽  
Jacquelyn Meyers ◽  
Nicholas Martin ◽  
Yvonne Ho ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Brain Activity ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome ◽  
Oscillatory Brain Activity

scholarly journals A Further Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (6) ◽  
pp. 538-545 ◽  
Author(s):  
W. D. Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Tissue Specific ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Quality Control Metrics

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

scholarly journals Trans-ancestry genome-wide association study identifies novel genetic mechanisms in rheumatoid arthritis

2021 ◽  
Author(s):  
Kazuyoshi Ishigaki ◽  
Saori Sakaue ◽  
Chikashi Terao ◽  
Yang Luo ◽  
Kyuto Sonehara ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Study ◽  
Fine Mapping ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Research ◽  
Genome Wide Association ◽  
Polygenic Risk Score ◽  
Genome Wide

AbstractTrans-ancestry genetic research promises to improve power to detect genetic signals, fine-mapping resolution, and performances of polygenic risk score (PRS). We here present a large-scale genome-wide association study (GWAS) of rheumatoid arthritis (RA) which includes 276,020 samples of five ancestral groups. We conducted a trans-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 were novel. Candidate genes at the novel loci suggested essential roles of the immune system (e.g., TNIP2 and TNFRSF11A) and joint tissues (e.g., WISP1) in RA etiology. Trans-ancestry fine mapping identified putatively causal variants with biological insights (e.g., LEF1). Moreover, PRS based on trans-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between European and East Asian populations. Our study provides multiple insights into the etiology of RA and improves genetic predictability of RA.

scholarly journals LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks

PLoS Genetics ◽  
2020 ◽  
Vol 16 (11) ◽  
pp. e1009077
Author(s):  
Jeffery A. Goldstein ◽  
Joshua S. Weinstock ◽  
Lisa A. Bastarache ◽  
Daniel B. Larach ◽  
Lars G. Fritsche ◽  
...  
Keyword(s):  
Health System ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Clinical Laboratory ◽  
Meta Analysis ◽  
Test Results ◽  
Genome Wide ◽  
Laboratory Test Results ◽  
Vanderbilt University ◽  
Novel Associations

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

scholarly journals A large-scale genome-wide association study meta-analysis of cannabis use disorder

2020 ◽  
Vol 7 (12) ◽  
pp. 1032-1045 ◽  
Author(s):  
Emma C Johnson ◽  
Ditte Demontis ◽  
Thorgeir E Thorgeirsson ◽  
Raymond K Walters ◽  
Renato Polimanti ◽  
...  
Keyword(s):  
Association Study ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Cannabis Use ◽  
Genome Wide Association ◽  
Cannabis Use Disorder ◽  
Genome Wide

scholarly journals Comment on “Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets” by Lam et al.

2017 ◽  
Author(s):  
William David Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Data Sets ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Short Commentary

Lam et al. (2017) reported a large-scale genome-wide association study (GWAS) of cognitive ability. They used the new analytical method of Multi-Trait Analysis of GWAS (MTAG) (Turley et al., 2017) to combine GWAS data sets on the correlated phenotypes of cognitive ability and education, deriving 70 loci that they described as “trait specific” to cognitive ability. The purpose of this short commentary is to examine whether the use of MTAG, in this case (Lam et al., 2017), has resulted in a phenotype more similar to education than cognitive ability.

scholarly journals Genetic risk factors and Covid-19 severity in Brazil: results from BRACOVID Study

2021 ◽  
Author(s):  
Alexandre Pereira ◽  
Taniela M Bes ◽  
Mariliza Velho ◽  
Emanuelle Marques ◽  
Cinthia Jannes ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Significant Heterogeneity ◽  
Disease Biology ◽  
Genome Wide ◽  
The World ◽  
Significant Locus ◽  
Rapid Deployment

The Covid-19 pandemic has changed the paradigms for disease surveillance and rapid deployment of scientific-based evidence for understanding disease biology, susceptibility, and treatment. We have organized a large-scale genome-wide association study in Sars-Cov-2 infected individuals in Sao Paulo, Brazil, one of the most affected areas of the pandemic in the country, itself one of the most affected in the world. Here we present the results of the initial analysis in the first 5,233 participants of the BRACOVID study. We have conducted a GWAS for Covid-19 hospitalization enrolling 3533 cases (hospitalized Covid-19 participants) and 1700 controls (non-hospitalized Covid-19 participants). Models were adjusted by age, sex and the 4 first principal components. A meta-analysis was also conducted merging BRACOVID hospitalization data with the Human Genetic Initiative (HGI) Consortia results. BRACOVID results validated most loci previously identified in the HGI meta-analysis. In addition, no significant heterogeneity according to ancestral group within the Brazilian population was observed for the two most important Covid-19 severity associated loci: 3p21.31 and Chr21 near IFNAR2. Using only data provided by BRACOVID a new genome-wide significant locus was identified on Chr1 near the genes DSTYK and RBBP5. The associated haplotype has also been previously associated with a number of blood cell related traits and might play a role in modulating the immune response in Covid-19 cases.

scholarly journals Genome-wide association study of suicide attempt in psychiatric disorders identifies association with major depression polygenic risk scores

2018 ◽  
Author(s):  
Niamh Mullins ◽  
Tim B. Bigdeli ◽  
Anders D Børglum ◽  
Jonathan R I Coleman ◽  
Ditte Demontis ◽  
...  
Keyword(s):  
Major Depression ◽  
Suicide Attempt ◽  
Psychiatric Disorders ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Risk Scores ◽  
Genetic Associations ◽  
Suicide Attempters ◽  
Polygenic Risk ◽  
Genome Wide

AbstractObjectiveOver 90% of suicide attempters have a psychiatric diagnosis, however twin and family studies suggest that the genetic etiology of suicide attempt (SA) is partially distinct from that of the psychiatric disorders themselves. Here, we present the largest genome-wide association study (GWAS) on suicide attempt using major depressive disorder (MDD), bipolar disorder (BIP) and schizophrenia (SCZ) cohorts from the Psychiatric Genomics Consortium.MethodSamples comprise 1622 suicide attempters and 8786 non-attempters with MDD, 3264 attempters and 5500 non-attempters with BIP and 1683 attempters and 2946 non-attempters with SCZ. SA GWAS were performed comparing attempters to non-attempters in each disorder followed by meta-analysis across disorders. Polygenic risk scoring investigated the genetic relationship between SA and the psychiatric disorders.ResultsThree genome-wide significant loci for SA were found: one associated with SA in MDD, one in BIP, and one in the meta-analysis of SA in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. Polygenic risk scores for major depression were significantly associated with SA in MDD (P=0.0002), BIP (P=0.0006) and SCZ (P=0.0006).ConclusionsThis study provides new information on genetic associations and the genetic etiology of SA across psychiatric disorders. The finding that polygenic risk scores for major depression predict suicide attempt across disorders provides a possible starting point for predictive modelling and preventative strategies. Further collaborative efforts to increase sample size hold potential to robustly identify genetic associations and gain biological insights into the etiology of suicide attempt.

scholarly journals LabWAS: novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks

2020 ◽  
Author(s):  
Jeffery A. Goldstein ◽  
Joshua S. Weinstock ◽  
Lisa A. Bastarache ◽  
Daniel B. Larach ◽  
Lars G. Fritsche ◽  
...  
Keyword(s):  
Health System ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Clinical Laboratory ◽  
Meta Analysis ◽  
Test Results ◽  
Genome Wide ◽  
Laboratory Test Results ◽  
Vanderbilt University ◽  
Novel Associations

ABSTRACTPhenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative lab measurements. We meta-analyzed 70 labs matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these labs, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 700 previous GWAS associations across 46 different labs. We discovered 31 novel associations at genome-wide significance for 22 distinct labs, including the first reported associations for two labs. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are available through an interactive webtool to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for lab data. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain labs. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative traits in EHRs.

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