Association of CACNA1C polymorphisms with serum BDNF levels in bipolar disorder

2019 ◽  
pp. 1-3
Author(s):  
Erik Smedler ◽  
Erik Pålsson ◽  
Kenji Hashimoto ◽  
Mikael Landén
Keyword(s):  
Bipolar Disorder ◽  
Genetic Variants ◽  
Association Studies ◽  
Serum Levels ◽  
Genome Wide Association Studies ◽  
Risk Alleles ◽  
Voltage Gated Calcium Channels ◽  
Genome Wide ◽  
Tissue Plasminogen ◽  
Serum Bdnf

Variation in the CACNA1C gene has been associated with bipolar disorder in several genome-wide association studies. This gene encodes the alpha 1C subunit of L-type voltage-gated calcium channels, which play an essential role in neurons. We analysed 39 biomarkers in either cerebrospinal fluid or serum in relation to six different CACNA1C variants in 282 patients with bipolar disorder and 90 controls. We report associations of CACNA1C risk alleles with serum levels of BDNF as well as tissue plasminogen activator, which converts pro-BDNF to mature BDNF. This sheds light on links between CACNA1C genetic variants and pathophysiological mechanisms in bipolar disorder.Declaration of interestNone.

scholarly journals Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009918
Author(s):  
Bernard Ng ◽  
William Casazza ◽  
Nam Hee Kim ◽  
Chendi Wang ◽  
Farnush Farhadi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Association Studies ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Cascading Effects ◽  
Risk Alleles ◽  
Gene Sets ◽  
Genome Wide ◽  
Regulatory Landscape

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

scholarly journals Host Genetic Variants Potentially Associated with SARS-Cov-2: A Multi-Population Analysis

2020 ◽  
Author(s):  
Maria K. Smatti ◽  
Yasser Al-Sarraj ◽  
Omar Albagha ◽  
Hadi M. Yassine
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Population Analysis ◽  
Fold Change ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Risk Alleles ◽  
Risk Variants ◽  
Genome Wide ◽  
Ifn Γ

Background: Clinical outcomes of Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and interpopulation differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. Methods: We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, n=6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). Results: A total of 74 SNPs, located in 10 genes: ICAM3, IFN-γ, CCL2, CCL5, AHSG, MBL, Furin, TMPRSS2, IL4, and CD209 promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (CCL2, MBL, CCL5, AHSG, and IL4) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in TMPRSS2, IFN-γ, ICAM3, and Furin were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Conclusion: Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2.

Migraine: Genetic Variants and Clinical Phenotypes

2019 ◽  
Vol 26 (34) ◽  
pp. 6207-6221 ◽  
Author(s):  
Innocenzo Rainero ◽  
Alessandro Vacca ◽  
Flora Govone ◽  
Annalisa Gai ◽  
Lorenzo Pinessi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Mthfr Gene ◽  
Genome Wide Association Studies ◽  
Clinical Phenotypes ◽  
Network Analyses ◽  
Genome Wide ◽  
Genomic Studies ◽  
The Common ◽  
The Relationship

Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals Pharmacogenomics of Lithium Response in Bipolar Disorder

2021 ◽  
Vol 14 (4) ◽  
pp. 287
Author(s):  
Courtney M. Vecera ◽  
Gabriel R. Fries ◽  
Lokesh R. Shahani ◽  
Jair C. Soares ◽  
Rodrigo Machado-Vieira
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Mood Stabilizer ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Lithium Response ◽  
Genetic Loading ◽  
Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals GWASdb: a database for human genetic variants identified by genome-wide association studies

2011 ◽  
Vol 40 (D1) ◽  
pp. D1047-D1054 ◽  
Author(s):  
Mulin Jun Li ◽  
Panwen Wang ◽  
Xiaorong Liu ◽  
Ee Lyn Lim ◽  
Zhangyong Wang ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Statistical Genetics

2018 ◽  
pp. 57-69 ◽  
Author(s):  
Till F. M. Andlauer ◽  
Bertram Müller-Myhsok ◽  
Stephan Ripke
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Power Calculation ◽  
Genome Wide Association Studies ◽  
Psychiatric Genetics ◽  
Factors Influencing ◽  
Genome Wide ◽  
Basic Concepts ◽  
Study Designs

Over more than the last decade, hypothesis-free genome-wide association studies (GWAS) have been widely used to detect genetic factors influencing phenotypes of interest. The basic principle of GWAS has been unchanged since the beginning: a series of univariate tests is conducted on all genetic variants available across the genome. We present study designs and commonly used methods for genome-wide studies, with a focus on the analysis of common variants. The basic concepts required for an application of GWAS in psychiatric genetics are introduced, from power calculation to meta-analysis. This chapter will help the reader in gaining the knowledge required for participation in and realization of GWAS of both qualitative and quantitative traits.

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