scholarly journals Patterns of convergence and divergence between bipolar disorder type I and type II: evidence from integrative genomic analyses

2021 ◽  
Author(s):  
Yunqi Huang ◽  
Yunjia Liu ◽  
Yulu Wu ◽  
Yiguo Tang ◽  
Siyi Liu ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Brain Regions ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Type Ii ◽  
Association Analyses ◽  
Convergence And Divergence ◽  
The Difference ◽  
Genetic Structures

Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about genetic structure of BD subtypes. We aimed to investigate genetic overlap and distinction of bipolar type I (BDI) & type II (BDII) by conducting integrative post-GWAS analyses. This study utilized single nucleotide polymorphism (SNP)-level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis including exploring the potential causal associations between BDI & II and drug responses and comparing the difference of genetic structures among four different psychiatric traits. Our results find SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184 and RPL10AP3 shared by BDI & II, while one locus (i.e., MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes effecting BDI & II through expression in specific brain regions (e.g., nucleus accumbens for BDI). Cross-phenotype analyses indicated that BDI & II have different drug response, but share continuous genetic structures with schizophrenia (SCZ) and major depression disorder (MDD), which help fill the gaps left by the dichotomy of mental disorder. These combined evidences illustrate genetic convergence and divergence between BDI & II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.

scholarly journals DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Zachary R. McCaw ◽  
Thomas Colthurst ◽  
Taedong Yun ◽  
Nicholas A. Furlotte ◽  
Andrew Carroll ◽  
...  
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Real Data ◽  
Interactive Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Linear Modeling ◽  
Association Analyses ◽  
Covariate Effects ◽  
Non Linear

AbstractGenome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

scholarly journals Polygenic risk scores across the extended psychosis spectrum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lukasz Smigielski ◽  
Sergi Papiol ◽  
Anastasia Theodoridou ◽  
Karsten Heekeren ◽  
Miriam Gerstenberg ◽  
...  
Keyword(s):  
At Risk ◽  
Association Studies ◽  
Bayesian Regression ◽  
Mitigation Measures ◽  
Risk Scores ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Type Ii ◽  
Polygenic Risk ◽  
Common Genetic Variants

AbstractAs early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke’s pseudo-R2: 1.3–7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

scholarly journals Genetics of bipolar disorder

2008 ◽  
Vol 10 (2) ◽  
pp. 141-152
Keyword(s):  
Bipolar Disorder ◽  
Candidate Genes ◽  
Current Knowledge ◽  
Association Studies ◽  
Genetic Maps ◽  
Behavioral Disorder ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Positional Candidate ◽  
Replication Studies

Bipolar disorder especially the most severe type (type I), has a strong genetic component. Family studies suggest that a small number of genes of modest effect are involved in this disorder. Family-based studies have identified a number of chromosomal regions linked to bipolar disorder, and progress is currently being made in identifying positional candidate genes within those regions. A number of candidate genes have also shown evidence of association with bipolar disorder, and genome-wide association studies are now under way, using dense genetic maps. Replication studies in larger or combined datasets are needed to definitively assign a role for specific genes in this disorder. This review covers our current knowledge of the genetics of bipolar disorder, and provides a commentary on current approaches used to identify the genes involved in this complex behavioral disorder.

scholarly journals Polymorphisms Associated with Age at Onset in Patients with Moderate-to-Severe Plaque Psoriasis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Rocío Prieto-Pérez ◽  
Guillermo Solano-López ◽  
Teresa Cabaleiro ◽  
Manuel Román ◽  
Dolores Ochoa ◽  
...  
Keyword(s):  
Plaque Psoriasis ◽  
Age Of Onset ◽  
Late Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Healthy Controls ◽  
Type Ii ◽  
Severe Plaque Psoriasis

Psoriasis is a chronic skin disease in which genetics play a major role. Although many genome-wide association studies have been performed in psoriasis, knowledge of the age at onset remains limited. Therefore, we analyzed 173 single-nucleotide polymorphisms in genes associated with psoriasis and other autoimmune diseases in patients with moderate-to-severe plaque psoriasis type I (early-onset, <40 years) or type II (late-onset, ≥40 years) and healthy controls. Moreover, we performed a comparison between patients with type I psoriasis and patients with type II psoriasis. Our comparison of a stratified population with type I psoriasisn=155and healthy controlsN=197is the first to reveal a relationship between theCLMN,FBXL19,CCL4L,C17orf51,TYK2,IL13,SLC22A4,CDKAL1, andHLA-B/MICAgenes. When we compared type I psoriasis with type II psoriasisN=36, we found a significant association between age at onset and the genesPSORS6,TNF-α,FCGR2A,TNFR1,CD226,HLA-C,TNFAIP3, andCCHCR1. Moreover, we replicated the association between rs12191877 (HLA-C) and type I psoriasis and between type I and type II psoriasis. Our findings highlight the role of genetics in age of onset of psoriasis.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

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.

A nonparametric test for association with multiple loci in the retrospective case-control study

2019 ◽  
Vol 29 (2) ◽  
pp. 589-602
Author(s):  
Chan Wang ◽  
Shufang Deng ◽  
Leiming Sun ◽  
Liming Li ◽  
Yue-Qing Hu
Keyword(s):  
Rare Variants ◽  
Association Studies ◽  
Nonparametric Test ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Retrospective Case ◽  
Multiple Loci ◽  
Common Diseases ◽  
The Difference

The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance–covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.

Blood Type and Bipolar Disorder

2002 ◽  
Vol 95 (3) ◽  
pp. 988-988
Author(s):  
Tamas Zonda ◽  
David Lester
Keyword(s):  
Bipolar Disorder ◽  
Blood Type ◽  
Type I ◽  
Type Ii ◽  
Bipolar Patients

Type I bipolar patients in Budapest were reported to have type O blood more often and types A and B blood less often than Type II bipolar patients.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

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