scholarly journals Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

2013 ◽  
Vol 45 (9) ◽  
pp. 984-994 ◽  
Author(s):  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Relationship ◽  
Genome Wide

scholarly journals Genetic factors influencing a neurobiological substrate for psychiatric disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Till F. M. Andlauer ◽  
Thomas W. Mühleisen ◽  
Felix Hoffstaedter ◽  
Alexander Teumer ◽  
Katharina Wittfeld ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Anterior Cingulate ◽  
Psychiatric Disease ◽  
Salience Network ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Common Substrate ◽  
The Common

AbstractA retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex—areas that constitute hub nodes of the salience network—represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

scholarly journals Detection and application of genome-wide variations in peach for association and genetic relationship analysis

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Liping Guan ◽  
Ke Cao ◽  
Yong Li ◽  
Jian Guo ◽  
Qiang Xu ◽  
...  
Keyword(s):  
Genetic Relationship ◽  
Dna Markers ◽  
High Throughput Sequencing ◽  
Prunus Persica ◽  
Genetic Research ◽  
Diploid Species ◽  
Sequencing Data ◽  
Relationship Analysis ◽  
Genome Wide ◽  
A Genome

Abstract Background Peach (Prunus persica L.) is a diploid species and model plant of the Rosaceae family. In the past decade, significant progress has been made in peach genetic research via DNA markers, but the number of these markers remains limited. Results In this study, we performed a genome-wide DNA markers detection based on sequencing data of six distantly related peach accessions. A total of 650,693~1,053,547 single nucleotide polymorphisms (SNPs), 114,227~178,968 small insertion/deletions (InDels), 8386~12,298 structure variants (SVs), 2111~2581 copy number variants (CNVs) and 229,357~346,940 simple sequence repeats (SSRs) were detected and annotated. To demonstrate the application of DNA markers, 944 SNPs were filtered for association study of fruit ripening time and 15 highly polymorphic SSRs were selected to analyze the genetic relationship among 221 accessions. Conclusions The results showed that the use of high-throughput sequencing to develop DNA markers is fast and effective. Comprehensive identification of DNA markers, including SVs and SSRs, would be of benefit to genetic diversity evaluation, genetic mapping, and molecular breeding of peach.

T46THE GENETIC RELATIONSHIP BETWEEN EDUCATIONAL ATTAINMENT AND COGNITIVE PERFORMANCE IN MAJOR PSYCHIATRIC DISORDERS

2019 ◽  
Vol 29 ◽  
pp. S241
Author(s):  
Ashley Lütticke ◽  
Fanny Senner ◽  
Monika Budde ◽  
Kristina Adorjan ◽  
Urs Heilbronner ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Psychiatric Disorders ◽  
Cognitive Performance ◽  
Genetic Relationship

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

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Shiqiang Cheng ◽  
Fanglin Guan ◽  
Mei Ma ◽  
Lu Zhang ◽  
Bolun Cheng ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Plasma Proteins ◽  
Genome Wide Association Study ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Tumor Necrosis Factor Receptor ◽  
Autism Spectrum ◽  
Human Blood Plasma ◽  
Plasma Proteome ◽  
Genome Wide

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.

scholarly journals Analysis of shared heritability in common disorders of the brain

Science ◽  
2018 ◽  
Vol 360 (6395) ◽  
pp. eaap8757 ◽  
Author(s):  
◽  
Verneri Anttila ◽  
Brendan Bulik-Sullivan ◽  
Hilary K. Finucane ◽  
Raymond K. Walters ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Neurological Disorders ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Brain Disorders ◽  
Cognitive Measures ◽  
Genome Wide ◽  
The Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

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