scholarly journals Asthma and affective traits in adults: a genetically informative study

2019 ◽  
Vol 53 (5) ◽  
pp. 1802142 ◽  
Author(s):  
Kelli Lehto ◽  
Nancy L. Pedersen ◽  
Catarina Almqvist ◽  
Yi Lu ◽  
Bronwyn K. Brew
Keyword(s):  
Genetic Susceptibility ◽  
Genetic Correlations ◽  
Risk Scores ◽  
Affective Traits ◽  
Causal Direction ◽  
Genome Wide ◽  
Dose Response Effect ◽  
Study Participants ◽  
Swedish Twin Registry ◽  
Shared Genetic

Depression, anxiety and high neuroticism (affective traits) are often comorbid with asthma. A causal direction between the affective traits and asthma is difficult to determine; however, there may be a common underlying pathway attributable to shared genetic factors. Our aim was to determine whether a common genetic susceptibility exists for asthma and each of the affective traits.An adult cohort from the Swedish Twin Registry underwent questionnaire-based health assessments (n=23 693) and genotyping (n=15 908). Firstly, questionnaire-based associations between asthma and affective traits were explored. This was followed by genetic analyses: 1) polygenic risk scores (PRS) for affective traits were used as predictors of asthma in the cohort, and 2) genome-wide association results from UK Biobank were used in linkage-disequilibrium score regression (LDSC) to quantify genetic correlations between asthma and affective traits. Analyses found associations between questionnaire-based asthma and affective traits (OR 1.67, 95% CI 1.50–1.86 major depression; OR 1.45, 95% CI 1.30–1.61 anxiety; and OR 1.60, 95% CI 1.40–1.82 high neuroticism). Genetic susceptibility for neuroticism explained the variance in asthma with a dose–response effect; that is, study participants in the highest neuroticism PRS quartile were more likely to have asthma than those in the lowest quartile (OR 1.37, 95% CI 1.17–1.61). Genetic correlations were found between depression and asthma (rg=0.17), but not for anxiety or neuroticism.We conclude that the observed comorbidity between asthma and the affective traits may in part be due to shared genetic influences between asthma and depression (LDSC) and neuroticism (PRS), but not anxiety.

scholarly journals Asthma and affective traits in adults: a genetically informative study

2019 ◽  
Author(s):  
Kelli Lehto ◽  
Nancy L. Pedersen ◽  
Catarina Almqvist ◽  
Yi Lu ◽  
Bronwyn K Brew
Keyword(s):  
Genetic Susceptibility ◽  
Genetic Factors ◽  
Genetic Correlations ◽  
Risk Scores ◽  
Affective Traits ◽  
Causal Direction ◽  
Genome Wide ◽  
Health Assessments ◽  
Dose Response Effect ◽  
Shared Genetic

ABSTRACTDepression, anxiety and high neuroticism (affective traits) are often comorbid with asthma. A causal direction between the affective traits and asthma is difficult to determine, however, it may be that there is a common underlying pathway attributable to shared genetic factors. Our aim was to determine whether a common genetic susceptibility exists for asthma and each of the affective traits.An adult twin cohort from the Swedish Twin Register underwent questionnaire-based health assessments (n=23 693) and genotyping (n=15 908). Firstly, questionnaire-based associations between asthma and affective traits were explored. This was followed by genetic analyses: a) polygenic risk scores (PRS) for affective traits were used as predictors of asthma, and b) linkage-disequilibrium score regression based on genome-wide association results from UK Biobank was used to quantify genetic correlations.Analyses found that the questionnaire-based associations between asthma and each affective trait were associated (OR 1.7, 95%CI 1.5-1.9 major depression, OR 1.5, 95%CI 1.3-1.6 anxiety, and OR 1.6, 95% 1.4-1.8 high neuroticism). Genetic susceptibility for neuroticism explained the variance in asthma with a dose response effect; that is, those in the highest neuroticism PRS quartile were more likely to have asthma than those in the lowest quartile (OR 1.4, 95%CI 1.2-1.6). Genetic correlations were found between depression and asthma (rg= 0.17), but not for anxiety or neuroticism score.We conclude that the observed comorbidity between asthma and the affective traits may in part be due to shared genetic influences between asthma and depression and neuroticism, but not anxiety.

scholarly journals Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia

2013 ◽  
Vol 203 (2) ◽  
pp. 107-111 ◽  
Author(s):  
Marian L. Hamshere ◽  
Evangelia Stergiakouli ◽  
Kate Langley ◽  
Joanna Martin ◽  
Peter Holmans ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Genetic Susceptibility ◽  
Attention Deficit ◽  
Genome Wide Association Study ◽  
Research Work ◽  
Data Set ◽  
Hyperactivity Disorder ◽  
Risk Alleles ◽  
Genome Wide ◽  
Shared Genetic

BackgroundThere is recent evidence of some degree of shared genetic susceptibility between adult schizophrenia and childhood attention-deficit hyperactivity disorder (ADHD) for rare chromosomal variants.AimsTo determine whether there is overlap between common alleles conferring risk of schizophrenia in adults with those that do so for ADHD in children.MethodWe used recently published Psychiatric Genome-wide Association Study (GWAS) Consortium (PGC) adult schizophrenia data to define alleles over-represented in people with schizophrenia and tested whether those alleles were more common in 727 children with ADHD than in 2067 controls.ResultsSchizophrenia risk alleles discriminated ADHD cases from controls (P = 1.04 × 104, R2 = 0.45%); stronger discrimination was given by alleles that were risk alleles for both adult schizophrenia and adult bipolar disorder (also derived from a PGC data-set) (P = 9.98 ×10−6, R2 × 0.59%).ConclusionsThis increasing evidence for a small, but significant, shared genetic susceptibility between adult schizophrenia and childhood ADHD highlights the importance of research work across traditional diagnostic boundaries.

scholarly journals Detecting Local Genetic Correlations with Scan Statistics

2019 ◽  
Author(s):  
Hanmin Guo ◽  
James J. Li ◽  
Qiongshi Lu ◽  
Lin Hou
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Scan Statistics ◽  
Genome Wide Association Studies ◽  
Scan Statistic ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Statistic Approach ◽  
Shared Genetic

AbstractGenetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to five phenotypically distinct but genetically correlated psychiatric disorders, we identified 49 non-overlapping genome regions associated with multiple disorders, including multiple hub regions showing concordant effects on more than two disorders. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

scholarly journals Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Kathryn S. Burch ◽  
Ruth Johnson ◽  
Malika K. Freund ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Empirical Bayes ◽  
Genetic Correlations ◽  
Causal Snps ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Snps ◽  
Genome Wide ◽  
Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

Abstract 447: Deep Transcriptomic Analysis of Human Vascular Cells Identifies Risk Genes for Common Vascular Diseases

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Sylvia T Nurnberg ◽  
YoSon Park ◽  
Jordi Vaquero-Garcia ◽  
Milos Pjanic ◽  
Susanna Elwyn ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genetic Susceptibility ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome ◽  
Allele Specific ◽  
Artery Disease ◽  
Shared Genetic

The most recent Genome-wide Association Study (GWAS) meta-analysis has reported a total of 58 genomic loci to be statistically significantly associated with genetic susceptibility to Coronary Artery Disease (CAD) (Consortium, 2015). Many of these loci also associate with other phenotypes, with the majority being lipid traits (Tada et al., 2014). But also hypertension, stroke (Dichgans et al., 2014) and migraine (Pickrell et al., 2016) appear to share genetic determinants with CAD. To functionally annotate the genomic loci harboring these association SNPs we sequenced the transcriptomes of 20 same donor human coronary artery endothelial (EC) and smooth muscle cell (SMC) lines. Deep RNA-Sequencing was used to assess Differential Gene Expression, Differential Splicing and Allele-Specific Expression. Focusing on GWAS loci for vascular phenotypes (CAD, stroke, migraine) we identified genes which display allele-specific differences in mRNA expression or splicing. We propose these genes as suitable targets for follow up studies. Consortium, C.A.D. (2015). A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nature genetics 47, 1121-1130. Tada, H., Won, H.H., Melander, O., Yang, J., Peloso, G.M., and Kathiresan, S. (2014). Multiple associated variants increase the heritability explained for plasma lipids and coronary artery disease. Circulation Cardiovascular genetics 7, 583-587. Dichgans, M., Malik, R., Konig, I.R., Rosand, J., Clarke, R., Gretarsdottir, S., Thorleifsson, G., Mitchell, B.D., Assimes, T.L., Levi, C., et al. (2014). Shared genetic susceptibility to ischemic stroke and coronary artery disease: a genome-wide analysis of common variants. Stroke; a journal of cerebral circulation 45, 24-36. Pickrell, J.K., Berisa, T., Liu, J.Z., Segurel, L., Tung, J.Y., and Hinds, D.A. (2016). Detection and interpretation of shared genetic influences on 42 human traits. Nature genetics 48, 709-717.

scholarly journals Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis

2020 ◽  
Vol 8 (1) ◽  
pp. e001140
Author(s):  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Genetic Architecture ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

ObjectiveWe aimed to estimate genetic correlation, identify shared loci and test causality between leptin levels and type 2 diabetes (T2D).Research design and methodsOur study consists of three parts. First, we calculated the genetic correlation of leptin levels and T2D or glycemic traits by using linkage disequilibrium score regression analysis. Second, we conducted a large-scale genome-wide cross-trait meta-analysis using cross-phenotype association to identify shared loci between trait pairs that showed significant genetic correlations in the first part. In the end, we carried out a bidirectional MR analysis to find out whether there is a causal relationship between leptin levels and T2D or glycemic traits.ResultsWe found positive genetic correlations between leptin levels and T2D (Rg=0.3165, p=0.0227), fasting insulin (FI) (Rg=0.517, p=0.0076), homeostasis model assessment-insulin resistance (HOMA-IR) (Rg=0.4785, p=0.0196), as well as surrogate estimates of β-cell function (HOMA-β) (Rg=0.4456, p=0.0214). We identified 12 shared loci between leptin levels and T2D, 1 locus between leptin levels and FI, 1 locus between leptin levels and HOMA-IR, and 1 locus between leptin levels and HOMA-β. We newly identified eight loci that did not achieve genome-wide significance in trait-specific genome-wide association studies. These shared genes were enriched in pancreas, thyroid gland, skeletal muscle, placenta, liver and cerebral cortex. In addition, we found that 1-SD increase in HOMA-IR was causally associated with a 0.329 ng/mL increase in leptin levels (β=0.329, p=0.001).ConclusionsOur results have shown the shared genetic architecture between leptin levels and T2D and found causality of HOMA-IR on leptin levels, shedding light on the molecular mechanisms underlying the association between leptin levels and T2D.

scholarly journals Evaluation of shared genetic susceptibility loci between autoimmune diseases and schizophrenia based on genome-wide association studies

2016 ◽  
Vol 71 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Louise K. Hoeffding ◽  
Anders Rosengren ◽  
Johan H. Thygesen ◽  
Henriette Schmock ◽  
Thomas Werge ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Genetic Susceptibility ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Genome Wide ◽  
Shared Genetic

scholarly journals Association between substance use disorder and polygenic liability to schizophrenia

2017 ◽  
Author(s):  
Sarah M. Hartz ◽  
Amy Horton ◽  
Mary Oehlert ◽  
Caitlin E. Carey ◽  
Arpana Agrawal ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorder ◽  
Meta Analysis ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Individual Substance ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Individual Dataset ◽  
Shared Genetic

AbstractBackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.MethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.ResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4×10-23).ConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

scholarly journals Genome-wide analyses in 1,987,836 participants identify 39 genetic loci associated with sleep apnoea

2020 ◽  
Author(s):  
Adrian I Campos ◽  
Nathan Ingold ◽  
Yunru Huang ◽  
Pik Fang Kho ◽  
Xikun Han ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Sleep Apnoea ◽  
Risk Scores ◽  
Genetic Associations ◽  
Genome Wide ◽  
A Genome

Rationale: Sleep apnoea is a complex disorder characterised by periods of halted breathing during sleep. Despite its association with serious health conditions such as cardiovascular disease, the aetiology of sleep apnoea remains understudied, and previous genetic studies have failed to identify replicable genetic risk factors. Objective: To advance our understanding of factors that increase susceptibility to sleep apnoea by identifying novel genetic associations. Methods: We conducted a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts, and a previously published GWAS of apnoea-hypopnea index (N Total =510,484). Further, we used multi-trait analysis of GWAS (MTAG) to boost statistical power, leveraging the high genetic correlations between apnoea, snoring and body mass index. Replication was performed in an independent sample from 23andMe, Inc (N Total =1,477,352; N cases =175,522). Results: Our results revealed 39 independent genomic loci robustly associated with sleep apnoea risk, and significant genetic correlations with multisite chronic pain, sleep disorders, diabetes, high blood pressure, osteoarthritis, asthma and BMI-related traits. We also derived polygenic risk scores for sleep apnoea in a leave-one-out independent cohort and predicted probable sleep apnoea in participants (OR=1.15 to 1.22; variance explained = 0.4 to 0.9%). Conclusions: We report novel genetic markers robustly associated with sleep apnoea risk and substantial molecular overlap with other complex traits, thus advancing our understanding of the underlying biological mechanisms of susceptibility to sleep apnoea.

scholarly journals Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders and known risk factors

2020 ◽  
Author(s):  
Niamh Mullins ◽  
Jooeun Kang ◽  
Adrian I Campos ◽  
Jonathan R I Coleman ◽  
Alexis C Edwards ◽  
...  
Keyword(s):  
Risk Factors ◽  
Suicide Attempt ◽  
Psychiatric Disorders ◽  
Risk Taking ◽  
Sleep Disturbances ◽  
Genetic Correlations ◽  
Genetic Etiology ◽  
Genome Wide ◽  
A Genome ◽  
Shared Genetic

AbstractSuicide is a leading cause of death worldwide and non-fatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both are known to have a substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium and conditioned the results on psychiatric disorders using GWAS summary statistics, to investigate their shared and divergent genetic architectures. Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, which remained associated after conditioning and has previously been implicated in risk-taking, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, lower socioeconomic status, pain, lower educational attainment, reproductive traits, risk-taking, sleep disturbances, and poorer overall general health. After conditioning, the genetic correlations between SA and psychiatric disorders decreased, whereas those with non-psychiatric traits remained largely unchanged. Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest the existence of a shared genetic etiology between SA and known risk factors that is not mediated by psychiatric disorders.

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