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 Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

2021 ◽  
Author(s):  
Kristin Tsuo ◽  
Wei Zhou ◽  
Ying Wang ◽  
Masahiro Kanai ◽  
Shinichi Namba ◽  
...  
Keyword(s):  
Statistical Power ◽  
Complex Disease ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Risk Scores ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Genome Wide ◽  
Order Of Magnitude ◽  
Novel Associations

Asthma is a complex disease that affects millions of people and varies in prevalence by an order of magnitude across geographic regions and populations. However, the extent to which genetic variation contributes to these disparities is unclear, as studies probing the genetics of asthma have been primarily limited to populations of European (EUR) descent. As part of the Global Biobank Meta-analysis Initiative (GBMI), we conducted the largest genome-wide association study of asthma to date (153,763 cases and 1,647,022 controls) via meta-analysis across 18 biobanks spanning multiple countries and ancestries. Altogether, we discovered 180 genome-wide significant loci (p < 5x10-8) associated with asthma, 49 of which are not previously reported. We replicate well-known associations such as IL1RL1 and STAT6, and find that overall the novel associations have smaller effects than previously-discovered loci, highlighting our substantial increase in statistical power. Despite the considerable range in prevalence among biobanks, from 3% to 24%, the genetic effects of associated loci are largely consistent across the biobanks and ancestries. To further investigate the polygenic architecture of asthma, we construct polygenic risk scores (PRS) using a multi-ancestry approach, which yields higher predictive power for asthma in non-EUR populations compared to PRS derived from previous asthma meta-analyses and using other methods. Additionally, we find considerable genetic overlap between asthma and chronic obstructive pulmonary disease (COPD) across ancestries but minimal overlap in enriched biological pathways. Our work underscores the multifactorial nature of asthma development and offers insight into the shared genetic architecture of asthma that may be differentially perturbed by environmental factors and contribute to variation in prevalence.

scholarly journals A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773)

2017 ◽  
Author(s):  
Weihua Meng ◽  
Mark J Adams ◽  
Harry L Hebert ◽  
Ian J Deary ◽  
Andrew M McIntosh ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Psychological Traits ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

AbstractHeadache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. We identified 3,343 SNPs which reached the genome-wide significance level of P < 5 × 10−8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 × 10−47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 × 10−15 in the LINC02210-CRHR1 gene was the top SNP.Positive relationships (P < 0.001) between multiple brain tissues and genetic associations were identified through tissue expression analysis, whereas no vascular related tissues showed significant relationships. We identified several significant positive genetic correlations between headache and other psychological traits including neuroticism, depressive symptoms, insomnia, and major depressive disorder.Our results suggest that brain function is closely related to broadly-defined headache. In addition, we also found that many psychological traits have genetic correlations with headache.

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 Genetic associations and architecture of asthma-chronic obstructive pulmonary disease overlap

2020 ◽  
Author(s):  
C. John ◽  
A.L. Guyatt ◽  
N. Shrine ◽  
R. Packer ◽  
T.A. Olafsdottir ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Meta Analysis ◽  
European Ancestry ◽  
Chronic Obstructive ◽  
Genetic Associations ◽  
Obstructive Pulmonary Disease ◽  
Genome Wide ◽  
A Genome ◽  
Stage 1

AbstractSome individuals have characteristics of both asthma and COPD (asthma-COPD overlap, ACO), and evidence suggests they experience worse outcomes than those with either condition alone. Improved knowledge of the genetic architecture would contribute to understanding whether determinants of risk in this group differ from those in COPD or asthma.We conducted a genome-wide association study in 8,068 cases and 40,360 controls of European ancestry from UK Biobank (stage 1). After excluding variants only associated with asthma or COPD we selected 31 variants for further investigation in 12 additional cohorts (stage 2), and discovered eight novel signals for ACO in a meta-analysis of stage 1 and 2 studies.Our signals include an intergenic signal on chromosome 5 not previously associated with asthma, COPD or lung function, and suggest a spectrum of shared and distinct genetic influences in asthma, COPD and ACO. A number of signals may represent loci that predispose to serious long-term consequences in people with asthma.

scholarly journals LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

2016 ◽  
Author(s):  
Jie Zheng ◽  
A. Mesut Erzurumluoglu ◽  
Benjamin L. Elsworth ◽  
Laurence Howe ◽  
Philip C. Haycock ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Web Interface ◽  
The Public ◽  
Genome Wide ◽  
Level Data ◽  
Centralized Database

AbstractMotivationLD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.ResultsIn this manuscript, we describe LD Hub – a centralized database of summary-level GWAS results for 177 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.Availability and implementationThe web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

scholarly journals Genome-wide association study of psychiatric and substance use comorbidity in Mexican individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Jaime Martínez-Magaña ◽  
Alma Delia Genis-Mendoza ◽  
Jorge Ameth Villatoro Velázquez ◽  
Marycarmen Bustos-Gamiño ◽  
Isela Esther Juárez-Rojop ◽  
...  
Keyword(s):  
Substance Use ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Logistic Models ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Multinomial Models ◽  
Gamma Receptor ◽  
Genome Wide ◽  
A Genome

AbstractThe combination of substance use and psychiatric disorders is one of the most common comorbidities. The objective of this study was to perform a genome-wide association study of this comorbidity (Com), substance use alone (Subs), and psychiatric symptomatology alone (Psych) in the Mexican population. The study included 3914 individuals of Mexican descent. Genotyping was carried out using the PsychArray microarray and genome-wide correlations were calculated. Genome-wide associations were analyzed using multiple logistic models, polygenic risk scores (PRSs) were evaluated using multinomial models, and vertical pleiotropy was evaluated by generalized summary-data-based Mendelian randomization. Brain DNA methylation quantitative loci (brain meQTL) were also evaluated in the prefrontal cortex. Genome-wide correlation and vertical pleiotropy were found between all traits. No genome-wide association signals were found, but 64 single-nucleotide polymorphism (SNPs) reached nominal associations (p < 5.00e−05). The SNPs associated with each trait were independent, and the individuals with high PRSs had a higher prevalence of tobacco and alcohol use. In the multinomial models all of the PRSs (Subs-PRS, Com-PRS, and Psych-PRS) were associated with all of the traits. Brain meQTL of the Subs-associated SNPs had an effect on the genes enriched in insulin signaling pathway, and that of the Psych-associated SNPs had an effect on the Fc gamma receptor phagocytosis pathway.

scholarly journals A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 477-494 ◽  
Author(s):  
Jakub Pecanka ◽  
Marianne A. Jonker ◽  
Zoltan Bochdanovits ◽  
Aad W. Van Der Vaart ◽  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Score Test ◽  
Interaction Model ◽  
Type I ◽  
Two Stage ◽  
Genome Wide ◽  
Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

A Proteome-Wide Association Study Identified Candidate Human Brain Proteins Associated with Coffee Consumption

2021 ◽  
Author(s):  
Chun'e Li ◽  
Xiao Liang ◽  
Yumeng Jia ◽  
Yan Wen ◽  
Huijie Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
Association Study ◽  
Plasma Proteins ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Coffee Consumption ◽  
Genetic Associations ◽  
Brain Proteins ◽  
Genome Wide ◽  
Brain Proteome

Abstract Background Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites. Methods First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites. Results For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216). Conclusions We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.

Abstract 16003: Genetic Variation Associated With Favorable Exercise Response in Patients With Systolic Heart Failure: A Hf-action Trial Substudy

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Sara Coles ◽  
Stephanie Giamberardino ◽  
Carol Haynes ◽  
Ruicong She ◽  
Hongsheng Gui ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Trial ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Systolic Heart Failure ◽  
Genome Wide Association ◽  
Supervised Exercise ◽  
Genome Wide ◽  
A Genome ◽  
Response To Exercise

Background: Exercise has shown benefit in patients with systolic heart failure, including in the clinical trial Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION). There is heterogeneity in who derives benefit from exercise, and the biologic mechanisms of favorable response to exercise in systolic heart failure are not well understood. Hypothesis: Genetic variation is an underlying factor influencing heterogeneity in response to exercise in patients with systolic heart failure. Methods: The HF-ACTION trial randomized individuals with systolic heart failure (left ventricular ejection fraction <35%) to supervised exercise versus usual care. In this study, we performed a genome wide association study (GWAS) in the HF-ACTION biorepository using the Axiom Biobank1 genotyping array (13,403,591 single nucleotide polymorphisms [SNPs] after quality control on directly genotyped and 1000 genomes imputed data), in N=377 study subjects who completed the supervised exercise arm. Using change in peak VO2 as our outcome, we ran within-ancestry GWASes, modeling SNP effects as both additive and dominant, and conducted across-ancestry meta-analysis within each genetic model. Results: Five loci met genome-wide significance in the European ancestry analyses, 5 loci in the African ancestry, and 8 in the meta-analyses. The two most significantly associated loci across both additive and dominant meta-analysis models were rs111577308 located in the histone acetylation for transcription elongator complex 3 gene ( ELP3, p=1.212x10 -9 ) and rs75444785 located in the phosphodiesterase 4D gene ( PDE4D , p=1.565x10 -9 ). ELP3 is responsible for histone modifications related to DNA transcription factor complexes, and PDE4D is involved in cyclic AMP cell signaling. In silico analysis of these loci showed that they are in linkage with regions associated with skeletal muscle and peripheral vascular disease phenotypes. Conclusions: Using a genome-wide association study in a well-phenotyped clinical trial of exercise in systolic heart failure, we found common genetic variants in genes involved in DNA transcription histone modification and cyclic AMP cell signaling that are associated with a more favorable response to exercise.

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