scholarly journals A global overview of genetically interpretable comorbidities among common diseases in UK Biobank

2021 ◽  
Author(s):  
Guiying Dong ◽  
Jianfeng Feng ◽  
Fengzhu Sun ◽  
Jingqi Chen ◽  
Xing-Ming Zhao
Keyword(s):  
Genetic Architecture ◽  
Hospital Inpatient ◽  
Uk Biobank ◽  
Network Decomposition ◽  
Genetic Components ◽  
Common Diseases ◽  
Cell Immunity ◽  
Genetic Risks ◽  
Network Modules ◽  
Shared Genetic

AbstractBackgroundComorbidities greatly increase global health burdens, but the landscapes of their genetic factors have not been systematically investigated.MethodsWe used the hospital inpatient data of 385,335 patients in UK Biobank to investigate the comorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify comorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for interpretable comorbidity networks to detect the hub diseases and the involved molecules in comorbidity modules.Results11,285 comorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture level. The comorbidities affecting the same and different physiological systems showed different patterns at the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by comorbidities mainly converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable comorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the comorbidity modules could help provide useful insights into the genetic contributors for comorbiditities.ConclusionsOur results provide a systematic resource for understanding the genetic predispositions of comorbidity, and indicate that hub diseases and converged molecules and functions may be the key for treating comorbidity. We have created an online database to facilitate researchers and physicians to browse, search or download these comorbidities (https://comorbidity.comp-sysbio.org).

scholarly journals A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Guiying Dong ◽  
Jianfeng Feng ◽  
Fengzhu Sun ◽  
Jingqi Chen ◽  
Xing-Ming Zhao
Keyword(s):  
Genetic Architecture ◽  
Hospital Inpatient ◽  
Uk Biobank ◽  
Network Decomposition ◽  
Genetic Components ◽  
Common Diseases ◽  
Cell Immunity ◽  
Genetic Risks ◽  
The Uk ◽  
Shared Genetic

Abstract Background Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated. Methods We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module. Results In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities. Conclusions Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities (https://multimorbidity.comp-sysbio.org).

scholarly journals Do Regional Brain Volumes and Major Depressive Disorder Share Genetic Architecture?: a study of Generation Scotland (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766)

2016 ◽  
Author(s):  
Eleanor M. Wigmore ◽  
Toni-Kim Clarke ◽  
Mark J. Adams ◽  
Ana M. Fernandez-Pujals ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Genetic Architecture ◽  
Hippocampal Volume ◽  
Uk Biobank ◽  
Major Depressive ◽  
Brain Volumes ◽  
Regional Brain ◽  
Generation Scotland ◽  
English Longitudinal Study ◽  
Shared Genetic

AbstractMajor depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from ENIGMA consortium’s genome-wide association study (GWAS) of regional brain volume, we sought to test whether there is shared genetic architecture between 8 subcortical brain volumes and MDD. Using LD score regression utilising summary statistics from ENIGMA and the Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. We also generated polygenic risk scores (PRS) to assess potential pleiotropy between regional brain volumes and MDD in three cohorts (Generation Scotland; Scottish Family Health Study (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766). We used logistic regression to examine volumetric PRS and MDD and performed a meta-analysis across the three cohorts. No regional volumetric PRS demonstrated significant association with MDD or recurrent MDD. In this study we provide some evidence that hippocampal volume and MDD may share genetic architecture, albeit this did not survive multiple testing correction and was in the opposite direction to most reported phenotypic correlations. We therefore found no evidence to support a shared genetic architecture for MDD and regional subcortical volumes.

scholarly journals Author Correction: A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases

2018 ◽  
Vol 50 (12) ◽  
pp. 1753-1753 ◽  
Author(s):  
Zhaozhong Zhu ◽  
Phil H. Lee ◽  
Mark D. Chaffin ◽  
Wonil Chung ◽  
Po-Ru Loh ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Allergic Diseases ◽  
Uk Biobank ◽  
Wide Cross ◽  
Trait Analysis ◽  
Genome Wide ◽  
A Genome ◽  
Shared Genetic

scholarly journals O21: GENOME-WIDE ASSOCIATION STUDY OF VARICOSE VEINS IN 810,625 INDIVIDUALS IDENTIFIES 45 GENETIC RISK LOCI

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
WUR Ahmed ◽  
A Wiberg ◽  
M Ng ◽  
D Furniss
Keyword(s):  
Association Study ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Varicose Veins ◽  
Phenotypic Expression ◽  
Cell Activity ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Genetic Components ◽  
Genome Wide

Abstract Introduction Varicose veins (VV) impact a third of the UK adult population; 10% of patients develop lipodermatosclerosis and ulceration. VV often requires surgical management, however, there is a high-risk of recurrence. VV is a complex disease, where genetic and non-genetic components contribute to overall phenotypic expression. The genetic architecture of VV is poorly understood; we aimed to uncover its genetic basis. Method We conducted hitherto the largest genome-wide association study of VV. In stage one, using UK Biobank, we compared 22,473 VV patients and 379,183 controls. In stage two, replication and meta-analysis were performed in an independent cohort of 113,041 VV cases and 295,928 controls from 23&Me (California, USA). In-silico analysis was conducted in FUMA, MAGMA, and XGR. Result 109 genome-wide significant (P≤ 5×10-8) loci were identified in UK Biobank, 45 of which successfully replicated in the 23&Me cohort. Twenty-seven loci have not been previously reported. FUMA positionally-mapped 128 genes at the replicated loci, with 84 having a combined annotation-dependent depletion score (CADD) >12.37, suggesting functional, deleterious variants. MAGMA analysis implicated pathways involved in cardiovascular system development (P=1.57×10-08) and tube morphogenesis (P=9.35×10-08). Furthermore, XGR revealed enriched pathways in downstream signalling in naive CD8+ T cells (P=0.0017), and encoding structural and core extracellular glycoproteins (both P=0.007). Conclusion We identified 45 variants conferring risk of VV, which provide insights into disease biology. Implicated genes are enriched in pathways involved in vascular development, immune cell activity and extracellular matrix function, and provide new targets for therapeutic development. Take-home message Unravelling the genetic architecture of varicose veins may facilitate our understanding of the disease and guide therapeutic approaches.

scholarly journals Hierarchical clustering of gene-level association statistics reveals shared and differential genetic architecture among traits in the UK Biobank

2019 ◽  
Author(s):  
Melissa R. McGuirl ◽  
Samuel Pattillo Smith ◽  
Björn Sandstede ◽  
Sohini Ramachandran
Keyword(s):  
Hierarchical Clustering ◽  
Genetic Architecture ◽  
Branch Length ◽  
European Ancestry ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Multiple Traits ◽  
Gene Level ◽  
The Uk ◽  
Shared Genetic

AbstractGenome-wide association (GWA) studies have generally focused on a single phenotype of interest. Emerging biobanks that pair genotype data from thousands of individuals with phenotype data using medical records or surveys enable testing for genetic associations in each phenotype assayed. However, methods for characterizing shared genetic architecture among multiple traits are lagging behind. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for characterizing shared and divergent genetic architecture among multiple phenotypes. The objective of WINGS (freely available at https://github.com/ramachandran-lab/PEGASUS-WINGS) is to identify groups of phenotypes, or “clusters”, that share a core set of genes enriched for mutations in cases. We show in simulations that WINGS can reliably detect phenotype clusters across a range of percent shared architecture and number of phenotypes included. We then use the gene-level association test PEGASUS with WINGS to characterize shared genetic architecture among 87 case-control and seven quantitative phenotypes in 349,468 unrelated European-ancestry individuals from the UK Biobank. We identify 10 significant phenotype clusters that contain two to eight phenotypes. One significant cluster of seven immunological phenotypes is driven by seven genes; these genes have each been associated with two or more of those same phenotypes in past publications. WINGS offers a precise and efficient new application of Ward hierarchical clustering to generate hypotheses regarding shared genetic architecture among phenotypes in the biobank era.

scholarly journals A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases

2018 ◽  
Vol 50 (6) ◽  
pp. 857-864 ◽  
Author(s):  
Zhaozhong Zhu ◽  
Phil H. Lee ◽  
Mark D. Chaffin ◽  
Wonil Chung ◽  
Po-Ru Loh ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Allergic Diseases ◽  
Uk Biobank ◽  
Wide Cross ◽  
Trait Analysis ◽  
Genome Wide ◽  
A Genome ◽  
Shared Genetic

scholarly journals Cardiovascular-related deaths at the beginning of the COVID-19 outbreak: a prospective analysis based on the UK Biobank

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046931
Author(s):  
Junren Wang ◽  
Jianwei Zhu ◽  
Huazhen Yang ◽  
Yao Hu ◽  
Yajing Sun ◽  
...  
Keyword(s):  
Primary Diagnosis ◽  
Secondary Outcome ◽  
Hospital Inpatient ◽  
Reference Period ◽  
Stress Reactions ◽  
Uk Biobank ◽  
The Uk ◽  
The Impact ◽  
Distinct Increase ◽  
Related Mortality

ObjectiveTo assess the impact of the COVID-19 outbreak on cardiovascular disease (CVD) related mortality and hospitalisation.DesignCommunity-based prospective cohort study.SettingThe UK Biobank.Participants421 372 UK Biobank participants who were registered in England and alive as of 1 January 2020.Primary and secondary outcome measuresThe primary outcome of interest was CVD-related death, which was defined as death with CVD as a cause in the death register. We retrieved information on hospitalisations with CVD as the primary diagnosis from the UK Biobank hospital inpatient data. The study period was 1 January 2020 to June 30 2020, and we used the same calendar period of the three preceding years as the reference period. In order to control for seasonal variations and ageing of the study population, standardised mortality/incidence ratios (SMRs/SIRs) with 95% CIs were used to estimate the relative risk of CVD outcomes during the study period, compared with the reference period.ResultsWe observed a distinct increase in CVD-related deaths in March and April 2020, compared with the corresponding months of the three preceding years. The observed number of CVD-related deaths (n=218) was almost double in April, compared with the expected number (n=120) (SMR=1.82, 95% CI 1.58 to 2.07). In addition, we observed a significant decline in CVD-related hospitalisations from March onwards, with the lowest SIR observed in April (0.45, 95% CI 0.41 to 0.49).ConclusionsThere was a distinct increase in the number of CVD-related deaths in the UK Biobank population at the beginning of the COVID-19 outbreak. The shortage of medical resources for hospital care and stress reactions to the pandemic might have partially contributed to the excess CVD-related mortality, underscoring the need of sufficient healthcare resources and improved instructions to the public about seeking healthcare in a timely way.

scholarly journals Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes

2020 ◽  
Vol 13 (6) ◽  
Author(s):  
Aldo Córdova-Palomera ◽  
Catherine Tcheandjieu ◽  
Jason A. Fries ◽  
Paroma Varma ◽  
Vincent S. Chen ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Aortic Valve Disease ◽  
Valve Disease ◽  
Genome Wide Association ◽  
Aortic Valve Area ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Shared Genetic ◽  
Valve Area

Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities. Results: A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1 , P =1.8×10 −9 ), rs35991305 (chr12:94191968, CRADD , P =3.4×10 −8 ), and chr17:45013271:C:T ( GOSR2 , P =5.6×10 −8 ). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14; P =2.3×10 −6 ). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions. Conclusions: These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

scholarly journals Shared genetic background between children and adults with attention deficit/hyperactivity disorder

2019 ◽  
Author(s):  
Paula Rovira ◽  
Ditte Demontis ◽  
Cristina Sánchez-Mora ◽  
Tetyana Zayats ◽  
Marieke Klein ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Genetic Variants ◽  
Genetic Background ◽  
Genetic Architecture ◽  
Neurodevelopmental Disorder ◽  
Genome Wide Association Studies ◽  
Hyperactivity Disorder ◽  
Common Genetic Variants ◽  
Shared Genetic

AbstractAttention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate symptoms of inattention, impulsivity and hyperactivity that persist into adulthood in the majority of the diagnosed children. Despite several risk factors during childhood predicting the persistence of ADHD symptoms into adulthood, the genetic architecture underlying the trajectory of ADHD over time is still unclear. We set out to study the contribution of common genetic variants to the risk for ADHD across the lifespan by conducting meta-analyses of genome-wide association studies on persistent ADHD in adults and ADHD in childhood separately and comparing the genetic background between them in a total sample of 17,149 cases and 32,411 controls. Our results show nine new independent loci and support a shared contribution of common genetic variants to ADHD in children and adults. No subgroup heterogeneity was observed among children, while this group consists of future remitting and persistent individuals. We report similar patterns of genetic correlation of ADHD with other ADHD-related datasets and different traits and disorders among adults, children and when combining both groups. These findings confirm that persistent ADHD in adults is a neurodevelopmental disorder and extend the existing hypothesis of a shared genetic architecture underlying ADHD and different traits to a lifespan perspective.

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