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 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 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 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.

Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank

2021 ◽  
pp. bjophthalmol-2021-319508
Author(s):  
Xianwen Shang ◽  
Zhuoting Zhu ◽  
Yu Huang ◽  
Xueli Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Heart Disease ◽  
Age Related Macular Degeneration ◽  
Hospital Inpatient ◽  
Uk Biobank ◽  
Age Related ◽  
Incident Dementia ◽  
Increased Risk ◽  
The Uk ◽  
Systemic Condition

AimsTo examine independent and interactive associations of ophthalmic and systemic conditions with incident dementia.MethodsOur analysis included 12 364 adults aged 55–73 years from the UK Biobank cohort. Participants were assessed between 2006 and 2010 at baseline and were followed up until the early of 2021. Incident dementia was ascertained using hospital inpatient, death records and self-reported data.ResultsOver 1 263 513 person-years of follow-up, 2304 cases of incident dementia were documented. The multivariable-adjusted HRs (95% CI) for dementia associated with age-related macular degeneration (AMD), cataract, diabetes-related eye disease (DRED) and glaucoma at baseline were 1.26 (1.05 to 1.52), 1.11 (1.00 to 1.24), 1.61 (1.30 to 2.00) and (1.07 (0.92 to 1.25), respectively. Diabetes, heart disease, stroke and depression at baseline were all associated with an increased risk of dementia. Of the combination of AMD and a systemic condition, AMD-diabetes was associated with the highest risk for incident dementia (HR (95% CI): 2.73 (1.79 to 4.17)). Individuals with cataract and a systemic condition were 1.19–2.29 times more likely to develop dementia compared with those without cataract and systemic conditions. The corresponding number for DRED and a systemic condition was 1.50–3.24. Diabetes, hypertension, heart disease, depression and stroke newly identified during follow-up mediated the association between cataract and incident dementia as well as the association between DRED and incident dementia.ConclusionsAMD, cataract and DRED but not glaucoma are associated with an increased risk of dementia. Individuals with both ophthalmic and systemic conditions are at higher risk of dementia compared with those with an ophthalmic or systemic condition only.

Abstract P147: Genome Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases Using the UK Biobank Data

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Yanjun Guo ◽  
Wonil Chung ◽  
Zhilei Shan ◽  
Liming Liang
Keyword(s):  
Cardiovascular Diseases ◽  
Genetic Correlation ◽  
Multiple Testing ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Uk Biobank ◽  
Increased Risk ◽  
The Uk ◽  
Cardiovascular Traits ◽  
Shared Genetic

Background: Patients with RA have a 2-10 folds increased risk of cardiovascular diseases (CVD) and CVD accounts for almost 50% of the excess mortality in patients with RA when compared with general population, but the mechanisms underlying such associations are largely unknown. Methods: We examined the genetic correlation, causality, and shared genetic variants between RA (Ncase=6,756, Ncontrol=452,476) and CVD (Ncase=44,246, Ncontrol=414,986) using LD Score regression (LDSC), generalized summary-data-based Mendelian Randomization (GSMR), and cross-trait meta-analysis in the UK Biobank Data. Results: In the present study, RA was significantly genetically correlated with MI, angina, CHD, and CVD after correcting for multiple testing (Rg ranges from 0.40 to 0.43, P<0.05/5). Interestingly, when stratified by frequent usage of aspirin and paracetamol, we observed increased genetic correlation between RA and CVD for participants without aspirin usage ( Rg increased to 0.54 [95%CI: 0.54, 0.78] for angina; P value=6.69х10 -6 ), and for participants with usage of paracetamol ( Rg increased to 0.75 [95%CI: 0.20, 1.29] for MI; P value=8.90х10 -3 ). Cross-trait meta-analysis identified 9 independent loci that were shared between RA and at least one of the genetically correlated CVD traits including PTPN22 at chr1p13.2 , BCL2L11 at chr2q13 , and CCR3 at chr3p21.31 ( P single trait <1х10 -3 and P meta <5х10 -8 ) highlighting potential shared etiology between them which include accelerating atherosclerosis and upregulating oxidative stress and vascular permeability. Finally, Mendelian randomization analyses observed inconsistent instrumental effects and were unable to conclude the causality and directionality between RA and CVD. Conclusion: Our results supported positive genetic correlation between RA and multiple cardiovascular traits, and frequent usage of aspirin and paracetamol may modify their associations, but instrumental analyses were unable to conclude the causality and directionality between them.

Sex differences in genetic architecture in the UK Biobank

2021 ◽  
Vol 53 (9) ◽  
pp. 1283-1289
Author(s):  
Elena Bernabeu ◽  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Andrea Talenti ◽  
James Prendergast ◽  
...  
Keyword(s):  
Sex Differences ◽  
Genetic Architecture ◽  
Uk Biobank ◽  
The Uk

scholarly journals Accurate Genomic Prediction Of Human Height

2017 ◽  
Author(s):  
Louis Lello ◽  
Steven G. Avery ◽  
Laurent Tellier ◽  
Ana I. Vazquez ◽  
Gustavo de los Campos ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Uk Biobank ◽  
Multiple Phenotypes ◽  
Human Height ◽  
Out Of Sample ◽  
Heel Bone ◽  
The Common ◽  
Actual Height ◽  
The Uk ◽  
Missing Heritability Problem

AbstractWe construct genomic predictors for heritable and extremely complex human quan-titative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ∼40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ∼0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem – i.e., the gap between prediction R-squared and SNP heritability. The ∼20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

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 Genetic correlations between pain phenotypes and depression and neuroticism

2019 ◽  
Vol 28 (3) ◽  
pp. 358-366 ◽  
Author(s):  
Weihua Meng ◽  
Mark J. Adams ◽  
Parminder Reel ◽  
Aravind Rajendrakumar ◽  
Yu Huang ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depressive Disorders ◽  
Genetic Correlations ◽  
The Body ◽  
Uk Biobank ◽  
Major Depressive ◽  
Genetic Mechanisms ◽  
Major Depressive Disorders ◽  
The Uk ◽  
Shared Genetic

Abstract Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. In this study, we estimated the genetic correlations of eight pain phenotypes (defined by the UK Biobank, n = 151,922–226,683) with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. We also used the LDSC software to calculate the genetic correlations among pain phenotypes. All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2 = 0.31, standard error = 0.072). Many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Our results suggest that pain, neuroticism and depression share partially overlapping genetic risk factors.

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