RISK LOCI IDENTIFICATION OF NOVEL GENETIC RISK FACTORS FOR ATRIAL SEPTAL DEFECT USING THE UK BIOBANK

AbstractWe present here genetic risk factors for survivability from infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for coronavirus disease 19 (COVID-19). At the time of writing it is too early to determine comprehensively and without doubt all risk factors, but there is an urgency due to the global pandemic crisis that merits this early analysis. We have nonetheless discovered 5 novel risk variants in 4 genes, discovered by examining 193 deaths from 1,412 confirmed infections in a group of 5,871 UK Biobank participants tested for the virus. We also examine the distribution of these genetic variants across broad ethnic groups and compare it to data from the UK Office of National Statistics for increased risk of death from SARS-CoV-2. We confidently identify the gene ERAP2 with a high-risk variant, as well as three other genes of potential interest. Although mostly rare, a common theme of genetic risk factors affecting survival might be the inability to launch or modulate an effective immune and stress response to infection from the SARS-CoV-2 virus.

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The impact of age on genetic risk for common diseases

10.1101/2020.07.17.208280 ◽

2020 ◽

Author(s):

Xilin Jiang ◽

Chris Holmes ◽

Gil McVean

Keyword(s):

Risk Factors ◽

Recent Work ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Individual Risk ◽

Uk Biobank ◽

Common Diseases ◽

The Impact ◽

The Relationship ◽

Over Time

AbstractInherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Recent work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the relationship between genetic risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic risk. The predominant pattern shows genetic risk factors having the greatest impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants although the magnitude and form of the decrease varies among diseases. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.Author summaryThe genes we inherit from our parents influence our risk for almost all diseases, from cancer to severe infections. With the explosion of genomic technologies, we are now able to use an individual’s genome to make useful predictions about future disease risk. However, recent work has shown that the predictive value of genetic information varies by context, including age, sex and ethnicity. In this paper we introduce, validate and apply new statistical methods for investigating the relationship between age and genetic risk. These methods allow us to ask questions such as whether risk is constant over time, precisely how risk changes over time and whether all genetic risk factors have similar age profiles. By applying the methods to data from the UK Biobank, a prospective study of 500,000 people, we show that there is a tendency for genetic risk to decline with increasing age. We consider a series of possible explanations for the observation and conclude that there must be processes acting that we are currently unaware of, such as distinct phases of life in which genetic risk manifests itself, or interactions between genes and the environment.

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Genome-Wide Association Study of Asthma-COPD Overlap (ACO) Among UK Biobank Participants Provides Insights into Its Genetic Risk Factors

10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a4216 ◽

2020 ◽

Author(s):

J. Joo ◽

B.D. Hobbs ◽

M.H. Cho ◽

B.E. Himes

Keyword(s):

Risk Factors ◽

Association Study ◽

Genetic Risk ◽

Genome Wide Association Study ◽

Genetic Risk Factors ◽

Genome Wide Association ◽

Uk Biobank ◽

Genome Wide

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Gene-environment interactions in Multiple Sclerosis: a UK Biobank study

10.1101/2020.03.01.971739 ◽

2020 ◽

Author(s):

Benjamin Meir Jacobs ◽

Alastair Noyce ◽

Jonathan Bestwick ◽

Daniel Belete ◽

Gavin Giovannoni ◽

...

Keyword(s):

Risk Factors ◽

Multiple Sclerosis ◽

Childhood Obesity ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Age At Menarche ◽

Targeted Prevention ◽

Inflammatory Disorder ◽

Uk Biobank ◽

Polygenic Risk

AbstractImportanceMultiple Sclerosis (MS) is a neuro-inflammatory disorder caused by a combination of environmental exposures and genetic risk factors. We sought to determine whether genetic risk modifies the effect of environmental MS risk factors.MethodsPeople with MS were identified within UK Biobank using ICD10-coded MS or self-report. Associations between environmental risk factors and MS risk were quantified with a case-control design using multivariable logistic regression. Polygenic risk scores (PRS) were derived using the clumping-and-thresholding approach with external weights from the largest genome-wide association study of MS. Separate scores were created including (PRSMHC) and excluding (PRSNon-MHC) the MHC locus. The best performing PRS were identified in 30% of the cohort and validated in the remaining 70%. Interaction between environmental and genetic risk factors was quantified using the Attributable Proportion due to interaction (AP) and multiplicative interaction.ResultsData were available for 2250 people with MS and 486,000 controls. Childhood obesity, earlier age at menarche, and smoking were associated with MS. The optimal PRS were strongly associated with MS in the validation cohort (PRSMHC: Nagelkerke’s Pseudo-R2 0.033, p=3.92×10−111; PRSNon-MHC: Nagelkerke’s Pseudo-R2 0.013, p=3.73×10−43). There was strong evidence of interaction between polygenic risk for MS and childhood obesity (PRSMHC: AP=0.17, 95% CI 0.06 - 0.25, p=0.004; PRSNon-MHC: AP=0.17, 95% CI 0.06 - 0.27, p=0.006).Conclusions and RelevanceThis study provides novel evidence for an interaction between childhood obesity and a high burden of autosomal genetic risk. These findings may have significant implications for our understanding of MS biology and inform targeted prevention strategies.

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Genome-wide association study reveals genetic risk factors for trigeminal neuralgia

10.1101/2021.02.08.21251349 ◽

2021 ◽

Author(s):

Andrew T. Hale ◽

Jing He ◽

Oluwatoyin Akinnusotu ◽

Rebecca L. Sale ◽

Janey Wang ◽

...

Keyword(s):

Risk Factors ◽

Trigeminal Neuralgia ◽

Genetic Risk ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Uk Biobank ◽

Genome Wide ◽

The Uk ◽

Genome Wide Significance

AbstractBackgroundWhile many clinical risk factors of trigeminal neuralgia (TN) have been identified, the genetic basis of TN is largely unknown. Here, we perform the first genome-wide association study (GWAS) for TN using three independent DNA biobanks – BioVU, the UK Biobank, and Finngen.ObjectiveTo elucidate the genetic basis of TN.MethodsUsing GWAS summary statistics generated from BioVU, the UK Biobank, and Finngen, we performed fixed-effect meta-analysis across 490,912 individuals (1,188 TN cases and 489,724 controls) to identify genetic risk factors for TN. Genome-wide significance was defined as p < 5.0×10−8.ResultsWe identify an intergenic locus on chromosome 1p22.2 flanked by ZNF326 and SNORD3G containing 5 SNPs (rs77449572, rs543311093, rs35117749, rs71666259, and rs116010656) reaching genome-wide significance (p < 5.0 x 10−8), where rs77449572 is the sentinel variant (p = 1.72 x 10−9). The SNP rs77449572 overlaps an enhancer element in cortex-derived neurospheres. In addition, rs71666259 and rs116010656 are located in enhancer elements in embryonic stem cells (HUES48), suggesting potential functional consequences of this locus. We also identify a second locus on chromosome 5q35.1 containing sentinel variant rs62376947 reaching genome-wide significance (p = 2.49 x 10−8).ConclusionsTo our knowledge, we perform the first GWAS of TN. Future studies should be aimed at understanding the extent to which genetic variation stratifies response to neuropathic pain medication and whether genetic information may be used to identify patients who are likely to benefit (or not) from surgical intervention.

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Systematic classification of shared components of genetic risk for common human diseases

10.1101/374207 ◽

2018 ◽

Cited By ~ 2

Author(s):

Adrian Cortes ◽

Calliope A. Dendrou ◽

Lars Fugger ◽

Gil McVean

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Disease Classification ◽

Human Diseases ◽

Systematic Classification ◽

Healthcare Data ◽

Distinct Disease ◽

The Uk

Disease classification is fundamental to clinical practice, but current taxonomies do not necessarily reflect the pathophysiological processes that are common or unique to different disorders, such as those determined by genetic risk factors. Here, we use routine healthcare data from the 500,000 participants in the UK Biobank to map genome-wide associations across 19,628 diagnostic terms. We find that 3,510 independent genetic risk loci affect multiple clinical phenotypes, which we cluster into 629 distinct disease association profiles. We use multiple approaches to link clusters to different underlying biological pathways and show how these clusters define the genetic architecture of common medical conditions, including hypertension and immune-mediated diseases. Finally, we demonstrate how clusters can be utilised to re-define disease relationships and to inform therapeutic strategies.One sentence summarySystematic classification of genetic risk factors reveals molecular connectivity of human diseases with clinical implications

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Abstract 71: Sex and Genetic Predisposition Synergistically Influence Risk of Stroke and Myocardial Infarction in Middle-Aged Persons Without Risk Factors

Stroke ◽

10.1161/str.51.suppl_1.71 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Guido J Falcone ◽

Julian Acosta ◽

Audrey C Leasure ◽

Kevin N Vanent ◽

Rommell B Noche ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Genetic Predisposition ◽

Genetic Risk ◽

Vascular Risk Factors ◽

Vascular Risk ◽

Uk Biobank ◽

Middle Aged ◽

High Genetic Risk ◽

The Uk

Background and Hypothesis: Driven by aging-related physiological changes, the incidence of stroke and myocardial infarction rises rapidly in persons aged >40 years. A significant proportion of these acute vascular events (AVE) take place in persons without vascular risk factors. We tested the hypothesis that sex and genetic predisposition synergistically increase the risk of AVE in middle-aged persons without vascular risk factors. Methods: We analyzed data from the UK Biobank, a prospective longitudinal study that enrolled persons aged 40 to 69 years. Our analysis was restricted to middle-aged participants, defined as those aged 40 to 60 years. Prevalent and incident cases of stroke (ischemic and hemorrhagic) and myocardial infarction were included. To quantify the genetic predisposition to sustain an AVE, we constructed a polygenic risk score using 68 independent (R 2 <0.1) genetic variants known to associate (p<5x10 -8 ) with AVE. Participants were classified as having low, intermediate or high genetic risk according to tertiles of the score. We used Cox models for association and interaction testing. Results: Of the 502,536 study participants enrolled in the UK Biobank, 303,295 (60%) did not have any vascular risk factors. During the follow-up period, there were 5,746 AVEs, including 1,954 strokes and 3,792 myocardial infarctions. The cumulative risk of AVE was 0.12% (n=352), 0.46% (n = 1,386) and 1.32% (n = 4,008) at ages 40, 50 and 60 years (test-for-trend p<0.001). The risk of AVE was 3 times greater in men than women (HR 3.30, 95%CI 3.08 - 3.53). Compared to persons with low genetic risk, those with intermediate and high genetic risk had a 22% (HR 1.22, 95%CI 1.13 - 1.32) and 52% (HR 1.52, 95%CI 1.41 - 1.65) increase in risk of AVE, respectively. There was significant synergy (interaction) between sex and genetic predisposition: compared to females with low genetic risk, males with high genetic risk had 4 times (HR 3.91, 95%CI 3.58 - 4.26) the risk of AVE (interaction analysis p<0.001). Conclusion: Genetic information constitutes a promising tool to risk stratify middle-aged persons without vascular risk factors. The synergistic effect of sex and genetic predisposition points to specific subgroups that could benefit from aggressive preventive interventions.

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Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort

Journal of the American Heart Association ◽

10.1161/jaha.121.022576 ◽

2021 ◽

Author(s):

Divya Sharma ◽

Neta Gotlieb ◽

Michael E. Farkouh ◽

Keyur Patel ◽

Wei Xu ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Liver Disease ◽

Genetic Risk ◽

Fatty Liver Disease ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Uk Biobank ◽

Nonalcoholic Fatty Liver ◽

The Uk

Background Nonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Cardiovascular disease (CVD) is the leading cause of mortality among patients with NAFLD. The aim of our study was to develop a machine learning algorithm integrating clinical, lifestyle, and genetic risk factors to identify CVD in patients with NAFLD. Methods and Results We created a cohort of patients with NAFLD from the UK Biobank, diagnosed according to proton density fat fraction from magnetic resonance imaging data sets. A total of 400 patients with NAFLD with subclinical atherosclerosis or clinical CVD, defined by disease codes, constituted cases and 446 NAFLD cases with no CVD constituted controls. We evaluated 7 different supervised machine learning approaches on clinical, lifestyle, and genetic variables for identifying CVD in patients with NAFLD. The most significant clinical and lifestyle variables observed by the predictive modeling were age (59 years [54.00–63.00 years]), hypertension (145 mm Hg [134.0–156.0 mm Hg] and 85 mm Hg [79.00–93.00 mm Hg]), waist circumference (98 cm [95.00–105.00 cm]), and sedentary lifestyle, defined as time spent watching TV >4 h/d. In the genetic data, single‐nucleotide polymorphisms in IL16 and ANKLE1 gene were most significant. Our proposed ensemble‐based integrative machine learning model achieved an area under the curve of 0.849 using the random forest modeling for CVD prediction. Conclusions We propose a machine learning algorithm that identifies CVD in patients with NAFLD through integration of significant clinical, lifestyle, and genetic risk factors. These patients with NAFLD at higher risk of CVD should be flagged for screening and aggressive treatment of their cardiometabolic risk factors to prevent cardiovascular morbidity and mortality.

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