Analysis of COVID-19 genetic risk susceptibility using UK Biobank SNP genotype data

2021 ◽  
Vol 25 (1/2) ◽  
pp. 1
Author(s):  
Taesung Park ◽  
Catherine Apio ◽  
Taewan Goo ◽  
Kyulhee Han
Keyword(s):  
Genetic Risk ◽  
Genotype Data ◽  
Uk Biobank

Analysis of COVID-19 genetic risk susceptibility using UK Biobank SNP genotype data

2021 ◽  
Vol 25 (1/2) ◽  
pp. 1
Author(s):  
Taewan Goo ◽  
Kyulhee Han ◽  
Catherine Apio ◽  
Taesung Park
Keyword(s):  
Genetic Risk ◽  
Genotype Data ◽  
Uk Biobank

scholarly journals Genetic susceptibility, elevated blood pressure and risk of atrial fibrillation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Nazarzadeh ◽  
A Pinho-Gomes ◽  
Z Bidel ◽  
D Canoy ◽  
A Dehghan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Genetic Susceptibility ◽  
Genetic Risk ◽  
Funding Source ◽  
Individual Participant Data ◽  
Elevated Blood ◽  
Uk Biobank ◽  
Drug Classes ◽  
Individual Participant

Abstract Background Whether elevated blood pressure (BP) is a modifiable risk factor for atrial fibrillation (AF) is not established. Purpose We tested (1) whether the association between BP and risk of AF is causal, (2) whether it varies according to individual's genetic susceptibility for AF, and (3) the extent to which specific BP-lowering drugs are expected to reduce this risk. Methods First, causality of association was assessed through two-sample Mendelian Randomization (MR), using data from two independent genome-wide association studies that included a total of one million European population. Second, UK Biobank individual participant data of 329,237 participants at baseline was used to study the effect of BP on AF according to genetic susceptibility of developing AF. Third, a possible treatment effect with BP-lowering drug classes on AF risk was predicted through genetic variants in druggable genes that code proteins related to the function of each drug class. Estimated drug effects were compared with effects on incident coronary heart disease, for which direct trial evidence exists. Results The two-sample MR analysis indicated that on average each 10-mm Hg increment in systolic BP increased the risk of AF (odds ratio [OR]: 1.23 [1.11 to 1.36]). This association was replicated in the UK biobank using individual participant data. However, in a further genetic risk-stratified analysis, there was evidence for a linear gradient in the relative effects of systolic BP on AF; while there was no conclusive evidence of an effect in those with low genetic risk, a strong effect was observed among those with high genetic susceptibility for AF (Figure). The indirect comparison of predicted treatment effects using genetic proxies for three main drug classes (angiotensin-converting enzyme inhibitors, beta-blockers and calcium channel blockers) suggested similar average effects for prevention of atrial fibrillation and coronary heart disease. Conclusions The association between elevated BP and higher risk of AF is likely to be causal, suggesting that BP-lowering treatment may be effective in AF prevention. However, average effects masked clinically important variations, with a more pronounced effect in individuals with high genetic susceptibility. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation

scholarly journals Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank

Diabetes Care ◽  
2018 ◽  
Vol 41 (4) ◽  
pp. 762-769 ◽  
Author(s):  
Céline Vetter ◽  
Hassan S. Dashti ◽  
Jacqueline M. Lane ◽  
Simon G. Anderson ◽  
Eva S. Schernhammer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Shift Work ◽  
Genetic Risk ◽  
Night Shift ◽  
Uk Biobank ◽  
Night Shift Work ◽  
The Uk

Genetic Risk Scores and Dementia Risk Across Different Ethnic Groups in UK Biobank

2021 ◽  
Author(s):  
Naaheed Mukadam ◽  
Olga Giannakopoulou ◽  
Nick Bass ◽  
Karoline Kuchenbaecker ◽  
Andrew McQuillin
Keyword(s):  
Ethnic Groups ◽  
Genetic Risk ◽  
Risk Scores ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
Dementia Risk

scholarly journals Parkinson’s disease determinants, prediction and gene–environment interactions in the UK Biobank

2020 ◽  
Vol 91 (10) ◽  
pp. 1046-1054 ◽  
Author(s):  
Benjamin Meir Jacobs ◽  
Daniel Belete ◽  
Jonathan Bestwick ◽  
Cornelis Blauwendraat ◽  
Sara Bandres-Ciga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Family History ◽  
Genetic Risk ◽  
Positive Family History ◽  
Risk Scores ◽  
Uk Biobank ◽  
Gene Environment ◽  
History Of

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.MethodsWe identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD.ResultsStrong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes.InterpretationHere, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

scholarly journals Identifying cross-disease components of genetic risk across hospital data in the UK Biobank

2019 ◽  
Vol 52 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Adrian Cortes ◽  
Patrick K. Albers ◽  
Calliope A. Dendrou ◽  
Lars Fugger ◽  
Gil McVean
Keyword(s):  
Genetic Risk ◽  
Uk Biobank ◽  
Hospital Data ◽  
The Uk

scholarly journals Genetic Risk Score for Predicting Schizophrenia Using Human Chromosomal-Scale Length Variation

2021 ◽  
Author(s):  
Christopher Toh ◽  
James P. Brody
Keyword(s):  
Risk Score ◽  
Genetic Risk ◽  
Germ Line ◽  
Genetic Risk Score ◽  
Single Gene ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Length Variation ◽  
Uk Biobank ◽  
Scale Length

Abstract Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539-0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.

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