scholarly journals The UK Biobank resource with deep phenotyping and genomic data

Nature ◽  
2018 ◽  
Vol 562 (7726) ◽  
pp. 203-209 ◽  
Author(s):  
Clare Bycroft ◽  
Colin Freeman ◽  
Desislava Petkova ◽  
Gavin Band ◽  
Lloyd T. Elliott ◽  
...  
Keyword(s):  
Genomic Data ◽  
Uk Biobank ◽  
The Uk ◽  
Deep Phenotyping

scholarly journals Software as a Service for the Genomic Prediction of Complex Diseases

2019 ◽  
Author(s):  
Alessandro Bolli ◽  
Paolo Di Domenico ◽  
Giordano Bottà
Keyword(s):  
Clinical Practice ◽  
Family History ◽  
High Risk ◽  
Genomic Data ◽  
Predictive Performance ◽  
Software As A Service ◽  
Uk Biobank ◽  
Disease Risks ◽  
The Uk ◽  
Cad Risk

AbstractIn the last decade the scientific community witnessed a large increase in Genome-Wide Association Study sample size, in the availability of large Biobanks and in the improvements of statistical methods to model genomes features. This have paved the way for the development of new prediction medicine tools that use genomic data to estimate disease risk. One of these tools is the Polygenic Risk Score (PRS), a metric that estimates the genetic risk of an individual to develop a disease, based on a combination of a large number of genetic variants.Using the largest prospective genotyped cohort available to date, the UK Biobank, we built a new PRS for Coronary Artery Disease (CAD) and assessed its predictive performances along with two additional PRS for Breast Cancer (BC), and Prostate Cancer (PC). When compared with previously published PRS, the newly developed PRS for CAD displayed higher AUC and positive predictive value. PRSs were able to stratify disease risks from 1.34% to 25.7% (CAD in men), from 0.26% to 8.62% (CAD in women), from 1.6% to 24.6% (BC), and from 1.4% to 24.3% (PC) in the lowest and highest percentiles, respectively. Additionally, the three PRSs were able to identify the 5% of the UK Biobank population with a relative risk for the diseases at least 3 times higher than the average.Family history is a well recognised risk factor of CAD, BC, and PC and it is currently used to identify individuals at high risk of developing the diseases. We show that individuals with family history can have completely different disease risks based on PRS stratification: from 2.1% to 33% (CAD in men), from 0.56% to 10% (CAD in women), from 2.3% to 35.8% (BC), and from 1.0% to 34.0% (PC) in the lowest and highest percentiles, respectively. Additionally, the PRSs demonstrated higher predictive performance (AUCs (including age) CAD: 0.81, PC: 0.80, and BC: 0.68) than family history (AUCs (including age) CAD: 0.79, PC: 0.73, and BC: 0.61) in predicting the onset of diseases.Hyperlipidemia is well known to be associated with higher CAD risk, but a predictive performance comparison between each lipoprotein and CAD PRS has never been assessed. PRS shows higher discrimination capacity and Odds ratio per Standard deviation than LDL, HDL, total cholesterol-HDL ratio, ApoA, ApoB, ApoB-ApoA ratio, and Lipoprotein(a). Comparing the empirical risk distribution between PRS and each lipoprotein, we show that lipoprotein thresholds, currently used in clinical practice, identify a population equal to or smaller than what can be identified with the PRS at the same CAD risk threshold. Moreover, there is not correlation (max ρ: 0.137) between PRS and each lipoprotein, indicating that PRS captures different component of CAD etiology and identifies different people at high risk than those identified by lipoproteins, demonstrating to be an invaluable tool in CAD prevention.One of the major impairment of the PRS usage in clinical practice is the computational complexity needed to calculate per-individual PRSs. Deep bioinformatics expertise is required to run the entire pipeline, from imputing genomic data, through quality control to result visualisation. For these reasons we developed a Software as a Service (SaaS) for genomic risk prediction of complex diseases. The SaaS is fully automated, GDPR complaint and has been certified as a CE marked medical device. We made the SaaS freely available for research purposes. Researchers willing to use the SaaS can contact [email protected]

scholarly journals Inferring population structure in biobank-scale genomic data

2021 ◽  
Author(s):  
Alec M Chiu ◽  
Erin K Molloy ◽  
Zilong Tan ◽  
Ameet Talwalkar ◽  
Sriram Sankararaman
Keyword(s):  
Population Structure ◽  
Genetic Variants ◽  
Genomic Data ◽  
Human Populations ◽  
Uk Biobank ◽  
Medical Genomic ◽  
Variation Data ◽  
Genomic Studies ◽  
Comparable Accuracy ◽  
The Uk

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. While a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

Low birthweight is associated with poorer limb muscle mass and grip strength in middle age: findings from the UK Biobank Imaging Enhancement

2019 ◽  
Author(s):  
Elizabeth Curtis ◽  
Justin Liu ◽  
Kate Ward ◽  
Karen Jameson ◽  
Zahra Raisi-Estabragh ◽  
...  
Keyword(s):  
Grip Strength ◽  
Muscle Mass ◽  
Low Birthweight ◽  
Middle Age ◽  
Limb Muscle ◽  
Uk Biobank ◽  
The Uk

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

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