Abstract 13296: Risk Loci of Hypertrophic Cardiomyopathy Identified via the UK Biobank

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Alex Gyftopoulos ◽  
Yi-Ju Chen ◽  
Libin Wang ◽  
Charles H Williams ◽  
Young Wook Chun ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Large Population ◽  
Phenotypic Expression ◽  
Coiled Coil ◽  
Intraflagellar Transport ◽  
Genetic Profile ◽  
Uk Biobank ◽  
Disease Manifestation ◽  
Pathogenic Variants ◽  
The Uk

Introduction: Hypertrophic cardiomyopathy (HCM) is the most commonly inherited cardiac disease affecting 1:500 to 1:200 individuals worldwide. HCM has a heterogeneous genetic profile and phenotypic expression. More than 1400 known pathogenic variants have been identified in 11 sarcomere genes. In about 40% of HCM patients, the genetic cause may not be identified. The same mutation may lead to different phenotypes and severity in different individuals. Identification of novel HCM genes and modifiers will expand our understanding of the signaling pathways that are responsible for phenotypic expression of HCM. Methods: The UK Biobank comprises clinical and genetic data for greater than 500,000 individuals. We used OASIS, an information system for analyzing, searching, and visualizing associations between phenotype and genotype data to analyze this data. We compared control individuals to HCM individuals identified by ICD-10 code (I42.1 and I42.2) in a 20-to-1 fashion. Related individuals and those with confounding diagnoses were excluded. Results: The analysis was performed with Plink’s GLM option, and we identified 84 variants with a minor allele frequency of 0.5% or greater in 65 genes associated with HCM with a p < 1x10 -6 , including 4 with p < 5x10 -8 . The identified genes encode lncRNAs, miRNAs, and membrane proteins. Variants with high significance were identified in the genes encoding putative ciliary components DNAL4 (dynein axonemal light chain 4; p = 2.9x10 -8 ), MYO1D (unconventional myosin 1D; p = 3.1x10 -8 ), ITFAP (intraflagellar transport associated protein; p = 9.5x10 -8 ), CABCOCO1 (ciliary associated calcium biding coiled-coil 1; p = 3.7x 10 -7 ), EVL (Enah-Vasp-like; p = 4.4x 10 -7 ) and IFT122 (intraflagellar transport 122; p = 8.0 x10 -7 ). Conclusion: While none of these have previously associated with HCM, our findings suggest ciliary structure and function may play a role in disease manifestation. Our method is unique by pooling individuals in a large population set to identify potential causative or contributing mutations. Bioinformatic tools, such as OASIS, allow for the identification of previously unrecognized variants that may play a role in the development of HCM. This approach has identified numerous novel genes as possible risk loci.

scholarly journals Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

2019 ◽  
Author(s):  
Cristopher V. Van Hout ◽  
Ioanna Tachmazidou ◽  
Joshua D. Backman ◽  
Joshua X. Hoffman ◽  
Bin Ye ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Large Scale ◽  
Sequence Data ◽  
Varicose Veins ◽  
Large Population ◽  
Uk Biobank ◽  
Loss Of Function ◽  
Phenotypic Data ◽  
Pathogenic Variants ◽  
The Uk

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

scholarly journals Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

BMJ ◽  
2021 ◽  
pp. n214
Author(s):  
Weedon MN ◽  
Jackson L ◽  
Harrison JW ◽  
Ruth KS ◽  
Tyrrell J ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Population Based ◽  
Genome Project ◽  
Personal Genome ◽  
Uk Biobank ◽  
Sequencing Data ◽  
Snp Chip ◽  
Pathogenic Variants ◽  
The Uk

Abstract Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

THUR 121 Identifying participants with parkinson’s disease in uk biobank

2018 ◽  
Vol 89 (10) ◽  
pp. A13.2-A13
Author(s):  
Bush Kathryn ◽  
Rannikmae Kristiina ◽  
Schnier Christian ◽  
Wilkinson Timothy ◽  
Nolan John ◽  
...  
Keyword(s):  
Primary Care ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Secondary Care ◽  
Large Population ◽  
Free Text ◽  
Uk Biobank ◽  
Predictive Values ◽  
Diagnostic Codes ◽  
The Uk

BackgroundLinkage to routinely collected NHS data from primary, secondary care and death certificates enables identification of participants with Parkinson’s Disease (PD) within the UK Biobank cohort of 5 00 000 adults. Validation of the accuracy of this data is required prior to their use in research studies.MethodIn this validation study participants (n=125) with a code indicating PD were identified from a sample of 17 000 participants in the cohort. Diagnoses were validated by expert adjudicators, based on free text electronic medical records. Positive predictive values (PPV,% of cases identified that are true cases) were calculated.ResultsPrimary care diagnostic codes identified 93% of PD cases, with a PPV of 95%. Combined secondary care and death data identified 42% of PD cases with a PPV of 84%.Combining diagnostic and medication codes identified more participants, but did not increase the PPV.ConclusionsThis study suggests that linkage to routinely collected healthcare data is a reliable method for identifying participants with PD in the UK Biobank cohort.Primary care diagnostic codes identified the highest proportion of participants and had the highest PPV, demonstrating the value of using primary care data to identify cases of disease in large population based cohort studies.

scholarly journals Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

Gut ◽  
2018 ◽  
Vol 68 (4) ◽  
pp. 672-683 ◽  
Author(s):  
Todd Smith ◽  
David C Muller ◽  
Karel G M Moons ◽  
Amanda J Cross ◽  
Mattias Johansson ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Prediction Models ◽  
Large Population ◽  
External Validation ◽  
Population Based ◽  
Prognostic Models ◽  
Uk Biobank ◽  
Asymptomatic Individuals ◽  
The Uk

ObjectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.DesignModels were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).ResultsThe systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.ConclusionSeveral of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

scholarly journals Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

2019 ◽  
Vol 25 (10) ◽  
pp. 2422-2430 ◽  
Author(s):  
Douglas M. Ruderfer ◽  
Colin G. Walsh ◽  
Matthew W. Aguirre ◽  
Yosuke Tanigawa ◽  
Jessica D. Ribeiro ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Suicide Attempt ◽  
Genetic Correlation ◽  
Genetic Profile ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Common Genetic Variation ◽  
Genetic Studies ◽  
Patient Reported ◽  
The Uk

Abstract Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

scholarly journals Large-scale analysis of iliopsoas muscle volumes in the UK Biobank

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Julie A. Fitzpatrick ◽  
Nicolas Basty ◽  
Madeleine Cule ◽  
Yi Liu ◽  
Jimmy D. Bell ◽  
...  
Keyword(s):  
Large Scale ◽  
Large Population ◽  
Psoas Muscle ◽  
Magnetic Resonance Images ◽  
Muscle Volume ◽  
Iliopsoas Muscle ◽  
Uk Biobank ◽  
Cross Sectional ◽  
Automated Method ◽  
The Uk

AbstractPsoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurement and manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5000 participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (average dice score coefficient of 0.9046 ± 0.0058 for six-fold cross-validation). Iliopsoas muscle volumes were successfully measured in all 5000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts.

scholarly journals 0154 A new, efficient web-based tool to collect and code lifetime job histories in large population-based studies: the COPD project in the UK Biobank cohort

2014 ◽  
Vol 71 (Suppl 1) ◽  
pp. A19.1-A19
Author(s):  
Sara De Matteis ◽  
Lesley Rushton ◽  
Debbie Jarvis ◽  
Magda Wheatley ◽  
Hadia Azhar ◽  
...  
Keyword(s):  
Large Population ◽  
Population Based ◽  
Uk Biobank ◽  
Web Based ◽  
The Uk

scholarly journals Optical Coherence Tomography in the UK Biobank Study – Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164095 ◽  
Author(s):  
Pearse A. Keane ◽  
Carlota M. Grossi ◽  
Paul J. Foster ◽  
Qi Yang ◽  
Charles A. Reisman ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Retinal Thickness ◽  
Large Population ◽  
Automated Analysis ◽  
Population Based ◽  
Optical Coherence ◽  
Uk Biobank ◽  
The Uk

scholarly journals Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

2020 ◽  
Author(s):  
Sean J. Jurgens ◽  
Seung Hoan Choi ◽  
Valerie N. Morrill ◽  
Mark Chaffin ◽  
James P. Pirruccello ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Genetic Variation ◽  
Exome Sequencing ◽  
Human Diseases ◽  
Disease Genes ◽  
Uk Biobank ◽  
Novel Genes ◽  
Pathogenic Variants ◽  
Rare Genetic Variation ◽  
The Uk

AbstractBackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank.Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35×10−8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5×10−8), while COL9A3 was associated with cataract (OR 3.4, P=6.7×10−8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes.ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

scholarly journals Race, Socioeconomic Deprivation, and Hospitalization for COVID-19 in English participants of a National Biobank

2020 ◽  
Author(s):  
Aniruddh P. Patel ◽  
Manish D. Paranjpe ◽  
Nina P. Kathiresan ◽  
Manuel A. Rivas ◽  
Amit V. Khera
Keyword(s):  
Household Income ◽  
Odds Ratio ◽  
Large Population ◽  
Socioeconomic Deprivation ◽  
Composite Measure ◽  
Uk Biobank ◽  
Hospitalization Rates ◽  
Increased Risk ◽  
The Uk ◽  
Biologic Factors

Preliminary reports suggest that the Coronavirus Disease 2019 (COVIDâ^’19) pandemic has led to disproportionate morbidity and mortality among historically disadvantaged populations. The extent to which these disparities are related to socioeconomic versus biologic factors is largely unknown. We investigate the racial and socioeconomic associations of COVIDâ^’19 hospitalization among 418,794 participants of the UK Biobank, of whom 549 (0.13%) had been hospitalized. Both black participants (odds ratio 3.4; 95%CI 2.4â^’4.9) and Asian participants (odds ratio 2.1; 95%CI 1.5â^’3.2) were at substantially increased risk as compared to white participants. We further observed a striking gradient in COVIDâ^’19 hospitalization rates according to the Townsend Deprivation Index â^’ a composite measure of socioeconomic deprivation â^’ and household income. Adjusting for such factors led to only modest attenuation of the increased risk in black participants, adjusted odds ratio 3.1 (95%CI 2.0â^’4.8). These observations confirm and extend earlier preliminary and lay press reports of higher morbidity in non-white individuals in the context of a large population of participants in a national biobank. The extent to which this increased risk relates to variation in pre-existing comorbidities, differences in testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study.

Sign in / Sign up

Export Citation Format

Share Document