scholarly journals Cohort profile: Copenhagen Hospital Biobank - Cardiovascular Disease Cohort (CHB-CVDC): Construction of a large-scale genetic cohort to facilitate a better understanding of heart diseases

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e049709
Author(s):  
Ina H Laursen ◽  
Karina Banasik ◽  
Amalie D Haue ◽  
Oscar Petersen ◽  
Peter C Holm ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Large Scale ◽  
Western Europe ◽  
Blood Donors ◽  
Sequence Data ◽  
Heart Diseases ◽  
Whole Genome Sequence ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Artery Disease

PurposeThe aim of Copenhagen Hospital Biobank-Cardiovascular Disease Cohort (CHB-CVDC) is to establish a cohort that can accelerate our understanding of CVD initiation and progression by jointly studying genetics, diagnoses, treatments and risk factors.ParticipantsThe CHB-CVDC is a large genomic cohort of patients with CVD. CHB-CVDC currently includes 96 308 patients. The cohort is part of CHB initiated in 2009 in the Capital Region of Denmark. CHB is continuously growing with ~40 000 samples/year. Patients in CHB were included in CHB-CVDC if they were above 18 years of age and assigned at least one cardiovascular diagnosis. Additionally, up-to 110 000 blood donors can be analysed jointly with CHB-CVDC. Linkage with the Danish National Health Registries, Electronic Patient Records, and Clinical Quality Databases allow up-to 41 years of medical history. All individuals are genotyped using the Infinium Global Screening Array from Illumina and imputed using a reference panel consisting of whole-genome sequence data from 8429 Danes along with 7146 samples from North-Western Europe. Currently, 39 539 of the patients are deceased.Findings to dateHere, we demonstrate the utility of the cohort by showing concordant effects between known variants and selected CVDs, that is, >93% concordance for coronary artery disease, atrial fibrillation, heart failure and cholesterol measurements and 85% concordance for hypertension. Furthermore, we evaluated multiple study designs and the validity of using Danish blood donors as part of CHB-CVDC. Lastly, CHB-CVDC has already made major contributions to studies of sick sinus syndrome and the role of phytosterols in development of atherosclerosis.Future plansIn addition to genetics, electronic patient records, national socioeconomic and health registries extensively characterise each patient in CHB-CVDC and provides a promising framework for improved understanding of risk and protective variants. We aim to include other measurable biomarkers for example, proteins in CHB-CVDC making it a platform for multiomics cardiovascular studies.

scholarly journals Whole-genome sequence data suggests environmental adaptation of Ethiopian sheep populations

2021 ◽  
Author(s):  
Pamela Wiener ◽  
Christelle Robert ◽  
Abulgasim Ahbara ◽  
Mazdak Salavati ◽  
Ayele Abebe ◽  
...  
Keyword(s):  
High Altitude ◽  
Environmental Variables ◽  
Large Scale ◽  
Sequence Data ◽  
Strong Association ◽  
Environmental Adaptation ◽  
Whole Genome Sequence ◽  
Single Nucleotide Variants ◽  
High Altitude Adaptation ◽  
Altitude Adaptation

Abstract Great progress has been made over recent years in the identification of selection signatures in the genomes of livestock species. This work has primarily been carried out in commercial breeds for which the dominant selection pressures, are associated with artificial selection. As agriculture and food security are likely to be strongly affected by climate change, a better understanding of environment-imposed selection on agricultural species is warranted. Ethiopia is an ideal setting to investigate environmental adaptation in livestock due to its wide variation in geo-climatic characteristics and the extensive genetic and phenotypic variation of its livestock. Here, we identified over three million single nucleotide variants across 12 Ethiopian sheep populations and applied landscape genomics approaches to investigate the association between these variants and environmental variables. Our results suggest that environmental adaptation for precipitation-related variables is stronger than that related to altitude or temperature, consistent with large-scale meta-analyses of selection pressure across species. The set of genes showing association with environmental variables was enriched for genes highly expressed in human blood and nerve tissues. There was also evidence of enrichment for genes associated with high-altitude adaptation although no strong association was identified with hypoxia-inducible-factor (HIF) genes. One of the strongest altitude-related signals was for a collagen gene, consistent with previous studies of high-altitude adaptation. Several altitude-associated genes also showed evidence of adaptation with temperature, suggesting a relationship between responses to these environmental factors. These results provide a foundation to investigate further the effects of climatic variables on small ruminant populations.

scholarly journals The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes

2014 ◽  
Author(s):  
Jason W Sahl ◽  
Greg Caporaso ◽  
David A Rasko ◽  
Paul S Keim
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Parallel Implementation ◽  
Genetic Relationships ◽  
Clinical Diagnostics ◽  
Whole Genome Sequence ◽  
Bacterial Isolates ◽  
Bacterial Genomes ◽  
E Coli ◽  
Blast Score

Background. As whole genome sequence data from bacterial isolates becomes cheaper to generate, computational methods are needed to correlate sequence data with biological observations. Here we present the large-scale BLAST score ratio (LS-BSR) pipeline, which rapidly compares the genetic content of hundreds to thousands of bacterial genomes, and returns a matrix that describes the relatedness of all coding sequences (CDSs) in all genomes surveyed. This matrix can be easily parsed in order to identify genetic relationships between bacterial genomes. Although pipelines have been published that group peptides by sequence similarity, no other software performs the large-scale, flexible, full-genome comparative analyses carried out by LS-BSR. Results. To demonstrate the utility of the method, the LS-BSR pipeline was tested on 96 Escherichia coli and Shigella genomes; the pipeline ran in 163 minutes using 16 processors, which is a greater than 7-fold speedup compared to using a single processor. The BSR values for each CDS, which indicate a relative level of relatedness, were then mapped to each genome on an independent core genome single nucleotide polymorphism (SNP) based phylogeny. Comparisons were then used to identify clade specific CDS markers and validate the LS-BSR pipeline based on molecular markers that delineate between classical E. coli pathogenic variant (pathovar) designations. Scalability tests demonstrated that the LS-BSR pipeline can process 1,000 E. coli genomes in ~60h using 16 processors. Conclusions. LS-BSR is an open-source, parallel implementation of the BSR algorithm, enabling rapid comparison of the genetic content of large numbers of genomes. The results of the pipeline can be used to identify specific markers between user-defined phylogenetic groups, and to identify the loss and/or acquisition of genetic information between bacterial isolates. Taxa-specific genetic markers can then be translated into clinical diagnostics, or can be used to identify broadly conserved putative therapeutic candidates.

scholarly journals Ankle-Brachial Index as a Predictor for Cardiovascular Disease in Postmenopausal Women

2019 ◽  
Vol 04 (01) ◽  
pp. 015-019
Author(s):  
Lakshmi Lasya Manchikanti ◽  
Madhuri Taranikanti ◽  
Akhila Dronamraju ◽  
Sudha Bala ◽  
Rohith Kumar Guntuka
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Postmenopausal Women ◽  
Heart Diseases ◽  
Ankle Brachial Index ◽  
Digital Data ◽  
Anthropometric Parameters ◽  
Cross Sectional ◽  
Mortality And Morbidity ◽  
Artery Disease

Abstract Background and Aim Menopausal women are at an increasing risk of cardiovascular diseases due to ovarian failure with estrogen deficiency. Redistribution of fat leading to abdominal obesity is a risk factor for cardiovascular disease. Dyslipidemia is one of the risk factors for peripheral artery disease (PAD) and coronary artery disease (CAD). Prevalence of PAD in women is similar or even higher than men, especially after menopause. ankle-brachial index (ABI) is a gold standard technique to diagnose PAD and acts as an independent prognostic marker to identify patients with high cardiovascular risk. This study aims to compare the ABI between pre- and postmenopausal women and to show that routine use of ABI as a screening tool can be valuable in predicting mortality and morbidity from heart diseases in peri- and postmenopausal women. Material and Methods A cross-sectional study was done on a total of 107 women with no prior medical diseases such as hypertension, diabetes mellitus, cardiovascular diseases, and history of smoking. Fifty pre- and 57 postmenopausal women were included in this study. Anthropometric parameters such as height, weight, and body mass index (BMI) were measured. ABI was calculated by measuring the systolic pressures at posterior tibial artery and brachial artery, as per the protocols using digital data acquisition system. Results BMI in postmenopausal women was significantly higher with p = 0.0023. Systolic and diastolic blood pressures were significantly higher in postmenopausal women (p = 0.000001), and ABI was found to be significantly lower in postmenopausal women particularly on the left side. Conclusion ABI serves as an efficient indicator of PAD. It becomes necessary to understand the progression of PAD as its presence can increase the risk of mortality and morbidity from CAD. Early diagnosis of cardiovascular disease through simple techniques such as ABI measurement would provide scope for early interventional strategies.

scholarly journals Finding functional disease-associated non-coding variation using next-generation sequencing

2016 ◽  
Author(s):  
Paolo Devanna ◽  
Xiaowei Sylvia Chen ◽  
Joses Ho ◽  
Dario Gajewski ◽  
Alessandro Gialluisi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Binding Sites ◽  
Large Scale ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Next Generation Sequencing Data ◽  
Whole Genome ◽  
Next Generation ◽  
Whole Exome ◽  
Generation Sequencing

ABSTRACTNext generation sequencing has opened the way for the large scale interrogation of cohorts at the whole exome, or whole genome level. Currently, the field largely focuses on potential disease causing variants that fall within coding sequences and that are predicted to cause protein sequence changes, generally discarding non-coding variants. However non-coding DNA makes up ~98% of the genome and contains a range of sequences essential for controlling the expression of protein coding genes. Thus, potentially causative non-coding variation is currently being overlooked. To address this, we have designed an approach to assess variation in one class of non-coding regulatory DNA; the 3′UTRome. Variants in the 3'UTR region of genes are of particular interest because 3'UTRs are responsible for modulating protein expression levels via their interactions with microRNAs. Furthermore they are amenable to large scale analysis as 3′UTR-microRNA interactions are based on complementary base pairing and as such can be predicted in silico at the genome-wide level. We report a strategy for identifying and functionally testing variants in microRNA binding sites within the 3'UTRome and demonstrate the efficacy of this pipeline in a cohort of language impaired children. Using whole exome sequence data from 43 probands, we extracted variants that lay within 3'UTR microRNA binding sites. We identified a common variant (SNP) in a microRNA binding site and found this SNP to be associated with an endophenotype of language impairment (non-word repetition). We showed that this variant disrupted microRNA regulation in cells and was linked to altered gene expression in the brain, suggesting it may represent a risk factor contributing to SLI. This work demonstrates that biologically relevant variants are currently being under-investigated despite the wealth of next-generation sequencing data available and presents a simple strategy for interrogating non-coding regions of the genome. We propose that this strategy should be routinely applied to whole exome and whole genome sequence data in order to broaden our understanding of how non-coding genetic variation underlies complex phenotypes such as neurodevelopmental disorders.

The ACE2 gene: its potential as a functional candidate for cardiovascular disease

2012 ◽  
Vol 124 (2) ◽  
pp. 65-76 ◽  
Author(s):  
Louise M. Burrell ◽  
Stephen B. Harrap ◽  
Elena Velkoska ◽  
Sheila K. Patel
Keyword(s):  
Cardiovascular Disease ◽  
Large Scale ◽  
Renin Angiotensin System ◽  
Experimental Models ◽  
Left Ventricular ◽  
Special Importance ◽  
Gene Variation ◽  
Hereditary Factors ◽  
Artery Disease ◽  
Ace2 Gene

The RAS (renin–angiotensin system) plays an important role in the pathophysiology of CVD (cardiovascular disease), and RAS blockade is an important therapeutic strategy in the management of CVD. A new counterbalancing arm of the RAS is now known to exist in which ACE (angiotensin-converting enzyme) 2 degrades Ang (angiotensin) II, the main effector of the classic RAS, and generates Ang-(1–7). Altered ACE2 expression is associated with cardiac and vascular disease in experimental models of CVD, and ACE2 is increased in failing human hearts and atherosclerotic vessels. In man, circulating ACE2 activity increases with coronary heart disease, as well as heart failure, and a large proportion of the variation in plasma ACE2 levels has been attributed to hereditary factors. The ACE2 gene maps to chromosome Xp22 and this paper reviews the evidence associating ACE2 gene variation with CVD and considers clues to potential functional ACE2 variants that may alter gene expression or transcriptional activity. Studies to date have investigated ACE2 gene associations in hypertension, left ventricular hypertrophy and coronary artery disease, but the results have been inconsistent. The discrepancies may reflect the sample size of the studies, the gender or ethnicity of subjects, the cardiovascular phenotype or the ACE2 SNP investigated. The frequent observation of apparent sex-dependence might be of special importance, if confirmed. As yet, there are no studies to concurrently assess ACE2 gene polymorphisms and circulating ACE2 activity. Large-scale carefully conducted clinical studies are urgently needed to clarify more precisely the potential role of ACE2 in the CVD continuum.

scholarly journals The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes

2014 ◽  
Author(s):  
Jason W Sahl ◽  
Greg Caporaso ◽  
David A Rasko ◽  
Paul S Keim
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Parallel Implementation ◽  
Genetic Relationships ◽  
Clinical Diagnostics ◽  
Whole Genome Sequence ◽  
Bacterial Isolates ◽  
Bacterial Genomes ◽  
E Coli ◽  
Blast Score

Background. As whole genome sequence data from bacterial isolates becomes cheaper to generate, computational methods are needed to correlate sequence data with biological observations. Here we present the large-scale BLAST score ratio (LS-BSR) pipeline, which rapidly compares the genetic content of hundreds to thousands of bacterial genomes, and returns a matrix that describes the relatedness of all coding sequences (CDSs) in all genomes surveyed. This matrix can be easily parsed in order to identify genetic relationships between bacterial genomes. Although pipelines have been published that group peptides by sequence similarity, no other software performs the large-scale, flexible, full-genome comparative analyses carried out by LS-BSR. Results. To demonstrate the utility of the method, the LS-BSR pipeline was tested on 96 Escherichia coli and Shigella genomes; the pipeline ran in 163 minutes using 16 processors, which is a greater than 7-fold speedup compared to using a single processor. The BSR values for each CDS, which indicate a relative level of relatedness, were then mapped to each genome on an independent core genome single nucleotide polymorphism (SNP) based phylogeny. Comparisons were then used to identify clade specific CDS markers and validate the LS-BSR pipeline based on molecular markers that delineate between classical E. coli pathogenic variant (pathovar) designations. Scalability tests demonstrated that the LS-BSR pipeline can process 1,000 E. coli genomes in ~60h using 16 processors. Conclusions. LS-BSR is an open-source, parallel implementation of the BSR algorithm, enabling rapid comparison of the genetic content of large numbers of genomes. The results of the pipeline can be used to identify specific markers between user-defined phylogenetic groups, and to identify the loss and/or acquisition of genetic information between bacterial isolates. Taxa-specific genetic markers can then be translated into clinical diagnostics, or can be used to identify broadly conserved putative therapeutic candidates.

scholarly journals Developing Large-scale Electronic Patient Records Conforming to the openEHR Architecture

2014 ◽  
Vol 16 ◽  
pp. 1281-1286 ◽  
Author(s):  
Gunnar Ellingsen ◽  
Bente Christensen ◽  
Line Silsand
Keyword(s):  
Large Scale ◽  
Patient Records ◽  
Electronic Patient Records

scholarly journals DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Po-Jung Huang ◽  
Jui-Huan Chang ◽  
Hou-Hsien Lin ◽  
Yu-Xuan Li ◽  
Chi-Ching Lee ◽  
...  
Keyword(s):  
Genome Analysis ◽  
Genetic Variants ◽  
Large Scale ◽  
Sequence Data ◽  
Classification Model ◽  
Whole Genome Sequence ◽  
Small Scale ◽  
Whole Genome ◽  
Gold Standard Method ◽  
Computing Framework

Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.

Sign in / Sign up

Export Citation Format

Share Document