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Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.
Pubmed ID: 23977022 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.
View all literature mentionsSoftware repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsAn interdisciplinary data resource on health, economic position and quality of life as people age. Longitudinal multidisciplinary data from a representative sample of the English population aged 50 and older have been collected. Both objective and subjective data are collected relating to health and disability, biological markers of disease, economic circumstance, social participation, networks and well-being. Participants are surveyed every two years to see how people''s health, economic and social circumstances may change over time. One of the study''s aims is to determine the relationships between functioning and health, social networks, resources and economic position as people plan for, move into and progress beyond retirement. It is patterned after the Health and Retirement Study, a similar study based in the United States. ELSA''s method of data collection includes face-to-face interview with respondents aged 50+; self-completion; and clinical, physical, and performance measurements (e.g., timed walk). Wave 2 added questions about quality of health care, literacy, and household consumption, and a visit by a nurse to obtain anthropometric, blood pressure, and lung function measurements, as well as saliva and blood samples, and to record results from tests of balance and muscle strength. Another new aspect of Wave 2 is the ''Exit Interview'' carried out with proxy informants to collect data about respondents who have died since Wave 1. This interview includes questions about the respondents'' physical and psychological health, the care and support they received, their memory and mood in the last year of their life, and details of what has happened to their finances after their death. Wave 3 data added questions related to mortgages and pensions. The intention is to conduct interviews every 2 years, and to have a nurse visit every 4 years. It also is envisioned that the ELSA data will ultimately be linked to available administrative data, such as death registry data, a cancer register, NHS hospital episodes data, National Insurance contributions, benefits, and tax credit records. The survey data are designed to be used for the investigation of a broad set of topics relevant to understanding the aging process. These include: * health trajectories, disability and healthy life expectancy; * the determinants of economic position in older age; * the links between economic position, physical health, cognition and mental health; * the nature and timing of retirement and post-retirement labour market activity; * household and family structure, social networks and social supports; * patterns, determinants and consequences of social, civic and cultural participation; * predictors of well-being. Current funding for ELSA will extend the panel to 12 years of study, giving significant potential for longitudinal analyses to examine causal processes. * Dates of Study: 2002-2007 * Study Features: Longitudinal, International, Anthropometric Measures * Sample Size: ** 2000-2003 (Wave 1): 12,100 ** 2004-2005 (Wave 2): 9,433 ** 2006-2007 (Wave 3): 9,771 ** 2008-2009 (Wave 4): underway Links * Economic and Social Data Service (ESDS): http://www.esds.ac.uk/longitudinal/about/overview.asp * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00139#scope-of-study
View all literature mentionsInternational collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
View all literature mentionsQTL analysis based on imputed dosages/posterior_probabilities.
View all literature mentionsOpen source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.
View all literature mentionsA dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.
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