1000 Genomes Project Releases Data from Pilot Projects on Path to Providing Database for 2,500 Human Genomes
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In addition, work has begun on the full-scale effort to build a public database containing information from the genomes of 2,500 people from 27 populations around the world.
Launched in 2008, the 1000 Genomes Project first conducted three pilot studies to test multiple strategies to produce a catalogue of genetic variants that are present in one percent or greater frequency in the different populations chosen for study (European, African and East Asian).
Disease researchers will use the catalogue, which is being developed over the next two years, to study the contribution of genetic variation to illness.
In addition to distributing the results on the Project’s own web sites, the pilot data set is available via the Amazon Web services (AWS) computing cloud to enable anyone to access this unprecedentedly large data set, even if they do not have capacity to download it locally.
“I am indebted to all the project researchers who are making this collaboration so successful,” said Richard Durbin, PhD, of the Wellcome Trust Sanger Institute, who is co-chair of the consortium. “In the pilot projects we have made significant progress in optimizing the use of next generation sequencing platforms to study human genetic variation, and we can now apply what we have learned to accelerate our efforts to sequence this reference collection of human genomes.”
“Completing the goals of the initial pilot projects has been critical to informing how to apply next-generation sequencing in human genetic research, and provides a solid foundation the next stage of the project,” said David Altshuler, MD, PhD, of the Broad Institute, Cambridge, Massachusetts, and co-chair of the project consortium. “We are eager to make rapid progress on the full set of 2,500 genomes and to provide the resulting data for use by the disease genetic community. I fully expect that these data will more precisely define genetic risk factors already discovered, and lead to the discovery of many new risk factors for disease.”
A previous public project, the International HapMap Project, provided an initial database of over 3 million human DNA variants present in 270 DNA samples. Information and methods developed by the HapMap Project fuelled a first generation of so-called ‘Genome Wide Association Studies’ (GWAS) that have localized over 600 novel genetic risk factors for common diseases such as diabetes, heart attack, inflammatory bowel disease, breast cancer, schizophrenia, and other disorders. These studies were limited by technology, however, to studying a subset of more common DNA variants (those with frequency greater than five to ten per cent).
The 1000 Genomes Project exploits next-generation DNA sequencing technologies to develop a much more complete database – one that goes much lower in frequency, and one that is extended to more human populations. This database will contain all forms of variation – single letter changes (termed ‘SNPs’), small insertions and deletions (termed ‘indels’) and large changes in the structure and copy number of chromosomes (termed ‘copy number variations’). This integrated map is a novel contribution, as previous studies have focused exclusively on one form of DNA variation (even though each of our genomes contains all variety of variation).
“The increased resolution of the 1000 Genomes map will provide researchers with far more detailed sequence information beyond common variants, including millions of less-common and rare variants”, said Elaine Mardis, PhD, co-director of the Washington University Genome Center and member of the project steering committee. “Researchers who have found regions of the genome associated with disease will be able to look at this data to see an almost complete set of genetic variants in those regions that might contribute directly to disease.”
A critical new component of the Project is the selection of 2,500 DNA samples from 27 populations around the world. Each participant has provided explicit consent for full and public release of DNA samples and full sequence data (including recognition of potential risks).
The free and public availability of Project data will fuel development of new methods and new approaches to genetic research – applications that would happen much more slowly (if at all) if there were only disease-specific datasets that can’t be shared freely on the web (due to more restrictive informed consent).
“We are committed to make these data public to make certain that any institution or researcher around the world can access and work with our datasets to better understand common disease,” said Jun Wang, PhD, associate director of the Beijing Genomics Institute in Shenzhen, China, and member of the 1000 Genomes Project steering committee. “We must work together if we are going to find those subtle differences in the human genome that lead to diseases like cancer and diabetes.”
The uses of Project data will be many. One clear use is to track down the causal mutations underlying initial localizations from GWAS. A second is making it possible to test less common DNA variants for contributions to disease. And a third is to help identify rare mutations that cause strongly inherited diseases: in studies aiming to find such rare mutations, it is very helpful to have a complete database of common variants that can be screened out to focus attention on those mutations that are unique to an individual or family.
But before such uses could be realized, many technical and analytical challenges had to be overcome. These were the focus of the pilot projects.
Pilot projects – testing essential aspects of project feasibility
The first pilot project involved sequencing the genomes of six people (two nuclear families each with two parents and a daughter) at high coverage. Each sample was sequenced an average of 20 to 60 times, and using a variety of sequencing technologies. Previous “personal genomes” were each based on only a single sequencing method, and thus were limited to what that method could detect. By using multiple methods, the Project has uncovered not only a more complete picture of DNA variation in these individuals, but also learned about the strengths and limitations of each of the current technologies. These data also served as a comparison group for the genome sequences analysed in the other pilot projects.
The six genomes were sequenced by academic centres in China, Germany, the UK, and the US, as well as by three companies, using platforms from the companies: 454 Life Sciences, a Roche company; Applied Biosystems, an Applera Corp. business; and Illumina Inc. All of the platforms were able to sequence 85 to 90 per cent of a genome and produce high-quality data.
The second pilot project sequenced the genomes of 179 people at low coverage – an average of three passes of the genome. Although sequencing costs are dropping, it is still very expensive to sequence the genomes of hundreds of people deeply enough to find all of the genetic variants in each genome accurately. An alternative approach is to sequence many genomes at light coverage, and then combine the data from many people to discover genetic variants that they share. The results of the pilot project confirmed that this strategy is effective and will allow the project to meet its goal of discovering sequence variants that are shared with other people.
The third pilot project involved sequencing the coding regions, called exons, of 1,000 genes in about 700 people to explore how best to obtain a detailed catalogue in the approximately two per cent of the genome that is composed of protein-coding genes. This Project provided unprecedented sample size to learn about the patterns of rare variation in the human population.
Data analysis and access – and first major release of biomedical data on the Amazon Web Services Cloud
The amount of data produced by the 1000 Genomes Project is unprecedented in biomedical research. Currently, the total size of the datasets is over 50 terabytes, or 50,000 gigabytes. That corresponds to almost eight trillion DNA base pairs, or terabases, of sequence data. Early in the project, merely copying the vast quantities of data between the European Bioinformatics Institute (EBI) in the UK and National Center for Biotechnology Information (NCBI), part of the US National Library of Medicine in the US consumed large fractions of both groups’ capacity on the Internet for several days.
For many researchers and institutions, especially those who lack the computer and analytical power to study such a massive data set, an economical option is being tested to access and analyze the pilot data. The pilot datasets of the 1000 Genomes Project (7.3 terabytes of data) are now available as a public dataset through Amazon Web Services (AWS) and integrated into the company’s Elastic Compute Cloud (Amazon EC2 and Simple Storage Service, S3). As new data become available and usage of this data increase on AWS, it is anticipated that additional data sets will be available in AWS.
The cost to researchers for computing through Amazon EC2 can be counted in tens of dollars per day compared to the hundreds of thousands of dollars it would cost to purchase the computer infrastructure needed to download and analyze this amount of data locally. Because 1000 Genomes Project data are publicly available from EBI and NCBI, other companies that provide similar computing services are also free to download and provide the data to their clients.
“The 1000 Genomes Project has a simple goal: peer more deeply into the genetic variations of the human genome to understand the genetic contribution to common human diseases,” said Eric D Green, MD, PhD, Director of the National Human Genome Research Institute, which provides major funding to the effort. “I am excited about the progress being made on this resource for use by scientists around the world and look forward to seeing what we learn from the next stage of the project.”
Consortium researchers are writing a paper that describes the pilot data and the design of the full project that is expected to be published in a peer-reviewed scientific journal later this year.