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University of Maryland Institute for Genome Sciences Contributes to 1000 Genome Project

Wednesday, October 31, 2012

 Scott Devine, PhD

International Human Genomics Project Aims to Identify Genetic Variations that Cause Disease

The world’s largest, most detailed catalog of human genetic variation has doubled in size as part of a project involving researchers at the University of Maryland School of Medicine’s Institute for Genome Sciences (IGS).

The catalog of genetic variation – used by disease researchers around the world – has expanded with the 1000 Genomes Project’s latest publication in the Oct. 31 issue of the journal Nature. The National Human Genome Research Institute (NHGRI), part of the National Institutes of Health, helps fund and direct this international public-private consortium of researchers in the United States, Britain, China, Germany and Canada. Four researchers from the Institute for Genome Sciences participate in the project, including co-principal investigator Scott E. Devine, PhD, associate professor of medicine at the University of Maryland School of Medicine.

Genetic variation explains in part why people look different and vary in their risk for diseases. The goal of the 1000 Genomes Project is to identify and compile variants in the human genome that occur at a frequency of at least one in 50 people. Although most of these genetic variants cause little if any effect, some contribute to disease, and others are beneficial. An example of a beneficial difference is a rare genetic variant that blocks the human immunodeficiency virus from infecting white blood cells and, thus, protects people exposed to HIV who carry this variant.

The expanded catalog allows medical researchers to locate genetic differences contributing to rare and common diseases more precisely. Identifying the genetic underpinnings of disease will help lead to new diagnostic tests and, in some cases, treatments. 

“This Nature paper reports the genome sequences of more than 1,000 humans from around the globe,” says Dr. Devine. “The genetic variation that has been detected in these 1,000 humans will allow us to understand how natural genetic variation influences human traits and diseases. It also opens the door to new therapeutic approaches in which physicians tailor medical treatments for each individual based on the genetic variation that is detected in a given patient's genome. The 1000 Genomes Project provides a solid foundation for this new enterprise of personalized genomic medicine.”

Dr. Devine works on the 1000 Genome Project with Luke J. Tallon, scientific director of the Genomics Resource Center at the IGS, Xinyue Liu, and Ankit Maroo, all members of the human genomics group led by Dr. Devine at IGS. The many researchers on the project are grouped into specific areas of focus. IGS researchers participated in the analysis and structural variation groups, and their work is funded by a grant from the NHGRI (R01 HG002898).

The University of Maryland scientists have contributed toward developing new approaches for identifying short insertions and deletions of DNA, know as INDELs. The INDELs are small chunks of DNA that are inserted or deleted in a given human genome. They are second most abundant type of human genome variation, with about 600,000 INDEL variants in a typical human genome.

“The long term goal of this work is to understand how short INDELs impact the genetic code of each person,” says Dr. Devine. “You can just begin to imagine the havoc that all those insertions and deletions might wreak on the instructions contained within the human genome. These changes can produce alterations in human biology and can produce diseases such as cancers, heart disease, diabetes, and more.”

So far, project researchers have sequenced the genomes of 1,092 people from 14 populations in Europe, East Asia, sub-Saharan Africa and the Americas. Ultimately, they will study more than 2,500 individuals from 26 populations. All of the participants consented to inclusion of sequence data derived from their anonymous DNA samples in an open online database. Each part of these genomes was read (or sequenced) an average of six times, which provides accurate information about common genetic variants but misses many rare variants.

“The 1000 Genomes Project is a large, international effort aiming to characterize human genetic variation, including people from many different populations,” said Eric D. Green, MD, PhD, NHGRI director. “The newly published findings provide deeper insights about the presence and pattern of variants in different people’s genomes, which is critical information for studying the genomic basis of human disease.”

To identify rare variants in the exome, the part of the genome that codes for proteins, the researchers sequenced the exomes of 15,000 genes in each genome an average of 80 times. The study discovered 99.8 percent of exome variants with a frequency of at least 1 percent and 99.3 percent of variants elsewhere in the genome with a frequency of at least 1 percent.

Phase one of the 1000 Genomes Project, the subject of the Nature paper, has produced a massive amount of genomic data. Simply recording the raw information takes some 180 terabytes of hard-drive space, enough to fill more than 40,000 DVDs. All of the information is freely available on the Internet through public databases such as ones at the National Center for Biotechnology Information at the U.S. National Library of Medicine in Bethesda, MD, and the European Bioinformatics Institute in Hinxton, England. Data from the project have been available to researchers since 2008.

The massive dataset became available in the cloud this year via Amazon Web Services (AWS). Cloud access enables users to analyze large amounts of the data much more quickly, as it eliminates the time-consuming download of data and lets users run their analyses over many servers at once. Researchers pay only for the additional AWS resources they need to further process or analyze the data.

All the genetic information for making an organism resides in the DNA, which is a set of long molecules made of units called bases.  Each base can be a chemical unit abbreviated A, C, G or T.  For this paper, the researchers identified 38 million single-nucleotide polymorphisms, or SNPs (pronounced "snips"), which are DNA variants that occur when a particular base in the genome sequence differs among people. These variants are the most common genetic differences among people. Each SNP is like a landmark, reflecting a specific position in the genome where the DNA spelling differs by one letter among people.

They also identified variants in the linear structure of the DNA, including 1.4 million short indels (insertions or deletions of DNA as small as a single base or as large as 50 bases) and 14,000 large deletions of DNA. SNPs and structural variants can help explain an individual’s susceptibility to disease, response to drugs, or reaction to environmental factors such as air pollution or stress.  Other studies have found an elevated rate of indels in diseases such as autism and schizophrenia, although it's not yet clear how they affect those diseases.

Data analysis is a vital part of the project, and approximately 260 analysts participated in analyzing the data reported in the recent Nature publication. They mapped and assembled the raw DNA sequence data relative to the reference human genome sequence. They then analyzed the aligned sequences to locate SNPs and structural variants. SNPs are relatively easy to find; structural variants (such as insertions, deletions and copy number differences) are much harder to find. A number of research groups are working to establish how to go from the raw sequence data to identifying structural variants.

Many research groups contributed to the generation of genome sequence data for this project, including NHGRI’s large-scale sequencing centers: the Human Genome Sequencing Center at the Baylor College of Medicine, Houston; The Broad Institute of MIT and Harvard University in Cambridge, MA, and The Genome Institute at the Washington University School of Medicine in St. Louis, MO. Other groups included the Wellcome Trust Sanger Institute in Hinxton, England; BGI Shenzhen in Shenzhen, China; the Max Planck Institute for Molecular Genetics in Berlin; and Illumina, Inc., in San Diego.

The 1000 Genomes Project eliminates time-consuming steps for researchers trying to find genetic variants that affect a disease. Genome-wide association studies aim to find regions of the genome that contain DNA variants relevant to a disease. They use technologies that provide information about hundreds of thousands to a couple of million SNPs in each studied genome; they can combine these data with 1000 Genomes Project data on tens of millions of variants to find regions affecting the disease more precisely.  The 1000 Genomes Project data then can be used to greatly enhance such studies by providing more detailed information about known variants. Instead of sequencing the genomes of all the people in a study – still an expensive prospect for thousands of people – researchers can use the 1000 Genomes Project data to find most of the variants in those regions that are associated with the disease.

“Once researchers find genes and variants of interest associated with disease by using the 1000 Genomes Project data, they have to return to basic biology to study them one at a time, to establish which genes and variants are causal for the disease and not just along for the ride,” said Lisa D. Brooks, PhD, program director of the Genetic Variation Program in NHGRI’s Division of Genome Sciences. “The 1000 Genomes Project data accelerate their ability to close in on those genes and variants.”

Planning for the $120 million project began in 2007. In 2010, researchers published data on three pilot studies. The 2012 data set will be followed by the last addition to the catalog in 2013. 

The 1000 Genomes Project data is available through:

• The 1000 Genomes website: www.1000genomes.org
• NCBI: ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
• EBI: ftp://ftp.1000genomes.ebi.ac.uk
• Amazon: http://s3.amazonaws.com/1000genomes

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