Genome-wide association studies (GWAS) represent a recently developed research technique with many implications on both a global and an individual scale. GWAS seek to identify the single nucleotide polymorphisms (SNPs, pronounced "snips") that are common to the human genome and to determine how these polymorphisms are distributed across different populations. On a broad scale, these studies help scientists uncover associations between individual SNPs and disorders that are passed from one generation to the next in Mendelian fashion. On a small scale, GWAS can be used to determine an individual's risk of developing a particular disorder. Although the impact of GWAS on medical genetics is undeniable, the true usefulness of these studies largely depends upon researchers' understanding of the interacting factors behind common genetic disorders.
SNPs: Variations in the Human Genome
With the completion of the Human Genome Project in 2003, researchers began to pinpoint areas of the genome that varied between individuals. Shortly thereafter, they discovered that the most common type of DNA sequence variation found in the genome is the single nucleotide polymorphism; in fact, there are an estimated 10 million SNPs that commonly occur in the human genome. A worldwide effort known as the HapMap Project seeks to identify and localize these and other genetic variants, and to learn how the variants are distributed within and among populations from different parts of the world. To date, the project has identified over 3.1 million SNPs across the human genome that are common to individuals of African, Asian, and European ancestry.
The HapMap information, which is available to the public, has facilitated a new type of research effort: the genome-wide association study (GWAS). In such a study, the distribution of SNPs is determined in hundreds or even thousands of people with and without a particular disease. By tallying which SNPs co-occur with disease symptoms, researchers can make a statistical estimate regarding the level of increased risk associated with each SNP. For instance, in a landmark 2007 study conducted in the United Kingdom, researchers identified people affected by seven common disorders, and they then genotyped 2,000 people in each disease category (for a total of 14,000 individuals studied). Next, these individuals were compared to 3,000 genotyped controls who did not have the disorders in question. As a result of these comparisons, the researchers were able to identify new genetic markers that point to an increased risk for multifactorial disorders such as heart disease and diabetes (Wellcome Trust Case Control Consortium, 2007). It was announced in July 2008 that this study will be expanded to include an additional 36,000 individuals and will focus on examining the genetic contributions to a total of 14 common disorders, as well as to individuals' responses to certain drugs.
The Role of DNA Microarrays in GWAS
Only a few years ago, a robust GWAS that genotyped thousands of individuals would have been prohibitively expensive. However, this is no longer the case thanks to the widespread use of an innovation known as the DNA microarray, which was first developed in the early 1990s. A DNA microarray (sometimes called a DNA chip) is a small glass slide with short DNA probes attached to it in a specific pattern. When a sample of fragmented DNA is washed over the microarray, pieces of this DNA hybridize to the chip and can then be detected by scanning software.
One manufacturer of DNA microarrays, Affymetrix, now offers a chip containing approximately 1.8 million different genetic markers. When a fragment of DNA from a test sample hybridizes to a probe on this microarray, the scanning software can document exactly which genetic marker is present in that DNA sample. This technology allows a single sample to be simultaneously queried for changes at almost 2 million known genetic variants (Figure 1).
Figure 1: Outline of a typical microarray experiment.
In a two-color experiment (panel a in the figure), DNA from different individuals of the same species or from different tissues in a single individual (e.g., normal and diseased cells) is extracted and differentially labeled with compatible fluorophores (e.g., Cy3 and Cy5). Equal amounts of labeled DNA are hybridized to the microarray, and the hybridization reaction is allowed to come to equilibrium over a period of >12 hours. At most probes, equal amounts of the two samples will hybridize (yellow features on the array), reflecting the fact that most loci in the two genomes are present in equal amounts (e.g., region 3). Regions that are deleted in the sample genome (e.g., region 1 of sample A) will result in probes with increased relative Cy3 signal (green features). Alternatively, amplified regions in the sample (e.g., region 2 of sample A) will result in features with an increased relative Cy5 signal (red features). Over the entire microarray, the signal ratios at each feature follow a Gaussian distribution, and candidate copy number variations are identified on the basis of deviation of a particular probe ratio, using statistical cut-offs. Although the diagram illustrates the protocol for array comparative genome hybridization, all array procedures, including SNP discovery and insertion site mapping, are carried out in this way. One-color experiments (panel b in the figure) are performed in a similar manner, except that the DNA is labeled with a single color and hybridized to a microarray without a reference sample. The difference between two-color and one-color experiments is that in the former case, two samples are compared within an experiment, whereas in the latter case, two separate experiments are required to compare the samples. For Affymetrix-manufactured microarrays, the method entails labeling DNA with biotin, then adding streptavidin conjugated to phycoerythrin after hybridization (represented by yellow circles). Rather than a ratio, an absolute value of hybridization is determined; following normalization, this value is compared with other experiments to detect genomic variation. A single two-color hybridization gives less variation at each probe than two independent one-colour hybridizations because the detailed conditions at every probe, such as salt concentration and temperature, are identical in the two-color experiment but are not necessarily identical in the two independent one-color experiments.
Using GWAS to Estimate Disease Risk
The practicality of identifying a correlation between a genetic change and the incidence of a complex disease is limited to statistical estimation of increased risk for developing the disorder, rather than a hard-and-fast prediction. This is due to the significant number of genetic and environmental variables that interact to cause the onset of a complex disease. Therefore, any genetic variant, such as a SNP, makes only a small contribution to an individual's overall risk.
In most cases, findings from a GWAS cannot be directly applied to the prevention or treatment of disease. Rather, before doctors are able to recommend medicinal, behavioral, and environmental interventions based on a SNP profile, the full pathway of disease development and the involvement of all variables must be understood. For example, one situation that makes the link between SNPs and disease difficult to understand is the case in which a SNP is not located within an exon of a gene. In such instances, studies are required to investigate the possibility that the SNP lies in a promoter or enhancer region and somehow affects regulation of the causal gene.
Occasionally, the results of a GWAS seem relatively straightforward; this is often the case when a gene is identified that contains a variant that confers susceptibility to a multifactorial disorder (Steinthorsdottir et al., 2007). However, the effect of that variant or other alleles of the gene is still just one of many factors influencing disease risk; therefore, the predictive power of the various alleles is not absolute. A well-known example of this is the link between certain alleles of the apolipoprotein E (ApoE) gene found on chromosome 19 and the development of Alzheimer's disease. ApoE codes for a protein that helps carry cholesterol in the bloodstream, and it has three common alleles: e2, e3, and e4. Research has shown that having one or two copies of the ApoE e4 allele significantly increases a person's risk for developing Alzheimer's disease, but it does not guarantee development of this disorder. It also remains unclear how certain forms of ApoE influence cerebral plaque formation, the hallmark of Alzheimer's disease (National Institute on Aging, 2008).
Personal SNP Profiles
Over the past few years, the scientific community has experienced a deluge of knowledge derived from the use of genome-wide association studies. As previously noted, because most SNPs are only partial contributors to an individual's risk for developing a disease, researchers must be cautious about giving too much weight to SNP profiles. Nonetheless, this has not deterred savvy entrepreneurs from capitalizing on existing GWAS research. For example, consumer genomics companies such as 23andMe, deCODE genetics, Navigenics, and Knome now offer a range of personal genotyping and sequencing services to clients who are interested in learning their estimated genome-based risk for developing a number of common disorders. Thus, for about U.S. $1,000, you can have your entire genome scanned for markers that have been identified by GWAS and receive personalized risk calculations that are updated as new knowledge becomes available.
In the coming years, social science research will reveal why people want this information, how well they understand the information they receive, and whether these risk assessments make a difference in patients' lives. One thing is certain, however: Personal genetic profiles will continue to increase in their medical value as researchers cultivate more and more knowledge about the genetic and environmental factors that interact to contribute to the development of common disorders.
Summary
Medical genetics has historically been a highly specialized field, and genome-wide association studies have played an important role in research to identify possible connections between SNPs and various disorders. Over time, researchers' knowledge of the genetic risk factors for disease has become more comprehensive, and this has made it possible to apply GWAS research to risk assessments for various disorders. It is important to understand, however, that GWAS are not absolute predictors of disease due to the role of environmental factors in complex disease development. Although the days of personalized medical care predicted by the planners of the Human Genome Project are drawing closer, that dream will only be realized through continued research into all of the many factors that contribute to complex disease susceptibility.
References and Recommended Reading
Gresham, D., et al. Comparing whole genomes using DNA microarrays. Nature Reviews Genetics 9, 291–302 (2008) (link to article)
National Institute on Aging. "Alzheimer's Disease Genetics Facts Sheet." (2008)
Steinthorsdottir, V., et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nature Genetics 39, 770-775 (2007) doi:10.1038/ng2043 (link to article)
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-682 (2007) doi:10.1038/nature05911 (link to article)
