Research conducted during the early 2000s revealed that sets of SNPs within distinct regions of the chromosome tend to be inherited together. If specific SNPs were contributing to common disease progression, identifying variants between distinct populations could theoretically help construct a map of the genome that identifies areas of potential association with disease. These initial observations led to the formation of an international consortium called the Haplotype Map (HapMap) project, which resulted in the creation of a HapMap, a catalogue of SNPs commonly found in a consensus human genome and their locations within the genome. Using the HapMap, a researcher can conduct a genome-wide association study (GWAS)—a rapid scan of many individuals’ entire genomes to discover alleles associated with a particular disorder. DNA microarray chips—miniature glass chips encoded with thousands of short, synthetic, single-stranded DNA sequences representing identified SNPs—have been developed to facilitate this approach. The technology relies on the discriminatory nature of DNA sequences to bind to exactly complementary pieces of DNA with high affinity. Genomic DNA, for example, can be denatured into single strands, cut into smaller pieces, labeled fluorescently, and allowed to bind to the DNA encoded on the chip. Differences between the binding pattern of DNA from individuals with a particular disease and the binding pattern of DNA from a control group can lead to the identification of specific alleles which may contribute to disease progression. A GWAS is particularly useful for finding alleles that contribute to common diseases, and it enables researchers to do so far more easily and cheaply than before. However, as sequencing costs continue to fall, many predict that the “$1,000 genome” will soon become a reality. At this price, it is conceivable that whole genome sequencing will eventually replace genome wide association studies.
Although a GWAS can be a powerful source of information about genetic predisposition to disease, so far these studies explain only a very small fraction of heritability and fail to capture other contributing factors such as multiple common genetic variants acting together, copy number variants, or epigenetics—chemical changes in base pairs, or physical changes in chromosome structure—that may greatly modulate phenotypic expression but that are themselves typically not heritable.
Therefore, a GWAS is a weak forecaster of an individual’s risk for a genetic disorder. Obesity linkages, for instance, account for less than 2 percent of variance in heritable body mass index. Furthermore, a GWAS has a major drawback: it misses rare genetic variants that, when present,