After the Human Genome Project was successfully completed in April 2003 and it was assured that humans are identical in the sequence of their genome by 99.9%, researchers are moving on to find more about the 0.1% left. Although our genome is made out of 3 billion bases (A’s, G’s, C’s and T’s), the 0.1% genetic difference is extremely significant. This is due to the fact that this small percentage holds the key to most of the consequences of such a variation between human beings (e.g susceptibility to diseases and also response to drugs). This introduced the term SNPs (single nucleotide polymorphism) which was found to be highly involved in variation of response to many drugs (like anti-cancers) and the list is increasing every day.

To be more focused on the human variations represented by the 0.1 percent, the NIH led the international HapMap project. Their work was performed on four populations groups: the Yoruba people in Ibadan Nigeria,the Japanese in Tokyo, the Han Chinese from Beijing and Utah residents from western and northern Europe. The work began in October 2002 and successfully ended in October 2005. The work of  three successive years helped the researchers invent a shortcut for studying SNPs. Scientists believe that there are about 10 million SNPs distributed among 3 billion base pairs which make up our genome, so scanning the whole genome of millions of people for such SNPs would be extremely expensive. After the HapMap project, researchers demonstrated that variants usually tend to cluster into neighborhoods (called haplotypes) and thus the number could be reduced to 300,000 SNPs only. This means that they could reduce the work load by about 30 folds.

The genome-wide association (GWA) studies aim to pinpoint the genetic differences, which cause a certain disease (or a biological trait) by comparing a group of people (who have the trait under research) to a control group (people who are free from this trait). Utilizing thousands of SNPs markers, we can identify regions (loci) which are statistically different between patient and control groups. Thus, we can identify the genetic difference between sick and control people, even though the difference was subtle. This means that the combination of slightly altered genes plus environmental factors could be well studied. The conventional ways, usually used to study genetic differences, are mainly based on selecting the candidate gene based on knowing or suspecting the mechanism of the disease. GWA helps scanning of the whole genome in a comprehensive unbiased manner. It will let us get the whole picture about other, non expected, contributing genes. In this way, GWA studies will help us study the multi-factorial diseases (like cancer and diabetes) in a more rationalised way.

Another challenge has come up: What about the genetic variations due to geographic ancestry? It is also a significant contributing factor to variation among humans and all the efforts are directed towards making a somewhat universal map of human genome to help develop individualized drugs. A group of scientists led by David Reich, an assistant professor at Harvard Medical School, described a quantitative method that can correct such errors due to geographical ancestry known collectively as “population stratification”. It will help if the disease groups, sharing the same trait, have differences in their geographic ancestry.