Myth 1: Personal Genomics is cure: NO, Personal genomics is just one another way to accelerate the discovery of new drugs. This time the claim is to find personal drugs. “Personal Genomics” literally, is just having access to ones own genomics information along with associated biological information. It is too early to predict if personal genomics will really help discover personal medicine. There are a few examples from the past which explain how
a. It took almost 60 years to invent antibiotics, after scientists discovered the existence of germs in the 1860s
b. Human genome was mapped in year 2003; it took 13 years of effort with almost 2.7 billion dollars being spent. Now in the year 2010, we still do not have a standard variation map for even a single set of population. Efforts are going on and it will take some more time to standardize.
c. It still takes more than 10 years to discover a new drug and
d. The finest example is BRCA. BRCA mutation was discovered in 1990, its almost 20 years now we still do not have a complete cure.
Myth 2: SNPs will predict disease: It is more than that. Just by finding variation in genome will not lead to conclusion. One variation is just a clue towards identifying the disease. There are many other factors responsible like environment, family history, habits, life style and exposure. The following will bust this myth.
a. Variance in risk prediction among the DTC companies. 23 and me, Navigenics and Decode have large sets of exclusive SNPs in their data sets. These differences are due to different SNP selection methods. For example, for type 2 diabetes SNP rs1111875. Navigenics cites “C” as the risk allele and Gene Essence cites “G.” The underlying reference cites “C” as the risk allele and “C” and “T” as the possible genotype values. The online GWAS catalog cites this study and two others with “C” as the risk allele, and one with “G” as the risk allele. In demonstration examples, deCODEme suggests a possible genotype value as “GG” and Gene Essence suggests “AA.” Again, not all four bases should be acceptable genotype values and there should be only one risk allele. (Multigenic condition risk assessment in direct-to-consumer genomic services)
b. There is an issue with accuracy of genotype data. Ideally the lower the error rate the better it is. Some times the error is due to sequencing technology.
c. Issue with accuracy of reference sequence.
d. It’s just not SNPs which are responsible for disease. There are other factors like gene expression levels, epigenomics and environment which are responsible too.
Myth 3: Genetic tests will help identify disease: These tests are like any other tests like blood test, urine test, scans etc. If one of the tests is not a help in identifying the disease then the doctor recommends another test to narrow down the possibility. More over the results are in probability. The problem with probabilities is that they hide some information and expose some and not sure how important is the hidden information.
Personal genomics is one step towards the promise for better health. We are in exciting times, a lot is happening. Year 2000, the scientific community was excited with the field of bioinformatics and the promise it brought along with it. Then came wave of gene expression studies, the belief that biomarkers will change the health industry, followed by the 2nd generation of sequencing technology. The truth is that every new technology came up with its own challenges: Microarrays had the problem of replication of results; next generation sequencing has variability and high throughput data issues. In addition, so are the genetic tests and personal genomics with SNP variability.
While we still find the right cure, EAT WELL, SLEEP WELL and LOVE WELL
