I’m mystified. It sounded so easy on paper and more accurate than gazing into a crystal ball to see what my future has in store. I only had to give a sample and let the experts decipher my future encased within my genetic code. Yet, science indicates that forecasting with the genetic code may be no more accurate than gazing into a crystal ball. Let me explain…
First, our genetics are only predictive. Just because we carry a gene does not mean that it is being actively used by our bodies. It could just be going along for the ride or be negated by external factors.
Second, many of us (if not all) are walking around with a smorgasbord of genomes. Evidence exists that people can have different genomes in different parts of their body. The acquisition of multiple genomes can happen in the early days in the womb between twins (Boklage, 2006), between mother and fetus (Boddy, Fortunato, Wilson Sayres, & Aktipis, 2015; Stevens, 2016) or because an embryonic cell develops a mutation that gets perpetuated to various parts of the body (but not the entire body) (Lupski, 2013). Also, genomes can be picked up from other people, such as via a bone marrow transplantation (Hung et al., 2009). As we age, mutations can occur in localized parts of the body too, which can contribute to cancer or age-related issues (Aguilera & Garcia-Muse, 2013). Pending the location that the sample was taken from, one can obtain very different genetic test results from the same person!
Third, the procurement of the genetic knowledge could have been compromised. It is not uncommon for samples to pick up mutations during the sequencing of the DNA, which were not present in the original sample (Chen, Liu, Evans, & Ettwiller, 2017). Hence, the resulting data set has become damaged. In a recent publication, it was found that 41% of the 1000 Genomes project and 73% of The Cancer Genome Atlas data sets showed damage (Chen et al., 2017). This can play significant havoc when inferring from the data sets or determining a course of medical treatment based on damaged data sets.
From sequencing our genetic material, we must step aside and ask ourselves what it is that we want to learn. If it is to see that we are 10% (insert favorite ethnicity here), then wonderful. If it is to glean serious medical information, we must remember that the information is only predictive, not absolute and could be an incomplete picture of our multigenome. It is also probable that an error occurred during the DNA sequencing, and does not truly reflect a change in the sequence of the gene in question. To complement genetic testing, additional parameters that may glean more insight about what is actively going on our bodies must be measured too.
Aguilera, A., & Garcia-Muse, T. (2013). Causes of genome instability. Annu Rev Genet, 47, 1-32. doi:10.1146/annurev-genet-111212-133232
Boddy, A. M., Fortunato, A., Wilson Sayres, M., & Aktipis, A. (2015). Fetal microchimerism and maternal health: a review and evolutionary analysis of cooperation and conflict beyond the womb. Bioessays, 37(10), 1106-1118. doi:10.1002/bies.201500059
Boklage, C. E. (2006). Embryogenesis of chimeras, twins and anterior midline asymmetries. Hum Reprod, 21(3), 579-591. doi:10.1093/humrep/dei370
Chen, L., Liu, P., Evans, T. C., Jr., & Ettwiller, L. M. (2017). DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science, 355(6326), 752-756. doi:10.1126/science.aai8690
Hung, E. C., Shing, T. K., Chim, S. S., Yeung, P. C., Chan, R. W., Chik, K. W., . . . Lo, Y. M. (2009). Presence of donor-derived DNA and cells in the urine of sex-mismatched hematopoietic stem cell transplant recipients: implication for the transrenal hypothesis. Clin Chem, 55(4), 715-722. doi:10.1373/clinchem.2008.113530
Lupski, J. R. (2013). Genetics. Genome mosaicism–one human, multiple genomes. Science, 341(6144), 358-359. doi:10.1126/science.1239503
Stevens, A. M. (2016). Maternal microchimerism in health and disease. Best Pract Res Clin Obstet Gynaecol, 31, 121-130. doi:10.1016/j.bpobgyn.2015.08.005