CAN GENOME SEQUENCING AFFECT THE DRUGS SOMEONE TAKES?
Even if a person is generally healthy, he or she may take medications on occasion or even regularly. As an example, a young woman had her genome sequenced and was found to have a variant that put her at risk for thrombosis. Based on this information, it is recommended that she should avoid certain oral contraceptives. In a second example, an individual with heart palpitations was found to have a variant responsible for an aberrancy in the electrical properties of the heart called long QT syndrome. It is recommended that this individual avoid certain drugs in the future, including some commonly prescribed antibiotics. Because we already know so much about the genetic effects on drug responses, much more can be learned about drug dosages and what drugs a person should avoid.
Analysis of the genomes of a number of people reveals that a person’s genome sequence typically has valuable information regarding about three to six drugs (Table 1). As an example, analysis of the author’s genome revealed useful information about several drugs related to type 2 diabetes: metformin, a widely used diabetes drug, and troglitazone, which is no longer prescribed in the United States. Both these medications were predicted to have a greater effect than would be expected for the average patient. The genome sequence also revealed useful information about statins that should not used so as to avoid side effects. Finally, it revealed the previously mentioned VKORC1– 1639G>A mutation, which increases the blood thinning effects of warfarin. Although it is possible that some or none of these drugs will ever be used, the information may be valuable in the future.
Table 1. Examples of Variants Predicting Drug Response in a Personal Genome. The variants listed in the Table were found in the genome of the author who has elevated glucose and are potentially relevant for his drug dosing should they ever be needed
|Gene||SNP||Patient Genotype||Treatment||Drug(s) Affected|
|CDKN2A/2B||rs1,08,11,661||C/T||type 2 diabetes||troglitazone (increased beta-cell function)|
|CYP2C19||rs1,22,48,560||C/T||atherosclerosis||clopidogrel (increased activation)|
|LPIN1||rs1,01,92,566||G/G||type 2 diabetes||rosiglitazone (increased effect)|
|SLC22A1||rs6,22,342||A/A||type 2 diabetes||metformin (increased effect)|
|VKORC1||rs99,23,231 (or-1639G>A)||C/T||atherosclerosis||warfarin (lower dose required)|