Traditional versus Genetic Risk Factors

Yesterday, I started reading this GenomeWeb article discussing the utility of genome-wide association studies (GWAS) in testing drug safety. This article largely agreed with my earlier post. In fact, the article mentions the utility of genetic diagnostics for prescribing abacavir (Ziagen, a HIV drug), as described in “The Language of Life.” The article also mentions new studies to determine genetic risk factors for flucloxacillin (an antibiotic) and clozapine (an antipsychotic). In general, this article provides good support for my earlier claim that drug sensitivity is the most exciting area of GWAS research.

However, the article also mentions that scientists may have overestimated the strength of GWAS in predicting risk to diseases such as heart disease and type II diabetes. This slightly contradicts my most recent post where I claim that genetic predictors of type II diabetes are probably “pretty good.” Therefore, I decided it might be worthwhile to reevaluate my claims.

I think the recent type II diabetes study is well designed, and I was surprised by the results. The authors calculate multiple models for traditional (e.g. age, sex, family history, waist circumference, body mass index, smoking behavior, cholesterol levels) and genetic risk factors. They found that traditional models identified onset of type II diabetes with a 20-30% success rate (at 5% false positive rate), while the gene-based model had only about a 6.5% success rate (at 5% false positive rate). The GenomeWeb article also mentions an interview with the senior director of research at 23andMe (a genetic diagnostic company), who claims that some rare variants may impart especially high risk to certain individuals and genetic tests should therefore complement risk assessment using traditional risk factors. Readers should also take a look at Web Table B of the diabetes study, which calculates predictive power of individual variants. For example, TCF7L2 had one of the strongest associations with type II diabetes in the GWAS Catalog, and this gene did in fact have a significantly higher rate of incidence for diabetes for individuals with a certain set of variants. However, the risk of developing diabetes only increased from 5.4% to 8.6%. Therefore, I think these time consuming diagnostic studies (lasting 10+ years) are unfortunately essential to determine the practicality of genetic tests for diseases whose genetic basis is either complex or unclear.

On the other hand, I don’t think the results of the heart disease study are very surprising. After checking the GWAS Catalog for genetic associations with myocardial infarction and stroke (the diseases examined in the study), I only found a small amount of information on these diseases. There was only one study on myocardial infarction, which only had one significant variant (rs10757278-G, p-value = 1 x 10^-20), and two studies on stroke with weak associations (p-values between 9 x 10^-6 and 1 x 10^-9) and no overlapping associations between the two studies. The heart disease study also used a gene model with 101 variants, which is much larger than the number of significant associations listed in the GWAS catalog (unless the authors considered diseases not explicitly described in the abstract). Unlike the diabetes study, the authors did not provide a table describing how accurately individual variants could predict the onset of heart disease.

Of course, genetic models could improve when novel variants are discovered and/or better models for complex diseases are developed. I also think genetic models would naturally be more accurate for simple diseases that are associated with mutations in single gene. In the meantime, I think consumers need to currently interpret genetic risk factors for complex diseases with a grain of salt, but I think it is still worth getting excited about using genomic tools to predict novel drug targets and discover genetic sensitivities to drugs that are currently on the market or in clinical trials.