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by marchenko
3268 days ago
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There are very few genetic risk score models that outperform traditional observational disease markers (here[1] is a non-paywalled discussion of cardiovascular GRS performance as an example). The best GRS results tend to be in relatively genetically-homogeneous populations that are similar to the population in which the GRS model was developed. In some cases, knowing the ethnicity or simple family history of a patient can buy you a good portion of the AUC of the relevant GRS. So if you have a classifier like ZIP, that (1) epidemiologists have done a bit of legwork correlating to classical markers like obesity (or their correlates, such as income and dietary/smoking/prescription patterns) and (2) tends to follow familial/ethnicity clusters in (3) a heterogeneous population, you can amass a fair bit of predictive power on the cheap for complex disorders where environmental variance plays a role, as well as beating the spread on behaviourally-determined mortality/morbidity factors. It is likely that the predictive power of GRS-based approaches will improve for many conditions in the future (they are of course already powerful for Mendelian disorders). [1]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527979/ |
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I do have some doubts that the zip code actually gives BETTER prediction than the genetic risk score. I have difficulty believing that if someone had done a genetic profile on a patient and was willing to tell me either the patient's zip code OR the genetic risk score, that I would be better off asking for the zip code because it had greater predictive value. It' certainly not impossible (because of environmental factors that correlate with zip code), but it is surprising and I haven't yet seen actual research supporting it.