We are not at Gattaca levels of DNA predictions, not merely because of lack of tech development, but because the DNA doesn't actually tell us that. Given the weak clues that DNA actually gives about the individual, Gattaca is a cautionary tale about what happens when people believe too much in bad predictions, and enshrine bad models as a metric of social worth. Any society could fall prey to this, but today it seems more likely the bad models will be based on predictive modellijg from monitoring social media than from DNA.
Federal research programs also bit too hard on the promises as well, and invested heavily in genome-wide-association-studies (GWAS) to very little effect. I chose my HN username as these were getting deployed, much out of frustration with that direction of research. The clues, even early on into a massive spend, we're that GWAS wouldn't be terribly productive.
I think the biggest risk of being oversold right now are the "polygenic risk scores" being used for various quantitative traits, which fail to generalize very well. As more advanced machine learning techniques are used, we are likely to get even worse models. The curse of dimensionality makes the problem much more difficult than with traditional machine learning fields. Each case with a single label has millions of features, but we only get a very small number of cases relative to the number of features. And we are unlikely to be able to transform our feature space in ways that have worked well for, say, images by using spatial data. My personal hypothesis, by no means unique to me, is that we need to use the network of gene interactions as the feature transformation, but we don't yet have that network explored fully.
How do the hits from large scale GWAS fail to generalize within a population? Why would you get worse models with more advanced techniques, presumably the more advanced techniques would only be used instead of the simple ones if they are proven to work.
Great question! I haven't revisited the question in quite some time, but one possible explanation is sometimes called the "winner's curse." There's a "measurement error" for each site in that genetics is never deterministic, and when you have millions of sites, and only thousands of cases split into yes/no categories. If a particular site explains only 10% of the "yes" cases, it could be that random sampling makes it look like 15% or 7% or whatever. And when you have a handful of sites, each with measurement error of this sort, when you sort the handful of sites, the genomic sites with the biggest effect are likely to come from random chance.
More advanced models have a greater ability to find spurious signals, to overfit the data, and to mispredict. Training with more parameters, correctly, often requires more data, and the biggest limitation to GWAS is the amount of data. For some rare conditions, there may not be bough humans alive to provide the data fully needed, if we are going only by GWAS. Because GWAS doesn't have any model for how cells work, and the naturally occurring variation in the human population is unlikely to provide enough data to reconstruct the relationships that are necessary to rebuild the parts of that physical model in order to make predictions from DNA alone.
There are also potentially different prevalence of causal variants in different populations, depending on the interactions between different genes. In the San Francisco East Bay, there's a mixing of many many previously geographically different human populations. Rare diseases that are most prevalent in one population are starting to present differently when there's admixture with more distantly related humans. The whole combination of minor differences can cause changes to what we think was a well-defined disease.
You mean this functional medicine? Or are you using the term in a different way?
> Functional medicine is a form of alternative medicine that encompasses a number of unproven and disproven methods and treatments.
> It has been described as pseudoscience,[5] quackery,[6] and at its essence a rebranding of complementary and alternative medicine.
Descriptions like the above which seek to attack and discredit any form of alternative medicine will always exist because mainstream medical practice first seeks to enrich corporations and only secondarily tries to help patients. (Not on an individual level, but structurally.)
I'd invite you to check out the YouTube channels or any of the books written by Dr. Mark Hyman or Dr. Gundry; the content they put out is incredible and has had a huge positive impact on my life.
The two people you mentioned seem to be regarded negatively by the scientific community.
Dr. Mark Hyman
> He was the editor-in-chief of, and is a contributing editor to, Alternative Therapies in Health and Medicine.
Looking at that journal it's definitely filled with crank nonsense.
And Dr. Gundry I assume is Steven Gundry, who is best known for false claims about lectins.
This doesn't constitute strong evidence and isn't how science works.
Note: I have 25+ years experience in this field, I'm not just spewing out random beliefs.
I have also got a full 30x sequencing from a bargain offer, but i haven't analyzed those. Never got to it, apart from downloading some app to browse them. Which stresses my point why the analysis that these services could provide is useful.