Steve Hsu: prediction

Transcript context

…Okay, so let's talk about your company Genomic Prediction. Do you want to talk about this company and give an intro about what it is? Yeah. So there are two ways to introduce it. One is the scientific view. The other is the IVF view. I can do a little of both. So scientifically, the issue is that we have more and more genomic data. If you give me the genomes of a bunch of people and then give me some information about each person, ex. Do they have diabetes? How tall are they? What's their IQ score? It’s a natural AI machine learning problem to figure out which features in the DNA variation between people are predictive of whatever variable you're trying to predict. This is the ancient scientific question of how you relate the genotype of the organism (the specific DNA pattern), to the phenotype (the expressed characteristics of the organism). If you think about it, this is what biology is! We had the molecular revolution and figured out that it’s people's DNA that stores the information which is passed along. Evolution selects on the basis of the variation in the DNA that’s expressed as phenotype, as that phenotype affects fitness/reproductive success. That's the whole ballgame for biology. As a physicist who's trained in mathematics and computation, I'm lucky that I arrived on the scene at a time when we're going to solve this basic fundamental problem of biology through brute force, AI, and machine learning. So that's how I got into this. Now you ask as an entrepreneur, “Okay, fine Steve, you're doing this in your office with your postdocs and collaborators on your computers. What use is it? ” The most direct application of this is in the following setting: Every year around the world, millions of families go through IVF—typically because they're having some fertility issues, and also mainly because the mother is in her 30s or maybe 40s. In the process of IVF, they use hormone stimulation to produce more eggs. Instead of one per cycle, depending on the age of the woman, they might produce anywhere between five to twenty, or even sixty to a hundred eggs for young women who are hormonally stimulated (egg donors). From there, it’s trivial because men produce sperm all the time. You can fertilize eggs pretty easily in a little dish, and get a bunch of embryos that grow. They start growing once they're fertilized. The problem is that if you're a family and produce more embryos than you’re going to use, you have the embryo choice problem. You have to figure out which embryo to choose out of say, 20 viable embryos. The most direct application of the science that I described is that we can now genotype those embryos from a small biopsy. I can tell you things about the embryos. I could tell you things like your fourth embryo being an outlier. For breast cancer risk, I would think carefully about using number four. Number ten is an outlier for cardiovascular disease risk. You might want to think about not using that one. The other ones are okay. So, that’s what genomic prediction does. We work with 200 or 300 different IVF clinics in six continents. Yeah, so the super fascinating thing about this is that the diseases you talked about—or at least their risk profiles—are polygenic. You can have thousands of SNPs (single nucleotide polymorphisms) determining whether you will get a disease. So, I'm curious to learn how you were able to transition to this space and how your knowledge of mathematics and physics was able to help you figure out how to make sense of all this data.…

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