Amazon, Google race to get human DNA into the cloud

A lot of the low-hanging fruit, in terms of statistical significance for heritable disease, has already been picked, in the sense of a naive search for SNP X in gene Y that causes disease Z. The more people continue to look for single SNPs or even single gene causal factors, the more we learn that they are of limited use, due to the complexity and redundancy of biological interaction networks (genes/proteins/epigenetic modifications/environment/etc)

One thing that people are definitely still interested in regarding single genes or SNPS is looking for biomarkers. For instance, it's pretty easy to look at RNA abundance with RNA-seq and make a model with good discriminatory power to predict who does or doesn't have (say) breast cancer. But Those tests can be expensive, and profiling the appropriate tissue can be difficult. On the other hand, if we could find SNPs that correlate well with the RNA expression model that predicts disease, then we could just do a cheaper test/faster/easier test for the SNPs. Even better if we can validate causation independently using the emerging, though incomplete corpus of biological pathways.

Beyond that, some folks are excited about the field of functional genomics, which aims to correlate genome-level data to the structure and function of gene products (eg proteins) to get a more low-level, causal look at things.

Biological network extraction is something I'm excited about. Here, you usually want to extract pairs of features, or better yet, higher order structures from data to learn about what factors are important in disease pathways. This puts you immediately in the regime of way more potential features than data points, even if you sequence everyone in the world. I think this consideration worth thinking about when people try to hype you that big data and machine learning will just "solve" biology or medicine. L1 regularization only gets you so far. But it gets you somewhere, and can be hugely useful in suggesting new experiments.

I also think it's important to think about how it may soon be cheaper to store actual RNA/DNA and sequence it on demand, than it will be to store the data itself. DNA sequences would be a fine thing to sequence once and store, but for stuff, maybe not so much.

Illumina is building a cloud product of its own and we still need sequencers. So, Illumina still looks like a good investment.

Can insurance companies in the U.S. still reject or charge more for new customers based on the patients' prior conditions?

That was one of the major reasons to keep patient genomic data offline.

Edit: Nevermind, looks like it's not a problem anymore (http://www.genome.gov/24519851)

There is way way more hype than reality on MTHFR . Placebo remains the most effective drug we have.

I wonder if they'll try to reassure people that they've "anonymized" your DNA.

I wouldn't even consider that my doctors' business. It's your business, full stop. Your doctor is a contractor whose job it is to help you interpret the results, same as a medical device.

Your genetic information can not be used to discriminate against you.

"The President has signed into law the Genetic Information Nondiscrimination Act (GINA) that will protect Americans against discrimination based on their genetic information when it comes to health insurance and employment."

http://www.genome.gov/24519851

Exciting but mundane. A lot has been invested but there have been very few useful results. It turns out the genome offers very little more than you already know from living in your body every day.

You should check out Gattaca [http://www.imdb.com/title/tt0119177/]. It's a great movie that explores some of the negatives that crop up from a society that genetically sequences all citizens from birth.