I'm a PhD student in genomics (read: argument to authority). Regulatory issues are definitely important and largely an impediment that should be removed, imo. That said, I think the larger issue is capturing and integrating good phenotypic and disease state data into datasets. Although there are large genomics data sets available, generally they have pretty sparse and poorly annotated phenotypic data. This is actually tied to other regulatory issues related to medicine. If you think this is important, please do consider getting involved in the area.


by [anonymous] 1 min read9th Aug 20158 comments


Since risk from individual SNP's 'should' not be aggregated to indicate an individual's risk based on multiple sources of evidence, how are the magnitudes for genosets determined?. Can bayes or another method be used to interpret a promethease report?

Even genetic epidemiology textbooks seem pessimistic: about the usefulness of the genetic research underpinning precision medicine:

‘...for the repeated failure to replicate positive findings in genetic epidemiology (102; 103) and remains the subejct of an important ongoing debate (101-105)’ -pg. 26 on chapter 1. An Introduction to Genetic Epidemiology

The references in question are about the impact of population stratification on genetic association studies. That doesn’t seem to substantiate such a broad stroke about the non-replicability of genetic epidemiology. I don't know what to make of these findings.

Here is a link to a screenshot of those references

It suprises me that entrepreneurial machine learning analysts don’t beg for genetic research to identify how combinatorial patterns of genes to be able to characterise individual risk. It seems like if/once they can get hold of that information, the sequence from genetic science to consumer actionable health information is bridged. So where are the 'lean gene learning machine' startups? I certainly don’t have the lean gene to do it myself. I don’t know machine learning.

Regulatory issues seems like the biggest hurdle. To the best of my google-fu, 23andme doesn't even disclose what it's 'Established Research' genes are. So, once regulatory hurdles are surmounted, lots of useful research will flood out.