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.
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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:
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.