The Future of Genomic Precision Medicine

Written by: Stephen Hsu

Primary Source: Information Processing, 01/30/2019.

As I mentioned in this earlier post, I’ll be in the UK next week for a Ditchley Foundation conference on the intersection of machine learning and genetic engineering.

I’ll present these slides at the meeting.

The slides review the rapidly evolving situation in genomic prediction, focusing on disease risk predicted using inexpensive genotyping. There are now 10-20 disease conditions for which we can identify, e.g., the top 1% outliers with 5-10x normal risk for the disease. The papers reporting these results have almost all appeared within the last year or so!

On the last slide I give a simple cost-benefit analysis of population wide genotyping and conclude that the net benefit is already positive given the tools we have. The numbers used are per capita. The UK NHS is already headed in this direction.

I use breast cancer as the example on the slide, but since the same genotype can be used for 10+ disease risks (including diabetes, atrial fibrillation, hypothyroidism, etc.) the net benefit is potentially much larger than what is obtained from breast cancer alone. The point is that G is really small compared to the potential benefit.

Cost benefit analysis of population wide genotyping

Details of breast cancer calculation below. I am sure one can do much better, but it provides a quick back of the envelope estimate of the numbers.

Spend $100 per person to genotype all women in the population. Identify those with top 10% risk score. About 33% of these individuals will get breast cancer. Treat the risk outliers by giving them, e.g., regular mammograms starting a decade earlier than usual (~$100 annual mammogram x 10y = $1k). In the slide I assume the average cost of the intervention / treatment is $1k and the average benefit is $12k. All of the high risk women (10%) get the intervention, but only the 33% percent that get breast cancer (or some subset of that group) benefit from early detection. This paper estimates that early detection of breast cancer saves typically tens of thousands of dollars per individual, so my numbers are conservative. If one uses multiple tens of thousands as the benefit amount, one could spend much more on early treatment and still have a positive net benefit.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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