Genomic Prediction: A Hypothetical (Embryo Selection), Part 2

Written by: Stephen Hsu

Primary Source: Information Processing, 8/19/18.

The figures below are from the recent paper Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations (Nature Genetics), discussed previously here.

As you can see, genomic prediction of risk allows to identify outliers for conditions like heart disease and diabetes. Individuals who are top 1% in polygenic risk score are many times (approaching an order of magnitude) more likely to exhibit the condition than the typical person.

In an earlier post, Genomic Prediction: A Hypothetical (Embryo Selection), I pointed out a similar situation with regard to the SSGAC predictor for Educational Attainment. Negative outliers on that polygenic score (e.g., bottom 1%) are much more likely to have difficulty in school. I then posed this hypothetical:

You are an IVF physician advising parents who have exactly 2 viable embryos, ready for implantation.

The parents want to implant only one embryo.

All genetic and morphological information about the embryos suggest that they are both viable, healthy, and free of elevated disease risk.

However, embryo A has polygenic score (as in figure above) in the lowest quintile (elevated risk of struggling in school) while embryo B has polygenic score in the highest quintile (less than average risk of struggling in school). We could sharpen the question by assuming, e.g., that embryo A has score in the bottom 1% while embryo B is in the top 1%.

You have no other statistical or medical information to differentiate between the two embryos.

What do you tell the parents? Do you inform them about the polygenic score difference between the embryos?

We can pose the analogous hypothetical for the risk scores displayed below. Should the parents be informed if, for instance, one of the embryos is in the top 1% risk for heart disease or Type 2 Diabetes? Is there a difference between the case of the EA predictor and disease risk predictors?

In the case of monogenic (Mendelian) genetic risk, e.g., Tay-Sachs, Cystic Fibrosis, BRCA, etc., deliberate genetic screening is increasingly common, even if penetrance is imperfect (i.e., the probability of the condition given the presence of the risk variant is less than 100%).

Note, the risk ratio between top 1% and bottom 1% individuals is potentially very large (see below), although more careful analysis is probably required to understand this better.

These hypotheticals will not be hypothetical for very much longer: the future is here.

Tables showing risk for CAD according to GPS.

(CAD = coronary artery disease.)

Table showing the genetic risk for atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer.

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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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