Outlier selection via noisy genomic predictors

Written by: Stephen Hsu

Primary Post:  Information Processing 

We recently used machine learning techniques to build polygenic predictors for a number of complex traits. One of these traits is bone density, for which the predictor correlates r ≈ 0.45 with actual bone density. This is far from perfect, but good enough to identify outliers, as illustrated above.

The figures above show the actual bone density distribution of individuals who are in the top or bottom 5 percent for predictor score. You can see that people with low/high scores are overwhelmingly likely to be below/above average on the phenotype, with a good chance of being in the extreme left/right tail of the distribution.

If, for example, very low bone density elevates likelihood of osteoporosis or fragile bones, then individuals with low polygenic score would have increased risk for those medical conditions and should receive extra care and additional monitoring as they age.

Similarly, if one had a cognitive ability predictor with r ≈ 0.45, the polygenic score would allow the identification of individuals likely to be well below or above average in ability.

I predict this will be the case relatively soon. Much sooner than most people think ;-)

Here is a recent talk I gave at MSU: Genomic Prediction of Complex Traits

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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.