Genomic Prediction of Complex Disease Risk (bioRxiv)

Written by: Stephen Hsu

Primary Source: Information Processing, 12/27/2018

Graph comparing risk prediction versus theoretical prediction of atrial fibrilation

Our new paper describes over a dozen genomic predictors for common disease risk, constructed via machine learning on hundreds of thousands of genotypes. The predictors use anywhere from a few tens (e.g., 20 or 50) to thousands of SNPs to compute the risk PGS (Poly-Genic Score) for a specific disease.

The figure above (Atrial Fibrillation) shows out-of-sample testing of risk prediction (black dots with error bars) compared to theoretical prediction (red line). The theoretical prediction uses the empirical fact that cases and controls are normally-distributed in PGS score, with the two distributions shifted relative to each other. Cases have, on average, higher risk scores, and come to dominate in high PGS percentile bins. So, conditional on a high PGS risk score (e.g., 99th percentile PGS), the probability of the condition can be significantly elevated (e.g., ~8 times typical probability of developing atrial fibrillation).

We can identify, from SNP genotype alone, a subset of the population with unusual risk for conditions like Atrial Fibrillation or Diabetes or Breast Cancer or Prostate Cancer.

Just a year or two ago this would have seemed like science fiction to biomedical researchers…

Empirical validation of risk is limited by availability of out-of-sample populations for whom we have genotype and disease status. However, it is clear from the results that the theoretical models do a good job of predicting odds ratios once the properties of the case and control normal distributions (mean and standard deviation of PGS) are known.

These predictors only require data from an inexpensive ~$50 SNP array. Once the ~1 million SNPs on the array are measured *all* of the disease risks can be computed for an individual patient. It is only a matter of time before genotyping of this kind becomes Standard of Care in health systems around the world.

In the paper we also analyze the rate of improvement of prediction AUC as training sample size increases. With more data these predictors will become significantly more accurate — the relevant timescale is just a few years!

Genomic Prediction of Complex Disease Risk

Louis Lello, Timothy Raben, Soke Yuen Yong, Laurent CAM Tellier, Stephen D. H. Hsu

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistive) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range  0.58 – 0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

https://www.biorxiv.org/content/early/2018/12/27/506600

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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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