Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

1. Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes Rui Chen,1,* George I. Mias,1,* Jennifer Li-Pook-Than,1,* Lihua Jiang,1,* Hugo Y. K. Lam,1Rong Chen,2 Elana Miriami,1 Konrad J. Karczewski,1 Manoj Hariharan,1 Frederick E. Dewey,3 Yong Cheng,1 Michael J. Clark,1 Hogune Im,1 Lukas Habegger,4,5 Suganthi Balasubramanian,4,5 Maeve O'Huallachain,1 Joel T. Dudley,2 Sara Hillenmeyer,1 Rajini Haraksingh,1 Donald Sharon,1 Ghia Euskirchen,1 Phil Lacroute,1 Keith Bettinger,1 Alan P. Boyle,1 Maya Kasowski,1 Fabian Grubert,1 Scott Seki,2 Marco Garcia,2 Michelle Whirl-Carrillo,1 Mercedes Gallardo,6,7 Maria A. Blasco,6 Peter L. Greenberg,8 Phyllis Snyder,1 Teri E. Klein,1 Russ B. Altman,1,9 Atul Butte,2 Euan A. Ashley,3 Kari C. Nadeau,2 Mark Gerstein,4,5,10 Hua Tang,1 and Michael Snyder1,§

2. Introduction • Integrative Personal Omics Profiling (iPOP): an analysis that combines genomic, transcriptomic, proteomic, metabolomics, and autoantibody profiles • To date, comprehensive omics profiles have been limited and have not been applied to the analysis of generally healthy individuals

3. Methodology • Performed extensive omics profiling of blood components from a generally healthy individual over a 14 month period • Determined the whole-genome sequence (WGS) of the subject, and together with transcriptomic, proteomic, metabolomics, and autoantibody profiles; used this information to generate an iPOP • Analyzed the iPOP of the individual over the course of healthy states and 2 viral infections (HRV and RSV) • Used PBMC’s (peripheral blood mononuclear cells), plasma, and sera from a 54- year old male volunteer Michael Snyder

5. Whole Genome Sequence (WGS) • 80-100 fold coverage • Able to identify sequences not present in the reference sequence (1,425 contigs) • SNP vs SNV • Identified variants likely to be associated with increased susceptibility to disease • Damaging mutation in TERT- acquired aplastic anemia • Hypertriglyceridemia • Diabetes

8. RiskOGram • Algorithm which integrates information from multiple alleles associated with disease risk • Revealed: Type 2 diabetes, Hypertiglyceridemia, Coronary Artery Disease, Basal Cell Carcinoma

10. Medical Phenotypes Monitoring • Glucose levels originally normal; but elevated after RSV infection, extending several months • High levels of glucose confirmed using HbA1 measurements • TERT mutation (aplastic anemia) – little/no decrease in telomere length- context specific • Hypertriglyceridemia- triglyceride levels were high (reduced with simvastatin) • Demonstrate that genome sequence can be used to estimate disease risk- monitoring traits with disease

13. Dynamic Omics Analysis • Transcriptome, proteome, metabolome • Searched for two types of patterns: • 1) Correlated patterns over time • 2) Single Unusal Events • Used Fourier spectral analysis- normalize various omics data for identifying common trends and features, and also accounts for data set variability, uneven sampling, and data gaps • Detects real-times changes in any kind of omics activity at differential time points • Total of 19,714 distinct transcript isoforms corresponding to 12,659 genes were tracked for that entire time course, and their expression response; autocorrelated and subdivided as displaying maxima or minima

14. Heteroallelic Variation • Allele specific expression (ASE) • Found 497 and 1047 genes that exhibited differential ASE during HRV and RSV infection, respectively • Concluded that differential ASE is pervasive in humans- particular distinct during healthy and infected states

17. Conclusion • First, study to perform extensive personal iPOP of an individual through healthy and diseased states • Revealed extensive complex and dynamics change in omics profiles • iPOP provides multidimensional view of medical states, including healthy states, response to viral infection, recovery and T2D onset • Disease risk can be assessed from a genome sequence and illustrates how traits associated with disease can be monitored to identify varying physiological states • Showed that large numbers of molecular components are present in blood samples and can be measured (>3 billion measurements taken over 20 time points)

18. Cons • Storage for large data sets • Genome sequence still relatively expensive • Support • Particular with high disease risk, individual can makes more informed/better decisions on the medical course of action • Integrated personal omics profiling is faciliting the discovery of novel genes and pathways and their interplay with the environment, allowing for improved health monitoring and targeted therapeutics, leading to better personalized health care

19. “In the future, we may not need to follow 40,000 variables,” said Snyder. “It’s possible that only a subset of them will be truly predictive of future health. But studies like these are important to know which are important and which don’t add much to our understanding.” • “Right now, this type of analysis is very expensive. But we have to expect that, like whole-genome sequencing, it will get much cheaper. And we also have to consider the savings to society from preventing disease.”