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Objective:  Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of &#x2013;omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.  Methods:  In this paper, we present &#x2013;omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.  Results:  To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating &#x2013;omic information into EHR.  Conclusion:   Big data analytics is able to address &#x2013;omic and EHR data challenges for paradigm shift toward precision medicine.  Significance:  Big data analytics makes sense of &#x2013;omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

–Omic and Electronic Health Record Big Data Analytics for Precision Medicine