LAUSR

The convergence of scientific capability and technology that generates vast health data at diminishing cost has generated opportunities, challenges, and anticipation surrounding future data-centric healthcare models. Individualized health data spanning biomolecular, physiological, and environmental dimensions comprise a personal omics profile. Here, we discuss methods and opportunities to bridge genome and dynamic physiology, detect disease at an early stage, and uncover lifestyle and environmental patterns associated with the disease. Significant challenges exist to aggregate, integrate, and protect personal omics data to advance our understanding of the disease, enable data-driven clinical decisions, and motivate individuals to sustain behavioral change. Since the first sequencing of the human genome in 2003, the relationship between genetic variants and phenotypes has remained a central challenge in medicine. Many diseases including coronary atherosclerosis are polygenic or indeed omnigenic wherein many variants work together to impact a phenotype.1 Potentially confounding factors and small study population size in comparison to the size of the human genome make it challenging to decipher genetic risk for complex and heterogeneous diseases. To better understand how genetic variation maps to complex traits, simultaneous measurements that bridge genotype and phenotype are required. This deep phenotyping is the goal of personal omics profiling,2 which combines measures of the genome, epigenome, transcriptome, proteome, metabolome, and additional omes (Figure [A]). Rapid advances in sequencing and mass spectrometry drive continued improvement in cost, accuracy, and throughput.3,4 Mobile and wearable technologies enable physiological, contextual, and environmental measurements. As we learn more about the symbiotic functions of the microbiome in human health, we also apply multiomic profiling to microbial populations (Figure [B]). Together these measurements provide a holistic profile of dynamic health and facilitate personalized, precision interventions based on predictive models (Figure [E]). Figure. Overview of personal omics. A , Omic measures span …