From genomic medicine to precision medicine: highlights of 2015

Emerging trends and research priorities in premature ovarian insufficiency genes: a bibliometric and visualization study

PURPOSE

To illustrate the results achieved by genes in premature ovarian insufficiency (POI) and collaborations in the field, and to explore key themes and future directions.

METHODS

Articles and reviews related to POI genes published between 1990 and 2022 were retrieved from the Web of Science core collection (WoSCC) for the total bibliometric analysis. Tools were analyzed for publication, country, institution, journal, authors, reference, keywords, subject categories, funding agencies, and research hotspots using a bibliometric online analysis platform, Bibliographic Co-occurrence Matrix Builder (BICOMB), CiteSpace V, and VOSviewer.

RESULTS

A total of 2,232 papers were included in this study. Articles were published in 52 countries, with the United States publishing the most, followed by China. A total of 308 institutions contributed to relevant publications. Shandong University published the most papers. Qin Y's team published the most relevant papers. Human reproduction and fertility and sterility are the two journals with the most papers. X-chromosome abnormalities, transcription factor mutations, and FMR1 genes are the directions of more POI, and DNA repair is the keyword of the research frontier in recent years.

CONCLUSIONS

This study summarizes the relevant literature on POI gene research for the first time and analyzes the current hotspots and future trends in this field. The findings can further reveal the etiology, diagnosis, and treatment of POI, which is beneficial for researchers to grasp the genetic dynamics of POI women.

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Genomic Medicine in Periodontal Disease: Old Issue, New Insights

Genetic variability is the main cause of phenotypic variation. Some variants may be associated with several diseases and can be used as risk biomarkers, identifying animals with higher susceptibility to develop the pathology. Genomic medicine uses this genetic information for risk calculation, clinical diagnosis and prognosis, allowing the implementation of more effective preventive strategies and/or personalized therapies. Periodontal disease (PD) is the inflammation of the periodontium induced mainly by bacterial plaque and is the leading cause of tooth loss. Microbial factors are responsible for the PD initiation; however, several studies support the genetic influence on the PD progression. The main purpose of the present publication is to highlight the main steps involved in the genomic medicine applied to veterinary patients, describing the flowchart from the characterization of the genetic variants to the identification of potential associations with specific clinical data. After investigating which genes might potentially be implicated in canine PD, the RANK gene, involved in the regulation of osteoclastogenesis, was selected to illustrate this approach. A case-control study was performed using DNA samples from a population of 90 dogs – 50 being healthy and 40 with PD. This analysis allowed for the discovery of four new intronic variations that were banked in GenBank (g.85A>G, g.151G>T, g.268A>G and g.492T>C). The results of this study are not intended to be applied exclusively to PD. On the contrary, this genetic information is intended to be used by other researchers as a foundation for the development of multiple applications in the veterinary clinical field.

COVIDSeq as Laboratory Developed Test (LDT) for Diagnosis of SARS-CoV-2 Variants of Concern (VOC)

Rapid classification and detection of SARS-CoV-2 variants have been critical in comprehending the virus's transmission dynamics. Clinical manifestation of the infection is influenced by comorbidities such as age, immune status, diabetes, and the infecting variant. Thus, clinical management may differ for new variants. For example, some monoclonal antibody treatments are variant-specific. Yet, a U.S. Food and Drug Administration (FDA)-approved test for detecting the SARS-CoV-2 variant is unavailable. A laboratory-developed test (LDT) remains a viable option for reporting the infecting variant for clinical intervention or epidemiological purposes. Accordingly, we have validated the Illumina COVIDSeq assay as an LDT according to the guidelines prescribed by the College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA). The limit of detection (LOD) of this test is Ct<30 (~15 viral copies) and >200X genomic coverage, and the test is 100% specific in the detection of existing variants. The test demonstrated 100% precision in inter-day, intra-day, and intra-laboratory reproducibility studies. It is also 100% accurate, defined by reference strain testing and split sample testing with other CLIA laboratories. Advanta Genetics LDT COVIDSeq has been reviewed by CAP inspectors and is under review by FDA for Emergency Use Authorization.

A Focus Group Study of Perceptions of Genetic Risk Disclosure in Members of the Public in Sweden: "I'll Phone the Five Closest Ones, but What Happens to the Other Ten?"

This study explores perceptions and preferences on receiving genetic risk information about hereditary cancer risk in members of the Swedish public. We conducted qualitative content analysis of five focus group discussions with participants (n = 18) aged between 24 and 71 years, recruited from various social contexts. Two prominent phenomena surfaced around the interplay between the three stakeholders involved in risk disclosure: the individual, healthcare, and the relative at risk. First, there is a genuine will to share risk information that can benefit others, even if this is difficult and causes discomfort. Second, when the duty to inform becomes overwhelming, compromises are made, such as limiting one’s own responsibility of disclosure or projecting the main responsibility onto another party. In conclusion, our results reveal a discrepancy between public expectations and the actual services offered by clinical genetics. These expectations paired with desire for a more personalized process and shared decision-making highlight a missing link in today’s risk communication and suggest a need for developed clinical routines with stronger healthcare–patient collaboration. Future research needs to investigate the views of genetic professionals on how to address these expectations to co-create a transparent risk disclosure process which can realize the full potential of personalized prevention.

Exploring the Role of Community Health Workers in Improving the Collection of Family Health History: A Pilot Study

Community health workers (CHWs) have been successful partners in addressing public health and health care challenges but have yet to be engaged in efforts to promote family health history (FHH) collection. FHH information is a key factor in determining disease risk and supporting screening and prevention across multiple diseases. The collection of FHH information could be facilitated by the existing cadre of CHWs already working alongside clients and families. In this qualitative study, we interviewed 30 CHWs from Georgia to better understand the current level of knowledge about FHH, perceptions of how FHH collection aligns with their role, and barriers and facilitators in order to support more active involvement of CHWs in FHH collection. Interviews were completed, transcribed, and double coded by three study team members. More than half of CHWs reported knowing their own FHH information. CHWs showed a strong interest and support for collecting FHH in their job, despite limited current engagement in this role. CHWs acknowledged the collection of FHH as being an opportunity to empower clients to have conversations with their providers. To better support this work, CHWs requested training in using and integrating FHH tools into their workflow and support in communicating about FHH with their clients. Our findings suggest that with support and training, CHWs are uniquely positioned to improve FHH collection among their client base. Ultimately, improving FHH collection skills among the population could allow for better integration of risk-stratified approaches that are informed by FHH information for the prevention, management, and treatment of disease.

Obesity cardiomyopathy: evidence, mechanisms, and therapeutic implications

The prevalence of heart failure is on the rise and imposes a major health threat, in part, due to the rapidly increased prevalence of overweight and obesity. To this point, epidemiological, clinical, and experimental evidence supports the existence of a unique disease entity termed “obesity cardiomyopathy,” which develops independent of hypertension, coronary heart disease, and other heart diseases. Our contemporary review evaluates the evidence for this pathological condition, examines putative responsible mechanisms, and discusses therapeutic options for this disorder. Clinical findings have consolidated the presence of left ventricular dysfunction in obesity. Experimental investigations have uncovered pathophysiological changes in myocardial structure and function in genetically predisposed and diet-induced obesity. Indeed, contemporary evidence consolidates a wide array of cellular and molecular mechanisms underlying the etiology of obesity cardiomyopathy including adipose tissue dysfunction, systemic inflammation, metabolic disturbances (insulin resistance, abnormal glucose transport, spillover of free fatty acids, lipotoxicity, and amino acid derangement), altered intracellular especially mitochondrial Ca²⁺ homeostasis, oxidative stress, autophagy/mitophagy defect, myocardial fibrosis, dampened coronary flow reserve, coronary microvascular disease (microangiopathy), and endothelial impairment. Given the important role of obesity in the increased risk of heart failure, especially that with preserved systolic function and the recent rises in COVID-19-associated cardiovascular mortality, this review should provide compelling evidence for the presence of obesity cardiomyopathy, independent of various comorbid conditions, underlying mechanisms, and offer new insights into potential therapeutic approaches (pharmacological and lifestyle modification) for the clinical management of obesity cardiomyopathy.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Liquid Biopsy Enables Quantification of the Abundance and Interindividual Variability of Hepatic Enzymes and Transporters

Variability in individual capacity for hepatic elimination of therapeutic drugs is well recognized and is associated with variable expression and activity of liver enzymes and transporters. Although genotyping offers some degree of stratification, there is often large variability within the same genotype. Direct measurement of protein expression is impractical due to limited access to tissue biopsies. Hence, determination of variability in hepatic drug metabolism and disposition using liquid biopsy (blood samples) is an attractive proposition during drug development and in clinical practice. This study used a multi-"omic" strategy to establish a liquid biopsy technology intended to assess hepatic capacity for metabolism and disposition in individual patients. Plasma exosomal analysis (n = 29) revealed expression of 533 pharmacologically relevant genes at the RNA level, with 147 genes showing evidence of expression at the protein level in matching liver tissue. Correction of exosomal RNA expression using a novel shedding factor improved correlation against liver protein expression for 97 liver-enriched genes. Strong correlation was demonstrated for 12 key drug-metabolizing enzymes and 4 drug transporters. The developed test allowed reliable patient stratification, and in silico trials demonstrated utility in adjusting drug dose to achieve similar drug exposure between patients with variable hepatic elimination. Accordingly, this approach can be applied in characterization of volunteers prior to enrollment in clinical trials and for patient stratification in clinical practice to achieve more precise individual dosing.

Precision health: A pragmatic approach to understanding and addressing key factors in autoimmune diseases

The past decade has witnessed a significant paradigm shift in the clinical approach to autoimmune diseases, lead primarily by initiatives in precision medicine, precision health and precision public health initiatives. An understanding and pragmatic implementation of these approaches require an understanding of the drivers, gaps and limitations of precision medicine. Gaining the trust of the public and patients is paramount but understanding that technologies such as artificial intelligences and machine learning still require context that can only be provided by human input or what is called augmented machine learning. The role of genomics, the microbiome and proteomics, such as autoantibody testing, requires continuing refinement through research and pragmatic approaches to their use in applied precision medicine.

Measuring the chronology of the translational process of molecular genetic discoveries

Background

The process of technology validation and transfer of new molecular diagnostic tests towards the clinic faces challenges and needs to be improved. There is no empirical measure of the chronology and pace of technology transfer of molecular genetic discoveries.

Methods

We studied these for 29 molecular genetic test discoveries in order to (1) provide estimates of the timeframe between discovery of a clinical application and complete clinical implementation, and (2) compare the trajectories between different new tests to identify common patterns. We identified 11 publicly available “timestamps” for the technology transfer process ranging from discovery of the marker to use in a clinical setting. For each test selected, we searched public databases to identify available timestamps and dates. We plotted and compared trajectories of individual tests, including chronology.

Results

We show that there is much variability in the chronology of transfer between biomarkers. The median time between discovery of the marker and availability of the clinical test was 9.5 years (minimum 1). There was a median time of 18 years between test discovery and FDA approval (minimum 7 years), and it took a median of 17 years between discovery and the availability of a certified reference material for the 10 assays that have one (minimum 9 years).

Conclusions

We conclude that new molecular genetic tests take significant time between discovery and clinical implementation, and that further work is needed to pinpoint key factors, including policy and organization factors, that may allow for improving and streamlining this process.

The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine

Background:

Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.

Objective:

This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.

Conclusion:

Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

Comparison of three variant callers for human whole genome sequencing

Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample. According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively. We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

What People Want to Know About Their Genes: A Critical Review of the Literature on Large-Scale Genome Sequencing Studies

From a public health perspective, the “All of Us” study provides an opportunity to isolate targeted and cost-effective prevention and early-detection strategies. Identifying motivations for participation in large-scale genomic sequencing (LSGS) studies, and motivations and preferences to receive results will help determine effective strategies for “All of Us” study implementation. This paper offers a critical review of the literature regarding LSGS for adult onset hereditary conditions where results could indicate an increased risk to develop disease. The purpose of this review is to synthesize studies which explored peoples’ motivations for participating in LSGS studies, and their desire to receive different types of genetic results. Participants were primarily motivated by altruism, desire to know more about their health, and curiosity. When asked about hypothetically receiving results, most participants in hypothetical studies wanted all results except those which were uncertain (i.e., a variant of uncertain significance (VUS)). However, participants in studies where results were returned preferred to receive only results for which an intervention was available, but also wanted VUS. Concerns about peoples’ understanding of results and possible psychosocial implications are noted. Most studies examined populations classified as “early adopters,” therefore, additional research on motivations and expectations among the general public, minority, and underserved populations is needed.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests

Motivation

Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power.

Results

We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data.

Availability and Implementation

The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html.

Contact

maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Can learning health systems help organisations deliver personalised care?

There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions.

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

Public health microbiology laboratories (PHLs) are on the cusp of unprecedented improvements in pathogen identification, antibiotic resistance detection, and outbreak investigation by using whole-genome sequencing (WGS). However, considerable challenges remain due to the lack of common standards. Here, we describe the validation of WGS on the Illumina platform for routine use in PHLs according to Clinical Laboratory Improvements Act (CLIA) guidelines for laboratory-developed tests (LDTs). We developed a validation panel comprising 10 Enterobacteriaceae isolates, 5 Gram-positive cocci, 5 Gram-negative nonfermenting species, 9 Mycobacterium tuberculosis isolates, and 5 miscellaneous bacteria. The genome coverage range was 15.71× to 216.4× (average, 79.72×; median, 71.55×); the limit of detection (LOD) for single nucleotide polymorphisms (SNPs) was 60×. The accuracy, reproducibility, and repeatability of base calling were >99.9%. The accuracy of phylogenetic analysis was 100%. The specificity and sensitivity inferred from multilocus sequence typing (MLST) and genome-wide SNP-based phylogenetic assays were 100%. The following objectives were accomplished: (i) the establishment of the performance specifications for WGS applications in PHLs according to CLIA guidelines, (ii) the development of quality assurance and quality control measures, (iii) the development of a reporting format for end users with or without WGS expertise, (iv) the availability of a validation set of microorganisms, and (v) the creation of a modular template for the validation of WGS processes in PHLs. The validation panel, sequencing analytics, and raw sequences could facilitate multilaboratory comparisons of WGS data. Additionally, the WGS performance specifications and modular template are adaptable for the validation of other platforms and reagent kits.

Global Proteomic Analysis of Human Liver Microsomes: Rapid Characterization and Quantification of Hepatic Drug-Metabolizing Enzymes

Many genetic and environmental factors lead to interindividual variations in the metabolism and transport of drugs, profoundly affecting efficacy and toxicity. Precision dosing, that is, targeting drug dose to a well characterized subpopulation, is dependent on quantitative models of the profiles of drug-metabolizing enzymes (DMEs) and transporters within that subpopulation, informed by quantitative proteomics. We report the first use of ion mobility-mass spectrometry for this purpose, allowing rapid, robust, label-free quantification of human liver microsomal (HLM) proteins from distinct individuals. Approximately 1000 proteins were identified and quantified in four samples, including an average of 70 DMEs. Technical and biological variabilities were distinguishable, with technical variability accounting for about 10% of total variability. The biological variation between patients was clearly identified, with samples showing a range of expression profiles for cytochrome P450 and uridine 5'-diphosphoglucuronosyltransferase enzymes. Our results showed excellent agreement with previous data from targeted methods. The label-free method, however, allowed a fuller characterization of the in vitro system, showing, for the first time, that HLMs are significantly heterogeneous. Further, the traditional units of measurement of DMEs (pmol mg^-1 HLM protein) are shown to introduce error arising from variability in unrelated, highly abundant proteins. Simulations of this variability suggest that up to 1.7-fold variation in apparent CYP3A4 abundance is artifactual, as are background positive correlations of up to 0.2 (Spearman correlation coefficient) between the abundances of DMEs. We suggest that protein concentrations used in pharmacokinetic predictions and scaling to in vivo clinical situations (physiologically based pharmacokinetics and in vitro-in vivo extrapolation) should be referenced instead to tissue mass.

Precision medicine in cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.