Variance quantitative trait loci reveal gene-gene interactions which alter blood traits.

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ~450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ~140,000 NIH All of Us and ~70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Clonal hematopoiesis of indeterminate potential-associated non-small cell lung cancer risk is potentiated by small particulate matter air pollution among non-smokers: a novel somatic variant-environment interaction.

Small particulate matter air pollution (PM 2.5 ) is a recognized driver of non-small cell lung cancer (NSCLC) among non-smoking individuals. Inhaled PM 2.5 recruits pro-inflammatory macrophages to the air-lung interface, which promotes malignant lung epithelial cell growth and progression to overt cancer. We sought to determine whether clonal hematopoiesis of indeterminate potential (CHIP), a common age-related condition characterized by hyperinflammatory macrophages, exacerbates PM 2.5 -associated NSCLC in non-smokers using genetic, environmental, and phenotypic data from 413,901 individuals in the UK Biobank. Among non-smokers, PM 2.5 is not associated with NSCLC and not associated with prevalence of CHIP, but CHIP is associated with a doubling of NSCLC risk (hazard ratio (HR) 2.01, 95% confidence interval (CI): 1.34-3.00). Moreover, CHIP-associated NSCLC risk is exacerbated in the setting of above-median PM 2.5 levels (HR 2.70, 95% CI: 1.60-4.55). PM 2.5 × CHIP is also associated with significantly greater markers of systemic inflammation (CRP, IL-6, and IL-1ß) than expected. Altogether, these results suggest CHIP and PM 2.5 form a novel gene × environment interaction promoting NSCLC tumorigenesis in non-smokers.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R 2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified TCL1A , NRIP1 , and TERT locus variants as modulators of mCA clonal expansion rate.

Genomewide Association Studies in Pharmacogenomics.

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

Altered pulmonary blood volume distribution as a biomarker for predicting outcomes in COVID-19 disease.

INTRODUCTION: Evidence suggests that vascular inflammation and thrombosis may be important drivers of poor clinical outcomes in patients with COVID-19. We hypothesised that a significant decrease in the percentage of blood volume in vessels with a cross-sectional area between 1.25 and 5 mm2 relative to the total pulmonary blood volume (BV5%) on chest computed tomography (CT) in COVID-19 patients is predictive of adverse clinical outcomes. METHODS: We performed a retrospective analysis of chest CT scans from 10 hospitals across two US states in 313 COVID-19-positive and 195 COVID-19-negative patients seeking acute medical care. RESULTS: BV5% was predictive of outcomes in COVID-19 patients in a multivariate model, with a BV5% threshold below 25% associated with OR 5.58 for mortality, OR 3.20 for intubation and OR 2.54 for the composite of mortality or intubation. A model using age and BV5% had an area under the receiver operating characteristic curve of 0.85 to predict the composite of mortality or intubation in COVID-19 patients. BV5% was not predictive of clinical outcomes in patients without COVID-19. CONCLUSIONS: The data suggest BV5% as a novel biomarker for predicting adverse outcomes in patients with COVID-19 seeking acute medical care.

Pathway and network embedding methods for prioritizing psychiatric drugs.

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

Computed tomography angiography evaluation of acute limb ischemia.

Acute limb ischemia (ALI), a subclass of critical limb ischemia, is a medical emergency. The cause of ALI is usually thrombotic or embolic in nature, and the specific etiology often dictates the appropriate therapy. While the diagnosis is a clinical with common presenting symptoms, advances in ultrasound, computed tomography, and magnetic resonance technology have impacted the diagnosis and subsequent therapy. In ALI, the time to revascularization is critical and computed tomography angiography (CTA) provides a highly sensitive and specific technique for rapidly identifying occlusions and precisely defining vascular anatomy prior to interventions. In patients with significant renal disease, magnetic resonance angiography with or without contrast provides effective alternatives at the expense of imaging time. Treatment can include a variety of endovascular or surgical interventions, including thromboembolectomy, angioplasty, or bypass. Proper evaluation of the etiology of the ischemia, affected vasculature, and medical history is critical to select appropriate treatment and improve patient outcomes. Here, we examine the presentation, evaluation, and treatment of ALI and the role of CTA in diagnosis and therapy.

Radiogenomics and Radiomics in Liver Cancers.

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor's molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

Irreversible Electroporation in Liver Cancers and Whole Organ Engineering.

Liver cancers contribute significantly to cancer-related mortality worldwide and liver transplants remain the cornerstone of curative treatment for select, early-stage patients. Unfortunately, because of a mismatch between demand and supply of donor organs, liver cancer patients must often wait extended periods of time prior to transplant. A variety of local therapies including surgical resection, transarterial chemoembolization, and thermal ablative methods exist in order to bridge to transplant. In recent years, a number of studies have examined the role of irreversible electroporation (IRE) as a non-thermal local ablative method for liver tumors, particularly for those adjacent to critical structures such as the vasculature, gall bladder, or bile duct. In addition to proving its utility as a local treatment modality, IRE has also demonstrated promise as a technique for donor organ decellularization in the context of whole-organ engineering. Through complete non-thermal removal of living cells, IRE allows for the creation of an acellular extra cellular matrix (ECM) scaffold that could theoretically be recellularized and implanted into a living host. Here, we comprehensively review studies investigating IRE, its role in liver cancer treatment, and its utility in whole organ engineering.

Liquid Biopsy in Gastrointestinal Cancers.

Liquid biopsy is the sampling of any biological fluid in an effort to enrich and analyze a tumor's genetic material. Peripheral blood remains the most studied liquid biopsy material, with circulating tumor cells (CTC's) and circulating tumor DNA (ctDNA) allowing the examination and longitudinal monitoring of a tumors genetic landscape. With applications in cancer screening, prognostic stratification, therapy selection and disease surveillance, liquid biopsy represents an exciting new paradigm in the field of cancer diagnostics and offers a less invasive and more comprehensive alternative to conventional tissue biopsy. Here, we examine liquid biopsies in gastrointestinal cancers, specifically colorectal, gastric, and pancreatic cancers, with an emphasis on applications in diagnostics, prognostics and therapeutics.

Three-dimensional (3D) printing and its applications for aortic diseases.

Three-dimensional (3D) printing is a process which generates prototypes from virtual objects in computer-aided design (CAD) software. Since 3D printing enables the creation of customized objects, it is a rapidly expanding field in an age of personalized medicine. We discuss the use of 3D printing in surgical planning, training, and creation of devices for the treatment of aortic diseases. 3D printing can provide operators with a hands-on model to interact with complex anatomy, enable prototyping of devices for implantation based upon anatomy, or even provide pre-procedural simulation. Potential exists to expand upon current uses of 3D printing to create personalized implantable devices such as grafts. Future studies should aim to demonstrate the impact of 3D printing on outcomes to make this technology more accessible to patients with complex aortic diseases.

Social Medicine: Twitter in Healthcare.

Social media enables the public sharing of information. With the recent emphasis on transparency and the open sharing of information between doctors and patients, the intersection of social media and healthcare is of particular interest. Twitter is currently the most popular form of social media used for healthcare communication; here, we examine the use of Twitter in medicine and specifically explore in what capacity using Twitter to share information on treatments and research has the potential to improve care. The sharing of information on Twitter can create a communicative and collaborative atmosphere for patients, physicians, and researchers and even improve quality of care. However, risks involved with using Twitter for healthcare discourse include high rates of misinformation, difficulties in verifying the credibility of sources, overwhelmingly high volumes of information available on Twitter, concerns about professionalism, and the opportunity cost of using physician time. Ultimately, the use of Twitter in healthcare can allow patients, healthcare professionals, and researchers to be more informed, but specific guidelines for appropriate use are necessary.

Volume-outcome relationships for transcatheter aortic valve replacement-risk-adjusted and volume stratified analysis of TAVR outcomes.

OBJECTIVES: This purpose of the study was to evaluate TAVR outcomes at low, intermediate and high volume institutions. BACKGROUND: For the care of complex patients, volume-outcome effect is well described. The initial US TAVR experience was limited to a few centers of excellence. The impact of institutional volume on outcomes after TAVR has not been systematically studied. METHODS: Within the Banner Health system, TAVR is performed at 3 institutions-a low volume, an intermediate volume and a high volume institution. 181 consecutive patients undergoing TAVR within these 3 institutions were the study cohort. To adjust for bias and confounders between the 3 groups, risk-adjusted multivariate logistic regression and propensity score analysis was performed. The primary endpoint was a composite of mortality, dialysis-dependent renal failure, cerebrovascular accident, need for new permanent pacemaker and readmission within 30days. RESULTS: The primary endpoint was reached in 38.8% of patients at the high volume institution and 76.2% of patients at the low volume institution (p<0.01). Having a TAVR procedure at a larger volume institution was an independent predictor of having improved outcomes (OR 0.33, 95% CI 0.16-0.68; p=0.003). These improved outcomes after the TAVR procedure noted at the large volume institution were seen in the most complex patients: age ≥80years, BMI >30, diabetes, hypertension, prior CAD, CKD and NYHA class III/IV heart failure. CONCLUSIONS: High-risk patients undergoing TAVR at a large volume institution have better 30-day outcomes compared to outcomes at intermediate and low volume centers.

Using Naïve Bayesian Analysis to Determine Imaging Characteristics of KRAS Mutations in Metastatic Colon Cancer.

Genotype, particularly Ras status, greatly affects prognosis and treatment of liver metastasis in colon cancer patients. This pilot aimed to apply word frequency analysis and a naive Bayes classifier on radiology reports to extract distinguishing imaging descriptors of wild-type colon cancer patients and those with v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations. In this institutional-review-board-approved study, we compiled a SNaPshot mutation analysis dataset from 457 colon adenocarcinoma patients. From this cohort of patients, we analyzed radiology reports of 299 patients (> 32,000 reports) who either were wild-type (147 patients) or had a KRAS (152 patients) mutation. Our algorithm determined word frequency within the wild-type and mutant radiology reports and used a naive Bayes classifier to determine the probability of a given word belonging to either group. The classifier determined that words with a greater than 50% chance of being in the KRAS mutation group and which had the highest absolute probability difference compared to the wild-type group included: "several", "innumerable", "confluent", and "numerous" (p < 0.01). In contrast, words with a greater than 50% chance of being in the wild type group and with the highest absolute probability difference included: "few", "discrete", and "[no] recurrent" (p = 0.03). Words used in radiology reports, which have direct implications on disease course, tumor burden, and therapy, appear with differing frequency in patients with KRAS mutations versus wild-type colon adenocarcinoma. Moreover, likely characteristic imaging traits of mutant tumors make probabilistic word analysis useful in identifying unique characteristics and disease course, with applications ranging from radiology and pathology reports to clinical notes.

Animal models of venous thrombosis.

Venous thrombosis (VT) is a prevalent clinical condition with significant adverse sequela or mortality. Anticoagulation and pharmacologic or pharmacomechanical thrombolytic therapies are the mainstays of VT treatment. An understanding of thrombosis biology will allow for more effective VT-tailored diagnosis and therapy. In vivo models of thrombosis provide indispensable tools to study the pathogenesis of thrombus formation and to evaluate novel therapeutic or preventive adjuncts for VT management or prevention. In this article, we review the most prominent in vivo models of VT created in rodents and swine species and outline how each model can serve as a useful tool to promote our understanding of VT pathogenesis and to examine novel therapies.

Elastography techniques in the evaluation of deep vein thrombosis.

Deep venous thrombosis (DVT) is a significant medical problem with an incidence of 1 in 1,000 adults and greatly reduces quality of life through post-thrombotic syndrome. Treatment choice for DVT can be influenced by the age of the clot. While new endovascular catheter techniques treat venous clots to potentially prevent post-thrombotic syndrome, they require improved imaging techniques to accurately determine clot age. This review investigates experimental and clinical evidence of elastography techniques for aging DVT. Strain elastography and shear wave elastography are the most common techniques to age thrombus. These elastography techniques can distinguish between acute and chronic clots by characterizing tissue stiffness. When clot age cannot be determined with ultrasound duplex analysis, elastography may offer a helpful adjunct. However, further investigation is required to validate accuracy and reproducibility for clinical implementation of this novel technique.