Integrated Pleiotropic Gene Set Unveils Comorbidity Insights across Digestive Cancers and Other Diseases.

Comorbidities are prevalent in digestive cancers, intensifying patient discomfort and complicating prognosis. Identifying potential comorbidities and investigating their genetic connections in a systemic manner prove to be instrumental in averting additional health challenges during digestive cancer management. Here, we investigated 150 diseases across 18 categories by collecting and integrating various factors related to disease comorbidity, such as disease-associated SNPs or genes from sources like MalaCards, GWAS Catalog and UK Biobank. Through this extensive analysis, we have established an integrated pleiotropic gene set comprising 548 genes in total. Particularly, there enclosed the genes encoding major histocompatibility complex or related to antigen presentation. Additionally, we have unveiled patterns in protein-protein interactions and key hub genes/proteins including TP53, KRAS, CTNNB1 and PIK3CA, which may elucidate the co-occurrence of digestive cancers with certain diseases. These findings provide valuable insights into the molecular origins of comorbidity, offering potential avenues for patient stratification and the development of targeted therapies in clinical trials.

The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.

Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.

Discerning asthma endotypes through comorbidity mapping.

Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.

Prevalence of Mental Health Conditions Among 6078 Individuals With Down Syndrome in the United States.

Findings from a recent study of the largest documented cohort of individuals with Down syndrome (DS) in the United States described prevalence of common disease conditions and strongly suggested significant disparity in mental health conditions among these individuals as compared with age- and sex-matched individuals without DS. The retrospective, descriptive study reported herein is a follow-up to document prevalence of 58 mental health conditions across 28 years of data from 6078 individuals with DS and 30,326 age- and sex-matched controls. Patient data were abstracted from electronic medical records within a large integrated health system. In general, individuals with DS had higher prevalence of mood disorders (including depression); anxiety disorders (including obsessive-compulsive disorder); schizophrenia; psychosis (including hallucinations); pseudobulbar affect; personality disorder; dementia (including Alzheimer's disease); mental disorder due to physiologic causes; conduct disorder; tic disorder; and impulse control disorder. Conversely, the DS cohort experienced lower prevalence of bipolar I disorder; generalized anxiety, panic, phobic, and posttraumatic stress disorders; substance use disorders (including alcohol, opioid, cannabis, cocaine, and nicotine disorders); and attention-deficit/hyperactivity disorder. Prevalence of many mental health conditions in the setting of DS vastly differs from comparable individuals without DS. These findings delineate a heretofore unclear jumping-off point for ongoing research.

Prevalence of Infectious Diseases Among 6078 Individuals With Down Syndrome in the United States.

A recent disease prevalence study of the largest documented Down syndrome (DS) cohort in the United States strongly suggested significant disparity in general infectious disease conditions among individuals with DS versus those without DS. In this follow-up retrospective analysis, we explored these differences in greater detail by calculating prevalence of 52 infectious diseases, across 28 years of data among 6078 individuals with DS and 30,326 age- and sex-matched controls, abstracted from electronic medical records within a large Midwestern health system. We found that the DS cohort had higher prevalence of pneumonias (including aspiration, viral, bacterial, pneumococcal, and unspecified/atypical); otitis externa; and the skin infections impetigo, abscess, and cellulitis. To the contrary, the DS cohort had lower prevalence of many respiratory infections other than pneumonia (including influenza, strep pharyngitis, upper respiratory infection, sinusitis, tonsillitis, laryngitis, bronchitis, scarlet fever, and otitis media); sexually transmitted infections (including bacterial vaginosis, chlamydia, genital herpes, HIV/AIDS, human papillomavirus, pelvic inflammatory disease, and trichomoniasis); mononucleosis; shingles; unspecified hepatitis; intestinal infections; and enteritis. These findings highlight that individuals with DS could be more or less prone to different infectious diseases than their non-DS matched counterparts. Additional research to understand why these differences exist and how they might affect the clinical approach to patients with DS is warranted.

Prevalence of Endocrine Disorders Among 6078 Individuals With Down Syndrome in the United States.

Findings from a recent study describing prevalence of common disease conditions in the largest documented cohort of individuals with Down syndrome (DS) in the United States strongly suggested significant disparity in endocrine disorders among these individuals when compared with age- and sex-matched individuals without DS. This retrospective, descriptive study is a follow-up report documenting prevalence of 21 endocrine disorder conditions, across 28 years of data, from 6078 individuals with DS and 30,326 age- and sex-matched controls, abstracted from electronic medical records within a large integrated health system. Overall, individuals with DS experienced higher prevalence of adrenal insufficiency and Addison's disease; thyroid disorders, including hypothyroidism, hyperthyroidism, Hashimoto's disease, and Graves' disease; prolactinoma/hyperprolactinemia; diabetes insipidus; type I diabetes mellitus; and gout. Conversely, those with DS had lower prevalence of polycystic ovary syndrome and type II diabetes mellitus. Many prevalences of endocrine conditions seen in individuals with DS significantly differ relative to their non-DS matched counterparts. These varied findings warrant further exploration into how screening for and treatment of endocrine conditions may need to be approached differently for individuals with DS.

Prevalence of Common Disease Conditions in a Large Cohort of Individuals With Down Syndrome in the United States.

PURPOSE: Given the current life expectancy and number of individuals living with Down syndrome (DS), it is important to learn common occurrences of disease conditions across the developmental lifespan. This study analyzed data from a large cohort of individuals with DS in an effort to better understand these disease conditions, inform future screening practices, tailor medical care guidelines, and improve utilization of health care resources. METHODS: This retrospective, descriptive study incorporated up to 28 years of data, compiled from 6078 individuals with DS and 30,326 controls matched on age and sex. Data were abstracted from electronic medical records within a large Midwestern health system. RESULTS: In general, individuals with DS experienced higher prevalence of testicular cancer, leukemias, moyamoya disease, mental health conditions, bronchitis and pneumonia, gastrointestinal conditions, thyroid disorder, neurological conditions, atlantoaxial subluxation, osteoporosis, dysphagia, diseases of the eyes/adnexa and of the ears/mastoid process, and sleep apnea, relative to matched controls. Individuals with DS experienced lower prevalence of solid tumors, heart disease conditions, sexually transmitted diseases, HIV, influenza, sinusitis, urinary tract infections, and diabetes. Similar rates of prevalence were seen for lymphomas, skin melanomas, stroke, acute myocardial infarction, hepatitis, cellulitis, and osteoarthritis. CONCLUSIONS: While it is challenging to draw a widespread conclusion about comorbidities in individuals with Down syndrome, it is safe to conclude that care for individuals with DS should not automatically mirror screening, prevention, or treatment guidelines for the general U.S. population. Rather, care for those with DS should reflect the unique needs and common comorbidities of this population.

Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease. METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort. RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan. CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman's ρ = 0.32, p < 10-16); and (3) the disease onset age and heritability are negatively correlated (ρ = -0.46, p < 10-16).

Ultra-multiplexed analysis of single-cell dynamics reveals logic rules in differentiation.

Dynamical control of cellular microenvironments is highly desirable to study complex processes such as stem cell differentiation and immune signaling. We present an ultra-multiplexed microfluidic system for high-throughput single-cell analysis in precisely defined dynamic signaling environments. Our system delivers combinatorial and time-varying signals to 1500 independently programmable culture chambers in week-long live-cell experiments by performing nearly 106 pipetting steps, where single cells, two-dimensional (2D) populations, or 3D neurospheres are chemically stimulated and tracked. Using our system and statistical analysis, we investigated the signaling landscape of neural stem cell differentiation and discovered "cellular logic rules" that revealed the critical role of signal timing and sequence in cell fate decisions. We find synergistic and antagonistic signal interactions and show that differentiation pathways are highly redundant. Our system allows dissection of hidden aspects of cellular dynamics and enables accelerated biological discovery.

Incremental parameter estimation of kinetic metabolic network models.

BACKGROUND: An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE). Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified). RESULTS: In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates) exceeds that of metabolites (chemical species). Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA) models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. CONCLUSIONS: The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.

Ensemble kinetic modeling of metabolic networks from dynamic metabolic profiles.

Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional "best-fit" models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.

Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method.

MOTIVATION: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints. RESULTS: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing.