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Abstract Gene therapy utilizes nucleic acid drugs to treat diseases, encompassing gene supplementation, gene replacement, gene silencing, and gene editing. It represents a distinct therapeutic approach from traditional medications and introduces novel strategies for genetic disorders. Over the past two decades, significant advancements have been made in the field of gene therapy, leading to the approval of various gene therapy drugs. Gene therapy was initially employed for treating genetic diseases and cancers, particularly monogenic conditions classified as orphan diseases due to their low prevalence rates; however, polygenic or complex diseases exhibit higher incidence rates within populations. Extensive research on the etiology of polygenic diseases has unveiled new therapeutic targets that offer fresh opportunities for their treatment. Building upon the progress achieved in gene therapy for monogenic diseases and cancers, extending its application to polygenic or complex diseases would enable targeting a broader range of patient populations. This review aims to discuss the strategies of gene therapy, methods of gene editing (mainly CRISPR-CAS9), and carriers utilized in gene therapy, and highlight the applications of gene therapy in polygenic or complex diseases focused on applications that have either entered clinical stages or are currently undergoing clinical trials. Graphical Abstract

Abstract Background Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis. Methods To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm’s parameters (parameter-based). Results Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients’ outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580). Conclusions By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

This editorial, published in Frontiers in Microbiology in July 2024, emphasizes the importance of data-driven approaches in understanding the relationship between human microbiota and complex diseases. The human microbiota, consisting of trillions of microorganisms in the body, plays a significant role in maintaining health and influencing disease states. Traditional statistical methods are insufficient for analyzing the vast amount of data generated from microbiota analysis, necessitating the use of advanced data-driven approaches like bioinformatics and machine learning. The editorial highlights the growing use of these techniques and their potential for improving diagnostics, treatments, and prevention strategies for complex diseases. The document also mentions specific research articles published in the same issue that investigate the causal associations between microbiota and various complex diseases using Mendelian randomization investigations. The articles cover a wide range of topics, including the relationship between gut microbiome and conditions such as psoriatic arthritis, androgenetic alopecia, and hemorrhagic stroke. Other articles focus on the development and evaluation of computational models for predicting microbe-disease associations. The document expresses gratitude to the contributors and reviewers of the research articles, and funding sources and potential conflicts of interest are disclosed. [Extracted from the article]

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R's ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented. [ABSTRACT FROM AUTHOR]

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases. [ABSTRACT FROM AUTHOR]

Background: Several studies showed that the perpetuation of consanguinity increases homozygosity and homogenizes the population's gene pool. This allows the expression of recessive deleterious mutations and increases the prevalence of genetic disorders and birth defects. Despite the reported negative health effects, consanguinity is still practiced in Morocco. This study aimed to evaluate the prevalence and socio-demographic determinants of consanguinity in the Souss region and to assess the association of this type of union with congenital disabilities and complex diseases. To meet this aim, a survey based on a cross-sectional approach was conducted between January 2019 and January 2020 among 520 randomly selected participants in the Souss region. Results: The findings revealed a high prevalence of consanguinity of 28.46%, with first-cousin unions accounting for 16.15% of all marriages. According to multivariate logistic regression analysis, early age at first marriage, men's occupation, endogamy, and parental consanguinity were predictive factors for consanguineous unions in the study population. Moreover, the results revealed a significant association between consanguinity and the incidence of physical disabilities (OR = 3.753; [95% CI 1.398–10.074]), mental retardation (OR = 5.219; [95% CI 1.545–17.631]), deafness-mutism (OR = 4.262; [95% CI 1.004–18.089]) and cardiovascular diseases (OR = 2.167; [95% CI 1.036–4.530]). However, no significant association was found between consanguinity and diabetes, cancer, asthma, epilepsy, and psychiatric disorders. Conclusion: Overall, our results suggest a high practice of consanguinity in the Souss population, associated with social, economic, and cultural factors. Consanguineous unions were associated with a high incidence of mental retardation, physical disabilities, deafness-mutism, and cardiovascular diseases. In this population, where marriage between relatives is highly preferred, awareness programs are not sufficient, and genetic studies on consanguinity-related diseases are necessary to provide specific premarital screening and thus increase the efficiency of genetic counseling. [ABSTRACT FROM AUTHOR]

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans but with limited evidence in other ancestries. Here, we present a multi-ancestry proteome-wide MR analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the putative causal effects of 1,545 proteins on eight diseases in African (32,658) and European (1,219,993) ancestries and identified 45 and 7 protein-disease pairs with MR and genetic colocalization evidence in the two ancestries, respectively. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence in both ancestries and seven pairs with specific effects in the two ancestries separately. Integrating these MR signals with clinical trial evidence, we prioritized 16 pairs for investigation in future drug trials. Our results highlight the value of proteome-wide MR in informing the generalizability of drug targets for disease prevention across ancestries and illustrate the value of meta-analysis of biobanks in drug development.

Abstract Genomic effect variants associated with survival and protection against complex diseases vary between populations due to microevolutionary processes. The aim of this study was to analyse diversity and distribution of effect variants in a context of potential positive selection. In total, 475 individuals of Lithuanian origin were genotyped using high-throughput scanning and/or sequencing technologies. Allele frequency analysis for the pre-selected effect variants was performed using the catalogue of single nucleotide polymorphisms. Comparison of the pre-selected effect variants with variants in primate species was carried out to ascertain which allele was derived and potentially of protective nature. Recent positive selection analysis was performed to verify this protective effect. Four variants having significantly different frequencies compared to European populations were identified while two other variants reached borderline significance. Effect variant in SLC30A8 gene may potentially protect against type 2 diabetes. The existing paradox of high rates of type 2 diabetes in the Lithuanian population and the relatively high frequencies of potentially protective genome variants against it indicate a lack of knowledge about the interactions between environmental factors, regulatory regions, and other genome variation. Identification of effect variants is a step towards better understanding of the microevolutionary processes, etiopathogenetic mechanisms, and personalised medicine.

Abstract Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.

Ectodermal dysplasia (ED) is one of the congenitally syndromic diseases with dental anomalies. This syndrome manifests in various forms and usually requires early restorative therapy to restore esthetics and function in young patients. The restorative approaches vary greatly and range from minimally invasive shape corrections using composite build-ups and removable partial or complete dental prostheses, to implantologic solutions, always based on the syndromic expression, the age of the patient, the residual growth, as well as the dentition itself. The present case report presents an innovative prosthetic management of a young patient suffering from ED with permanent tooth aplasia and persistent primary teeth using maxillomandibular individually veneered tooth-colored CAD/CAM polycarbonate splints. The CAD phase has been significantly improved by including the analysis of 3D face scans. This advanced technical development makes it possible to avoid any time-consuming try-in and start directly with the splint production, ensuring a much faster complete rehabilitation and support for the young patient. [ABSTRACT FROM AUTHOR]

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R’s ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented.

Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa. [ABSTRACT FROM AUTHOR]

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic‐based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high‐throughput molecular technologies, together with large collections of health data and novel data‐driven approaches, offers promise toward improved individual health through precision medicine. [ABSTRACT FROM AUTHOR]

Complex diseases are affected by various factors, and single-nucleotide polymorphisms (SNPs) are the basis for their susceptibility by affecting protein structure and gene expression. Complex diseases often arise from the interactions of multiple SNPs and are investigated using epistasis detection algorithms. Nevertheless, the computational burden associated with the "combination explosion" hinders these algorithms' ability to detect these interactions. To perform multi-SNP analysis in complex diseases, the iterative exclusion of compatible samples (IECS) workflow is proposed in this work. In the IECS workflow, qualitative comparative analysis (QCA) is firstly employed as the calculation engine to calculate the solution; secondly, the pattern is extracted from the prime implicants with the greatest raw coverage in the solution; then, the pattern is tested with the chi-square test in the source dataset; finally, all compatible samples are excluded from the current dataset. This process is repeated until the QCA calculation has no solution or reaches the iteration threshold. The workflow was applied to analyze simulated datasets and the Alzheimer's disease dataset, and its performance was compared with that of the BOOST and MDR algorithms. The findings illustrated that IECS exhibits greater power with less computation and can be applied to perform multi-SNP analysis in complex diseases. [ABSTRACT FROM AUTHOR]

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The oralome is the summary of the dynamic interactions orchestrated between the ecological community of oral microorganisms (comprised of up to approximately 1000 species of bacteria, fungi, viruses, archaea and protozoa - the oral microbiome) that live in the oral cavity and the host. These microorganisms form a complex ecosystem that thrive in the dynamic oral environment in a symbiotic relationship with the human host. However, the microbial composition is significantly affected by interspecies and host-microbial interactions, which in turn, can impact the health and disease status of the host. In this review, we discuss the composition of the oralome and inter-species and host-microbial interactions that take place in the oral cavity and examine how these interactions change from healthy (eubiotic) to disease (dysbiotic) states. We further discuss the dysbiotic signatures associated with periodontitis and caries and their sequalae, (e.g., tooth/bone loss and pulpitis), and the systemic diseases associated with these oral diseases, such as infective endocarditis, atherosclerosis, diabetes, Alzheimer's disease and head and neck/oral cancer. We then discuss current computational techniques to assess dysbiotic oral microbiome changes. Lastly, we discuss current and novel techniques for modulation of the dysbiotic oral microbiome that may help in disease prevention and treatment, including standard hygiene methods, prebiotics, probiotics, use of nano-sized drug delivery systems (nano-DDS), extracellular polymeric matrix (EPM) disruption, and host response modulators.

Objective. To map existing organizational care pathways in clinical centers of expertise that care for pregnant women affected by rare and complex connective tissue diseases (rcCTDs). Methods. An international working group composed of experts in the field of pregnancy in rcCTDs co-designed a survey focused on organizational aspects related to the patient's pathway before, during, and after pregnancy. The survey was distributed to subject experts through referral sampling. Results. Answers were collected from 69 centers in 21 countries. Patients with systemic lupus erythematosus and/or antiphospholipid syndrome were followed by more than 90% of centers, whereas those with disorders such as IgG4-related diseases were rarely covered. In the majority of centers, a multidisciplinary team was involved, including an obstetrician/gynecologist in 91.3% of cases and other healthcare professionals less frequently. Respondents indicated that 96% of the centers provided routine pre-pregnancy care, whereas the number of patient visits during pregnancy varied across centers. A formalized care pathway was described in 49.2% of centers, and 20.3% of centers had a predefined protocol for the monitoring of pregnant patients. Access to therapies during pregnancy also was heterogeneous among different centers. Conclusion. In international referral centers, a high level of care is provided to patients with rcCTDs before, during, and after pregnancy. No significant discrepancies were found between European and non-European countries. However, this work highlights a potential benefit to streamlining the care approaches across countries to optimize pregnancy and perinatal outcomes among patients with rcCTDs. [ABSTRACT FROM AUTHOR]

Environmental toxicants have the potential to contribute to the pathophysiology of multiple complex diseases, but the underlying mechanisms remain obscure. One such toxicant is the widely used fungicide ziram, a dithiocarbamate known to have neurotoxic effects and to increase the risk of Parkinson's disease. We have used Drosophila melanogaster as an unbiased discovery tool to identify novel molecular pathways by which ziram may disrupt neuronal function. Consistent with previous results in mammalian cells, we find that ziram increases the probability of synaptic vesicle release by dysregulation of the ubiquitin signaling system. In addition, we find that ziram increases neuronal excitability. Using a combination of live imaging and electrophysiology, we find that ziram increases excitability in both aminergic and glutamatergic neurons. This increased excitability is phenocopied and occluded by null mutant animals of the ether a-go-go (eag) potassium channel. A pharmacological inhibitor of the temperature sensitive hERG (human ether-a-go-go related gene) phenocopies the excitability effects of ziram but only at elevated temperatures. seizure (sei), a fly ortholog of hERG, is thus another candidate target of ziram. Taken together, the eag family of potassium channels emerges as a candidate for mediating some of the toxic effects of ziram. We propose that ziram may contribute to the risk of complex human diseases by blockade of human eag and sei orthologs, such as hERG.

Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine.

Abstract Background The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. Results scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. Conclusions We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

BackgroundGenome-wide association studies (GWASs) have revealed relationships between over 57,000 genetic variants and diseases. However, unlike Mendelian diseases, complex diseases arise from the interplay of multiple genetic and environmental factors. Natural selection has led to a high tendency of risk alleles to be enriched in minor alleles in Mendelian diseases. Therefore, an allele that was previously advantageous or neutral may later become harmful, making it a risk allele.MethodsUsing data in the NHGRI-EBI Catalog and the VARIMED database, we investigated whether (1) GWASs more easily detect risk alleles and (2) facilitate evolutionary insights by comparing risk allele frequencies of different diseases. We conducted computer simulations of P-values for association tests when major and minor alleles were risk alleles. We compared the expected proportion of SNVs whose risk alleles were minor alleles with the observed proportion.ResultsOur statistical results revealed that risk alleles were enriched in minor alleles, especially for variants with low minor allele frequencies (MAFs  50% risk alleles were minor alleles because of the larger difference in the power of GWASs to differentiate between minor and major alleles, especially with low MAFs or when the number of controls exceeds the number of cases. However, the observed ratios between minor and major alleles in low MAFs (

Complex diseases are prevalent medical conditions which are characterized by inter-patient heterogeneity with regards to symptom profiles, disease trajectory, comorbidities, and treatment response. Their pathophysiology involves a combination of genetic, environmental, and psychosocial factors. The intricacies of complex diseases, encompassing different levels of biological organization in the context of environmental and psychosocial factors, makes them difficult to study, understand, prevent, and treat. The field of network medicine has progressed our understanding of these complex mechanisms and highlighted mechanistic overlap between diagnoses as well as patterns of symptom co-occurrence. These observations call into question the traditional conception of complex diseases, where diagnoses are treated as distinct entities, and prompts us to reconceptualize our nosological models. Thus, this manuscript presents a novel model, in which the individual disease burden is determined as a function of molecular, physiological, and pathological factors simultaneously, and represented as a state vector. In this conceptualization the focus shifts from identifying the underlying pathophysiology of diagnosis cohorts towards identifying symptom-determining traits in individual patients. This conceptualization facilitates a multidimensional approach to understanding human physiology and pathophysiology in the context of complex diseases. This may provide a useful concept to address both the significant interindividual heterogeneity of diagnose cohorts as well as the lack of clear distinction between diagnoses, health, and disease, thus facilitating the progression towards personalized medicine.

BackgroundCirculating vitamin D has been associated with multiple clinical diseases in observational studies, but the association was inconsistent due to the presence of confounders. We conducted a bidirectional Mendelian randomization (MR) study to explore the healthy atlas of vitamin D in many clinical traits and evaluate their causal association.MethodsBased on a large-scale genome-wide association study (GWAS), the single nucleotide polymorphism (SNPs) instruments of circulating 25-hydroxyvitamin D (25OHD) from 443,734 Europeans and the corresponding effects of 10 clinical diseases and 42 clinical traits in the European population were recruited to conduct a bidirectional two-sample Mendelian randomization study. Under the network of Mendelian randomization analysis, inverse-variance weighting (IVW), weighted median, weighted mode, and Mendelian randomization (MR)–Egger regression were performed to explore the causal effects and pleiotropy. Mendelian randomization pleiotropy RESidual Sum and Outlier (MR-PRESSO) was conducted to uncover and exclude pleiotropic SNPs.ResultsThe results revealed that genetically decreased vitamin D was inversely related to the estimated BMD (β = −0.029 g/cm2, p = 0.027), TC (β = −0.269 mmol/L, p = 0.006), TG (β = −0.208 mmol/L, p = 0.002), and pulse pressure (β = −0.241 mmHg, p = 0.043), while positively associated with lymphocyte count (β = 0.037%, p = 0.015). The results did not reveal any causal association of vitamin D with clinical diseases. On the contrary, genetically protected CKD was significantly associated with increased vitamin D (β = 0.056, p = 2.361 × 10−26).ConclusionThe putative causal effects of circulating vitamin D on estimated bone mass, plasma triglyceride, and total cholesterol were uncovered, but not on clinical diseases. Vitamin D may be linked to clinical disease by affecting health-related metabolic markers.

Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment. [ABSTRACT FROM AUTHOR]

Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene–disease association. [ABSTRACT FROM AUTHOR]

Complex diseases are affected by various factors, and single-nucleotide polymorphisms (SNPs) are the basis for their susceptibility by affecting protein structure and gene expression. Complex diseases often arise from the interactions of multiple SNPs and are investigated using epistasis detection algorithms. Nevertheless, the computational burden associated with the “combination explosion” hinders these algorithms’ ability to detect these interactions. To perform multi-SNP analysis in complex diseases, the iterative exclusion of compatible samples (IECS) workflow is proposed in this work. In the IECS workflow, qualitative comparative analysis (QCA) is firstly employed as the calculation engine to calculate the solution; secondly, the pattern is extracted from the prime implicants with the greatest raw coverage in the solution; then, the pattern is tested with the chi-square test in the source dataset; finally, all compatible samples are excluded from the current dataset. This process is repeated until the QCA calculation has no solution or reaches the iteration threshold. The workflow was applied to analyze simulated datasets and the Alzheimer’s disease dataset, and its performance was compared with that of the BOOST and MDR algorithms. The findings illustrated that IECS exhibits greater power with less computation and can be applied to perform multi-SNP analysis in complex diseases.

Abstract Background Targeted Next Generation Sequencing is a common and powerful approach used in both clinical and research settings. However, at present, a large fraction of the acquired genetic information is not used since pathogenicity cannot be assessed for most variants. Further complicating this scenario is the increasingly frequent description of a poli/oligogenic pattern of inheritance showing the contribution of multiple variants in increasing disease risk. We present an approach in which the entire genetic information provided by target sequencing is transformed into binary data on which we performed statistical, machine learning, and network analyses to extract all valuable information from the entire genetic profile. To test this approach and unbiasedly explore the presence of recurrent genetic patterns, we studied a cohort of 112 patients affected either by genetic Creutzfeldt–Jakob (CJD) disease caused by two mutations in the PRNP gene (p.E200K and p.V210I) with different penetrance or by sporadic Alzheimer disease (sAD). Results Unsupervised methods can identify functionally relevant sources of variation in the data, like haplogroups and polymorphisms that do not follow Hardy–Weinberg equilibrium, such as the NOTCH3 rs11670823 (c.3837 + 21 T > A). Supervised classifiers can recognize clinical phenotypes with high accuracy based on the mutational profile of patients. In addition, we found a similar alteration of allele frequencies compared the European population in sporadic patients and in V210I-CJD, a poorly penetrant PRNP mutation, and sAD, suggesting shared oligogenic patterns in different types of dementia. Pathway enrichment and protein–protein interaction network revealed different altered pathways between the two PRNP mutations. Conclusions We propose this workflow as a possible approach to gain deeper insights into the genetic information derived from target sequencing, to identify recurrent genetic patterns and improve the understanding of complex diseases. This work could also represent a possible starting point of a predictive tool for personalized medicine and advanced diagnostic applications.

A growing patient population is adolescents and young adults who have had one or more serious medical problems and are aging into adulthood. This group of patients has unique medical needs, which has resulted in the development of a specialized area of medicine: transitional care medicine. The case reviews of two of these patients are described. Patient 1 was a 23-year-old man with hereditary pancreatitis. His genetic condition resulted in the need for pancreatic splenectomy and removal of part of his small bowel, resulting in insulin-dependent diabetes and malnutrition. These complex clinical issues and the challenges of chronic pain were further complicated by severe anxiety disorder and substance abuse. He presented to the University of Rochester Medical Center's Complex Care Center (CCC), an interdisciplinary clinic that provides care for adults with pediatric onset conditions, staffed with both dentists and physicians, with acute pain from a grossly decayed premolar tooth. His blood glucose measured > 500 mg/dL and he was experiencing an acute episode of anxiety. With the expertise and experience of center staff his care needs could be met. Patient 2 was a 32-year-old woman with chronic juvenile rheumatoid arthritis, drug-associated lupus, and mental health problems including depression. This condition requires her to be managed with broad spectrum immunosuppression to prevent joint inflammation that results in significant joint destruction and bone loss. She presented to the CCC with an abscessed molar tooth, which prevented her from receiving her required immunotherapy, IV tocilizamab. While monitored by on-site physicians, a center dentist could safely proceed with the extraction. These cases illustrate that, as the population of transitional care patients grows, general dentists can learn to work on-site with physicians and allied health per - sonnel to meet the need. [ABSTRACT FROM AUTHOR]

Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K -fold cross validations and case studies seem to be must-have procedures, the value of K , the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction. [ABSTRACT FROM AUTHOR]

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb. A wide range of health-related, lifestyle, and environmental variables impact disease risks; however, they are often unmeasured. By generating and analyzing polygenic scores that captured these factor's heritable component, we highlight their roles across medical phenotypes of two large biobanks and showcase the scores' ability to improve disease prediction models. [ABSTRACT FROM AUTHOR]

Since the problem proposed in late 2000s, microRNA–disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance. [ABSTRACT FROM AUTHOR]

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches., (© The Author(s) 2024. Published by Oxford University Press.)

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce. [ABSTRACT FROM AUTHOR]

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia. [ABSTRACT FROM AUTHOR]

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce.

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.

Heritability is a fundamental concept in genetic studies, measuring the genetic contribution to complex traits and bringing insights about disease mechanisms. The advance of high-throughput technologies has provided many resources for heritability estimation. Linkage disequilibrium (LD) score regression (LDSC) estimates both heritability and confounding biases, such as cryptic relatedness and population stratification, among single-nucleotide polymorphisms (SNPs) by using only summary statistics released from genome-wide association studies. However, only partial information in the LD matrix is utilized in LDSC, leading to loss in precision. In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by making full use of the LD information. Compared to state-of-the-art heritability estimating methods, LDER provides more accurate estimates of SNP heritability and better distinguishes the inflation caused by polygenicity and confounding effects. We demonstrate the advantages of LDER both theoretically and with extensive simulations. We applied LDER to 814 complex traits from UK Biobank, and LDER identified 363 significantly heritable phenotypes, among which 97 were not identified by LDSC. [ABSTRACT FROM AUTHOR]

The increasingly appreciated prevalence of complicated stressor‐to‐phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell‐ and tissue‐specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor‐induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context‐dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single‐alteration perspective in disease to a comprehensive network‐based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The gut microbiota contributes to mammalian host biology by supplying metabolites from nutrients and pro-inflammatory molecules. We have recently shown that urinary hippurate is associated with reduced risk of obesity, increased gut microbiome diversity and gene richness, and functional modules for microbial production of its precursor benzoate. Obese mice infused with hippurate exhibit profound alterations of glucose homeostasis. Here, we tested the biological effects of chronic administration of benzoate on cardiometabolic phenotypes in lean and obese mice. Benzoate induced glucose intolerance, enhanced insulin secretion, increased adiposity and stimulated liver inflammation in lean mice fed control diet. In contrast, in condition of obesity and diabetes induced by high fat diet feeding, benzoate infusion resulted in reduction of glucose intolerance, stimulation of both glucose-induced insulin secretion and β-cell proliferation, and reduction of liver triglyceride and collagen accumulation. These results combined with those previously obtained in mice treated with hippurate underline the importance of the benzoate-hippurate pathway in cardiometabolic diseases and pave the way to diagnostic and therapeutic solutions. [Display omitted] • Benzoate treatment in lean mice impairs glucose tolerance and increases adiposity. • Benzoate infusion in lean mice regulates insulin secretion and β-cell proliferation. • Benzoate in obese mice reduces glucose intolerance and stimulates insulin secretion. [ABSTRACT FROM AUTHOR]

Metabolic and inflammatory disorders such as autoimmune and neurodegenerative diseases are increasing at alarming rates. Many of these are not tissue-specific occurrences but complex, systemic pathologies of unknown origin for which no cure exists. Such complexity obscures causal relationships among factors regulating disease progression. Emerging technologies mimicking human physiology, such as microphysiological systems (MPSs), offer new possibilities to provide clarity in systemic metabolic and inflammatory diseases. Controlled interaction of multiple MPSs and the scalability of biological complexity in MPSs, supported by continuous multiomic monitoring, might hold the key to identifying novel relationships between interorgan crosstalk, metabolism, and immunity. In this perspective, I aim to discuss the current state of modeling multiorgan physiology and evaluate current opportunities and challenges. [ABSTRACT FROM AUTHOR]