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Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA–mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

Genomic technologies are now utilized for the genetic diagnosis of vascular anomalies. This provides the opportunity for genetic counselors to make a significant contribution to patient care for this complex disease. The aim of this study was to explore Australian healthcare professionals' perspectives on the relatively recent integration of molecular diagnostic testing for vascular anomalies, with or without genetic counseling support. Nine semi‐structured interviews were conducted with Australian healthcare professionals involved in the provision of care for individuals with vascular anomalies. Thematic analysis identified six themes: (1) Molecular diagnosis is beneficial; (2) psychosocial needs can motivate families to pursue a molecular diagnosis; (3) molecular genetic testing for vascular anomalies is complex; (4) genetic service provision is not a one size fits all; (5) a client‐centered approach for genetic service provision can go a long way; and (6) the value of genetic counselors. Based on our findings, implementation of a vascular anomalies genetic diagnostic program inclusive of genetic counseling may be challenging, yet such programs are likely to benefit both patients and their families, as well as healthcare professionals. As this paradigm shift unfolds, genetic counselors have an opportunity to contribute to the vascular anomaly field by educating healthcare professionals and patients, by participating in multidisciplinary clinics to support complex cases and by raising awareness regarding their practice and potential contributions. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a group of diseases characterized by hyperglycemia and its consequences, affecting over 34 million individuals in the United States and 422 million worldwide. While most diabetes is polygenic and is classified as type 1 (T1D), type 2 (T2D), or gestational diabetes (GDM), at least 0.4% of all diabetes is monogenic in nature. Correct diagnosis of monogenic diabetes has important implications for glycemic management and genetic counseling. We provide this Practice Resource to familiarize the genetic counseling community with (1) the existence of monogenic diabetes, (2) how it differs from more common polygenic/complex diabetes types, (3) the advantage of a correct diagnosis, and (4) guidance for identifying, counseling, and testing patients and families with suspected monogenic diabetes. This document is intended for genetic counselors and other healthcare professionals providing clinical services in any setting, with the goal of maximizing the likelihood of a correct diagnosis of monogenic diabetes and access to related care. [ABSTRACT FROM AUTHOR]

Background: There is evidence that complex diseases and mortality are associated with DNA methylation and age acceleration. Numerous epigenetic clocks, including Horvath, Hannum, DNA PhenoAge, DNA GrimAge, and Dunedin Pace of Aging Methylation, continue to be developed in this young scientific field. The most well-known epigenetic clocks are presented here, along with information about how they relate to chronic disease. Summary: We examined all the literature until January 2023, investigating associations between measures of age acceleration and complex and age-related diseases. We focused on the scientific literature and research that are most strongly associated with epigenetic clocks and that have shown promise as biomarkers for obesity, cardiovascular illness, type 2 diabetes, and neurodegenerative disease. Key Messages: Understanding the complex interactions between accelerated epigenetic clocks and chronic diseases may have significant effects on both the early diagnosis of disease and health promotion. Additionally, there is a lot of interest in developing treatment plans that can delay the onset of illnesses or, at the very least, alter the underlying causes of such disorders. [ABSTRACT FROM AUTHOR]

Haplotype-based association analysis has several advantages over single-SNP association analysis. However, to date all haplotype-disease associations have not excluded recombination interference among multiple loci and hence some results might be confounded by recombination interference. Association of sister haplotypes with a complex disease, based on recombination disequilibrium (RD) was presented. Sister haplotypes can be determined by translating notation of DNA base haplotypes to notation of genetic genotypes. Sister haplotypes provide haplotype pairs available for haplotype-disease association analysis. After performing RD tests in control and case cohorts, a two-by-two contingency table can be constructed using sister haplotype pair and case-control pair. With this standard two-by-two table, one can perform classical Chi-square test to find statistical haplotype-disease association. Applying this method to a haplotype dataset of Alzheimer disease (AD), association of sister haplotypes containing ApoE3/4 with risk for AD was identified under no RD. Haplotypes within gene IL-13 were not associated with risk for breast cancer in the case of no RD and no association of haplotypes in gene IL-17A with risk for coronary artery disease were detected without RD. The previously reported associations of haplotypes within these genes with risk for these diseases might be due to strong RD and/or inappropriate haplotype pairs. [ABSTRACT FROM AUTHOR]

In complex diseases, the interactions among genes are commonly elucidated through the lens of graphs. Amongst these genes, certain ones form bi-functional modules within the graph, contingent upon their (anti)correlation with a specific functional state, such as susceptibility to a genetic disorder of non-Mendelian traits. Consequently, a disease can be delineated by a finite number of these discernible modules. Within each module, there exist allelic variants that pose a genetic risk, thus qualifying as genetic risk factors. These factors precipitate a permissive state, which if all other modules also align in the same permissive state, can ultimately lead to the onset of the disease in an individual. To gain a deeper insight into the incidence of a disease, it becomes imperative to acquire a comprehensive understanding of the genetic transmission of these factors. In this work, we present a non-linear model for this transmission, drawing inspiration from the classic theory of the Bell experiment. This model aids in elucidating the variances observed in SNP interactions concerning the risk of disease. [ABSTRACT FROM AUTHOR]

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, the importance of developing multi-target drugs as a strategy to address complex diseases such as NASH has been growing; however, their manufacturing processes remain time- and cost-intensive and inefficient. To overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. We designed a tetra-specific compound, C-192, targeting glucagon-like peptide 1 (GLP-1), glucagon (GCG), fibroblast growth factor 21 (FGF21), and interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH using UniStac. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine-choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumulation, and the non-alcoholic fatty liver disease activity score. In this study, we demonstrated the feasibility of UniStac in creating multi-specific drugs and confirmed the therapeutic potential of C-192, a drug that integrates multiple mechanisms into a single molecule for the treatment of NASH. [ABSTRACT FROM AUTHOR]

The interactions between genetic and environmental risk factors contribute to the aetiology of complex human diseases. Genome‐wide association studies (GWAS) have revealed that most of the genetic variants associated with complex diseases are located in the non‐coding part of the genome, preferentially within enhancers. Enhancers are distal cis‐regulatory elements composed of clusters of transcription factors binding sites that positively regulate the expression of their target genes. The generation of genome‐wide maps for histone marks (e.g., H3K27ac), chromatin accessibility and transcription factor and coactivator (e.g., p300) binding profiles have enabled the identification of enhancers across many human cell types and tissues. Nonetheless, the functional and pathological consequences of the majority of disease‐associated genetic variants located within enhancers seem to be rather minor under normal conditions, thus questioning their medical relevance. Here we propose that, due to the prevalence of enhancer redundancy, the pathological effects of many disease‐associated non‐coding genetic variants might be preferentially (or even only) manifested under environmental stress. [ABSTRACT FROM AUTHOR]

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The article explores the profound impact of personal dedication and family involvement in pediatric care through two contrasting patient experiences. Topics discussed include the role of hope and perseverance in challenging cases, the limitations and strengths of modern medicine, and the importance of holistic and family-centered care approaches.

Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome's functional landscape and enabling variant interpretation that takes 3D structure into account.

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying diseaserelated genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment. [ABSTRACT FROM AUTHOR]

Apical necrosis and premature fruit drop of walnut result in significant yield losses, while these symptoms are associated with different microorganisms. Brown apical necrosis (BAN) is caused by Fusarium and Alternaria species, although this symptomatology can also be caused by Xanthomonas arboricola pv. juglandis (Xaj), causal agent of walnut blight disease. The objectives of this study were to determine the prevalence of the main microorganisms causing apical necrosis in walnut orchards of south-central and southern Chile; evaluate the effects of single vs. multiple species inoculation of microorganisms involved in the expression of apical necrosis and fruit drop; and determine the developmental stage(s) in which walnut flower or fruit is most susceptible to infection by those microorganisms under field conditions. Results showed that Xaj was the most prevalent pathogen causing apical necrosis in walnut orchards, while the incidence of BAN fungal microorganisms associated with apical necrosis in walnuts fruits varied between orchards and seasons. Infection trials demonstrated that the three microorganisms, acting either individually or as a mixed infection, can cause symptoms and premature drop of inoculated fruits, while the combined infection resulted in more frequent infections than those caused by each fungal pathogen separately. Full female flowering (Ff2) and stigma wilting (Gf) were the developmental stages with the greatest susceptibility to these microrganisms, while copper sprays decreased disease incidence. These results suggest that apical necrosis is caused by a complex of microorganisms and that the infection depends on the phenological stage at which the infection occurs. [ABSTRACT FROM AUTHOR]

Genomic testing increasingly challenges health care providers and patients to understand, share, and use information. The provision of polygenic risks is anticipated to complicate comprehension, communication, and risk perception further. This manuscript aims to illuminate the challenges confronting families with multiple genetic risks for Parkinson's disease. Identifying and planning for such issues may prove valuable to family members now and in the future, should neuroprotective or genotype‐specific therapies become available. We present qualitative data from interviews with a multi‐generational family carrying pathogenic variants in the glucocerebrosidase (GBA1) and leucine‐rich repeat kinase 2 (LRRK2) genes which are associated with an increased risk for developing Parkinson's disease (PD). The family includes two brothers (heterozygous for LRRK2 p.G2019S and homozygous for GBA1 p.N409S) and their four descendants. The brothers were concordant for GD and discordant for PD. Genetic counseling and testing were provided to four of the six participants. Two years later, semi‐structured interviews were conducted with the initial participants (n = 4) and two additional first‐degree relatives. Interviews were transcribed and thematically analyzed, providing the basis for this report. Illuminated topics include the perceived risk of developing PD, recall of genetic information, and family communication. With the expanding use of exome and genome sequencing, we anticipate that genetic counselors will increasingly face the challenges demonstrated by this case involving multiple genetic risks for PD, limited data to clarify risk, and the inherent variability of family communication, genetic knowledge, and risk perception. This clinical case report provides a compelling narrative demonstrating the need for additional research exploring these multifaceted topics relevant to both families facing these challenges and providers striving to assist, support and guide their journey. [ABSTRACT FROM AUTHOR]

Families in Ireland often wait over 1 year to see a genetic counselor (GC). This qualitative study aimed to explore the views of families who received a diagnosis of 22q11DS in Ireland regarding the need for timely access to genetic counseling at the point of diagnosis. Twenty participants were recruited through the '22q Ireland' support group, giving a response rate of approximately 10% of the total support group members. Semi‐structured interviews were conducted online and by telephone which explored experiences of receiving diagnoses, medical care, genetic counseling, mental health, and coping with the diagnosis. Interviews were transcribed verbatim and analyzed using thematic analysis. The experiences of 20 participants were classified into five main themes: Receiving Diagnosis, Interactions with Healthcare Professionals (HCPs, excluding GCs), Medical Care, Information, and Impact of Condition. Participants reported receiving diagnoses for their children in a sub‐optimal manner due to inappropriate settings and insufficient information, support, and pre‐test counseling. Parents reported feeling responsible for managing their child's complex and fragmented medical care. Participants reported insufficient empathy and little awareness of 22q11DS among HCPs. Participants perceived genetic counseling to be associated with family planning and reported delayed, if any, access to services. Mental health was a particular worry among participants. Conferences about 22q11DS are the main source of information for parents. Participants reported a range of emotions after diagnosis and described the family impact. The findings suggest both an association between HCPs' poor understanding of 22q11DS and the perceived lack of empathy from HCPs and fragmented medical care. There is an identified need for advocacy of the GC profession in Ireland to support these families. Increased awareness of 22q11DS among HCPs and the development of a coordinated care pathway for 22q11DS, with timely access to genetic counseling, may improve care and lead to better outcomes. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) provided fundamental insight into the genetic determinants of complex allergic diseases. For eczema, 58 susceptibility loci were reported. Protein-changing variants were associated with eczema at genome-wide significance at 12 loci. The majority of risk variants were, however, located in non-coding, regulatory regions of the genome. Prioritized target genes were enriched in pathways of the immune response and of epithelial barrier function. Interestingly, a large overlap in the genetic architecture underlying different allergic diseases was identified pointing to common pathomechanisms for eczema, asthma, hay fever, and food allergy. Here, we review the most recent findings from GWAS for eczema including the role of rare variants and genetic heterogeneity in ethnically diverse populations. In addition, we provide an overview of genes underlying Mendelian disorders featuring eczematous skin inflammation. [ABSTRACT FROM AUTHOR]

Background: The SAGES University Colorectal Masters Program is a structured educational curriculum that is designed to aid practicing surgeons develop and maintain knowledge and technical skills for laparoscopic colorectal surgery. The Colorectal Pathway is based on three anchoring procedures (laparoscopic right colectomy, laparoscopic left and sigmoid colectomy for uncomplicated and complex disease, and intracorporeal anastomosis for minimally invasive right colectomy) corresponding to three levels of performance (competency, proficiency and mastery). This manuscript presents focused summaries of the top 10 seminal articles selected for laparoscopic left and sigmoid colectomy for complex benign and malignant disease. Methods: A systematic literature search of Web of Science for the most cited articles on the topic of laparoscopic complex left/sigmoid colectomy yielded 30 citations. These articles were reviewed and ranked by the SAGES Colorectal Task Force and invited subject experts according to their citation index. The top 10 ranked articles were then reviewed and summarized, with emphasis on relevance and impact in the field, study findings, strength and limitations and conclusions. Results: The top 10 seminal articles selected for the laparoscopic left/sigmoid colectomy for complex disease anchoring procedure include advanced procedures such as minimally invasive splenic flexure mobilization techniques, laparoscopic surgery for complicated and/or diverticulitis, splenic flexure tumors, complete mesocolic excision, and other techniques (e.g., Deloyers or colonic transposition in cases with limited colonic reach after extended left-sided resection). Conclusions: The SAGES Colorectal Masters Program top 10 seminal articles selected for laparoscopic left and sigmoid colectomy for complex benign and malignant disease anchoring procedure are presented. These procedures were the most essential in the armamentarium of practicing surgeons that perform minimally invasive surgery for complex left and sigmoid colon pathology. [ABSTRACT FROM AUTHOR]

Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In this paper, we extend and evaluate two adaptive Fisher's methods, namely AFp and AFz, from the p-value combination perspective for phenotype–mRNA association analysis. The proposed method effectively aggregates heterogeneous phenotype–gene effects, allows association with different data types of phenotypes, and performs the selection of the associated phenotypes. Variability indices of the phenotype–gene effect selection are calculated by bootstrap analysis, and the resulting co-membership matrix identifies gene modules clustered by phenotype–gene effect. Extensive simulations demonstrate the superior performance of AFp compared to existing methods in terms of type I error control, statistical power and biological interpretation. Finally, the method is separately applied to three sets of transcriptomic and clinical datasets from lung disease, breast cancer, and brain aging and generates intriguing biological findings. [ABSTRACT FROM AUTHOR]

Brazilian quilombos are communities formed by enslaved Africans and their descendants all over the country during slavery and shortly after its abolition. Quilombos harbor a great fraction of the largely unknown genetic diversity of the African diaspora in Brazil. Thus, genetic studies in quilombos have the potential to provide important insights not only into the African roots of the Brazilian population but also into the genetic bases of complex traits and human adaptation to diverse environments. This review summarizes the main results of genetic studies performed on quilombos so far. Here, we analyzed the patterns of African, Amerindian, European, and subcontinental ancestry (within Africa) of quilombos from the five different geographic regions of Brazil. In addition, uniparental markers (from the mtDNA and the Y chromosome) studies are analyzed together to reveal demographic processes and sex-biased admixture that occurred during the formation of these unique populations. Lastly, the prevalence of known malaria-adaptive African mutations and other African-specific variants discovered in quilombos, as well as the genetic bases of health-related traits, are discussed here, together with their implication for the health of populations of African descent. [ABSTRACT FROM AUTHOR]

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

In complex diseases, the interactions among genes are commonly elucidated through the lens of graphs. Amongst these genes, certain ones form bi-functional modules within the graph, contingent upon their (anti)correlation with a specific functional state, such as susceptibility to a genetic disorder of non-Mendelian traits. Consequently, a disease can be delineated by a finite number of these discernible modules. Within each module, there exist allelic variants that pose a genetic risk, thus qualifying as genetic risk factors. These factors precipitate a permissive state, which if all other modules also align in the same permissive state, can ultimately lead to the onset of the disease in an individual. To gain a deeper insight into the incidence of a disease, it becomes imperative to acquire a comprehensive understanding of the genetic transmission of these factors. In this work, we present a non-linear model for this transmission, drawing inspiration from the classic theory of the Bell experiment. This model aids in elucidating the variances observed in SNP interactions concerning the risk of disease.

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Genomic changes specific to higher primates are regarded as primate-specific genomic information (PSI). Using PSI to inform genetic studies is highly desirable but hampered by three factors: heterogeneity among PSI studies, lack of integrated profiles of the identified PSI elements and dearth of relevant functional information. We report a database of 19,767 PSI elements collated from nine types of brain-related studies, which form 19,473 non-overlapping PSI regions that distribute unevenly but jointly cover only 0.81% of the genome. About 2.5% of the PSI regions colocalized with variants identified in genome-wide association studies, with disease loci more likely colocalized than quantitative trait loci (p = 1.6 × 10−5), particularly in regions without obvious regulatory roles. We further showed an LRP4 exemplar region with PSI elements orchestrated with common and rare disease variants and other functional elements. Our results render PSI elements as a valuable source to inform genetic studies of complex diseases.

In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, the importance of developing multi-target drugs as a strategy to address complex diseases such as NASH has been growing; however, their manufacturing processes remain time- and cost-intensive and inefficient. To overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. We designed a tetra-specific compound, C-192, targeting glucagon-like peptide 1 (GLP-1), glucagon (GCG), fibroblast growth factor 21 (FGF21), and interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH using UniStac. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine-choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumulation, and the non-alcoholic fatty liver disease activity score. In this study, we demonstrated the feasibility of UniStac in creating multi-specific drugs and confirmed the therapeutic potential of C-192, a drug that integrates multiple mechanisms into a single molecule for the treatment of NASH.

Abstract Background/aim The polygenic risk score (PRS) shows promise as a potentially effective approach to summarize genetic risk for complex diseases such as alcohol use disorder that is influenced by a combination of multiple variants, each of which has a very small effect. Yet, conventional PRS methods tend to over-adjust confounding factors in the discovery sample and thus have low power to predict the phenotype in the target sample. This study aims to address this important methodological issue. Methods This study proposed a new method to construct PRS by (1) approximating the polygenic model using a few principal components selected based on eigen-correlation in the discovery data; and (2) conducting principal component projection on the target data. Secondary data analysis was conducted on two large scale databases: the Study of Addiction: Genetics and Environment (SAGE; discovery data) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; target data) to compare performance of the conventional and proposed methods. Result and conclusion The results show that the proposed method has higher prediction power and can handle participants from different ancestry backgrounds. We also provide practical recommendations for setting the linkage disequilibrium (LD) and p value thresholds.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

Abstract Background July 20th, 2022, marks the 200th anniversary of the “Father of Genetics,” Gregor Mendel’s birth. His experiments with pea plants established the fundamental principles of genetic inheritance. Main text In this article, a succinct review of literature is hereby done to answer two key questions: (1) How Mendel’s principles of genetic inheritance helped us understand Mendelian disorders? and (2) How the study of Mendelian disorders can help us understand complex diseases? Conclusion This literature review concludes that continued effort to understand the genetic basis of Mendelian disorders will improve our understanding and treatment strategies for complex diseases.

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research. [ABSTRACT FROM AUTHOR]

Ehlers‐Danlos syndrome, hypermobility type (hEDS) is a heritable connective tissue disorder that currently does not have a known molecular etiology. Previous studies have explored the complex symptomology, clinical diagnosis, and psychological aspects of hEDS. Genetics providers currently aid in the diagnosis and management guidance of patients with hEDS, but there is limited data describing the needs and expectations of individuals with hEDS from a clinical genetics appointment. Our study sought to explore these items through the use of an online survey to assess participants' beliefs, needs and expectations (BNE) for genetic counseling as well as questions about demographics, hEDS symptoms, and current medical care. A total of 460 respondents with hEDS completed the survey. Most participants felt joint pain/weakness (n = 392; 88%) was one of the most disruptive symptoms of hEDS and 63% (n = 289) reported having psychiatric conditions. BNE scores were highest in two domains: expectations to have psychosocial concerns addressed during a genetic counseling appointment (mean score = 4.4/5; SD = 0.56) and desire for positive feelings after a genetic counseling session (mean score = 4.3/5; SD = 0.59). Participants who previously had genetic counseling felt less unsure about their diagnosis (p = 0.02) and had lower need for information about hEDS (p < 0.001). Majority of participants did not feel that their doctors were knowledgeable about hEDS (n = 269; 58%) and strongly supported a multidisciplinary approach to their care (n = 445; 97%). This research provides a framework for genetics providers and other healthcare professionals to assess the needs and expectations of patients with hEDS and consider re‐structuring their appointment formats to service this population. [ABSTRACT FROM AUTHOR]

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities. [ABSTRACT FROM AUTHOR]

Background: The identification of the causal SNPs of complex diseases in largescale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired. Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data. Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data. [ABSTRACT FROM AUTHOR]

An epilepsy diagnosis has large consequences for an individual but is often difficult to make in clinical practice. Novel biomarkers are thus greatly needed. Here, we give an overview of how thousands of common genetic factors that increase the risk for epilepsy can be summarized as epilepsy polygenic risk scores (PRS). We discuss the current state of research on how epilepsy PRS can serve as a biomarker for the risk for epilepsy. The high heritability of common forms of epilepsy, particularly genetic generalized epilepsy, indicates a promising potential for epilepsy PRS in diagnosis and risk prediction. Small sample sizes and low ancestral diversity of current epilepsy genome-wide association studies show, however, a need for larger and more diverse studies before epilepsy PRS could be properly implemented in the clinic. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA-mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

The rapid development of multi-omics (transcriptome, proteome, cistrome, imaging, and regulome) mediated wide association studies methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multi-omics mediated wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association studies (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multi-omics mediated wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Background: With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk., Results: We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies., Conclusions: Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance., Competing Interests: Competing interests There are no financial and non-financial competing interests declared by authors. Additional Declarations: No competing interests reported.

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA-disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNAdisease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA-disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA-disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA's performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies. [ABSTRACT FROM AUTHOR]

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle‐aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long‐lived individuals. We further stratified PRSs into cell‐type groups and significance‐level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p‐values (p ≤ 1x10‐5) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10‐5 < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade‐offs between complex diseases and longevity. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual's likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10−14), 62.3% increase in risk for severe obesity (p = 1 × 10−6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10−15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing. [ABSTRACT FROM AUTHOR]

Due to the high prevalence of mental illness in the general population, genetic counselors are likely to encounter patients with mental illness in practice, regardless of specialty. However, previous studies have shown that recent graduates of genetic counseling programs do not feel comfortable discussing mental illness in clinical encounters. One possible explanation for this discomfort is stigma toward mental illness, a well‐documented phenomenon both in society and in the healthcare field. Previous studies of this phenomenon in genetic counselors and trainees have relied on self‐report measures and are vulnerable to social desirability bias. We sought to gain a holistic understanding of attitudes toward mental illness held by genetic counseling trainees by measuring implicit and explicit biases. This study assessed 141 responses from genetic counseling students and recent graduates from master's graduate programs across North America. They were asked to complete a survey, which included a demographic questionnaire, a scale that has been validated for use for a variety of healthcare professionals (Nordt et al. 2006, Schizophrenia Bulletin, 32, 709), measuring explicit attitudes toward those with depression and schizophrenia (i.e., social distance and stereotype endorsement), and an implicit association test. Mean scores on the social distance and stereotype endorsement scales were higher for schizophrenia than depression, indicating higher levels of explicit bias toward the former than the latter. Participants held slightly significant implicit bias toward individuals with either physical or mental illness. These data suggest that unconscious or implicit bias may not contribute to unpreparedness to address psychiatric disorders in clinical practice that has been previously reported by new graduates. Therefore, genetic counseling trainees may be receptive to clinically relevant education pertaining to mental illness. These results could inform the curriculum of genetic counseling programs and facilitate provision of services to this population. [ABSTRACT FROM AUTHOR]