What is complex about complex disorders?

Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples

PURPOSE

Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears.

METHODS

A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs.

RESULTS

The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears.

CONCLUSION

The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.

The Role of FKBPs in Complex Disorders: Neuropsychiatric Diseases, Cancer, and Type 2 Diabetes Mellitus

Stress is a common denominator of complex disorders and the FK-506 binding protein (FKBP)51 plays a central role in stress. Hence, it is not surprising that multiple studies imply the involvement of the FKBP51 protein and/or its coding gene, FKBP5, in complex disorders. This review summarizes such reports concentrating on three disorder clusters-neuropsychiatric, cancer, and type 2 diabetes mellitus (T2DM). We also attempt to point to potential mechanisms suggested to mediate the effect of FKBP5/FKBP51 on these disorders. Neuropsychiatric diseases considered in this paper include (i) Huntington's disease for which increased autophagic cellular clearance mechanisms related to decreased FKBP51 protein levels or activity is discussed, Alzheimer's disease for which increased FKBP51 activity has been shown to induce Tau phosphorylation and aggregation, and Parkinson's disease in the context of which FKBP12 is mentioned; and (ii) mental disorders, for which significant association with the single nucleotide polymorphism (SNP) rs1360780 of FKBP5 intron 7 along with decreased DNA methylation were revealed. Since cancer is a large group of diseases that can start in almost any organ or tissue of the body, FKBP51's role depends on the tissue type and differences among pathways expressed in those tumors. The FKBP51-heat-shock protein-(Hsp)90-p23 super-chaperone complex might function as an oncogene or as a tumor suppressor by downregulating the serine/threonine protein kinase (AKt) pathway. In T2DM, two potential pathways for the involvement of FKBP51 are highlighted as affecting the pathogenesis of the disease-the peroxisome proliferator-activated receptor-γ (PPARγ) and AKt.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

Genetic risk assessment based on association and prediction studies

The genetic basis of phenotypic emergence provides valuable information for assessing individual risk. While association studies have been pivotal in identifying genetic risk factors within a population, complementing it with insights derived from predictions studies that assess individual-level risk offers a more comprehensive approach to understanding phenotypic expression. In this study, we established personalized risk assessment models using single-nucleotide polymorphism (SNP) data from 200 Korean patients, of which 100 experienced hepatitis B surface antigen (HBsAg) seroclearance and 100 patients demonstrated high levels of HBsAg. The risk assessment models determined the predictive power of the following: (1) genome-wide association study (GWAS)-identified candidate biomarkers considered significant in a reference study and (2) machine learning (ML)-identified candidate biomarkers with the highest feature importance scores obtained by using random forest (RF). While utilizing all features yielded 64% model accuracy, using relevant biomarkers achieved higher model accuracies: 82% for 52 GWAS-identified candidate biomarkers, 71% for three GWAS-identified biomarkers, and 80% for 150 ML-identified candidate biomarkers. Findings highlight that the joint contributions of relevant biomarkers significantly influence phenotypic emergence. On the other hand, combining ML-identified candidate biomarkers into the pool of GWAS-identified candidate biomarkers resulted in the improved predictive accuracy of 90%, demonstrating the capability of ML as an auxiliary analysis to GWAS. Furthermore, some of the ML-identified candidate biomarkers were found to be linked with hepatocellular carcinoma (HCC), reinforcing previous claims that HCC can still occur despite the absence of HBsAg.

Cytosine base editors induce off-target mutations and adverse phenotypic effects in transgenic mice

Base editors have been reported to induce off-target mutations in cultured cells, mouse embryos and rice, but their long-term effects in vivo remain unknown. Here, we develop a Systematic evaluation Approach For gene Editing tools by Transgenic mIce (SAFETI), and evaluate the off-target effects of BE3, high fidelity version of CBE (YE1-BE3-FNLS) and ABE (ABE7.10^F148A) in ~400 transgenic mice over 15 months. Whole-genome sequence analysis reveals BE3 expression generated de novo mutations in the offspring of transgenic mice. RNA-seq analysis reveals both BE3 and YE1-BE3-FNLS induce transcriptome-wide SNVs, and the numbers of RNA SNVs are positively correlated with CBE expression levels across various tissues. By contrast, ABE7.10^F148A shows no detectable off-target DNA or RNA SNVs. Notably, we observe abnormal phenotypes including obesity and developmental delay in mice with permanent genomic BE3 overexpression during long-time monitoring, elucidating a potentially overlooked aspect of side effects of BE3 in vivo.

Modeling the enigma of complex disease etiology

BACKGROUND

Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.

METHODS

Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.

RESULTS

We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.

CONCLUSIONS

The Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

What are the challenges with multi-targeted drug design for complex diseases?

INTRODUCTION

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.

AREAS COVERED

In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.

EXPERT OPINION

Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

Cerebral Polymorphisms for Lateralisation: Modelling the Genetic and Phenotypic Architectures of Multiple Functional Modules

Recent fMRI and fTCD studies have found that functional modules for aspects of language, praxis, and visuo-spatial functioning, while typically left, left and right hemispheric respectively, frequently show atypical lateralisation. Studies with increasing numbers of modules and participants are finding increasing numbers of module combinations, which here are termed cerebral polymorphisms—qualitatively different lateral organisations of cognitive functions. Polymorphisms are more frequent in left-handers than right-handers, but it is far from the case that right-handers all show the lateral organisation of modules described in introductory textbooks. In computational terms, this paper extends the original, monogenic McManus DC (dextral-chance) model of handedness and language dominance to multiple functional modules, and to a polygenic DC model compatible with the molecular genetics of handedness, and with the biology of visceral asymmetries found in primary ciliary dyskinesia. Distributions of cerebral polymorphisms are calculated for families and twins, and consequences and implications of cerebral polymorphisms are explored for explaining aphasia due to cerebral damage, as well as possible talents and deficits arising from atypical inter- and intra-hemispheric modular connections. The model is set in the broader context of the testing of psychological theories, of issues of laterality measurement, of mutation-selection balance, and the evolution of brain and visceral asymmetries.

Polypharmacology: The science of multi-targeting molecules

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

DGK and DZHK position paper on genome editing: basic science applications and future perspective

For a long time, gene editing had been a scientific concept, which was limited to a few applications. With recent developments, following the discovery of TALEN zinc-finger endonucleases and in particular the CRISPR/Cas system, gene editing has become a technique applicable in most laboratories. The current gain- and loss-of function models in basic science are revolutionary as they allow unbiased screens of unprecedented depth and complexity and rapid development of transgenic animals. Modifications of CRISPR/Cas have been developed to precisely interrogate epigenetic regulation or to visualize DNA complexes. Moreover, gene editing as a clinical treatment option is rapidly developing with first trials on the way. This article reviews the most recent progress in the field, covering expert opinions gathered during joint conferences on genome editing of the German Cardiac Society (DGK) and the German Center for Cardiovascular Research (DZHK). Particularly focusing on the translational aspect and the combination of cellular and animal applications, the authors aim to provide direction for the development of the field and the most frequent applications with their problems.

Apolipoprotein C-III and cardiovascular diseases: when genetics meet molecular pathologies

Cardiovascular diseases (CVD) have overtaken infectious diseases and are currently the world's top killer. A quite strong linkage between this type of ailments and elevated plasma levels of triglycerides (TG) has been always noticed. Notably, this risk factor is mired in deep confusion, since its role in atherosclerosis is uncertain. One of the explanations that aim to decipher this persistent enigma was provided by apolipoprotein C-III (apoC-III), a small protein historically recognized as an important regulator of TG metabolism. Preeminently, hundreds of studies have been carried out in order to explore the APOC3 genetic background, as well as to establish a correlation between its variants and dyslipidemia-related disorders, pointing to an earnest predictive power for future outcomes. Among several polymorphisms reported within the APOC3, the SstI site in its 3'-untranslated region (3'-UTR) was the most consistently and robustly associated with an increased CVD risk. As more genetic data supporting its importance in cardiovascular events aggregate, it was declared, correspondingly, that apoC-III exerts various atherogenic effects, either by intervening in the function and catabolism of many lipoproteins, or by inducing endothelial inflammation and smooth muscle cells (SMC) proliferation. This review was designed to shed the light on the structural and functional aspects of the APOC3 gene, the existing association between its SstI polymorphism and CVD, and the specific molecular mechanisms that underlie apoC-III pathological implications. In addition, the translation of all these gathered knowledges into preventive and therapeutic benefits will be detailed too.

Genetic Inheritance and Its Contribution to Tinnitus

Tinnitus is the abnormal perception of sound that affects more than 15% of adult population around the globe. Severe tinnitus is considered a complex disorder that arises as result of the interaction of genetic and environmental factors, and it is associated with several comorbidities such as hearing loss, anxiety, and insomnia. We begin this review with an introduction to human molecular genetics and the role of genetic variation on the inheritance. There are some genetic reports on tinnitus heritability including concordance studies in twins and adoptees or aggregation in families providing some evidence for familial aggregation in patients with severe tinnitus and high concordance in monozygotic twins with bilateral tinnitus. So, sex differences in familial aggregation and heritability of bilateral tinnitus suggest a potential sexual dimorphism in tinnitus inheritance.Molecular genetic studies have been demonstrated to be a useful tool to understand the role of genetic variation in rare diseases and complex disorders. The reported associations in common variants in neurotrophic factors such as GDNF, BDNF, or potassium channels genes were underpowered, and the lack of replication questions these findings. Although candidate gene approaches have failed in replicating these genetic associations, the development of high throughput sequencing technology and the selection of extreme phenotypes are strategies that will allow the clinicians and researchers to combine genetic information with clinical data to implement a personalized diagnosis and therapy in patients with tinnitus.

Type 2 diabetes mellitus: pathogenesis and genetic diagnosis

Type 2 diabetes mellitus (T2DM) is a heterogeneous condition that is related to both defective insulin secretion and peripheral insulin resistance. Beta cells are the major organ for secreting insulin hence, it is important to maintain an adequate beta-cell mass in response to various changes. Insulin resistance is a major cause of T2DM leads to elevated free fatty acid (FFA) levels which increases beta-cell mass and insulin secretion to compensate for insulin insensitivity. Chronic increase of plasma FFA levels results in disturbances in lipid metabolism, which contributes to decreased beta-cell function and lipotoxicity thus promoting T2DM. In the present review, we have discussed the process of beta-cell destruction, the role of genes in contributing to the fast increase in the progression of T2DM in detail. More than 130 variants in various T2DM susceptibility and candidate genes have been discovered to be associated with T2DM. Still, these variants elucidate only a small amount of total heritability of T2DM. Further, there is also an inventory of presently used therapeutic tools and a review of novel therapeutic approaches like incretin-based therapies or sodium-glucose transporter-2 inhibitors. Additionally, providing a concise but comprehensive update, this review will be essential to every clinician involved in the treatment of diabetes mellitus.

Rare genetic causes of complex kidney and urological diseases

Although often considered a single-entity, chronic kidney disease (CKD) comprises many pathophysiologically distinct disorders that result in persistently abnormal kidney structure and/or function, and encompass both monogenic and polygenic aetiologies. Rare inherited forms of CKD frequently span diverse phenotypes, reflecting genetic phenomena including pleiotropy, incomplete penetrance and variable expressivity. Use of chromosomal microarray and massively parallel sequencing technologies has revealed that genomic disorders and monogenic aetiologies contribute meaningfully to seemingly complex forms of CKD across different clinically defined subgroups and are characterized by high genetic and phenotypic heterogeneity. Investigations of prevalent genomic disorders in CKD have integrated genetic, bioinformatic and functional studies to pinpoint the genetic drivers underlying their renal and extra-renal manifestations, revealing both monogenic and polygenic mechanisms. Similarly, massively parallel sequencing-based analyses have identified gene- and allele-level variation that contribute to the clinically diverse phenotypes observed for many monogenic forms of nephropathy. Genome-wide sequencing studies suggest that dual genetic diagnoses are found in at least 5% of patients in whom a genetic cause of disease is identified, highlighting the fact that complex phenotypes can also arise from multilocus variation. A multifaceted approach that incorporates genetic and phenotypic data from large, diverse cohorts will help to elucidate the complex relationships between genotype and phenotype for different forms of CKD, supporting personalized medicine for individuals with kidney disease.

microRNAs in Autism Spectrum Disorders

BACKGROUND

Efforts to unravel the extensive impact of the non-coding elements of the human genome on cell homeostasis and pathological processes have gained momentum over the last couple of decades. miRNAs refer to short, often 18-25 nucleotides long, non-coding RNA molecules which can regulate gene expression. Each miRNA can regulate several mRNAs.

METHODS

This article reviews the literature on the roles of miRNAs in autism.

RESULTS

Considering the fact that ~ 1% of the human DNA encodes different families of miRNAs, their overall impact as critical regulators of gene expression in the mammalian brain should be immense. Though the autism spectrum disorders (ASDs) are predominantly genetic in nature and several candidate genes are already identified, the highly heterogeneous and multifactorial nature of the disorder makes it difficult to identify common genetic risk factors. Several studies have suggested that the environmental factors may interact with the genetic factors to increase the risk. miRNAs could possibly be one of those factors which explain this link between genetics and the environment.

CONCLUSION

In the present review, we have summarized our current knowledge on miRNAs and their complex roles in ASD, and also on their therapeutic applications.

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

BACKGROUND

Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.

RESULTS

In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study.

CONCLUSIONS

Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.

Excess of Rare Missense Variants in Hearing Loss Genes in Sporadic Meniere Disease

Meniere's disease (MD) is a clinical spectrum of rare disorders characterized by vertigo attacks, associated with sensorineural hearing loss (SNHL) and tinnitus involving low to medium frequencies. Although it shows familial aggregation with incomplete phenotypic forms and variable expressivity, most cases are considered sporadic. The aim of this study was to investigate the burden for rare variation in SNHL genes in patients with sporadic MD. We conducted a targeted-sequencing study including SNHL and familial MD genes in 890 MD patients to compare the frequency of rare variants in cases using three independent public datasets as controls. Patients with sporadic MD showed a significant enrichment of missense variants in SNHL genes that was not found in the controls. The list of genes includes GJB2, USH1G, SLC26A4, ESRRB, and CLDN14. A rare synonymous variant with unknown significance was found in the MARVELD2 gene in several unrelated patients with MD. There is a burden of rare variation in certain SNHL genes in sporadic MD. Furthermore, the interaction of common and rare variants in SNHL genes may have an additive effect on MD phenotype. This study will contribute to design a gene panel for the genetic diagnosis of MD.

Primary Immunodeficiency and Cancer Predisposition Revisited: Embedding Two Closely Related Concepts Into an Integrative Conceptual Framework

Common understanding suggests that the normal function of a "healthy" immune system safe-guards and protects against the development of malignancies, whereas a genetically impaired one might increase the likelihood of their manifestation. This view is primarily based on and apparently supported by an increased incidence of such diseases in patients with specific forms of immunodeficiencies that are caused by high penetrant gene defects. As I will review and discuss herein, such constellations merely represent the tip of an iceberg. The overall situation is by far more varied and complex, especially if one takes into account the growing difficulties to define what actually constitutes an immunodeficiency and what defines a cancer predisposition. The enormous advances in genome sequencing, in bioinformatic analyses and in the functional in vitro and in vivo assessment of novel findings together with the availability of large databases provide us with a wealth of information that steadily increases the number of sequence variants that concur with clinically more or less recognizable immunological problems and their consequences. Since many of the newly identified hard-core defects are exceedingly rare, their tumor predisposing effect is difficult to ascertain. The analyses of large data sets, on the other hand, continuously supply us with low penetrant variants that, at least in statistical terms, are clearly tumor predisposing, although their specific relevance for the respective carriers still needs to be carefully assessed on an individual basis. Finally, defects and variants that affect the same gene families and pathways in both a constitutional and somatic setting underscore the fact that immunodeficiencies and cancer predisposition can be viewed as two closely related errors of development. Depending on the particular genetic and/or environmental context as well as the respective stage of development, the same changes can have either a neutral, predisposing and, in some instances, even a protective effect. To understand the interaction between the immune system, be it "normal" or "deficient" and tumor predisposition and development on a systemic level, one therefore needs to focus on the structure and dynamic functional organization of the entire immune system rather than on its isolated individual components alone.

Bioinformatics methodologies for coeliac disease and its comorbidities

Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e.g. a list of comorbidities from the literature), using bioinformatics methods such as gene set enrichment analysis and semantic similarity. Our pipeline is written in R language, available at the following link: http://bioinformatica.isa.cnr.it/COELIAC_DISEASE/SCRIPTS/. We found a list of common differential expressed genes, gene ontology terms and pathways among CD and comorbidities and the closeness among the selected pathologies by means of disease ontology terms. Physicians and other researchers, such as molecular biologists, systems biologists and pharmacologists can use it to analyze pathology in detail, from differential expressed genes to ontologies, performing a comparison with the pathology comorbidities or with other diseases.

The neuronal migration hypothesis of dyslexia: A critical evaluation 30 years on

The capacity for language is one of the key features underlying the complexity of human cognition and its evolution. However, little is known about the neurobiological mechanisms that mediate normal or impaired linguistic ability. For developmental dyslexia, early postmortem studies conducted in the 1980s linked the disorder to subtle defects in the migration of neurons in the developing neocortex. These early studies were reinforced by human genetic analyses that identified dyslexia susceptibility genes and subsequent evidence of their involvement in neuronal migration. In this review, we examine recent experimental evidence that does not support the link between dyslexia and neuronal migration. We critically evaluate gene function studies conducted in rodent models and draw attention to the lack of robust evidence from histopathological and imaging studies in humans. Our review suggests that the neuronal migration hypothesis of dyslexia should be reconsidered, and the neurobiological basis of dyslexia should be approached with a fresh start.

Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP

Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance-a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.

Targeted exome sequencing in anti-factor H antibody negative HUS reveals multiple variations

BACKGROUND

Genetic susceptibility to atypical hemolytic uremic syndrome (aHUS) may lie within genes regulating or activating the alternate complement and related pathways converging on endothelial cell activation.

METHODS

We tested 32 Indian patients of aHUS negative for antibodies to complement factor H for genetic variations in a panel of 15 genes, i.e., CFH, CFHR1-5, CFI, CFB, C3, CD46, MASP2, DGKE, ADAMTS13, THBD and PLG using next-generation DNA sequencing and for copy number variation in CFHR1-3.

RESULTS

Despite absence of a public database of exome variations in the Indian population and limited functional studies, we could establish a genetic diagnosis in 6 (18.8%) patients using a stringent scheme of prioritization. One patient carried a likely pathogenic variation. The number of patients carrying possibly pathogenic variation was as follows: 1 variation: 5 patients, 2 variations: 9 patients, 3 variations: 5 patients, 4 variations: 9 patients, 5 variations: 2 patients and 6 variations: 2 patients. Homozygous deletion of CFHR1-3 was present in five patients; none of these carried a diagnostic genetic variation. Patients with or without diagnostic variation did not differ significantly in terms of enrichment of genetic variations that were rare/novel or predicted deleterious, or for possible environmental triggers.

CONCLUSION

We conclude that genetic testing for multiple genes in patients with aHUS negative for anti-FH antibodies reveals multiple candidate variations that require prioritization. Population data on variation frequency of the Indian population and supportive functional studies are likely to improve diagnostic yield.

New drug developments in psychosis: Challenges, opportunities and strategies

All currently approved drugs for schizophrenia work mainly by dopaminergic antagonism. While they are efficacious for psychotic symptoms, their efficacy is limited for negative symptoms and cognitive deficits which underlie the substantive disability in this illness. Recent insights into the biological basis of schizophrenia, especially in relation to non-dopaminergic mechanisms, have raised the efforts to find novel and effective drug targets, though with relatively little success thus far. Potential impediments to novel drug discovery include the continued use of symptom based disease definitions which leads to etiological and pathophysiological heterogeneity, lack of valid preclinical models for drug testing, and design limitations in clinical trials. These roadblocks can be addressed by (i) characterizing trans-diagnostic, translational pathophysiological dimensions as potential treatment targets, (ii) efficiency, accountability and, transparency in approaches to the clinical trials process, and (iii) leveraging recent advances in genetics and in vitro phenotypes. Accomplishing these goals is urgent given the significant unmet needs in the pharmacological treatment of schizophrenia. As this happens, it is imperative that clinicians employ optimal dosing, measurement-based care, and other best practices in utilizing existing treatments to optimize outcomes for their patients today.

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases.

Alteration of Neuronal Excitability and Short-Term Synaptic Plasticity in the Prefrontal Cortex of a Mouse Model of Mental Illness

Using a genetic mouse model that faithfully recapitulates a DISC1 genetic alteration strongly associated with schizophrenia and other psychiatric disorders, we examined the impact of this mutation within the prefrontal cortex. Although cortical layering, cytoarchitecture, and proteome were found to be largely unaffected, electrophysiological examination of the mPFC revealed both neuronal hyperexcitability and alterations in short-term synaptic plasticity consistent with enhanced neurotransmitter release. Increased excitability of layer II/III pyramidal neurons was accompanied by consistent reductions in voltage-activated potassium currents near the action potential threshold as well as by enhanced recruitment of inputs arising from superficial layers to layer V. We further observed reductions in both the paired-pulse ratios and the enhanced short-term depression of layer V synapses arising from superficial layers consistent with enhanced neurotransmitter release at these synapses. Recordings from layer II/III pyramidal neurons revealed action potential widening that could account for enhanced neurotransmitter release. Significantly, we found that reduced functional expression of the voltage-dependent potassium channel subunit K_v1.1 substantially contributes to both the excitability and short-term plasticity alterations that we observed. The underlying dysregulation of K_v1.1 expression was attributable to cAMP elevations in the PFC secondary to reduced phosphodiesterase 4 activity present in Disc1 deficiency and was rescued by pharmacological blockade of adenylate cyclase. Our results demonstrate a potentially devastating impact of Disc1 deficiency on neural circuit function, partly due to K_v1.1 dysregulation that leads to a dual dysfunction consisting of enhanced neuronal excitability and altered short-term synaptic plasticity.SIGNIFICANCE STATEMENT Schizophrenia is a profoundly disabling psychiatric illness with a devastating impact not only upon the afflicted but also upon their families and the broader society. Although the underlying causes of schizophrenia remain poorly understood, a growing body of studies has identified and strongly implicated various specific risk genes in schizophrenia pathogenesis. Here, using a genetic mouse model, we explored the impact of one of the most highly penetrant schizophrenia risk genes, DISC1, upon the medial prefrontal cortex, the region believed to be most prominently dysfunctional in schizophrenia. We found substantial derangements in both neuronal excitability and short-term synaptic plasticity-parameters that critically govern neural circuit information processing-suggesting that similar changes may critically, and more broadly, underlie the neural computational dysfunction prototypical of schizophrenia.

The symptom matrix: Using a formalism-based approach to address complex syndromes systematically

Complex rheumatological syndromes such as Systemic lupus erythematosus, Sjogren's Syndrome and many connective tissue disorders can be a challenge to classify and diagnose, due to their wide-ranging signs and symptoms, not all of which will necessarily be present in all patients. This can result in difficulties for the clinician, patient and researcher if signs and symptoms are either overlooked or are incorrectly included in the nosology or classification of diseases. This article presents a formalism-based approach to describing syndromes. This approach offers a more systematic way of representing signs and symptoms, to aid in diagnosis and classification of complex, heterogeneous and little understood syndromes. To illustrate this approach, Ehlers-Danlos Syndrome - Hypermobility Type is used as a worked example. This approach can also be applied to other syndromes in both clinical and educational settings, to assist with research, diagnosis, choice of treatment or intervention and nosology revision.

Complex Genetics of Type 2 Diabetes and Effect Size: What have We Learned from Isolated Populations?

Genetic studies in large outbred populations have documented a complex, highly polygenic basis for type 2 diabetes (T2D). Most of the variants currently known to be associated with T2D risk have been identified in large studies that included tens of thousands of individuals who are representative of a single major ethnic group such as European, Asian, or African. However, most of these variants have only modest effects on the risk for T2D; identification of definitive 'causal variant' or 'causative loci' is typically lacking. Studies in isolated populations offer several advantages over outbred populations despite being, on average, much smaller in sample size. For example, reduced genetic variability, enrichment of rare variants, and a more uniform environment and lifestyle, which are hallmarks of isolated populations, can reduce the complexity of identifying disease-associated genes. To date, studies in isolated populations have provided valuable insight into the genetic basis of T2D by providing both a deeper understanding of previously identified T2D-associated variants (e.g. demonstrating that variants in KCNQ1 have a strong parent-of-origin effect) or providing novel variants (e.g. ABCC8 in Pima Indians, TBC1D4 in the Greenlandic population, HNF1A in Canadian Oji-Cree). This review summarizes advancements in genetic studies of T2D in outbred and isolated populations, and provides information on whether the difference in the prevalence of T2D in different populations (Pima Indians vs. non-Hispanic Whites and non-Hispanic Whites vs. non-Hispanic Blacks) can be explained by the difference in risk allele frequencies of established T2D variants.

Emerging roles of ARHGAP33 in intracellular trafficking of TrkB and pathophysiology of neuropsychiatric disorders

Intracellular trafficking of receptor proteins is essential for neurons to detect various extracellular factors during the formation and refinement of neural circuits. However, the precise mechanisms underlying the trafficking of neurotrophin receptors to synapses remain elusive. Here, we demonstrate that a brain-enriched sorting nexin, ARHGAP33, is a new type of regulator for the intracellular trafficking of TrkB, a high-affinity receptor for brain-derived neurotrophic factor. ARHGAP33 knockout (KO) mice exhibit reduced expression of synaptic TrkB, impaired spine development and neuropsychiatric disorder-related behavioural abnormalities. These deficits are rescued by specific pharmacological enhancement of TrkB signalling in ARHGAP33 KO mice. Mechanistically, ARHGAP33 interacts with SORT1 to cooperatively regulate TrkB trafficking. Human ARHGAP33 is associated with brain phenotypes and reduced SORT1 expression is found in patients with schizophrenia. We propose that ARHGAP33/SORT1-mediated TrkB trafficking is essential for synapse development and that the dysfunction of this mechanism may be a new molecular pathology of neuropsychiatric disorders.

The molecular mechanisms of neurotrophin receptor trafficking are only partially understood. Here the authors show that ARHGAP33 interacts with SORT1 to regulate TrkB trafficking, the dysfunction of which impairs synapse development and leads to schizophrenia-related behavioural abnormalities in mice.

Using GWAS to identify genetic predisposition in hepatic autoimmunity

Primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH) represent the three major hepatic autoimmune conditions. Patient morbidity and mortality remain high across these three diseases, and an unmet need for rational therapy exists. Disease understanding has focused on combining clinical and laboratory based science to provide better insights into the joint host and environmental factors necessary for the initiation, and perpetuation, of hepato-biliary inflammation. Twin studies, family studies, population studies and an inter-relationship with other autoimmune phenomena suggest a genetic component to risk for each disease. Until recently, understanding of this genetic risk has been limited to HLA haplotypes. Associations with risk-conferring and protective HLA haplotypes are present in all three diseases. Over the last few years, genome-wide association studies (GWAS), and related genetic association studies, have greatly increased understanding of the genetic risk signature of these three diseases and autoimmunity in general. Here we consider the rationale for GWAS in general and with specific reference to hepatic autoimmunity. We consider the process of GWAS, and highlight major findings to date. Potential functional implications of key findings are discussed including the IL-12/STAT4 pathway in PBC and the CD28/IL-2 pathway in PSC. We describe the marked pleiotropy demonstrated by PBC and PSC, which is consistent with other autoimmune diseases. Further, we focus on specific gene associations including SH2B3, which is common to all three diseases, and FUT2 in PSC, which represents a link between environment and genetics. We review attempts to translate GWAS findings into basic laboratory models including in vivo systems and highlight where clinical observations relate to genetics. Finally we describe deficiencies in GWAS to date and consider future study of genetics in hepatic autoimmunity.

Integrating Diverse Types of Genomic Data to Identify Genes that Underlie Adverse Pregnancy Phenotypes

Progress in understanding complex genetic diseases has been bolstered by synthetic approaches that overlay diverse data types and analyses to identify functionally important genes. Pre-term birth (PTB), a major complication of pregnancy, is a leading cause of infant mortality worldwide. A major obstacle in addressing PTB is that the mechanisms controlling parturition and birth timing remain poorly understood. Integrative approaches that overlay datasets derived from comparative genomics with function-derived ones have potential to advance our understanding of the genetics of birth timing, and thus provide insights into the genes that may contribute to PTB. We intersected data from fast evolving coding and non-coding gene regions in the human and primate lineage with data from genes expressed in the placenta, from genes that show enriched expression only in the placenta, as well as from genes that are differentially expressed in four distinct PTB clinical subtypes. A large fraction of genes that are expressed in placenta, and differentially expressed in PTB clinical subtypes (23–34%) are fast evolving, and are associated with functions that include adhesion neurodevelopmental and immune processes. Functional categories of genes that express fast evolution in coding regions differ from those linked to fast evolution in non-coding regions. Finally, there is a surprising lack of overlap between fast evolving genes that are differentially expressed in four PTB clinical subtypes. Integrative approaches, especially those that incorporate evolutionary perspectives, can be successful in identifying potential genetic contributions to complex genetic diseases, such as PTB.

Understanding Language from a Genomic Perspective

Language is a defining characteristic of the human species, but its foundations remain mysterious. Heritable disorders offer a gateway into biological underpinnings, as illustrated by the discovery that FOXP2 disruptions cause a rare form of speech and language impairment. The genetic architecture underlying language-related disorders is complex, and although some progress has been made, it has proved challenging to pinpoint additional relevant genes with confidence. Next-generation sequencing and genome-wide association studies are revolutionizing understanding of the genetic bases of other neurodevelopmental disorders, like autism and schizophrenia, and providing fundamental insights into the molecular networks crucial for typical brain development. We discuss how a similar genomic perspective, brought to the investigation of language-related phenotypes, promises to yield equally informative discoveries. Moreover, we outline how follow-up studies of genetic findings using cellular systems and animal models can help to elucidate the biological mechanisms involved in the development of brain circuits supporting language.

The impact of human copy number variation on gene expression

Recent years have witnessed a flurry of important technological and methodological developments in the discovery and analysis of copy number variations (CNVs), which are increasingly enabling the systematic evaluation of their impact on a broad range of phenotypes from molecular-level (intermediate) traits to higher-order clinical phenotypes. Like single nucleotide variants in the human genome, CNVs have been linked to complex traits in humans, including disease and drug response. These recent developments underscore the importance of incorporating complex forms of genetic variation into disease mapping studies and promise to transform our understanding of genome function and the genetic basis of disease. Here we review some of the findings that have emerged from transcriptome studies of CNVs facilitated by the rapid advances in -omics technologies and corresponding methodologies.

Genetics of type 2 diabetes-pitfalls and possibilities

Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.

Insights into the genetic foundations of human communication

The human capacity to acquire sophisticated language is unmatched in the animal kingdom. Despite the discontinuity in communicative abilities between humans and other primates, language is built on ancient genetic foundations, which are being illuminated by comparative genomics. The genetic architecture of the language faculty is also being uncovered by research into neurodevelopmental disorders that disrupt the normally effortless process of language acquisition. In this article, we discuss the strategies that researchers are using to reveal genetic factors contributing to communicative abilities, and review progress in identifying the relevant genes and genetic variants. The first gene directly implicated in a speech and language disorder was FOXP2. Using this gene as a case study, we illustrate how evidence from genetics, molecular cell biology, animal models and human neuroimaging has converged to build a picture of the role of FOXP2 in neurodevelopment, providing a framework for future endeavors to bridge the gaps between genes, brains and behavior.

Homogeneous case subgroups increase power in genetic association studies

Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect.

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

Exome sequencing identifies DLG1 as a novel gene for potential susceptibility to Crohn's disease in a Chinese family study

BACKGROUND

Genetic variants make some contributions to inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC). More than 100 susceptibility loci were identified in Western IBD studies, but susceptibility gene has not been found in Chinese IBD patients till now. Sequencing of individuals with an IBD family history is a powerful approach toward our understanding of the genetics and pathogenesis of IBD. The aim of this study, which focuses on a Han Chinese CD family, is to identify high-risk variants and potentially novel loci using whole exome sequencing technique.

METHODS

Exome sequence data from 4 individuals belonging to a same family were analyzed using bioinformatics methods to narrow down the variants associated with CD. The potential risk genes were further analyzed by genotyping and Sanger sequencing in family members, additional 401 healthy controls (HC), 278 sporadic CD patients, 123 UC cases, a pair of monozygotic CD twins and another Chinese CD family.

RESULTS

From the CD family in which the father and daughter were affected, we identified a novel single nucleotide variant (SNV) c.374T>C (p.I125T) in exon 4 of discs large homolog 1 (DLG1), a gene has been reported to play multiple roles in cell proliferation, T cell polarity and T cell receptor signaling. After genotyping among case and controls, a PLINK analysis showed the variant was of significance (P<0.05). 4 CD patients of the other Chinese family bore another non-synonymous variant c.833G>A (p.R278Q) in exon 9 of DLG1.

CONCLUSIONS

We have discovered novel genetic variants in the coding regions of DLG1 gene, the results support that DLG1 is a novel potential susceptibility gene for CD in Chinese patients.

The total burden of rare, non-synonymous exome genetic variants is not associated with childhood or late-life cognitive ability

Human cognitive ability shows consistent, positive associations with fitness components across the life-course. Underlying genetic variation should therefore be depleted by selection, which is not observed. Genetic variation in general cognitive ability (intelligence) could be maintained by a mutation-selection balance, with rare variants contributing to its genetic architecture. This study examines the association between the total number of rare stop-gain/loss, splice and missense exonic variants and cognitive ability in childhood and old age in the same individuals. Exome array data were obtained in the Lothian Birth Cohorts of 1921 and 1936 (combined N = 1596). General cognitive ability was assessed at age 11 years and in late life (79 and 70 years, respectively) and was modelled against the total number of stop-gain/loss, splice, and missense exonic variants, with minor allele frequency less than or equal to 0.01, using linear regression adjusted for age and sex. In both cohorts and in both the childhood and late-life models, there were no significant associations between rare variant burden in the exome and cognitive ability that survived correction for multiple testing. Contrary to our a priori hypothesis, we observed no evidence for an association between the total number of rare exonic variants and either childhood cognitive ability or late-life cognitive ability.