Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model

Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

Major depressive disorder is a debilitating condition with diverse neuroimaging correlates, including cortical thinning in medial prefrontal cortex and altered functional connectivity of cortical association networks. However, the molecular bases of these imaging markers remain ambiguous, despite a need for treatment targets and mechanisms. Here, we advance cross-modal approaches to identify cell types and gene transcripts associated with depression-implicated cortex. Across multiple population-imaging datasets (combined N ≥ 23,723) and ex vivo patient cortical tissue, somatostatin interneurons and astrocytes emerge as replicable cell-level correlates of depression and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging markers of depression and offer a roadmap for integrating in vivo clinical imaging with genetic and postmortem patient transcriptional data.

Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

Local and global features of genetic networks supporting a phenotypic switch

Phenotypic switches are associated with alterations in the cell's gene expression profile and are vital to many aspects of biology. Previous studies have identified local motifs of the genetic regulatory network that could underlie such switches. Recent advancements allowed the study of networks at the global, many-gene, level; however, the relationship between the local and global scales in giving rise to phenotypic switches remains elusive. In this work, we studied the epithelial-mesenchymal transition (EMT) using a gene regulatory network model. This model supports two clusters of stable steady-states identified with the epithelial and mesenchymal phenotypes, and a range of intermediate less stable hybrid states, whose importance in cancer has been recently highlighted. Using an array of network perturbations and quantifying the resulting landscape, we investigated how features of the network at different levels give rise to these landscape properties. We found that local connectivity patterns affect the landscape in a mostly incremental manner; in particular, a specific previously identified double-negative feedback motif is not required when embedded in the full network, because the landscape is maintained at a global level. Nevertheless, despite the distributed nature of the switch, it is possible to find combinations of a few local changes that disrupt it. At the level of network architecture, we identified a crucial role for peripheral genes that act as incoming signals to the network in creating clusters of states. Such incoming signals are a signature of modularity and are expected to appear also in other biological networks. Hybrid states between epithelial and mesenchymal arise in the model due to barriers in the interaction between genes, causing hysteresis at all connections. Our results suggest emergent switches can neither be pinpointed to local motifs, nor do they arise as typical properties of random network ensembles. Rather, they arise through an interplay between the nature of local interactions, and the core-periphery structure induced by the modularity of the cell.

Canalization and Robustness in Human Genetics and Disease

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.

Multitissue Multiomics Systems Biology to Dissect Complex Diseases

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

Precision medicine and therapies of the future

Precision medicine in the epilepsies has gathered much attention, especially with gene discovery pushing forward new understanding of disease biology. Several targeted treatments are emerging, some with considerable sophistication and individual‐level tailoring. There have been rare achievements in improving short‐term outcomes in a few very select patients with epilepsy. The prospects for further targeted, repurposed, or novel treatments seem promising. Along with much‐needed success, difficulties are also arising. Precision treatments do not always work, and sometimes are inaccessible or do not yet exist. Failures of precision medicine may not find their way to broader scrutiny. Precision medicine is not a new concept: It has been boosted by genetics and is often focused on genetically determined epilepsies, typically considered to be driven in an individual by a single genetic variant. Often the mechanisms generating the full clinical phenotype from such a perceived single cause are incompletely understood. The impact of additional genetic variation and other factors that might influence the clinical presentation represent complexities that are not usually considered. Precision success and precision failure are usually equally incompletely explained. There is a need for more comprehensive evaluation and a more rigorous framework, bringing together information that is both necessary and sufficient to explain clinical presentation and clinical responses to precision treatment in a precision approach that considers the full picture not only of the effects of a single variant, but also of its genomic and other measurable environment, within the context of the whole person. As we may be on the brink of a treatment revolution, progress must be considered and reasoned: One possible framework is proposed for the evaluation of precision treatments.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes.

A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.

Genome-wide association studies and genetic testing: understanding the science, success, and future of a rapidly developing field

Dog owners are increasingly interested in using commercially available testing panels to learn about the genetics of their pets, both to identify breed ancestry and to screen for specific genetic diseases. Helping owners interpret and understand results from genetic screening panels is becoming an important issue facing veterinarians. The objective of this review article is to introduce basic concepts behind genetic studies and current genetic screening tests while highlighting their value in veterinary medicine. The potential uses and limitations of commercially available genetic testing panels as screening tests are discussed, including appropriate cautions regarding the interpretation of results. Future directions, particularly with regard to the study of common complex genetic diseases, are also described.

Genetic Underpinnings of Increased BMI and Its Association With Late Midlife Cognitive Abilities

Objectives: First, we test for differences in various cognitive abilities across trajectories of body mass index (BMI) over the later life course. Second, we examine whether genetic risk factors for unhealthy BMIs—assessed via polygenic risk scores (PRS)—predict cognitive abilities in late-life. Methods: The study used a longitudinal sample of Vietnam veteran males to explore the associations between BMI trajectories, measured across four time points, and later cognitive abilities. The sample of 977 individuals was drawn from the Vietnam Era Twin Study of Aging. Cognitive abilities evaluated included executive function, abstract reasoning, episodic memory, processing speed, verbal fluency, and visual spatial ability. Multilevel linear regression models were used to estimate the associations between BMI trajectories and cognitive abilities. Then, BMI PRS was added to the models to evaluate polygenic associations with cognitive abilities. Results: There were no significant differences in cognitive ability between any of the BMI trajectory groups. There was a significant inverse relationship between BMI-PRS and several cognitive ability measures. Discussion: While no associations emerged for BMI trajectories and cognitive abilities at the phenotypic levels, BMI PRS measures did correlate with key cognitive domains. Our results suggest possible polygenic linkages cutting across key components of the central and peripheral nervous system.

KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters

Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at https://github.com/YinLiLin/KAML.

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.

Update on NAFLD genetics: From new variants to the clinic

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver diseases in high-income countries and the burden of NAFLD is increasing at an alarming rate. The risk of developing NAFLD and related complications is highly variable among individuals and is determined by environmental and genetic factors. Genome-wide association studies have uncovered robust and reproducible associations between variations in genes such as PNPLA3, TM6SF2, MBOAT7, GCKR, HSD17B13 and the natural history of NAFLD. These findings have provided compelling new insights into the biology of NAFLD and highlighted potentially attractive pharmaceutical targets. More recently the development of polygenic risk scores, which have shown promising results for the clinical risk prediction of other complex traits (such as cardiovascular disease and breast cancer), have provided new impetus for the clinical validation of genetic variants in NAFLD risk stratification. Herein, we review current knowledge on the genetic architecture of NAFLD, including gene-environment interactions, and discuss the implications for disease pathobiology, drug discovery and risk prediction. We particularly focus on the potential clinical translation of recent genetic advances, discussing methodological hurdles that must be overcome before these discoveries can be implemented in everyday practice.

Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common, complex genetic disorder affecting up to 15% of reproductive-age women worldwide, depending on the diagnostic criteria applied. These diagnostic criteria are based on expert opinion and have been the subject of considerable controversy. The phenotypic variation observed in PCOS is suggestive of an underlying genetic heterogeneity, but a recent meta-analysis of European ancestry PCOS cases found that the genetic architecture of PCOS defined by different diagnostic criteria was generally similar, suggesting that the criteria do not identify biologically distinct disease subtypes. We performed this study to test the hypothesis that there are biologically relevant subtypes of PCOS.

METHODS AND FINDINGS

Using biochemical and genotype data from a previously published PCOS genome-wide association study (GWAS), we investigated whether there were reproducible phenotypic subtypes of PCOS with subtype-specific genetic associations. Unsupervised hierarchical cluster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a genotyped cohort of 893 PCOS cases (median and interquartile range [IQR]: age = 28 [25-32], body mass index [BMI] = 35.4 [28.2-41.5]). The clusters were replicated in an independent, ungenotyped cohort of 263 PCOS cases (median and IQR: age = 28 [24-33], BMI = 35.7 [28.4-42.3]). The clustering revealed 2 distinct PCOS subtypes: a "reproductive" group (21%-23%), characterized by higher luteinizing hormone (LH) and sex hormone binding globulin (SHBG) levels with relatively low BMI and insulin levels, and a "metabolic" group (37%-39%), characterized by higher BMI, glucose, and insulin levels with lower SHBG and LH levels. We performed a GWAS on the genotyped cohort, limiting the cases to either the reproductive or metabolic subtypes. We identified alleles in 4 loci that were associated with the reproductive subtype at genome-wide significance (PRDM2/KAZN, P = 2.2 × 10-10; IQCA1, P = 2.8 × 10-9; BMPR1B/UNC5C, P = 9.7 × 10-9; CDH10, P = 1.2 × 10-8) and one locus that was significantly associated with the metabolic subtype (KCNH7/FIGN, P = 1.0 × 10-8). We developed a predictive model to classify a separate, family-based cohort of 73 women with PCOS (median and IQR: age = 28 [25-33], BMI = 34.3 [27.8-42.3]) and found that the subtypes tended to cluster in families and that carriers of previously reported rare variants in DENND1A, a gene that regulates androgen biosynthesis, were significantly more likely to have the reproductive subtype of PCOS. Limitations of our study were that only PCOS cases of European ancestry diagnosed by National Institutes of Health (NIH) criteria were included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicated.

CONCLUSIONS

In conclusion, we have found reproducible reproductive and metabolic subtypes of PCOS. Furthermore, these subtypes were associated with novel, to our knowledge, susceptibility loci. Our results suggest that these subtypes are biologically relevant because they appear to have distinct genetic architecture. This study demonstrates how phenotypic subtyping can be used to gain additional insights from GWAS data.

Phylotranscriptomics of the Pentapetalae Reveals Frequent Regulatory Variation in Plant Local Responses to the Fungal Pathogen Sclerotinia sclerotiorum

Quantitative disease resistance (QDR) is a conserved form of plant immunity that limits infections caused by a broad range of pathogens. QDR has a complex genetic determinism. The extent to which molecular components of the QDR response vary across plant species remains elusive. The fungal pathogen Sclerotinia sclerotiorum, causal agent of white mold diseases on hundreds of plant species, triggers QDR in host populations. To document the diversity of local responses to S. sclerotiorum at the molecular level, we analyzed the complete transcriptomes of six species spanning the Pentapetalae (Phaseolus vulgaris, Ricinus communis, Arabidopsis [Arabidopsis thaliana], Helianthus annuus, Solanum lycopersicum, and Beta vulgaris) inoculated with the same strain of S. sclerotiorum About one-third of plant transcriptomes responded locally to S. sclerotiorum, including a high proportion of broadly conserved genes showing frequent regulatory divergence at the interspecific level. Evolutionary inferences suggested a trend toward the acquisition of gene induction relatively recently in several lineages. Focusing on a group of ABCG transporters, we propose that exaptation by regulatory divergence contributed to the evolution of QDR. This evolutionary scenario has implications for understanding the QDR spectrum and durability. Our work provides resources for functional studies of gene regulation and QDR molecular mechanisms across the Pentapetalae.

The dark genome and pleiotropy: challenges for precision medicine

Surprisingly we remain ignorant of the function of the majority of genes in the human and mouse genomes. The dark genome is a major obstacle to the interpretation of the function of human genetic variation and its impact on disease. At the same time, pleiotropy, how individual variants influence multiple phenotypes, is key to understanding gene function and the role of genes and genetic networks in disease systems. Both understanding the genetics of disease and developing new therapeutic approaches and advances in precision medicine are all compromised by our limited knowledge of gene function and pleiotropic effects. Illuminating the dark genome and revealing pleiotropy across the genome requires a highly coordinated and international effort to acquire and analyse high-dimensional phenotype data from model organisms. We describe briefly how the International Mouse Phenotyping Consortium is addressing these challenges and the novel features of the pleiotropic landscape that are revealed by functional genomics programmes at genome-wide scale.

What's new in IBD therapy: An "omics network" approach

The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social structure, and so on. Almost certainly because this rapid evolution has outpaced the ability of the body to adapt to a number of environmental and behavioral changes, there has been a parallel emergence of several chronic inflammatory diseases, among which are inflammatory bowel diseases (IBD), primarily ulcerative colitis and Crohn's disease. The ability to treat these conditions has progressively improved in the last 50 years, particularly in the last couple of decades with the introduction of biological therapy targeting primarily soluble mediators produced by inflammatory cells. A large number of biologics are now available, but all of them induce similarly unsatisfactory (<50%) rates of clinical response and remission, and most of them lose efficacy over time, requiring dose escalation or switching from one biologic to another. So, treatment of IBD still needs improvement that will occur only if different approaches are taken. A reason why even the most recent forms of IBD therapy are unsatisfactory is because they target only selected components of an exceedingly complex pathophysiological process, a reality that must be honestly considered if better IBD therapies are to be achieved. Brand new approaches must integrate all relevant factors in their totality - the "omes" - and identify the key controllers of biological responses. This can be accomplished by using systems biology-based approaches and advanced bioinformatics tools, which together represent the essence of network medicine. This review looks at the past and the present of IBD pathogenesis and therapy, and discusses how to develop new therapies based on a network medicine approach.

Genome optimization for improvement of maize breeding

We propose a new model to improve maize breeding that incorporates doubled haploid production, genomic selection, and genome optimization. Breeding 4.0 has been considered the next era of plant breeding. It is clear that the Breeding 4.0 era for maize will feature the integration of multi-disciplinary technologies including genomics and phenomics, gene editing and synthetic biology, and Big Data and artificial intelligence. The breeding approach of passively selecting ideal genotypes from designated genetic pools must soon evolve to virtual design of optimized genomes by pyramiding superior alleles using computational simulation. An optimized genome expressing optimal phenotypes, which may never actually be created, can function as a blueprint for breeding programs to use minimal materials and hybridizations to achieve maximum genetic gain. We propose a new breeding pipeline, "genomic design breeding," that incorporates doubled haploid production, genomic selection, and genome optimization and is facilitated by different scales of trait predictions and decision-making models. Successful implementation of the proposed model will facilitate the evolution of maize breeding from "art" to "science" and eventually to "intelligence," in the Breeding 4.0 era.

Translating insights from neuropsychiatric genetics and genomics for precision psychiatry

The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.

On the diagnostic and neurobiological origins of bipolar disorder

Psychiatry is constructed around a taxonomy of several hundred diagnoses differentiated by nuances in the timing, co-occurrence, and severity of symptoms. Bipolar disorder (BD) is notable among these diagnoses for manic, depressive, and psychotic symptoms all being core features. Here, we trace current understanding of the neurobiological origins of BD and related diagnoses. To provide context, we begin by exploring the historical origins of psychiatric taxonomy. We then illustrate how key discoveries in pharmacology and neuroscience gave rise to a generation of neurobiological hypotheses about the origins of these disorders that facilitated therapeutic innovation but failed to explain disease pathogenesis. Lastly, we examine the extent to which genetics has succeeded in filling this void and contributing to the construction of an objective classification of psychiatric disturbance.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

An atlas of evidence-based phenotypic associations across the mouse phenome

To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.

CRISPR-based functional evaluation of schizophrenia risk variants

As expanding genetic and genomic studies continue to implicate a growing list of variants contributing risk to neuropsychiatric disease, an important next step is to understand the functional impact and points of convergence of these risk factors. Here, with a focus on schizophrenia, we survey the most recent findings of the rare and common variants underlying genetic risk for schizophrenia. We discuss the ongoing efforts to validate these variants in post-mortem brain tissue, as well as new approaches to combine CRISPR-based genome engineering with patient-specific human induced pluripotent stem cell (hiPSC)-based models, in order to identify putative causal schizophrenia loci that regulate gene expression and cellular function. We consider the current limitations of hiPSC-based approaches as well as the future advances necessary to improve the fidelity of this human model. With the objective of utilizing patient genotype data to improve diagnosis and predict treatment response, the integration of CRISPR-genome engineering and hiPSC-based models represent an important strategy with which to systematically demonstrate the cell-type-specific effects of schizophrenia-associated variants.

A Genetics Perspective on the Role of the (Neuro)Immune System in Schizophrenia

The immune system has long been hypothesized to play a role in schizophrenia pathogenesis based on data from diverse disciplines. Recent reports of the identification of schizophrenia-associated genetic variants and their initial biological characterization have renewed investigation of the role of the immune system in schizophrenia. In the current review, the plausibility of a role of the immune system in schizophrenia pathogenesis is examined, by revisiting epidemiology, neuroimaging, pharmacology, and developmental biology from a genetics perspective, as well as by synthesizing diverse findings from the emerging and dynamic schizophrenia genomics field. Genetic correlations between schizophrenia and immunological disorders are inconsistent and often contradictory, as are neuroimaging studies of microglia markers. Small therapeutic trials of anti-inflammatory agents targeting immune function have been consistently negative. Some gene expression analyses of post-mortem brains of patients with schizophrenia have reported an upregulation of genes of immune function though others report downregulation, and overall transcriptome profiling to date does not support an upregulation of immune pathways associated with schizophrenia genetic risk. The currently reviewed genetic data do not converge to reveal consistent evidence of the neuroimmune system in schizophrenia pathogenesis, and indeed, a substantive role for the neuroimmune system in schizophrenia has yet to be established.

Next-generation drug repurposing using human genetics and network biology

Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatments have high costs that are difficult to justify for small markets that include rare diseases. Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the potential to increase the speed of drug discovery at a lower cost. This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common diseases with monogenic or polygenic etiologies. Also, highlighted here are important features of this next generation approach to drug repurposing, which can be combined with machine learning methods to meet the challenges of personalized medicine.

Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis

Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.

Systematic Review: Genetic Associations for Prognostic Factors of Urinary Bladder Cancer

INTRODUCTION

Many germline associations have been reported for urinary bladder cancer (UBC) outcomes and prognostic characteristics. It is unclear whether there are overlapping genetic patterns for various prognostic endpoints. We aimed to review contemporary literature on genetic associations with UBC prognostic outcomes and to identify potential overlap in reported genes.

METHODS

EMBASE, MEDLINE, and PubMed databases were queried for relevant articles in English language without date restrictions. The initial search identified 1346 articles. After exclusions, 112 studies have been summarized. Cumulatively, 316 single-nucleotide polymorphisms (SNPs) were reported across prognostic outcomes (recurrence, progression, death) and characteristics (tumor stage, grade, size, age, risk group). There were considerable differences between studied outcomes in the context of genetic associations. The most commonly reported SNPs were located in OGG1, TP53, and MDM2. For outcomes with the highest number of reported associations (ie, recurrence and death), functional enrichment annotation yields different terms, potentially indicating separate biological mechanisms.

CONCLUSIONS

Our study suggests that all UBC prognostic outcomes may have different biological origins with limited overlap. Further validation of these observations is essential to target a phenotype that could best predict patient outcome and advance current management practices.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Assessing the potential for assisted gene flow using past introduction of Norway spruce in southern Sweden: Local adaptation and genetic basis of quantitative traits in trees

Norway spruce (Picea abies) is a dominant conifer species of major economic importance in northern Europe. Extensive breeding programs were established to improve phenotypic traits of economic interest. In southern Sweden, seeds used to create progeny tests were collected on about 3,000 trees of outstanding phenotype ('plus' trees) across the region. In a companion paper, we showed that some were of local origin but many were recent introductions from the rest of the natural range. The mixed origin of the trees together with partial sequencing of the exome of >1,500 of these trees and phenotypic data retrieved from the Swedish breeding program offered a unique opportunity to dissect the genetic basis of local adaptation of three quantitative traits (height, diameter and bud-burst) and assess the potential of assisted gene flow. Through a combination of multivariate analyses and genome-wide association studies, we showed that there was a very strong effect of geographical origin on growth (height and diameter) and phenology (bud-burst) with trees from southern origins outperforming local provenances. Association studies revealed that growth traits were highly polygenic and bud-burst somewhat less. Hence, our results suggest that assisted gene flow and genomic selection approaches could help to alleviate the effect of climate change on P. abies breeding programs in Sweden.

A large-scale genomic investigation of susceptibility to infection and its association with mental disorders in the Danish population

Infections and mental disorders are two of the major global disease burdens. While correlations between mental disorders and infections have been reported, the possible genetic links between them have not been assessed in large-scale studies. Moreover, the genetic basis of susceptibility to infection is largely unknown, as large-scale genome-wide association studies of susceptibility to infection have been lacking. We utilized a large Danish population-based sample (N = 65,534) linked to nationwide population-based registers to investigate the genetic architecture of susceptibility to infection (heritability estimation, polygenic risk analysis, and a genome-wide association study (GWAS)) and examined its association with mental disorders (comorbidity analysis and genetic correlation). We found strong links between having at least one psychiatric diagnosis and the occurrence of infection (P = 2.16 × 10-208, OR = 1.72). The SNP heritability of susceptibility to infection ranged from ~2 to ~7% in samples of differing psychiatric diagnosis statuses (suggesting the environment as a major contributor to susceptibility), and polygenic risk scores moderately but significantly explained infection status in an independent sample. We observed a genetic correlation of 0.496 (P = 2.17 × 10-17) between a diagnosis of infection and a psychiatric diagnosis. While our GWAS did not identify genome-wide significant associations, we found 90 suggestive (P ≤ 10-5) associations for susceptibility to infection. Our findings suggest a genetic component in susceptibility to infection and indicate that the occurrence of infections in individuals with mental illness may be in part genetically driven.

Genomic and epigenomic bases of transgressive segregation - New breeding paradigm for novel plant phenotypes

For a holistic approach in developing the stress-resilient crops of the 21^st century, modern genomic biology will need to re-envision the underappreciated phenomena in classical genetics, and incorporate them into the new plant breeding paradigm. Advances in evolutionary genomics support a theory that genetic recombination under genome shock during hybridization of widely divergent parents is an important driver of adaptive speciation, by virtue of the novelties of rare hybrids and recombinants. The enormous potential of genetic network rewiring to generate developmental or physiological novelties with adaptive advantage to special ecological niches has been appreciated. Developmental and physiological reconfiguration through network rewiring involves intricate molecular synergies controlled both at the genetic and epigenetic levels, as typified by the phenomenon of transgressive segregation, observed in both natural and breeding populations. This paper presents modern views on the possible molecular underpinnings of transgressive phenotypes as they are created in plant breeding, expanded from classical explanations through the Omnigenic Theory for quantitative traits and modern paradigms of epigenetics. Perspectives on how genomic biology can fully exploit this phenomenon to create novel phenotypes beyond what could be achieved through the more reductionist approach of functional genomics are presented in context of genomic modeling.

From genetics to biology: advancing mental health research in the Genomics ERA

The Genomics Workgroup of the National Advisory Mental Health Council (NAMHC) recently issued a set of recommendations for advancing the NIMH psychiatric genetics research program and prioritizing subsequent follow-up studies. The report emphasized the primacy of rigorous statistical support from properly designed, well-powered studies for pursuing genetic variants robustly associated with disease. Here we discuss the major points NIMH program staff consider when assessing research applications based on common and rare variants, as well as genetic syndromes, associated with psychiatric disorders. These are broad guiding principles for investigators to consider prior to submission of their applications. NIMH staff weigh these points in the context of reviewer comments, the existing literature, and current investments in related projects. Following the recommendations of the NAMHC, statistical strength and robustness of the underlying genetic discovery weighs heavily in our funding considerations as does the suitability of the proposed experimental approach. We specifically address our evaluation of applications motivated in whole, or in part, by an association between human DNA sequence variation and a disease or trait relevant to the mission of the NIMH.

Psychiatric Genetics, Epigenetics, and Cellular Models in Coming Years

Psychiatric genetic studies have uncovered hundreds of loci associated with various psychiatric disorders. We take the opportunity to review achievements in the past and provide our view of what is coming in the fields of molecular genetics, epigenetics, and cellular models. We expect that SNP-array and sequencing-based studies of genetic associations will continue to expand, covering more disorders, drug responses, phenotypes, and diverse populations. Epigenetic studies of psychiatric disorders will be another promising field with the growing recognition that environmental factors impact the risk for psychiatric disorders by modulating epigenetic factors. Functional studies of genetic findings will be needed in cellular models to provide important connections between genetic and epigenetic variants and biological phenotypes.

Genetics and Omics Analysis of Autoimmune Skin Blistering Diseases

Autoimmune blistering diseases (AIBDs) of the skin are characterized by autoantibodies against different intra-/extracellular structures within the epidermis and at the basement membrane zone (BMZ). Binding of the antibodies to their target antigen leads to inflammation at the respective binding site and degradation of these structures, resulting in the separation of the affected skin layers. Clinically, blistering, erythema and lesions of the skin and/or mucous membranes can be observed. Based on the localization of the autoantigen, AIBDs can be divided into pemphigus (intra-epidermal blistering diseases) and pemphigoid diseases (sub-epidermal blistering diseases), respectively. Although autoantigens have been extensively characterized, the underlying causes that trigger the diseases are still poorly understood. Besides the environment, genetic factors seem to play an important role in a predisposition to AIBDs. Here, we review currently known genetic and immunological mechanisms that contribute to the pathogenesis of AIBDs. Among the most commonly encountered genetic predispositions for AIBDs are the HLA gene region, and deleterious mutations of key genes for the immune system. Particularly, HLA class II genes such as the HLA-DR and HLA-DQ alleles have been shown to be prevalent in patients. This has prompted further epidemiological studies as well as unbiased Omics approaches on the transcriptome, microbiome, and proteome level to elucidate common and individual genetic risk factors as well as the molecular pathways that lead to the pathogenesis of AIBDs.

Synergistic effects of common schizophrenia risk variants

The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders are unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells, to evaluate the effects of schizophrenia (SZ)-associated common variants predicted to function as SZ expression quantitative trait loci (eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platform resolves pre- and postsynaptic neuronal deficits, recapitulates genotype-dependent gene expression differences and identifies convergence downstream of SZ eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.

Verifying Feighner's Hypothesis; Anorexia Nervosa Is Not a Psychiatric Disorder

Mental causation takes explanatory priority over evolutionary biology in most accounts of eating disorders. The evolutionary threat of starvation has produced a brain that assists us in the search for food and mental change emerges as a consequence. The major mental causation hypothesis: anxiety causes eating disorders, has been extensively tested and falsified. The subsidiary hypothesis: anxiety and eating disorders are caused by the same genotype, generates inconsistent results because the phenotypes are not traits, but vary along dimensions. Challenging the mental causation hypothesis in Feighner et al. (1972) noted that anorexic patients are physically hyperactive, hoarding for food, and they are rewarded for maintaining a low body weight. In 1996, Feighner's hypothesis was formalized, relating the patients' behavioral phenotype to the brain mechanisms of reward and attention (Bergh and Södersten, 1996), and in 2002, the hypothesis was clinically verified by training patients how to eat normally, thus improving outcomes (Bergh et al., 2002). Seventeen years later we provide evidence supporting Feighner's hypothesis by demonstrating that in 2012, 20 out of 37 patients who were referred by a psychiatrist, had a psychiatric diagnosis that differed from the diagnosis indicated by the SCID-I. Out of the 174 patients who were admitted in 2012, most through self-referral, there was significant disagreement between the outcomes of the SCID-I interview and the patient's subjective experience of a psychiatric problem in 110 of the cases. In addition, 358 anorexic patients treated to remission scored high on the Comprehensive Psychopathological Rating Scale, but an item response analysis indicated one (unknown) underlying dimension, rather than the three dimensions the scale can dissociate in patients with psychiatric disorders. These results indicate that psychiatric diagnoses, which are reliable and valid in patients with psychiatric disorders, are less well suited for patients with anorexia. The results are in accord with the hypothesis of the present Research Topic, that eating disorders are not always caused by disturbed psychological processes, and support the alternative, clinically relevant hypothesis that the behavioral phenotype of the patients should be addressed directly.

Getting to the Cores of Autism

The genetic architecture of autism spectrum disorder (ASD) is itself a diverse allelic spectrum that consists of rare de novo or inherited variants in hundreds of genes and common polygenic risk at thousands of loci. ASD susceptibility genes are interconnected at the level of transcriptional and protein networks, and many function as genetic regulators of neurodevelopment or synaptic proteins that regulate neural activity. So that the core underlying neuropathologies can be further elucidated, we emphasize the importance of first defining subtypes of ASD on the basis of the phenotypic signatures of genes in model systems and humans.

Extreme Polygenicity of Complex Traits Is Explained by Negative Selection

Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (Me), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate Me, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread ∼4× more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.

Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

CONTEXT

Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.

OBJECTIVE

The objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.

DESIGN, PATIENTS, AND METHODS

We performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.

RESULTS

We found rare variants in DENND1A (P = 5.31 × 10-5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.

CONCLUSIONS

Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

Genome-wide association meta-analysis of cocaine dependence: Shared genetics with comorbid conditions

Cocaine dependence is a complex psychiatric disorder that is highly comorbid with other psychiatric traits. Twin and adoption studies suggest that genetic variants contribute substantially to cocaine dependence susceptibility, which has an estimated heritability of 65-79%. Here we performed a meta-analysis of genome-wide association studies of cocaine dependence using four datasets from the dbGaP repository (2085 cases and 4293 controls, all of them selected by their European ancestry). Although no genome-wide significant hits were found in the SNP-based analysis, the gene-based analysis identified HIST1H2BD as associated with cocaine-dependence (10% FDR). This gene is located in a region on chromosome 6 enriched in histone-related genes, previously associated with schizophrenia (SCZ). Furthermore, we performed LD Score regression analysis with comorbid conditions and found significant genetic correlations between cocaine dependence and SCZ, ADHD, major depressive disorder (MDD) and risk taking. We also found, through polygenic risk score analysis, that all tested phenotypes are significantly associated with cocaine dependence status: SCZ (R² = 2.28%; P = 1.21e-26), ADHD (R² = 1.39%; P = 4.5e-17), risk taking (R² = 0.60%; P = 2.7e-08), MDD (R² = 1.21%; P = 4.35e-15), children's aggressive behavior (R² = 0.3%; P = 8.8e-05) and antisocial behavior (R² = 1.33%; P = 2.2e-16). To our knowledge, this is the largest reported cocaine dependence GWAS meta-analysis in European-ancestry individuals. We identified suggestive associations in regions that may be related to cocaine dependence and found evidence for shared genetic risk factors between cocaine dependence and several comorbid psychiatric traits. However, the sample size is limited and further studies are needed to confirm these results.

Compensatory trans-regulatory alleles minimizing variation in TDH3 expression are common within Saccharomyces cerevisiae

Heritable variation in gene expression is common within species. Much of this variation is due to genetic differences outside of the gene with altered expression and is trans-acting. This trans-regulatory variation is often polygenic, with individual variants typically having small effects, making the genetic architecture and evolution of trans-regulatory variation challenging to study. Consequently, key questions about trans-regulatory variation remain, including the variability of trans-regulatory variation within a species, how selection affects trans-regulatory variation, and how trans-regulatory variants are distributed throughout the genome and within a species. To address these questions, we isolated and measured trans-regulatory differences affecting TDH3 promoter activity among 56 strains of Saccharomyces cerevisiae, finding that trans-regulatory backgrounds varied approximately twofold in their effects on TDH3 promoter activity. Comparing this variation to neutral models of trans-regulatory evolution based on empirical measures of mutational effects revealed that despite this variability in the effects of trans-regulatory backgrounds, stabilizing selection has constrained trans-regulatory differences within this species. Using a powerful quantitative trait locus mapping method, we identified ∼100 trans-acting expression quantitative trait locus in each of three crosses to a common reference strain, indicating that regulatory variation is more polygenic than previous studies have suggested. Loci altering expression were located throughout the genome, and many loci were strain specific. This distribution and prevalence of alleles is consistent with recent theories about the genetic architecture of complex traits. In all mapping experiments, the nonreference strain alleles increased and decreased TDH3 promoter activity with similar frequencies, suggesting that stabilizing selection maintained many trans-acting variants with opposing effects. This variation may provide the raw material for compensatory evolution and larger scale regulatory rewiring observed in developmental systems drift among species.

A global overview of pleiotropy and genetic architecture in complex traits

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

Network medicine-travelling with the insulin receptor: Encounter of the second type

Important progress has been made in understanding many aspects of insulin action in the last 10 years. Attention will be focused here on the physical protein interaction network of the internalized insulin receptor (IR) and its relationships with the genetic architecture of type 2 diabetes mellitus (T2D). The IR recognizes signals from the outside (circulating insulin) and engages the insulin signaling response. Within seconds, the IR is also involved in insulin internalization and its subsequent degradation in endosomes (physiological clearance of insulin). A T2D disease module sharing functional similarities with insulin secretion in pancreatic islets was recently identified in the close neighborhood of the internalized IR in liver. This module brought a new light on the apparent functional heterogeneity of numerous genes at risk to T2D by linking them to a few noncanonical layers of signaling feedback loops. These findings should be translated into a better understanding of the primary mechanisms of the disease and consequently a more precise sub-classification of T2D, ultimately leading to precision medicine and the development of new therapeutical drugs.

Depression: more treatment but no drop in prevalence: how effective is treatment? And can we do better?

PURPOSE OF REVIEW

Since the 70s, treatment of depression, especially pharmacologically, has expanded enormously. However, epidemiological studies show that 12-month population prevalence rates have not dropped. This observation raises multiple questions. How good are treatments of depression actually? Do they improve long-term outcomes? Have the treatment gaps narrowed? And how can we make mental healthcare more effective at the population level?

RECENT FINDINGS

Recent publications suggest some answers. Controlled treatment trials show that effectiveness of specific treatments (pharmacological, psychological) is modest and probably overestimated owing to substantial spontaneous recovery and nonspecific therapeutic effects. Treatment gaps are still substantial and prevention has unclear long-term effects and is not structurally embedded. Future relevance of genetic information for better personalized treatment is potentially high but uncertain. Increasingly, the potential of treatment to improve long-term outcome is being questioned.

SUMMARY

To reduce prevalence, it is essential to narrow the treatment gaps, provide timely interventions and high-quality treatment, eradicate waiting lists, prescribe antidepressants more cautiously and better managed, consider psychological alternatives, and provide more psychosocial treatment in primary care with physician-assistants. In addition, research is needed on long-term outcome of different treatment modalities, and least but not last the value of structurally socially embedded preventive interventions.

Molecular Origins of Complex Heritability in Natural Genotype-to-Phenotype Relationships

The statistical complexity of heredity has long been evident, but its molecular origins remain elusive. To investigate, we charted 90 comprehensive genotype-to-phenotype maps in a large population of wild diploid yeast. In contrast to long-standing assumptions, all types of genetic variation contributed similarly to phenotype. Causal synonymous and regulatory variants exhibited distinct molecular signatures, as did nonlinearities in heterozygote fitness that likely contribute to hybrid vigor. Highly pleiotropic variants altered disordered sequences within signaling hubs, and their effects correlated across environments-even when antagonistic-suggesting that large fitness gains bring concomitant costs. Natural genetic networks defined by the causal loci differed from those determined by precise gene deletions or protein-protein interactions. Finally, we found that traits that would appear omnigenic in less powered studies do in fact have finite genetic determinants. Integrating these molecular principles will be crucial as genome reading and writing become routine in research, industry, and medicine.

The Genetic Basis of Metabolic Disease

Recent developments in genetics and genomics are providing a detailed and systematic characterization of the genetic underpinnings of common metabolic diseases and traits, highlighting the inherent complexity within systems for homeostatic control and the many ways in which that control can fail. The genetic architecture underlying these common metabolic phenotypes is complex, with each trait influenced by hundreds of loci spanning a range of allele frequencies and effect sizes. Here, we review the growing appreciation of this complexity and how this has fostered the implementation of genome-scale approaches that deliver robust mechanistic inference and unveil new strategies for translational exploitation.

The genetics of depression: successful genome-wide association studies introduce new challenges

The recent successful genome-wide association studies (GWASs) for depression have yielded more than 80 replicated loci and brought back the excitement that had evaporated during the years of negative GWAS findings. The identified loci provide anchors to explore their relevance for depression, but this comes with new challenges. Using the watershed model of genotype-phenotype relationships as a conceptual aid and recent genetic findings on other complex phenotypes, we discuss why it took so long and identify seven future challenges. The biggest challenge involves the identification of causal mechanisms since GWAS associations merely flag genomic regions without a direct link to underlying biological function. Furthermore, the genetic association with the index phenotype may also be part of a more extensive causal pathway (e.g., from variant to comorbid condition) or be due to indirect influences via intermediate traits located in the causal pathways to the final outcome. This challenge is highly relevant for depression because even its narrow definition of major depressive disorder captures a heterogeneous set of phenotypes which are often measured by even more broadly defined operational definitions consisting of a few questions (minimal phenotyping). Here, Mendelian randomization and future discovery of additional genetic variants for depression and related phenotypes will be of great help. In addition, reduction of phenotypic heterogeneity may also be worthwhile. Other challenges include detecting rare variants, determining the genetic architecture of depression, closing the "heritability gap", and realizing the potential for personalized treatment. Along the way, we identify pertinent open questions that, when addressed, will advance the field.