Distilling pathophysiology from complex disease genetics

AI models and the future of genomic research and medicine: True sons of knowledge?: Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field

The increasing availability of large-scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that-while capable of generating predictive knowledge at unprecedented pace and scale-if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self-conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine.

Therapeutic Challenges and Emerging Treatment Targets for Pulmonary Hypertension in Left Heart Disease

Pulmonary hypertension (PH) attributable to left heart disease (LHD) is believed to be the most common form of PH and is strongly associated with increased mortality and morbidity in this patient population. Specific therapies for PH‐LHD have not yet been identified and the use of pulmonary artery hypertension‐targeted therapies in PH‐LHD are not recommended. Endothelin receptor antagonists, phosphodiesterase‐5 inhibitors, guanylate cyclase stimulators, and prostacyclins have all been studied in PH‐LHD with conflicting results. Understanding the mechanisms underlying PH‐LHD could potentially provide novel therapeutic targets. Fibrosis, oxidative stress, and metabolic syndrome have been proposed as pathophysiological components of PH‐LHD. Genetic associations have also been identified, offering additional mechanisms with biological plausibility. This review summarizes the evidence and challenges for treatment of PH‐LHD and focuses on underlying mechanisms on the horizon that could develop into potential therapeutic targets for this disease.

Lethal Infectious Diseases as Inborn Errors of Immunity: Toward a Synthesis of the Germ and Genetic Theories

It was first demonstrated in the late nineteenth century that human deaths from fever were typically due to infections. As the germ theory gained ground, it replaced the old, unproven theory that deaths from fever reflected a weak personal or even familial constitution. A new enigma emerged at the turn of the twentieth century, when it became apparent that only a small proportion of infected individuals die from primary infections with almost any given microbe. Classical genetics studies gradually revealed that severe infectious diseases could be driven by human genetic predisposition. This idea gained ground with the support of molecular genetics, in three successive, overlapping steps. First, many rare inborn errors of immunity were shown, from 1985 onward, to underlie multiple, recurrent infections with Mendelian inheritance. Second, a handful of rare and familial infections, also segregating as Mendelian traits but striking humans resistant to other infections, were deciphered molecularly beginning in 1996. Third, from 2007 onward, a growing number of rare or common sporadicinfections were shown to result from monogenic, but not Mendelian, inborn errors. A synthesis of the hitherto mutually exclusive germ and genetic theories is now in view.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Crossing Phenotype Heritability and Candidate Gene Expression in Grafted Black-Lipped Pearl Oyster Pinctada margaritifera, an Animal Chimera

Grafting mantle tissue of a donor pearl oyster into the gonad of a recipient oyster results in the formation of a chimera, the pearl sac. The phenotypic variations of this chimera are hypothesized to be the result of interactions between the donor and recipient genomes. In this study, the heritability of phenotypic variation and its association with gene expression were investigated for the first time during Pinctada margaritifera pearl production. Genetic variance was evaluated at different levels, 1) before the graft operation (expression in graft tissue), 2) after grafting (pearl sac tissue expression in chimera), and 3) on the product of the graft (pearl phenotype traits) based on controlled biparental crosses and the F1 generation. Donor-related genetic parameter estimates clearly demonstrate heritability for nacre weight and thickness, darkness and color, and surface defects and grade, which signifies a genetic basis in the donor oyster. In graft relative gene expression, the value of heritability was superior to 0.20 in for almost all genes; whereas in pearl sac, heritability estimates were low (h2 < 0.10; except for CALC1 and Aspein). Pearl sac expression seems to be more influenced by residual variance than the graft, which can be explained by environmental effects that influence pearls sac gene expression and act as a recipient additive genetic component. The interactions between donor and recipient are very complex, and further research is required to understand the role of the recipient oysters on pearl phenotypic and gene expression variances.

A framework for the investigation of rare genetic disorders in neuropsychiatry

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Homozygosity for TYK2 P1104A underlies tuberculosis in about 1% of patients in a cohort of European ancestry

The human genetic basis of tuberculosis (TB) has long remained elusive. We recently reported a high level of enrichment in homozygosity for the common TYK2 P1104A variant in a heterogeneous cohort of patients with TB from non-European countries in which TB is endemic. This variant is homozygous in ∼1/600 Europeans and ∼1/5,000 people from other countries outside East Asia and sub-Saharan Africa. We report a study of this variant in the UK Biobank cohort. The frequency of P1104A homozygotes was much higher in patients with TB (6/620, 1%) than in controls (228/114,473, 0.2%), with an odds ratio (OR) adjusted for ancestry of 5.0 [95% confidence interval (CI): 1.96-10.31, P = 2 × 10-3]. Conversely, we did not observe enrichment for P1104A heterozygosity, or for TYK2 I684S or V362F homozygosity or heterozygosity. Moreover, it is unlikely that more than 10% of controls were infected with Mycobacterium tuberculosis, as 97% were of European genetic ancestry, born between 1939 and 1970, and resided in the United Kingdom. Had all of them been infected, the OR for developing TB upon infection would be higher. These findings suggest that homozygosity for TYK2 P1104A may account for ∼1% of TB cases in Europeans.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration

Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1-3. We generated transcriptional profiles of postmortem retinas from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at more than 9 million common SNPs for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets4,5. Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.

Pathways to neurodegeneration: lessons learnt from unbiased genetic screens in Drosophila

Neurodegenerative diseases are a complex set of disorders that are known to be caused by environmental as well as genetic factors. In the recent past, mutations in a large number of genes have been identified that are linked to several neurodegenerative diseases. The pathogenic mechanisms in most of these disorders are unknown. Recently, studies of genes that are linked to neurodegeneration in Drosophila, the fruit flies, have contributed significantly to our understanding of mechanisms of neuroprotection and degeneration. In this review, we focus on forward genetic screens in Drosophila that helped in identification of novel genes and pathogenic mechanisms linked to neurodegeneration. We also discuss identification of four novel pathways that contribute to neurodegeneration upon mitochondrial dysfunction.

Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model through the Application of Quantitative Systems Pharmacology

Quantitative Systems Pharmacology (QSP) is a drug discovery approach that integrates computational and experimental methods in an iterative way to gain a comprehensive, unbiased understanding of disease processes to inform effective therapeutic strategies. We report the implementation of QSP to Huntington’s Disease, with the application of a chemogenomics platform to identify strategies to protect neuronal cells from mutant huntingtin induced death. Using the STHdh^Q111 cell model, we investigated the protective effects of small molecule probes having diverse canonical modes-of-action to infer pathways of neuronal cell protection connected to drug mechanism. Several mechanistically diverse protective probes were identified, most of which showed less than 50% efficacy. Specific combinations of these probes were synergistic in enhancing efficacy. Computational analysis of these probes revealed a convergence of pathways indicating activation of PKA. Analysis of phospho-PKA levels showed lower cytoplasmic levels in STHdh^Q111 cells compared to wild type STHdh^Q7 cells, and these levels were increased by several of the protective compounds. Pharmacological inhibition of PKA activity reduced protection supporting the hypothesis that protection may be working, in part, through activation of the PKA network. The systems-level studies described here can be broadly applied to any discovery strategy involving small molecule modulation of disease phenotype.

NRL-Regulated Transcriptome Dynamics of Developing Rod Photoreceptors

Gene regulatory networks (GRNs) guiding differentiation of cell types and cell assemblies in the nervous system are poorly understood because of inherent complexities and interdependence of signaling pathways. Here, we report transcriptome dynamics of differentiating rod photoreceptors in the mammalian retina. Given that the transcription factor NRL determines rod cell fate, we performed expression profiling of developing NRL-positive (rods) and NRL-negative (S-cone-like) mouse photoreceptors. We identified a large-scale, sharp transition in the transcriptome landscape between postnatal days 6 and 10 concordant with rod morphogenesis. Rod-specific temporal DNA methylation corroborated gene expression patterns. De novo assembly and alternative splicing analyses revealed previously unannotated rod-enriched transcripts and the role of NRL in transcript maturation. Furthermore, we defined the relationship of NRL with other transcriptional regulators and downstream cognate effectors. Our studies provide the framework for comprehensive system-level analysis of the GRN underlying the development of a single sensory neuron, the rod photoreceptor.

Base-resolution stratification of cancer mutations using functional variomics

A complete understanding of human cancer variants requires new methods to systematically and efficiently assess the functional effects of genomic mutations at a large scale. Here, we describe a set of tools to rapidly clone and stratify thousands of cancer mutations at base resolution. This protocol provides a massively parallel pipeline to achieve high stringency and throughput. The approach includes high-throughput generation of mutant clones by Gateway, confirmation of variant identity by barcoding and next-generation sequencing, and stratification of cancer variants by multiplexed interaction profiling. Compared with alternative site-directed mutagenesis methods, our protocol requires less sequencing effort and enables robust statistical calling of allele-specific effects. To ensure the precision of variant interaction profiling, we further describe two complementary methods-a high-throughput enhanced yeast two-hybrid (HT-eY2H) assay and a mammalian-cell-based Gaussia princeps luciferase protein-fragment complementation assay (GPCA). These independent assays with standard controls validate mutational interaction profiles with high quality. This protocol provides experimentally derived guidelines for classifying candidate cancer alleles emerging from whole-genome or whole-exome sequencing projects as 'drivers' or 'passengers'. For ∼100 genomic mutations, the protocol-including target primer design, variant library construction, and sequence verification-can be completed within as little as 2-3 weeks, and cancer variant stratification can be completed within 2 weeks.

Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 × 10^-8) and PPP4R4/SERPINA1 (P = 1.01 × 10^-8) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, ∼0.6), and accounted for a mean 0.9-1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function.

Using Drosophila nephrocytes in genetic kidney disease

Renal diseases are a growing health burden, and innovative models to study their pathomechanisms are greatly needed. Here, we highlight how the fruit fly Drosophila melanogaster can be used to model kidney diseases. We focus on the nephrocyte that has recently been shown to exhibit podocyte and proximal tubular cell features. These cells can be manipulated with precise genetic tools to dissect filtration and reabsorption mechanisms. Thus, they represent a novel and easy-to-use alternative in experimental nephrology.

Functional variomics and network perturbation: connecting genotype to phenotype in cancer

Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.

Method for generating multiple risky barcodes of complex diseases using ant colony algorithm

Background

Susceptible barcode recognition plays an important role in the diagnosis and treatment of complex diseases. Numerous approaches have been proposed to identify risky barcodes involved in the progress of complex diseases. However, some methods only consider differences in barcode frequencies between the control and disease groups; as such, these methods may be partial or even wrong. For example, some barcodes with a high risk ratio yield a low frequency on cases or exhibit a high frequency on controls, which may unreasonable from a statistical point.

Results

In our study, a stricter criteria, maximum discrepancy and maximum constituency, is designed to evaluate each barcode and ant colony algorithm is used to search combination space of epistasis. For complex diseases with multi-subtypes, our method can list several potential barcodes contributing to different subtypes of complex diseases. Another contribution of this work is to introduce a method for determining the length of barcodes and excluding noisy barcodes whose frequencies are abnormal. In addition, common pathogenic genes shared by different risky barcodes are also recognized, which may provide key clue for further study, such as gene function analysis.

Conclusions

Experimental results reveal that our method can find multiple risky barcodes whose risk ratio and odds ratio are >1. These barcodes could be related to different subtypes of complex diseases.

Drosophila and genome-wide association studies: a review and resource for the functional dissection of human complex traits

Human genome-wide association studies (GWAS) have successfully identified thousands of susceptibility loci for common diseases with complex genetic etiologies. Although the susceptibility variants identified by GWAS usually have only modest effects on individual disease risk, they contribute to a substantial burden of trait variation in the overall population. GWAS also offer valuable clues to disease mechanisms that have long proven to be elusive. These insights could lead the way to breakthrough treatments; however, several challenges hinder progress, making innovative approaches to accelerate the follow-up of results from GWAS an urgent priority. Here, we discuss the largely untapped potential of the fruit fly, Drosophila melanogaster, for functional investigation of findings from human GWAS. We highlight selected examples where strong genomic conservation with humans along with the rapid and powerful genetic tools available for flies have already facilitated fine mapping of association signals, elucidated gene mechanisms, and revealed novel disease-relevant biology. We emphasize current research opportunities in this rapidly advancing field, and present bioinformatic analyses that systematically explore the applicability of Drosophila for interrogation of susceptibility signals implicated in more than 1000 human traits, based on all GWAS completed to date. Thus, our discussion is targeted at both human geneticists seeking innovative strategies for experimental validation of findings from GWAS, as well as the Drosophila research community, by whom ongoing investigations of the implicated genes will powerfully inform our understanding of human disease.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions.

An extended set of yeast-based functional assays accurately identifies human disease mutations

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

Genetics of Bipolar Disorder: Recent Update and Future Directions

Although genetic studies of Bipolar Disorder have been pursued for decades, it has only been in the last several years that clearly replicated findings have emerged. These findings, typically of modest effects, point to a polygenic genetic architecture consisting of multiple common and rare susceptibility variants. While larger genome-wide association studies are ongoing, the advent of whole exome and genome sequencing should lead to the identification of rare, and potentially more penetrant, variants. Progress along both fronts will provide novel insights into the biology of Bipolar Disorder and help usher in a new era of personalized medicine and improved treatments.

Host genetics of severe influenza: from mouse Mx1 to human IRF7

Influenza viruses cause mild to moderate respiratory illness in most people, and only rarely devastating or fatal infections. The virulence factors encoded by viral genes can explain seasonal or geographic differences at the population level but are unlikely to account for inter-individual clinical variability. Inherited or acquired immunodeficiencies may thus underlie severe cases of influenza. The crucial role of host genes was first demonstrated by forward genetics in inbred mice, with the identification of interferon (IFN)-α/β-inducible Mx1 as a canonical influenza susceptibility gene. Reverse genetics has subsequently characterized the in vivo role of other mouse genes involved in IFN-α/β and -λ immunity. A series of in vitro studies with mouse and human cells have also refined the cell-intrinsic mechanisms of protection against influenza viruses. Population-based human genetic studies have not yet uncovered variants with a significant impact. Interestingly, human primary immunodeficiencies affecting T and B cells were also not found to predispose to severe influenza. Recently however, human IRF7 was shown to be essential for IFN-α/β- and IFN-λ-dependent protective immunity against primary influenza in vivo, as inferred from a patient with life-threatening influenza revealed to be IRF7-deficient by whole exome sequencing. Next generation sequencing of human exomes and genomes will facilitate the analysis of the human genetic determinism of severe influenza.

Pancreatic Beta Cell Survival and Signaling Pathways: Effects of Type 1 Diabetes-Associated Genetic Variants

Type 1 diabetes (T1D) is a complex autoimmune disease in which pancreatic beta cells are specifically destroyed by the immune system. The disease has an important genetic component and more than 50 loci across the genome have been associated with risk of developing T1D. The molecular mechanisms by which these putative T1D candidate genes modulate disease risk, however, remain poorly characterized and little is known about their effects in pancreatic beta cells. Functional studies in in vitro models of pancreatic beta cells, based on techniques to inhibit or overexpress T1D candidate genes, allow the functional characterization of several T1D candidate genes. This requires a multistage procedure comprising two major steps, namely accurate selection of genes of potential interest and then in vitro and/or in vivo mechanistic approaches to characterize their role in pancreatic beta cell dysfunction and death in T1D. This chapter details the methods and settings used by our groups to characterize the role of T1D candidate genes on pancreatic beta cell survival and signaling pathways, with particular focus on potentially relevant pathways in the pathogenesis of T1D, i.e., inflammation and innate immune responses, apoptosis, beta cell metabolism and function.

Genomics of Immune Diseases and New Therapies

Genomic DNA sequencing technologies have been one of the great advances of the 21st century, having decreased in cost by seven orders of magnitude and opening up new fields of investigation throughout research and clinical medicine. Genomics coupled with biochemical investigation has allowed the molecular definition of a growing number of new genetic diseases that reveal new concepts of immune regulation. Also, defining the genetic pathogenesis of these diseases has led to improved diagnosis, prognosis, genetic counseling, and, most importantly, new therapies. We highlight the investigational journey from patient phenotype to treatment using the newly defined XMEN disease, caused by the genetic loss of the MAGT1 magnesium transporter, as an example. This disease illustrates how genomics yields new fundamental immunoregulatory insights as well as how research genomics is integrated into clinical immunology. At the end, we discuss two other recently described diseases, CHAI/LATAIE (CTLA-4 deficiency) and PASLI (PI3K dysregulation), as additional examples of the journey from unknown immunological diseases to new precision medicine treatments using genomics.

Disruption of sonic hedgehog signaling in Ellis-van Creveld dwarfism confers protection against bipolar affective disorder

Ellis-van Creveld syndrome, an autosomal recessively inherited chondrodysplastic dwarfism, is frequent among Old Order Amish of Pennsylvania. Decades of longitudinal research on bipolar affective disorder (BPAD) revealed cosegregation of high numbers of EvC and Bipolar I (BPI) cases in several large Amish families descending from the same pioneer. Despite the high prevalence of both disorders in these families, no EvC individual has ever been reported with BPI. The proximity of the EVC gene to our previously reported chromosome 4p16 BPAD locus with protective alleles, coupled with detailed clinical observations that EvC and BPI do not occur in the same individuals, led us to hypothesize that the genetic defect causing EvC in the Amish confers protection from BPI. This hypothesis is supported by a significant negative association of these two disorders when contrasted with absence of disease (P=0.029, Fisher's exact test, two-sided, verified by permutation to estimate the null distribution of the test statistic). As homozygous Amish EVC mutations causing EvC dwarfism do so by disrupting sonic hedgehog (Shh) signaling, our data implicate Shh signaling in the underlying pathophysiology of BPAD. Understanding how disrupted Shh signaling protects against BPI could uncover variants in the Shh pathway that cause or increase risk for this and related mood disorders.

The Convergence of Systems and Reductionist Approaches in Complex Trait Analysis

Research into the genetic and environmental factors behind complex trait variation has traditionally been segregated into distinct scientific camps. The reductionist approach aims to decrypt phenotypic variability bit by bit, founded on the underlying hypothesis that genome-to-phenome relations are largely constructed from the additive effects of their molecular players. In contrast, the systems approach aims to examine large-scale interactions of many components simultaneously, on the premise that interactions in gene networks can be both linear and non-linear. Both approaches are complementary, and they are becoming increasingly intertwined due to developments in gene editing tools, omics technologies, and population resources. Together, these strategies are beginning to drive the next era in complex trait research, paving the way to improve agriculture and toward more personalized medicine.

Genetic basis of autoimmunity

Autoimmune diseases affect up to approximately 10% of the population. While rare Mendelian autoimmunity syndromes can result from monogenic mutations disrupting essential mechanisms of central and peripheral tolerance, more common human autoimmune diseases are complex disorders that arise from the interaction between polygenic risk factors and environmental factors. Although the risk attributable to most individual nucleotide variants is modest, genome-wide association studies (GWAS) have the potential to provide an unbiased view of biological pathways that drive human autoimmune diseases. Interpretation of GWAS requires integration of multiple genomic datasets including dense genotyping, cis-regulatory maps of primary immune cells, and genotyped studies of gene expression in relevant cell types and cellular conditions. Improved understanding of the genetic basis of autoimmunity may lead to a more sophisticated understanding of underlying cellular phenotypes and, eventually, novel diagnostics and targeted therapies.

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease

Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases.

Genome-wide association studies of late-onset cardiovascular disease

Human genetics is a powerful tool for discovering causal mediators of human disease and physiology. Cardiovascular diseases with late onset in the lifecourse have historically not been considered genetic diseases, but in recent years the contribution of a heritable factor has been established. More importantly, over the last decade genome-wide association studies (GWASs) have identified many loci associated with late-onset cardiovascular diseases including coronary artery disease, carotid artery disease, ischemic stroke, aortic aneurysm, peripheral vascular disease, atrial fibrillation, valvular disease and correlates of vascular and myocardial function. Here we review findings from GWASs considered statistically robust with regard to multiple testing (p<5×10(-8)) for late-onset cardiovascular diseases and traits. Although for only a handful of the 92 genetic loci described here have the mechanisms underlying disease association been established, new and previously unsuspected pathways have been implicated for several conditions. Examples include a role for NO signaling in myocardial repolarization and sudden cardiac death and a role for the protein sortilin in lipid metabolism and coronary artery disease. Genetic loci with multiple trait associations have also provided novel biological insights. For example, of the 46 genetic loci associated with coronary artery disease, only 16 are also associated with conventional risk factors for cardiovascular disease whereas the remaining two thirds may reflect novel pathways. Much work remains to functionally characterize genetic loci and for clinical utility, but accruing insights into the biological basis of cardiovascular aging in human populations promise to point to novel therapeutic and preventive strategies. This article is part of a Special Issue entitled 'SI:CV Aging'.

Drosophila and experimental neurology in the post-genomic era

For decades, the fruit fly, Drosophila melanogaster, has been among the premiere genetic model systems for probing fundamental neurobiology, including elucidation of mechanisms responsible for human neurologic disorders. Flies continue to offer virtually unparalleled versatility and speed for genetic manipulation, strong genomic conservation, and a nervous system that recapitulates a range of cellular and network properties relevant to human disease. I focus here on four critical challenges emerging from recent advances in our understanding of the genomic basis of human neurologic disorders where innovative experimental strategies are urgently needed: (1) pinpointing causal genes from associated genomic loci; (2) confirming the functional impact of allelic variants; (3) elucidating nervous system roles for novel or poorly studied genes; and (4) probing network interactions within implicated regulatory pathways. Drosophila genetic approaches are ideally suited to address each of these potential translational roadblocks, and will therefore contribute to mechanistic insights and potential breakthrough therapies for complex genetic disorders in the coming years. Strategic collaboration between neurologists, human geneticists, and the Drosophila research community holds great promise to accelerate progress in the post-genomic era.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Recent genetic findings in schizophrenia and their therapeutic relevance

Over 100 loci are now associated with schizophrenia risk as identified by single nucleotide polymorphisms (SNPs) in genome-wide association studies. These findings mean that 'genes for schizophrenia' have unquestionably been found. However, many questions remain unanswered, including several which affect their therapeutic significance. The SNPs individually have minor effects, and even cumulatively explain only a modest fraction of the genetic predisposition. The remainder likely results from many more loci, from rare variants, and from gene-gene and gene-environment interactions. The risk SNPs are almost all non-coding, meaning that their biological significance is unclear; probably their effects are mediated via an influence on gene regulation, and emerging evidence suggests that some key molecular events occur during early brain development. The loci include novel genes of unknown function as well as genes and pathways previously implicated in the pathophysiology of schizophrenia, e.g. NMDA receptor signalling. Genes in the latter category have the clearer therapeutic potential, although even this will be a challenging process because of the many complexities concerning the genetic architecture and mediating mechanisms. This review summarises recent schizophrenia genetic findings and some key issues they raise, particularly with regard to their implications for identifying and validating novel drug targets.

From candidate gene studies to GWAS and post-GWAS analyses in breast cancer

There are now more than 90 established breast cancer risk loci, with 57 new ones, revealed through genome-wide-association studies (GWAS) during the last two years. Established high, moderate and low penetrance genetic variants currently explain ∼49% of familial breast cancer risk. GWAS-discovered variants account for 14%, and it is estimated that another 1000 yet-to-be-discovered loci could contribute an additional ∼14% of familial risk. Polygenic risk scores can already be used to stratify breast cancer risk in the female population and could improve the targeting of mammographic screening programmes, which are at present largely based on age-specific risks. Fine-scale mapping and functional analyses are revealing candidate causal variants and the molecular mechanisms by which GWAS-hits may act. Better-powered GWAS and genome-wide sequencing projects are likely to continue identifying new breast cancer causal variants.

Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

At the molecular level, living cells are enormously complicated complex adaptive systems in which intertwined genomic, transcriptomic, proteomic and metabolic networks all play a crucial role. At the same time, cells are spatially heterogeneous systems in which subcellular compartmentalization of different functions is ubiquitous and requires efficient cross-compartmental communication. Dynamic redistribution of multitudinous proteins to different subcellular locations in response to cellular functional state is increasingly recognized as a crucial characteristic of cellular function that seems to be at least as important as overall changes in protein abundance. Characterization of the subcellular spatial dynamics of protein distribution is a major challenge for proteomics and recent results with MCF7 breast cancer cells suggest that this may be of particular importance for cancer cells.

Genetic Variations and Gastric Cancer

<b><i>Background:</i></b> Gastric cancer (GC) has an apparent hereditary component. However, in a large fraction of gastric cases, no known genetic syndrome or family history can be identified, suggesting the presence of ‘missing heritability' in GC etiology. Genome-wide association studies (GWAS) and traditional candidate gene studies have both led to the identification of multiple replicable common genetic variants associated with GC risk. <b><i>Summary:</i></b> We summarize the genetic variants associated with GC risk identified up to date. Achievements derived from translational cancer research including the following aspects: (a) contribution to the our understanding of gastric tumorigenesis, (b) guidance to individualized treatment and (c) prediction of patient prognosis. We also prospect future research direction such as post-GWAS analyses and rare variants studies. <b><i>Key Message:</i></b> Many genetic variants were found through GWAS or candidate gene studies, and interpreting their underlying mechanisms will help us translate risk profiles generated from these variations into use in the clinical setting for targeted screening and treatment. <b><i>Practical Implications:</i></b> Investigation of the potential use of genetic variations as prognostic and predictive markers is a developing field. Many people could benefit from a better understanding of genetic polymorphisms to potentially identify a priori individuals who might have the best chance of survival and therefore derive most clinical benefit from treatment. Outcomes of particular scientific interest for molecular epidemiologic studies should include overall survival, recurrence- and progression-free survival, response to treatment, and early and late toxicities stemming from chemotherapy and radiation.

Selecting causal genes from genome-wide association studies via functionally coherent subnetworks

Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ∼100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease.

Genetic variants at the IFNL3 locus and their association with hepatitis C virus infections reveal novel insights into host-virus interactions

Human genetic variation plays a critical role in both spontaneous clearance of and response to interferon (IFN)-based therapies against hepatitis C virus (HCV) as shown by the success of recent genome-wide association studies (GWAS). Several GWAS and later validation studies have shown that single nucleotide polymorphisms (SNPs) at the IFNL3 (formerly IL28B) locus on chromosome 19 are involved in eliminating HCV in human patients. No doubt that this information is helping clinicians worldwide in making better clinical decisions in anti-HCV therapy, but the biological mechanisms involving the SNPs leading to differential responses to therapy and spontaneous clearance of HCV remain elusive. Recent reports including the discovery of a novel IFN (IFN-λ4) gene at the IFNL3 locus and in vitro functional studies implicating 2 SNPs as causal variants lead to novel conclusions and perhaps to new directions in research. An attempt is made in this review to summarize the major findings of the GWAS, the efforts involved in the discovery of causal SNPs; and to explain the biological basis for spontaneous clearance and response to treatment in HCV infections.