A systematic approach to understand the functional consequences of non-protein coding risk regions

A primary goal of genetic association studies is to elucidate genes and novel biological mechanisms involved in disease. Recently, genome-wide association studies have identified many common genetic variants that are significantly associated with complex diseases such as cancer. In contrast to mutation-causing Mendelian disorders, a sizable fraction of the variants lies outside known protein-coding regions; therefore, understanding their biological consequences presents a major challenge in human genetics. Here we describe an integrated framework to allow non-protein coding loci to be annotated with respect to regulatory functions. This will facilitate identification of target genes as well as prioritize variants for functional testing.     

Ionizing radiation and genetic risks. X. The potential "disease phenotypes" of radiation-induced genetic damage in humans: perspectives from human molecular biology and radiation genetics.

Estimates of genetic risks of radiation exposure of humans are traditionally expressed as expected increases in the frequencies of genetic diseases (single-gene, chromosomal and multifactorial) over and above those of naturally-occurring ones in the population. An important assumption in expressing risks in this manner is that gonadal radiation exposures can cause an increase in the frequency of mutations and that this would result in an increase in the frequency of genetic diseases under study. However, despite compelling evidence for radiation-induced mutations in experimental systems, no increases in the frequencies of genetic diseases of concern or other adverse effects (i.e., those which are not formally classified as genetic diseases), have been found in human studies involving parents who have sustained radiation exposures. The known differences between spontaneous mutations that underlie naturally-occurring single-gene diseases and radiation-induced mutations studied in experimental systems now permit us to address and resolve these issues to some extent. The fact that spontaneous mutations (among which are point mutations and DNA deletions generally restricted to the gene) originate through a number of different mechanisms and that the latter are intimately related to the DNA organization of the genes, are now well-documented. Further, spontaneous mutations include those that cause diseases through loss of function as well as gain of function of genes. In contrast, most radiation-induced mutations studied in experimental systems (although identified through the phenotypes of the marker genes) are predominantly multigene deletions which cause loss of function; the recoverability of an induced deletion in a livebirth seems dependent on whether the gene and the genomic region in which it is located can tolerate heterozygosity for the deletion and yet be compatible with viability. In retrospect, the successful mutation test systems (such as the mouse specific locus test) used in radiation studies have involved genes which are non-essential for survival and are also located in genomic regions, likewise non-essential for survival. In contrast, most of the human genes at which induced mutations have been looked for, do not seem to have these attributes. The inference therefore is that the failure to find induced germline mutations in humans is not due to the resistance of human genes to induced mutations but due to the structural and functional constraints associated with their recoverability in livebirths. Since the risk of inducible genetic diseases in humans is estimated using rates of "recovered" mutations in mice, there is a need to introduce appropriate correction factors to bridge the gap between these rates and the rates at which mutations causing diseases are potentially recoverable in humans. Since the whole genome is the "target" for radiation-induced genetic damage, the failure to find increases in the frequencies of specific single-gene diseases of societal concern does not imply that there are no genetic risks of radiation exposures: the problem lies in delineating the phenotypes of recoverable genetic damage that are recognizable in livebirths. Data from studies of naturally-occurring microdeletion syndromes in humans and those from mouse radiation studies are instructive in this regard. They (i) support the view that growth retardation, mental retardation and multisystem developmental abnormalities are likely to be among the quantitatively more important adverse effects of radiation-induced genetic damage than mutations in a few selected genes and (ii) underscore the need to expand the focus in risk estimation from known genetic diseases (as has been the case thus far) to include these induced adverse developmental effects although most of these are not formally classified as "genetic diseases". (ABSTRACT TRUNCATED)     

Modifier genes in mice and humans

An emerging theme of studies with spontaneous, engineered and induced mutant mice is that phenotypes often depend on genetic background, implying that genetic modifiers have a role in guiding the functional consequences of genetic variation. Understanding the molecular and cellular basis by which modifier genes exert their influence will provide insights into developmental and physiological pathways that are critical to fundamental biological processes, as well as into novel targets for therapeutic interventions in human diseases.     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Gene functional similarity search tool (GFSST)

Background  

With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity.     

Most rare missense alleles are deleterious in humans: implications for complex disease and association studies

The accumulation of mildly deleterious missense mutations in individual human genomes has been proposed to be a genetic basis for complex diseases. The plausibility of this hypothesis depends on quantitative estimates of the prevalence of mildly deleterious de novo mutations and polymorphic variants in humans and on the intensity of selective pressure against them. We combined analysis of mutations causing human Mendelian diseases, of human-chimpanzee divergence, and of systematic data on human genetic variation and found that ~20% of new missense mutations in humans result in a loss of function, whereas ~27% are effectively neutral. Thus, the remaining 53% of new missense mutations have mildly deleterious effects. These mutations give rise to many low-frequency deleterious allelic variants in the human population, as is evident from a new data set of 37 genes sequenced in >1,500 individual human chromosomes. Surprisingly, up to 70% of low-frequency missense alleles are mildly deleterious and are associated with a heterozygous fitness loss in the range 0.001-0.003. Thus, the low allele frequency of an amino acid variant can, by itself, serve as a predictor of its functional significance. Several recent studies have reported a significant excess of rare missense variants in candidate genes or pathways in individuals with extreme values of quantitative phenotypes. These studies would be unlikely to yield results if most rare variants were neutral or if rare variants were not a significant contributor to the genetic component of phenotypic inheritance. Our results provide a justification for these types of candidate-gene (pathway) association studies and imply that mutation-selection balance may be a feasible evolutionary mechanism underlying some common diseases.     

Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.     

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.     

Cardiac disease modeling using induced pluripotent stem cell-derived human cardiomyocytes

Causative mutations and variants associated with cardiac diseases have been found in genes encoding cardiac ion channels, accessory proteins, cytoskeletal components, junctional proteins, and signaling molecules. In most cases the functional evaluation of the genetic alterationhas been carried out by expressing the mutated proteins in in-vitro heterologous systems. While these studies have provided a wealth of functional details that have greatly enhanced the understanding of the pathological mechanisms, it has always been clear that heterologous expression of the mutant protein bears the intrinsic limitation of the lack of a proper intracellular environment and the lack of pathological remodeling. The results obtained from the application of the next generation sequencing technique to patients suffering from cardiac diseases have identified several loci, mostly in non-coding DNA regions, which still await functional analysis. The isolation and culture of human embryonic stem cells has initially provided a constant source of cells from which cardiomyocytes(CMs) can be obtained by differentiation. Furthermore, the possibility to reprogram cellular fate to a pluripotent state, has opened this process to the study of genetic diseases. Thus induced pluripotent stem cells(i PSCs) represent a completely new cellular model that overcomes the limitations of heterologous studies. Importantly, due to the possibility to keep spontaneously beating CMs in culture for several months, during which they show a certain degree of maturation/aging, this approach will also provide a system in which to address the effect of long-term expression of the mutated proteins or any other DNA mutation, in terms of electrophysiological remodeling. Moreover, since i PSC preserve the entire patients’ genetic context, the system will help the physicians in identifying the most appropriate pharmacological intervention to correct the functional alteration. This article summarizes the current knowledge of cardiac genetic diseases modelled with i PSC.     

Genetic architecture of human brain evolution

Comparative studies of hominids have long sought to identify mutational events that shaped the evolution of the human nervous system. However, functional genetic differences are outnumbered by millions of nearly neutral mutations, and the developmental mechanisms underlying human nervous system specializations are difficult to model and incompletely understood. Candidate-gene studies have attempted to map select human-specific genetic differences to neurodevelopmental functions, but it remains unclear how to contextualize the relative effects of genes that are investigated independently. Considering these limitations, we discuss scalable approaches for probing the functional contributions of human-specific genetic differences. We propose that a systems-level view will enable a more quantitative and integrative understanding of the genetic, molecular and cellular underpinnings of human nervous system evolution.     

Identifying host targets for drug development with knowledge from genome-wide studies: lessons from HIV-AIDS

Advances in human genomics are now being effectively applied to the search for host factors underlying susceptibility to common diseases. From the steady stream of studies showing association of host genetic factors with viral diseases, it has become clear that host factors contribute substantially to the variability of viral infections in humans. Candidate gene studies that seek to show associations between single-nucleotide polymorphisms (SNPs) with a disease outcome have predominated, but whole-genome association studies (GWAS) have recently appeared. A major goal of these studies is to understand how human genetic variation contributes to individual differences in susceptibility and to exploit this knowledge for targeted drug development.     

Evolution, revolution and heresy in the genetics of infectious disease susceptibility

Infectious pathogens have long been recognized as potentially powerful agents impacting on the evolution of human genetic diversity. Analysis of large-scale case-control studies provides one of the most direct means of identifying human genetic variants that currently impact on susceptibility to particular infectious diseases. For over 50 years candidate gene studies have been used to identify loci for many major causes of human infectious mortality, including malaria, tuberculosis, human immunodeficiency virus/acquired immunodeficiency syndrome, bacterial pneumonia and hepatitis. But with the advent of genome-wide approaches, many new loci have been identified in diverse populations. Genome-wide linkage studies identified a few loci, but genome-wide association studies are proving more successful, and both exome and whole-genome sequencing now offer a revolutionary increase in power. Opinions differ on the extent to which the genetic component to common disease susceptibility is encoded by multiple high frequency or rare variants, and the heretical view that most infectious diseases might even be monogenic has been advocated recently. Review of findings to date suggests that the genetic architecture of infectious disease susceptibility may be importantly different from that of non-infectious diseases, and it is suggested that natural selection may be the driving force underlying this difference.     

ETph: enhancers and their targets in pig and human database

Enhancers, as the genomic non-coding sequences, play a key role in the activation of gene expression. They have been widely identified in the human genome. Pig is an important biomedical model for human health. Few studies have been performed to explore the enhancers in the pig genome. The human enhancer information may be useful to identify enhancers in the pig genome. In addition, the genetic background of pig traits could be useful to annotate human enhancers and diseases. Thus, in order to further study enhancers and their potential roles in human and pig, we developed a public database, ETph (Enhancers and their Targets in pig and human). ETph integrates the information on human enhancers, pig putative enhancers, target genes, pig QTL terms, human diseases, GO terms and the KEGG pathway. A total of 25 182 enhancers were identified in the pig genome using the human homology sequence information. Among them, 6232 high-confidence enhancers were used to build the ETph. ETph provides a convenient platform to search, browse and download data. Moreover, a web-based analytical tool was designed to visualize networks and topology graphs among pig putative enhancers, target genes, pig QTL traits and human diseases. ETph might provide a useful tool for researchers to investigate the genetic background of pig traits and human diseases. ETph is freely accessible at http://klab.sjtu.edu.cn/enhancer/ .     

The genetics of human personality

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. Twin and family studies have showed that personality traits are moderately heritable, and can predict various lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psychiatric diseases as extremes of normal tendencies, including specific personality traits. This implies that heritable variation in personality traits, such as neuroticism, would share a common genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable efforts over the past several decades, the genetic variants that influence personality are only beginning to be identified. We review these recent and increasingly rapid developments, which focus on the assessment of personality via several commonly used personality questionnaires in healthy human subjects. Study designs covered include twin, linkage, candidate gene association studies, genome‐wide association studies and polygenic analyses. Findings from genetic studies of personality have furthered our understanding about the genetic etiology of personality, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses have showed genetic correlations between personality and psychopathology, confirming that genetic studies of personality can help to elucidate the etiology of several neuropsychiatric diseases.     

Functional Analysis of Variance for Association Studies

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.     

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases

Background

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.

Principal Findings

We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.

Conclusions

For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.

Availability

The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.