Hunting human disease genes: lessons from the past, challenges for the future

The concept that a specific alteration in an individual’s DNA can result in disease is central to our notion of molecular medicine. The molecular basis of more than 3,500 Mendelian disorders has now been identified. In contrast, the identification of genes for common disease has been much more challenging. We discuss historical and contemporary approaches to disease gene identification, focusing on novel opportunities such as the use of population extremes and the identification of rare variants. While our ability to sequence DNA has advanced dramatically, assigning function to a given sequence change remains a major challenge, highlighting the need for both bioinformatics and functional approaches to appropriately interpret these data. We review progress in mapping and identifying human disease genes and discuss future challenges and opportunities for the field.     

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.     

Novel integrative genomics strategies to identify genes for complex traits

Forward genetics is a common approach to dissecting complex traits like common human diseases. The ultimate aim of this approach was the identification of genes that are causal for disease or other phenotypes of interest. However, the forward genetics approach is by definition restricted to the identification of genes that have incurred mutations over the course of evolution or that incurred mutations as a result of chemical mutagenesis, and that as a result lead to disease or to variations in other phenotypes of interest. Genes that harbour no such mutations, but that play key roles in parts of the biological network that lead to disease, are systematically missed by this class of approaches. Recently, a class of novel integrative genomics approaches has been devised to elucidate the complexity of common human diseases by intersecting genotypic, molecular profiling, and clinical data in segregating populations. These novel approaches take a more holistic view of biological systems and leverage the vast network of gene–gene interactions, in combination with DNA variation data, to establish causal relationships among molecular profiling traits and Fbetween molecular profiling and disease (or other classic phenotypes). A number of novel genes for disease phenotypes have been identified as a result of these approaches, highlighting the utility of integrating orthogonal sources of data to get at the underlying causes of disease.     

Genomic clocks and evolutionary timescales

For decades, molecular clocks have helped to illuminate the evolutionary timescale of life, but now genomic data pose a challenge for time estimation methods. It is unclear how to integrate data from many genes, each potentially evolving under a different model of substitution and at a different rate. Current methods can be grouped by the way the data are handled (genes considered separately or combined into a 'supergene') and the way gene-specific rate models are applied (global versus local clock). There are advantages and disadvantages to each of these approaches, and the optimal method has not yet emerged. Fortunately, time estimates inferred using many genes or proteins have greater precision and appear to be robust to different approaches.     

Anomalies génétiques et infertilité masculine

Fifteen percent of couples are infertile and in about 50% of cases the cause is of male origin. The aetiology is still unknown in more than 90% of cases and there may be genetic or environmental causes. Three approaches are used to detect genetic causes for male infertility: 1) cytogenetics, resulting in particular from progress made in molecular cytogenetics and the direct analysis of gametes by in situ molecular hybridation techniques. When a chromosome anomaly, the most common cause of infertility, including deletion of the Y chromosome, is discovered, it is not easy to distinguish between gene anomalies resulting from change and mechanical anomalies that are an integral part of meiosis; 2) the analysis of candidate genes, which often uses data obtained from animal, usually murine, models. This approach, frequently described in the literature, tends to be lengthy, expensive and rarely results in the discovery of an abnormal gene, as is the case, for example, with meiotic genes; 3) Mendel’s approach is clearly the preferred choice, studying as it does cases of inherited infertility, which is much more widespread than we might think.     

A Laplace mixture model for identification of differential expression in microarray experiments

Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.     

Molecular dating in the genomic era

The comparison of DNA and protein sequences of extant species might be informative for reconstructing the chronology of evolutionary events on Earth. A phylogenetic tree inferred from molecular data directly depicts the evolutionary affinities of species and indirectly allows estimating the age of their origin and diversification. Molecular dating is achieved by assuming the molecular clock hypothesis, i.e., that the rate of change of nucleotide and amino acid sequences is on average constant over geological time. If paleontological calibrations are available, then absolute divergence times of species can be estimated. However, three major difficulties potentially hamper molecular dating : (1) a limited sample of genes and organisms, (2) a limited number of fossil references, and (3) pervasive variations of molecular evolutionary rates among genomes and species. To circumvent these problems, different solutions have been recently proposed. Larger data sets are built with more genes and more species sampled through the mining of an increasing number of genomes. Moreover, independent key fossils are identified to calibrate molecular clocks, and the uncertainty on their age is integrated in subsequent analyses. Finally, models of molecular rate variations are constructed, and incorporated in the so-called relaxed molecular clock approaches. As an illustration of these improvements, we mention that the debated age of the animal (bilaterian metazoans) diversification may have occurred between 642-761 million years ago (Mya), roughly 100 Ma before the Cambrian explosion. Among mammals, the initial diversification of major placental groups may have taken place around 100 Mya, well before the Cretaceous/Tertiary boundary marking the extinction of dinosaurs.     

Hormonal regulation of gene expression

The involvement of plant hormones in the regulation of gene expression is well-recognized. Current research using molecular approaches has resulted in the isolation and characterization of a number of hormone-responsive genes and cDNAs. These genes are proving to be valuable molecular probes to study the mode of action of plant hormones. This review will briefly describe some recent molecular data from selected hormone-responsive plant systems. Results of these studies indicate potential complexity in the regulation of these genes. These results and future challenges are discussed.     

Messina: A Novel Analysis Tool to Identify Biologically Relevant Molecules in Disease

Background

Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes.

Methodology/Principal Findings

Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer.

Conclusions/Significance

Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package.     

The molecular genetic basis of plant adaptation

How natural selection on adaptive traits is filtered to the genetic level remains largely unknown. Theory and quantitative trait locus (QTL) mapping have provided insights into the number and effect of genes underlying adaptations, but these results have been hampered by questions of applicability to real biological systems and poor resolution, respectively. Advances in molecular technologies have expedited the cloning of adaptive genes through both forward and reverse genetic approaches. Forward approaches start with adaptive traits and attempt to characterize their underlying genetic architectures through linkage disequilibrium mapping, QTL mapping, and other methods. Reverse screens search large sequence data sets for genes that possess the signature of selection. Though both approaches have been successful in identifying adaptive genes in plants, very few, if any, of these adaptations' molecular bases have been fully resolved. The continued isolation of plant adaptive genes will lead to a more comprehensive understanding of natural selection's effect on genes and genomes.     

Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach.

Rates of molecular evolution vary widely between lineages, but quantification of how rates change has proven difficult. Recently proposed estimation procedures have mainly adopted highly parametric approaches that model rate evolution explicitly. In this study, a semiparametric smoothing method is developed using penalized likelihood. A saturated model in which every lineage has a separate rate is combined with a roughness penalty that discourages rates from varying too much across a phylogeny. A data-driven cross-validation criterion is then used to determine an optimal level of smoothing. This criterion is based on an estimate of the average prediction error associated with pruning lineages from the tree. The methods are applied to three data sets of six genes across a sample of land plants. Optimally smoothed estimates of absolute rates entailed 2- to 10-fold variation across lineages.     

Combining data in phylogenetic analysis

Systematists have access to multiple sources of character information in phylogenetic analysis. For example, it is not unusual to have nucleotide sequences from several different genes, or to have molecular and morphological data. How should diverse data be analyzed in phylogenetic analysis? Several methods have been proposed for the treatment of partitioned data: the total evidence, separate analysis, and conditional combination approaches. Here, we review some of the advantages and disadvantages of the different approaches, with special concentration on which methods help us to discern the evolutionary process and provide the most accurate estimates of phylogeny.     

Expression Dynamics of Genes and microRNAs at Different Growth Stages and Heat Treatments in Contrasting High Temperature Responsive Rice Genotypes

The global warming-driven climate change is becoming a major challenge for rice cultivation in Asia and Africa. High-temperature stress impairs the physiology and growth of rice plant, and ultimately results in reduced grain yield. This study was aimed to decipher the physiological and molecular changes occurring during different growth stages of heat-tolerant (N22) and -susceptible (Vandana) rice cultivars under three different heat treatments. Chlorophyll content, membrane integrity, gas exchange parameters and expression of genes and miRNAs were analyzed in N22 and Vandana at seedling, vegetative, and reproductive growth stages after exposing to short and long duration of high temperature stress, and recovery. A number of genes and miRNAs showed dynamic changes in their expression patterns at different growth stages and heat treatments, highlighting the necessity to understand gene regulation before employing the genes for modification through transgenic or gene editing approaches. Predominantly N22 showed distinct and unique capability to reprogram its physiological and molecular machinery during prolonged heat stress at reproductive stage, suggesting that the dynamics in gene regulation is crucial to determine its heat tolerant ability. The study has larger implications in deploying genes for the development of heat tolerant rice cultivars through breeding, transgenic, and genome editing approaches.