Modular epistasis in yeast metabolism

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.     

PDGF signaling specificity is mediated through multiple immediate early genes

Growth factor signaling leads to the induction or repression of immediate early genes, but how these genes act collectively as effectors of downstream processes remains unresolved. We have used gene trap-coupled microarray analysis to identify and mutate multiple platelet-derived growth factor (PDGF) intermediate early genes in mice. Mutations in these genes lead to a high frequency of phenotypes that affect the same cell types and processes as those controlled by the PDGF pathway. We conclude that these genes form a network that controls specific processes downstream of PDGF signaling.     

A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans

The fundamental aim of genetics is to understand how an organism's phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions-we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets.     

Reverse engineering of regulatory networks in human B cells

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.     

Pervasive joint influence of epistasis and plasticity on mutational effects in Escherichia coli

The effects of mutations on phenotype and fitness may depend on the environment (phenotypic plasticity), other mutations (genetic epistasis) or both. Here we examine the fitness effects of 18 random insertion mutations in E. coli in two resource environments and five genetic backgrounds. We tested each mutation for plasticity and epistasis by comparing its fitness effects across these ecological and genetic contexts. Some mutations had no measurable effect in any of these contexts. None of the mutations had effects on phenotypic plasticity that were independent of genetic background. However, half the mutations had epistatic interactions such that their effects differed among genetic backgrounds, usually in an environment-dependent manner. Also, the pattern of mutational effects across backgrounds indicated that epistasis had been shaped primarily by unique events in the evolutionary history of a population rather than by repeatable events associated with shared environmental history.     

Distributions of epistasis in microbes fit predictions from a fitness landscape model

How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions.     

Genome-wide association study of leaf architecture in the maize nested association mapping population

US maize yield has increased eight-fold in the past 80 years, with half of the gain attributed to selection by breeders. During this time, changes in maize leaf angle and size have altered plant architecture, allowing more efficient light capture as planting density has increased. Through a genome-wide association study (GWAS) of the maize nested association mapping panel, we determined the genetic basis of important leaf architecture traits and identified some of the key genes. Overall, we demonstrate that the genetic architecture of the leaf traits is dominated by small effects, with little epistasis, environmental interaction or pleiotropy. In particular, GWAS results show that variations at the liguleless genes have contributed to more upright leaves. These results demonstrate that the use of GWAS with specially designed mapping populations is effective in uncovering the basis of key agronomic traits.     

TTC21B contributes both causal and modifying alleles across the ciliopathy spectrum

Ciliary dysfunction leads to a broad range of overlapping phenotypes, collectively termed ciliopathies. This grouping is underscored by genetic overlap, where causal genes can also contribute modifier alleles to clinically distinct disorders. Here we show that mutations in TTC21B, which encodes the retrograde intraflagellar transport protein IFT139, cause both isolated nephronophthisis and syndromic Jeune asphyxiating thoracic dystrophy. Moreover, although resequencing of TTC21B in a large, clinically diverse ciliopathy cohort and matched controls showed a similar frequency of rare changes, in vivo and in vitro evaluations showed a significant enrichment of pathogenic alleles in cases (P < 0.003), suggesting that TTC21B contributes pathogenic alleles to ～5% of ciliopathy cases. Our data illustrate how genetic lesions can be both causally associated with diverse ciliopathies and interact in trans with other disease-causing genes and highlight how saturated resequencing followed by functional analysis of all variants informs the genetic architecture of inherited disorders.     

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.     

Bayesian inference of epistatic interactions in case-control studies

Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.     

Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.     

Complex interactions between genes controlling trafficking in primary cilia

Cilia-associated human genetic disorders are striking in the diversity of their abnormalities and their complex inheritance. Inactivation of the retrograde ciliary motor by mutations in DYNC2H1 causes skeletal dysplasias that have strongly variable expressivity. Here we define previously unknown genetic relationships between Dync2h1 and other genes required for ciliary trafficking. Mutations in mouse Dync2h1 disrupt cilia structure, block Sonic hedgehog signaling and cause midgestation lethality. Heterozygosity for Ift172, a gene required for anterograde ciliary trafficking, suppresses cilia phenotypes, Sonic hedgehog signaling defects and early lethality of Dync2h1 homozygotes. Ift122, like Dync2h1, is required for retrograde ciliary trafficking, but reduction of Ift122 gene dosage also suppresses the Dync2h1 phenotype. These genetic interactions illustrate the cell biology underlying ciliopathies and argue that mutations in intraflagellar transport genes cause their phenotypes because of their roles in cilia architecture rather than direct roles in signaling.     

Identification of acquired somatic mutations in the gene encoding chromatin-remodeling factor ATRX in the alpha-thalassemia myelodysplasia syndrome (ATMDS)

Inherited mutations of specific genes have elucidated the normal roles of the proteins they encode by relating specific mutations to particular phenotypes. But many potentially informative mutations in such genes are lethal early in development. Consequently, inherited mutations may not reflect all the functional roles of such proteins. Acquired, somatic defects should reflect a wider spectrum of mutations because they are not prone to negative selection in development. It has been difficult to identify such mutations so far, but microarray analysis provides a new opportunity to do so. Using this approach, we have shown that in individuals with myelodysplasia associated with alpha-thalassemia (ATMDS), somatic mutations of the gene encoding the chromatin remodeling factor ATRX cause an unexpectedly severe hematological phenotype compared with the wide spectrum of inherited mutations affecting this gene. These findings cast new light on this pleiotropic cofactor, which appears to be an essential component rather than a mere facilitator of globin gene expression.     

DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray

We describe a new synthetic lethality analysis by microarray (SLAM) technique that uses approximately 4,600 Saccharomyces cerevisiae haploid deletion mutants with molecular 'bar codes' (TAGs). We used SGS1 and SRS2, two 3'-->5' DNA helicase genes, as 'queries' to identify their redundant and unique biological functions. We introduced these 'query mutations' into a haploid deletion pool by integrative transformation to disrupt the query gene in every cell, generating a double mutant pool. Optimization of integrative transformation efficiency was essential to the success of SLAM. Synthetic interactions defined a DNA helicase genetic network and predicted a role for SRS2 in processing damaged replication forks but, unlike SGS1, not in rDNA replication, DNA topology or lagging strand synthesis. SGS1 and SRS2 have synthetic defects with MRC1 but not RAD9, suggesting that SGS1 and SRS2 function in a parallel pathway with MRC1 to transduce the DNA replication stress signal to the general DNA damage checkpoint pathway. Both helicase genes have rad51-reversible synthetic defects with 5'-->3' DNA helicase RRM3, suggesting that RRM3 helps prevent formation of toxic recombination intermediates. SLAM detects synthetic lethality efficiently and ranks candidate genetic interactions, making it an especially useful method.     

Progress and prospects in rat genetics: a community view

The rat is an important system for modeling human disease. Four years ago, the rich 150-year history of rat research was transformed by the sequencing of the rat genome, ushering in an era of exceptional opportunity for identifying genes and pathways underlying disease phenotypes. Genome-wide association studies in human populations have recently provided a direct approach for finding robust genetic associations in common diseases, but identifying the precise genes and their mechanisms of action remains problematic. In the context of significant progress in rat genomic resources over the past decade, we outline achievements in rat gene discovery to date, show how these findings have been translated to human disease, and document an increasing pace of discovery of new disease genes, pathways and mechanisms. Finally, we present a set of principles that justify continuing and strengthening genetic studies in the rat model, and further development of genomic infrastructure for rat research.