BACKGROUND: Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs. RESULTS: In this paper, we use a Bayesian logistic regression model as the QTL model for binary traits that includes both main and epistatic effects. Our logistic regression model employs hierarchical priors for regression coefficients similar to the ones used in the Bayesian LASSO linear model for multiple QTL mapping for continuous traits. We develop efficient empirical Bayesian algorithms to infer the logistic regression model. Our simulation study shows that our algorithms can easily handle a QTL model with a large number of main and epistatic effects on a personal computer, and outperform five other methods examined including the LASSO, HyperLasso, BhGLM, RVM and the single-QTL mapping method based on logistic regression in terms of power of detection and false positive rate. The utility of our algorithms is also demonstrated through analysis of a real data set. A software package implementing the empirical Bayesian algorithms in this paper is freely available upon request. CONCLUSIONS: The EBLASSO logistic regression method can handle a large number of effects possibly including the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTLs mapping for complex binary traits.
Establishing genotype-phenotype relationship is the key to understand the molecular mechanism of phenotypic adaptation. This initial step may be untangled by analyzing appropriate ancestral molecules, but it is a daunting task to recapitulate the evolution of non-additive (epistatic) interactions of amino acids and function of a protein separately. To adapt to the ultraviolet (UV)-free retinal environment, the short wavelength-sensitive (SWS1) visual pigment in human (human S1) switched from detecting UV to absorbing blue light during the last 90 million years. Mutagenesis experiments of the UV-sensitive pigment in the Boreoeutherian ancestor show that the blue-sensitivity was achieved by seven mutations. The experimental and quantum chemical analyses show that 4,008 of all 5,040 possible evolutionary trajectories are terminated prematurely by containing a dehydrated nonfunctional pigment. Phylogenetic analysis further suggests that human ancestors achieved the blue-sensitivity gradually and almost exclusively by epistasis. When the final stage of spectral tuning of human S1 was underway 45-30 million years ago, the middle and long wavelength-sensitive (MWS/LWS) pigments appeared and so-called trichromatic color vision was established by interprotein epistasis. The adaptive evolution of human S1 differs dramatically from orthologous pigments with a major mutational effect used in achieving blue-sensitivity in a fish and several mammalian species and in regaining UV vision in birds. These observations imply that the mechanisms of epistatic interactions must be understood by studying various orthologues in different species that have adapted to various ecological and physiological environments.
Mutations conferring resistance to antibiotics are typically costly in the absence of the drug, but bacteria can reduce this cost by acquiring compensatory mutations. Thus, the rate of acquisition of compensatory mutations and their effects are key for the maintenance and dissemination of antibiotic resistances. While compensation for single resistances has been extensively studied, compensatory evolution of multiresistant bacteria remains unexplored. Importantly, since resistance mutations often interact epistatically, compensation of multiresistant bacteria may significantly differ from that of single-resistant strains. We used experimental evolution, next-generation sequencing, in silico simulations, and genome editing to compare the compensatory process of a streptomycin and rifampicin double-resistant Escherichia coli with those of single-resistant clones. We demonstrate that low-fitness double-resistant bacteria compensate faster than single-resistant strains due to the acquisition of compensatory mutations with larger effects. Strikingly, we identified mutations that only compensate for double resistance, being neutral or deleterious in sensitive or single-resistant backgrounds. Moreover, we show that their beneficial effects strongly decrease or disappear in conditions where the epistatic interaction between resistance alleles is absent, demonstrating that these mutations compensate for the epistasis. In summary, our data indicate that epistatic interactions between antibiotic resistances, leading to large fitness costs, possibly open alternative paths for rapid compensatory evolution, thereby potentially stabilizing costly multiple resistances in bacterial populations.
Premature fusion of the cranial sutures (craniosynostosis), affecting 1 in 2,000 newborns, is treated surgically in infancy to prevent adverse neurologic outcomes. To identify mutations contributing to common non-syndromic midline (sagittal and metopic) craniosynostosis, we performed exome sequencing of 132 parent-offspring trios and 59 additional probands. Thirteen probands (7%) had damaging de novo or rare transmitted mutations in SMAD6, an inhibitor of BMP - induced osteoblast differentiation (P < 10-20). SMAD6 mutations nonetheless showed striking incomplete penetrance (<60%). Genotypes of a common variant near BMP2 that is strongly associated with midline craniosynostosis explained nearly all the phenotypic variation in these kindreds, with highly significant evidence of genetic interaction between these loci via both association and analysis of linkage. This epistatic interaction of rare and common variants defines the most frequent cause of midline craniosynostosis and has implications for the genetic basis of other diseases.
- TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
- Published about 6 years ago
Resistance of eggplant against Ralstonia solanacearum phylotype I strains was assessed in a F(6) population of recombinant inbred lines (RILs) derived from a intra-specific cross between S. melongena MM738 (susceptible) and AG91-25 (resistant). Resistance traits were determined as disease score, percentage of wilted plants, and stem-based bacterial colonization index, as assessed in greenhouse experiments conducted in Réunion Island, France. The AG91-25 resistance was highly efficient toward strains CMR134, PSS366 and GMI1000, but only partial toward the highly virulent strain PSS4. The partial resistance found against PSS4 was overcome under high inoculation pressure, with heritability estimates from 0.28 to 0.53, depending on the traits and season. A genetic map was built with 119 AFLP, SSR and SRAP markers positioned on 18 linkage groups (LG), for a total length of 884 cM, and used for quantitative trait loci (QTL) analysis. A major dominant gene, named ERs1, controlled the resistance to strains CMR134, PSS366, and GMI1000. Against strain PSS4, this gene was not detected, but a significant QTL involved in delay of disease progress was detected on another LG. The possible use of the major resistance gene ERs1 in marker-assisted selection and the prospects offered for academic studies of a possible gene for gene system controlling resistance to bacterial wilt in solanaceous plants are discussed.
Genome-wide association studies (GWAS) have been successful in finding numerous new risk variants for complex diseases, but the results almost exclusively rely on single-marker scans. Methods that can analyze joint effects of many variants in GWAS data are still being developed and trialed. To evaluate the performance of such methods it is essential to have a GWAS data simulator that can rapidly simulate a large number of samples, and capture key features of real GWAS data such as linkage disequilibrium (LD) among single-nucleotide polymorphisms (SNPs) and joint effects of multiple loci (multilocus epistasis). In the current study, we combine techniques for specifying high-order epistasis among risk SNPs with an existing program GWAsimulator [Li and Li, 2008] to achieve rapid whole-genome simulation with accurate modeling of complex interactions. We considered various approaches to specifying interaction models including the following: departure from product of marginal effects for pairwise interactions, product terms in logistic regression models for low-order interactions, and penetrance tables conforming to marginal effect constraints for high-order interactions or prescribing known biological interactions. Methods for conversion among different model specifications are developed using penetrance table as the fundamental characterization of disease models. The new program, called simGWA, is capable of efficiently generating large samples of GWAS data with high precision. We show that data simulated by simGWA are faithful to template LD structures, and conform to prespecified diseases models with (or without) interactions.
Independently evolving populations may adapt to similar selection pressures via different genetic changes. The interactions between such changes, such as in a hybrid individual, can inform us about what course adaptation may follow and allow us to determine whether gene flow would be facilitated or hampered following secondary contact. We used Saccharomyces cerevisiae to measure the genetic interactions between first-step mutations that independently evolved in the same biosynthetic pathway following exposure to the fungicide nystatin. We found that genetic interactions are prevalent and predominantly negative, with the majority of mutations causing lower growth when combined in a double mutant than when alone as a single mutant (sign epistasis). The prevalence of sign epistasis is surprising given the small number of mutations tested and runs counter to expectations for mutations arising in a single biosynthetic pathway in the face of a simple selective pressure. Furthermore, in one third of pairwise interactions, the double mutant grew less well than either single mutant (reciprocal sign epistasis). The observation of reciprocal sign epistasis among these first adaptive mutations arising in the same genetic background indicates that partial postzygotic reproductive isolation could evolve rapidly between populations under similar selective pressures, even with only a single genetic change in each. The nature of the epistatic relationships was sensitive, however, to the level of drug stress in the assay conditions, as many double mutants became fitter than the single mutants at higher concentrations of nystatin. We discuss the implications of these results both for our understanding of epistatic interactions among beneficial mutations in the same biochemical pathway and for speciation.
Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.
Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.
Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.