Genetic interactions between transcription factors cause natural variation in yeast

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally occurring variants of HXT7 Our study highlights the complex nature of the genotype-to-phenotype map within and between environments.

Intrinsic incompatibilities evolving as a by-product of divergent ecological selection: Considering them in empirical studies on divergence with gene flow

The possibility of intrinsic barriers to gene flow is often neglected in empirical research on local adaptation and speciation with gene flow, for example when interpreting patterns observed in genome scans. However, we draw attention to the fact that, even with gene flow, divergent ecological selection may generate intrinsic barriers involving both ecologically selected and other interacting loci. Mechanistically, the link between the two types of barriers may be generated by genes that have multiple functions (i.e., pleiotropy), and/or by gene interaction networks. Because most genes function in complex networks, and their evolution is not independent of other genes, changes evolving in response to ecological selection can generate intrinsic barriers as a by-product. A crucial question is to what extent such by-product barriers contribute to divergence and speciation-that is whether they stably reduce gene flow. We discuss under which conditions by-product barriers may increase isolation. However, we also highlight that, depending on the conditions (e.g., the amount of gene flow and the strength of selection acting on the intrinsic vs. the ecological barrier component), the intrinsic incompatibility may actually destabilize barriers to gene flow. In practice, intrinsic barriers generated as a by-product of divergent ecological selection may generate peaks in genome scans that cannot easily be interpreted. We argue that empirical studies on divergence with gene flow should consider the possibility of both ecological and intrinsic barriers. Future progress will likely come from work combining population genomic studies, experiments quantifying fitness and molecular studies on protein function and interactions.

Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated Caenorhabditis elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute and is instead dependent upon multiple factors such as the age and genetic background of an organism. In order to understand the mechanistic basis of these changes, we characterized heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused in part by a mutation in a component of the NURF chromatin remodeling complex, that regulates reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animal’s reproductive lifespan. Here, we use a previously published macroscale model of the egg-laying rate in C. elegans to show that time-dependent effect-size is explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate key predictions of this model with controlled mating experiments and quantification of oogenesis and sperm use. Incorporation of this model into QTL mapping allows us to identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of the unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.

Complex traits are influenced by the individual effects of genetic variants in addition to the interactions of the variants with the environment, age, and each other. While complex genetic architectures are ubiquitous in natural traits, little is known about the causal mechanisms that create their complex genetic architectures. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. We use computational modeling to demonstrate how age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how antagonistic pleiotropy can emerge from multicellular feedback systems.

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell's inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes' decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.

Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.

Powerful decomposition of complex traits in a diploid model

Explaining trait differences between individuals is a core and challenging aim of life sciences. Here, we introduce a powerful framework for complete decomposition of trait variation into its underlying genetic causes in diploid model organisms. We sequence and systematically pair the recombinant gametes of two intercrossed natural genomes into an array of diploid hybrids with fully assembled and phased genomes, termed Phased Outbred Lines (POLs). We demonstrate the capacity of this approach by partitioning fitness traits of 6,642 Saccharomyces cerevisiae POLs across many environments, achieving near complete trait heritability and precisely estimating additive (73%), dominance (10%), second (7%) and third (1.7%) order epistasis components. We map quantitative trait loci (QTLs) and find nonadditive QTLs to outnumber (3:1) additive loci, dominant contributions to heterosis to outnumber overdominant, and extensive pleiotropy. The POL framework offers the most complete decomposition of diploid traits to date and can be adapted to most model organisms.

Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90

The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models, quantitative genetics, and evolutionary theory. But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur. Here, we quantify Hsp90’s ability to buffer or potentiate (i.e., diminish or enhance) the effects of genetic variation on single-cell morphological features in budding yeast. We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations. However, we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure. Specifically, Hsp90 tends to enhance, rather than diminish, the effects of spontaneous mutations and recombinations. This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation. Instead, natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness.

Most biologists appreciate that natural selection filters new mutations (e.g., by eliminating deleterious ones), such that genetic variation in nature is biased. The idea that selection also skews the types of genetic interactions that exist in nature is less appreciated. For example, studies spanning diverse species have shown that the protein Hsp90, which helps other proteins to fold properly, tends to diminish the observable effects of genetic variation. This observation has led to the assumption that Hsp90 also buffers the effects of new mutations. This untested assumption has served as a rationale for cancer-treatment strategies and shaped our understanding of variation in complex traits. We measured the effects of new mutations on the shapes and sizes of individual yeast cells and found that Hsp90 does not tend to buffer these effects. Instead, Hsp90 interacts with new mutations in diverse ways, sometimes buffering, but more often enhancing mutational effects on cell shape and size. We conclude that selection preferentially allows buffered mutations to persist in natural populations. This result alters common perceptions about why cryptic (i.e., buffered) genetic variation exists and casts doubt on cancer-treatment strategies aiming to target presumed buffers of mutational effects.

Meiotic Interactors of a Mitotic Gene TAO3 Revealed by Functional Analysis of its Rare Variant

Studying the molecular consequences of rare genetic variants has the potential to identify novel and hitherto uncharacterized pathways causally contributing to phenotypic variation. Here, we characterize the functional consequences of a rare coding variant of TAO3, previously reported to contribute significantly to sporulation efficiency variation in Saccharomyces cerevisiae. During mitosis, the common TAO3 allele interacts with CBK1—a conserved NDR kinase. Both TAO3 and CBK1 are components of the RAM signaling network that regulates cell separation and polarization during mitosis. We demonstrate that the role of the rare allele TAO3(4477C) in meiosis is distinct from its role in mitosis by being independent of ACE2—a RAM network target gene. By quantitatively measuring cell morphological dynamics, and expressing the TAO3(4477C) allele conditionally during sporulation, we show that TAO3 has an early role in meiosis. This early role of TAO3 coincides with entry of cells into meiotic division. Time-resolved transcriptome analyses during early sporulation identified regulators of carbon and lipid metabolic pathways as candidate mediators. We show experimentally that, during sporulation, the TAO3(4477C) allele interacts genetically with ERT1 and PIP2, regulators of the tricarboxylic acid cycle and gluconeogenesis metabolic pathways, respectively. We thus uncover a meiotic functional role for TAO3, and identify ERT1 and PIP2 as novel regulators of sporulation efficiency. Our results demonstrate that studying the causal effects of genetic variation on the underlying molecular network has the potential to provide a more extensive understanding of the pathways driving a complex trait.

Predicting quantitative traits from genome and phenome with near perfect accuracy

In spite of decades of linkage and association studies and its potential impact on human health, reliable prediction of an individual's risk for heritable disease remains difficult. Large numbers of mapped loci do not explain substantial fractions of heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice. Here, we use a genome sequenced population of ∼7,000 yeast strains of high but varying relatedness, and predict growth traits from family information, effects of segregating genetic variants and growth in other environments with an average coefficient of determination R(2) of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability and is higher than achieved with a single assay in the laboratory. Our results prove that very accurate prediction of complex traits is possible, and suggest that additional data from families rather than reference cohorts may be more useful for this purpose.

Exploiting budding yeast natural variation for industrial processes

For the last two decades, the natural variation of the yeast Saccharomyces cerevisiae has been massively exploited with the aim of understanding ecological and evolutionary processes. As a result, many new genetic variants have been uncovered, providing a large catalogue of alleles underlying complex traits. These alleles represent a rich genetic resource with the potential to provide new strains that can cope with the growing demands of industrial fermentation processes. When surveyed in detail, several of these variants have proven useful in wine and beer industries by improving nitrogen utilisation, fermentation kinetics, ethanol production, sulphite resistance and aroma production. Here, I illustrate how allele-specific expression and polymorphisms within the coding region of GDB1 underlie fermentation kinetic differences in synthetic wine must. Nevertheless, the genetic basis of how GDB1 variants and other natural alleles interact in foreign genetic backgrounds remains unclear. Further studies in large sets of strains, recombinant hybrids and multiple parental pairs will broaden our knowledge of the molecular and genetic basis of trait adaptation for utilisation in applied and industrial processes.

Natural variation in non-coding regions underlying phenotypic diversity in budding yeast

Linkage mapping studies in model organisms have typically focused their efforts in polymorphisms within coding regions, ignoring those within regulatory regions that may contribute to gene expression variation. In this context, differences in transcript abundance are frequently proposed as a source of phenotypic diversity between individuals, however, until now, little molecular evidence has been provided. Here, we examined Allele Specific Expression (ASE) in six F1 hybrids from Saccharomyces cerevisiae derived from crosses between representative strains of the four main lineages described in yeast. ASE varied between crosses with levels ranging between 28% and 60%. Part of the variation in expression levels could be explained by differences in transcription factors binding to polymorphic cis-regulations and to differences in trans-activation depending on the allelic form of the TF. Analysis on highly expressed alleles on each background suggested ASN1 as a candidate transcript underlying nitrogen consumption differences between two strains. Further promoter allele swap analysis under fermentation conditions confirmed that coding and non-coding regions explained aspartic and glutamic acid consumption differences, likely due to a polymorphism affecting Uga3 binding. Together, we provide a new catalogue of variants to bridge the gap between genotype and phenotype.

Armour GE, Mackay TFC, Anholt RRH. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In this study, we implemented a powerful screen to quantify the extent of epistasis as well as identify candidate interacting variants using 203 inbred wild-derived lines with sequenced genomes of the Drosophila melanogaster Genetic Reference Panel (DGRP). We crossed the DGRP lines to P[GT1]-element insertion mutants in Sema-5c and neuralized (neur), two neurodevelopmental loci which affect olfactory behavior, and to their coisogenic wild-type control. We observed significant variation in olfactory responses to benzaldehyde among F1 genotypes and for the DGRP line by mutant genotype interactions for both loci, showing extensive nonadditive genetic variation. We performed genome-wide association analyses to identify the candidate modifier loci. None of these polymorphisms were in or near the focal genes; therefore, epistasis is the cause of the nonadditive genetic variance. Candidate genes could be placed in interaction networks. Several candidate modifiers are associated with neural development. Analyses of mutants of candidate epistatic partners with neur (merry-go-round (mgr), prospero (pros), CG10098, Alhambra (Alh) and CG12535) and Sema-5c (CG42540 and bruchpilot (brp)) showed aberrant olfactory responses compared with coisogenic controls. Thus, integrating genome-wide analyses of natural variants with mutations at defined genomic locations in a common coisogenic background can unmask specific epistatic modifiers of behavioral phenotypes.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

This study uses a large number of crosses between a common lab strain and vineyard-isolated strain of yeast, and estimates the phenotypic variance for various quantitative traits. Using this data set, the authors show additive quantitative trait loci (QTL) and QTL–QTL interactions to be on average 43% and 9%, respectively.

Population perspectives on functional genomic variation in yeast

Advances in high-throughput sequencing have facilitated large-scale surveys of genomic variation in the budding yeast,Saccharomyces cerevisiae These surveys have revealed extensive sequence variation between yeast strains. However, much less is known about how such variation influences the amount and nature of variation for functional genomic traits within and between yeast lineages. We review population-level studies of functional genomic variation, with a particular focus on how population functional genomic approaches can provide insights into both genome function and the evolutionary process. Although variation in functional genomics phenotypes is pervasive, our understanding of the consequences of this variation, either in physiological or evolutionary terms, is still rudimentary and thus motivates increased attention to appropriate null models. To date, much of the focus of population functional genomic studies has been on gene expression variation, but other functional genomic data types are just as likely to reveal important insights at the population level, suggesting a pressing need for more studies that go beyond transcription. Finally, we discuss how a population functional genomic perspective can be a powerful approach for developing a mechanistic understanding of the processes that link genomic variation to organismal phenotypes through gene networks.

Intersecting transcription networks constrain gene regulatory evolution

Epistasis—the non-additive interactions between different genetic loci—constrains evolutionary pathways, blocking some and permitting others^1–8. For biological networks such as transcription circuits, the nature of these constraints and their consequences are largely unknown. Here we describe the evolutionary pathways of a transcription network that controls the response to mating pheromone in yeasts⁹. A component of this network, the transcription regulator Ste12, has evolved two different modes of binding to a set of its target genes. In one group of species, Ste12 binds to specific DNA binding sites, while in another lineage it occupies DNA indirectly, relying on a second transcription regulator to recognize DNA. We show, through the construction of various possible evolutionary intermediates, that evolution of the direct mode of DNA binding was not directly accessible to the ancestor. Instead, it was contingent on a lineage-specific change to an overlapping transcription network with a different function, the specification of cell type. These results show that analyzing and predicting the evolution of cis-regulatory regions requires an understanding of their positions in overlapping networks, as this placement constrains the available evolutionary pathways.

Understanding how cis-regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences

Genome-scale methods have identified thousands of candidate cis-regulatory elements (CREs), but methods to directly assay the regulatory function of these elements on a comparably large scale have not been available. The inability to directly test and perturb the regulatory activity of large numbers of DNA sequences has hindered efforts to discover how cis-regulatory function is encoded in genomic sequence. Recently developed massively parallel reporter gene assays combine next generation sequencing with high-throughput oligonucleotide synthesis to offer the capacity to test and mutationally perturb thousands of specifically chosen or designed cis-regulatory sequences in a single experiment. These assays are the basis of recent studies that include large-scale functional validation of genomic CREs, exhaustive mutational analyses of individual regulatory sequences, and tests of large libraries of synthetic CREs. The results demonstrate how massively parallel reporter assays with libraries of designed sequences provide the statistical power required to address previously intractable questions about cis-regulatory function.

Ecological and Genetic Barriers Differentiate Natural Populations of Saccharomyces cerevisiae

How populations that inhabit the same geographical area become genetically differentiated is not clear. To investigate this, we characterized phenotypic and genetic differences between two populations of Saccharomyces cerevisiae that in some cases inhabit the same environment but show relatively little gene flow. We profiled stress sensitivity in a group of vineyard isolates and a group of oak-soil strains and found several niche-related phenotypes that distinguish the populations. We performed bulk-segregant mapping on two of the distinguishing traits: The vineyard-specific ability to grow in grape juice and oak-specific tolerance to the cell wall damaging drug Congo red. To implicate causal genes, we also performed a chemical genomic screen in the lab-strain deletion collection and identified many important genes that fell under quantitative trait loci peaks. One gene important for growth in grape juice and identified by both the mapping and the screen was SSU1, a sulfite-nitrite pump implicated in wine fermentations. The beneficial allele is generated by a known translocation that we reasoned may also serve as a genetic barrier. We found that the translocation is prevalent in vineyard strains, but absent in oak strains, and presents a postzygotic barrier to spore viability. Furthermore, the translocation was associated with a fitness cost to the rapid growth rate seen in oak-soil strains. Our results reveal the translocation as a dual-function locus that enforces ecological differentiation while producing a genetic barrier to gene flow in these sympatric populations.

Systems Medicine 2.0: potential benefits of combining electronic health care records with systems science models

Background

The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment.

Objective

Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases.

Methods

We undertook an unsystematic review of peer-reviewed literature and technology websites.

Results

Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome.

Conclusions

Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.

Research on single nucleotide polymorphisms interaction detection from network perspective

Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions.

Dominant Epistasis Between Two Quantitative Trait Loci Governing Sporulation Efficiency in Yeast Saccharomyces cerevisiae

Sporulation efficiency in the yeast Saccharomyces cerevisiae is a well-established model for studying quantitative traits. A variety of genes and nucleotides causing different sporulation efficiencies in laboratory, as well as in wild strains, has already been extensively characterised (mainly by reciprocal hemizygosity analysis and nucleotide exchange methods). We applied a different strategy in order to analyze the variation in sporulation efficiency of laboratory yeast strains. Coupling classical quantitative genetic analysis with simulations of phenotypic distributions (a method we call phenotype modelling) enabled us to obtain a detailed picture of the quantitative trait loci (QTLs) relationships underlying the phenotypic variation of this trait. Using this approach, we were able to uncover a dominant epistatic inheritance of loci governing the phenotype. Moreover, a molecular analysis of known causative quantitative trait genes and nucleotides allowed for the detection of novel alleles, potentially responsible for the observed phenotypic variation. Based on the molecular data, we hypothesise that the observed dominant epistatic relationship could be caused by the interaction of multiple quantitative trait nucleotides distributed across a 60--kb QTL region located on chromosome XIV and the RME1 locus on chromosome VII. Furthermore, we propose a model of molecular pathways which possibly underlie the phenotypic variation of this trait.

Genetic basis of haloperidol resistance in Saccharomyces cerevisiae is complex and dose dependent

The genetic basis of most heritable traits is complex. Inhibitory compounds and their effects in model organisms have been used in many studies to gain insights into the genetic architecture underlying quantitative traits. However, the differential effect of compound concentration has not been studied in detail. In this study, we used a large segregant panel from a cross between two genetically divergent yeast strains, BY4724 (a laboratory strain) and RM11_1a (a vineyard strain), to study the genetic basis of variation in response to different doses of a drug. Linkage analysis revealed that the genetic architecture of resistance to the small-molecule therapeutic drug haloperidol is highly dose-dependent. Some of the loci identified had effects only at low doses of haloperidol, while other loci had effects primarily at higher concentrations of the drug. We show that a major QTL affecting resistance across all concentrations of haloperidol is caused by polymorphisms in SWH1, a homologue of human oxysterol binding protein. We identify a complex set of interactions among the alleles of the genes SWH1, MKT1, and IRA2 that are most pronounced at a haloperidol dose of 200 µM and are only observed when the remainder of the genome is of the RM background. Our results provide further insight into the genetic basis of drug resistance.

Variation in response to a drug can be determined by many factors. In the model organism baker's yeast, many studies of chemical resistance traits have uncovered a complex genetic basis of such resistance. However, an in-depth study of how drug dose alters the effects of underlying genetic factors is lacking. Here, we employed linkage analysis to map the specific genetic loci underlying response to haloperidol, a small molecule therapeutic drug, using a large panel of segregants from a cross between two genetically divergent yeast strains BY (a laboratory strain) and RM (a vineyard strain). We found that loci associated with haloperidol resistance are dose-dependent. We also showed that variants in the oxysterol-binding-protein-like domain of the gene SWH1 underlie the major locus detected at all doses of haloperidol. Genetic interactions among genes SWH1, MKT1, and IRA2 in the RM background contribute to the differential response at high concentrations of haloperidol.

A model to investigate SNPs' interaction in GWAS studies

Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP-SNP interaction's role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.

Tiled ChrI RHS collection: a pilot high-throughput screening tool for identification of allelic variants

Reciprocal hemizygosity analysis is a genetic technique that allows phenotypic determination of the allelic effects of a gene in a genetically uniform background. Expanding this single gene technique to generate a genome-wide collection is termed as reciprocal hemizygosity scanning (RHS). The RHS collection should circumvent the need for linkage mapping and provide the power to identify all possible allelic variants for a given phenotype. However, the published RHS collections based on the existing genome-wide haploid deletion library reported a high rate of false positives. In this study, we report de novo construction of a RHS collection that is not based on the yeast deletion library. This collection has been constructed for the shortest yeast chromosome, ChrI. Using this ChrI RHS collection, we identified 13 allelic variants for the previously mapped loci and novel allelic variants for the growth differences in different environments. A few of these novel variants, which were fine mapped to a gene level, identified novel genetic variation for the previously studied environmental conditions. The availability of a genome-wide RHS collection would thus help us uncover a comprehensive list of allelic variants and better our understanding of the molecular pathways modulating a quantitative trait.

Higher-order genetic interactions and their contribution to complex traits

The contribution of genetic interactions involving three or more loci to complex traits is poorly understood. These higher-order genetic interactions (HGIs) are difficult to detect in genetic mapping studies, therefore, few examples of them have been described. However, the lack of data on HGIs should not be misconstrued as proof that this class of genetic effect is unimportant. To the contrary, evidence from model organisms suggests that HGIs frequently influence genetic studies and contribute to many complex traits. Here, we review the growing literature on HGIs and discuss the future of research on this topic.

Identification of loci that cause phenotypic variation in diverse species with the reciprocal hemizygosity test

The reciprocal hemizygosity test is a straightforward genetic test that can positively identify genes that have evolved to contribute to a phenotypic difference between strains or between species. The test involves a comparison between hybrids that are genetically identical throughout the genome except at the test locus, which is rendered hemizygous for alternative alleles from the two parental strains. If the two reciprocal hemizygotes display different phenotypes, then the two parental alleles must have evolved. New methods for targeted mutagenesis will allow application of the reciprocal hemizygosity test in many organisms. This review discusses the principles, advantages, and limitations of the test.

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

A strategy for genome-wide identification of gene based polymorphisms in rice reveals non-synonymous variation and functional genotypic markers

The genetic diversity of plants has traditionally been employed to improve crop plants to suit human needs, and in the future feed the increasing population and protect crops from environmental stresses and climate change. Genome-wide sequencing is a reality and can be used to make association to crop traits to be utilized by high-throughput marker based selection methods. This study describes a strategy of using next generation sequencing (NGS) data from the rice genome to make comparisons to the high-quality reference genome, identify functional polymorphisms within genes that might result in function changes and be used to study correlations to traits and employed in genetic mapping. We analyzed the NGS data of Oryza sativa ssp indica cv. G4 covering 241 Mb with ∼20X coverage and compared to the reference genome of Oryza sativa ssp. japonica to describe the genome-wide distribution of gene-based single nucleotide polymorphisms (SNPs). The analysis shows that the 63% covered genome consists of 1.6 million SNPs with 6.9 SNPs/Kb, and including 80,146 insertions and 92,655 deletions (INDELs) genome-wide. There are a total of 1,139,801 intergenic SNPs, 295,136 SNPs in intronic/non-coding regions, 195,098 in coding regions, 23,242 SNPs at the five-prime (5′) UTR regions and 22,686 SNPs at the three-prime (3′) UTR region. SNP variation was found in 40,761 gene loci, which include 75,262 synonymous and 119,836 non-synonymous changes, and functional reading frame changes through 3,886 inducing STOP-codon (isSNP) and 729 preventing STOP-codon (psSNP) variation. There are quickly evolving 194 high SNP hotspot genes (>100 SNPs/gene), and 1,513 out of 2,458 transcription factors displaying 2,294 non-synonymous SNPs that can be a major source of phenotypic diversity within the species. All data is searchable at https://plantstress-pereira.uark.edu/oryza2. We envision that this strategy will be useful for the identification of genes for crop traits and molecular breeding of rice cultivars.

Causal variation in yeast sporulation tends to reside in a pathway bottleneck

There has been extensive debate over whether certain classes of genes are more likely than others to contain the causal variants responsible for phenotypic differences in complex traits between individuals. One hypothesis states that input/output genes positioned in signal transduction bottlenecks are more likely than other genes to contain causal natural variation. The IME1 gene resides at such a signaling bottleneck in the yeast sporulation pathway, suggesting that it may be more likely to contain causal variation than other genes in the sporulation pathway. Through crosses between natural isolates of yeast, we demonstrate that the specific causal nucleotides responsible for differences in sporulation efficiencies reside not only in IME1 but also in the genes that surround IME1 in the signaling pathway, including RME1, RSF1, RIM15, and RIM101. Our results support the hypothesis that genes at the critical decision making points in signaling cascades will be enriched for causal variants responsible for phenotypic differences.

Distinguishing the small number of genetic variants that impact phenotypes from the huge number of innocuous variants within an individual's genome is a difficult problem. Several hypotheses concerning the location of causal variants have been put forward based on the fact that genes are often organized into signaling cascades where the activation of a gene at the top of a pathway in turn activates large numbers of downstream genes. One hypothesis states that causal variations are more likely to reside in the genes at the top of these pathways because their effects are amplified by the signaling cascade. Here we provide support for this hypothesis by showing that causal genetic variants in yeast sporulation cluster around a gene at the top of the sporulation signaling cascade. Our result suggests a way to focus the search for causal genetic variants, including those that cause disease, on a smaller number of genes that are more likely to harbor important variations.

Heritability and genetic basis of protein level variation in an outbred population

The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population’s average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.

Single nucleotide variants in transcription factors associate more tightly with phenotype than with gene expression

Mapping the polymorphisms responsible for variation in gene expression, known as Expression Quantitative Trait Loci (eQTL), is a common strategy for investigating the molecular basis of disease. Despite numerous eQTL studies, the relationship between the explanatory power of variants on gene expression versus their power to explain ultimate phenotypes remains to be clarified. We addressed this question using four naturally occurring Quantitative Trait Nucleotides (QTN) in three transcription factors that affect sporulation efficiency in wild strains of the yeast, Saccharomyces cerevisiae. We compared the ability of these QTN to explain the variation in both gene expression and sporulation efficiency. We find that the amount of gene expression variation explained by the sporulation QTN is not predictive of the amount of phenotypic variation explained. The QTN are responsible for 98% of the phenotypic variation in our strains but the median gene expression variation explained is only 49%. The alleles that are responsible for most of the variation in sporulation efficiency do not explain most of the variation in gene expression. The balance between the main effects and gene-gene interactions on gene expression variation is not the same as on sporulation efficiency. Finally, we show that nucleotide variants in the same transcription factor explain the expression variation of different sets of target genes depending on whether the variant alters the level or activity of the transcription factor. Our results suggest that a subset of gene expression changes may be more predictive of ultimate phenotypes than the number of genes affected or the total fraction of variation in gene expression variation explained by causative variants, and that the downstream phenotype is buffered against variation in the gene expression network.

Mapping small effect mutations in Saccharomyces cerevisiae: impacts of experimental design and mutational properties

Genetic variants identified by mapping are biased toward large phenotypic effects because of methodologic challenges for detecting genetic variants with small phenotypic effects. Recently, bulk segregant analysis combined with next-generation sequencing (BSA-seq) was shown to be a powerful and cost-effective way to map small effect variants in natural populations. Here, we examine the power of BSA-seq for efficiently mapping small effect mutations isolated from a mutagenesis screen. Specifically, we determined the impact of segregant population size, intensity of phenotypic selection to collect segregants, number of mitotic generations between meiosis and sequencing, and average sequencing depth on power for mapping mutations with a range of effects on the phenotypic mean and standard deviation as well as relative fitness. We then used BSA-seq to map the mutations responsible for three ethyl methanesulfonate−induced mutant phenotypes in Saccharomyces cerevisiae. These mutants display small quantitative variation in the mean expression of a fluorescent reporter gene (−3%, +7%, and +10%). Using a genetic background with increased meiosis rate, a reliable mating type marker, and fluorescence-activated cell sorting to efficiently score large segregating populations and isolate cells with extreme phenotypes, we successfully mapped and functionally confirmed a single point mutation responsible for the mutant phenotype in all three cases. Our simulations and experimental data show that the effects of a causative site not only on the mean phenotype, but also on its standard deviation and relative fitness should be considered when mapping genetic variants in microorganisms such as yeast that require population growth steps for BSA-seq.

Divergence in a master variator generates distinct phenotypes and transcriptional responses

Genetic basis of phenotypic differences in individuals is an important area in biology and personalized medicine. Analysis of divergent Saccharomyces cerevisiae strains grown under different conditions revealed extensive variation in response to both drugs (e.g., 4-nitroquinoline 1-oxide [4NQO]) and different carbon sources. Differences in 4NQO resistance were due to amino acid variation in the transcription factor Yrr1. Yrr1(YJM789) conferred 4NQO resistance but caused slower growth on glycerol, and vice versa with Yrr1(S96), indicating that alleles of Yrr1 confer distinct phenotypes. The binding targets of Yrr1 alleles from diverse yeast strains varied considerably among different strains grown under the same conditions as well as for the same strain under different conditions, indicating that distinct molecular programs are conferred by the different Yrr1 alleles. Our results demonstrate that genetic variations in one important control gene (YRR1), lead to distinct regulatory programs and phenotypes in individuals. We term these polymorphic control genes "master variators."

Discovering pair-wise genetic interactions: an information theory-based approach

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.

Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments

One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increase the predictability of adaptive evolution.

We studied adaptive evolution in different nitrogen-limited environments using long-term selection of asexually reproducing Saccharomyces cerevisiae populations in chemostats. Using next generation sequencing and DNA microarrays, we identified all acquired genetic variation associated with increased fitness, in both individual lineages and entire populations. We find that amplification alleles that include nutrient transporter genes specific to the molecular form of the nitrogen present in the environment are a common mechanism underlying increased fitness. In addition, we identified a general strategy for adaptation to nitrogen-limited environments that entails remodeling of phospholipid biogenesis required for producing important cellular components including vacuoles and autophagosomes. More general strategies for adaptation to nutrient-limited environments point to a role for re-wiring of signaling pathways that coordinate cell growth with nutrient availability. We reconstructed the evolutionary dynamics of a population evolving in ammonium-limited conditions and find that a multi-locus genotype is repeatedly selected within the population and constrained by epistasis. We propose that this genotype constitutes a “gene network polymorphism (GNP),” which may be a common outcome of adaptive evolution. Our study suggests that when the selective pressure is understood the molecular basis of adaptive evolution in large microbial populations may be predicted with reasonable precision.

Epistasis and quantitative traits: using model organisms to study gene-gene interactions

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

The molecular basis of phenotypic variation in yeast

The power of yeast genetics has now been extensively applied to phenotypic variation among strains of Saccharomyces cerevisiae. As a result, over 100 genes and numerous sequence variants have been identified, providing us with a general characterization of mutations underlying quantitative trait variation. Most quantitative trait alleles exert considerable phenotypic effects and alter conserved amino acid positions within protein coding sequences. When examined, quantitative trait alleles influence the expression of numerous genes, most of which are unrelated to an allele's phenotypic effect. The profile of quantitative trait alleles has proven useful to reverse quantitative genetics approaches and supports the use of systems genetics approaches to synthesize the molecular basis of trait variation across multiple strains.

Multiple MAPK cascades regulate the transcription of IME1, the master transcriptional activator of meiosis in Saccharomyces cerevisiae

The choice between alternative developmental pathways is primarily controlled at the level of transcription. Induction of meiosis in budding yeasts in response to nutrient levels provides a system to investigate the molecular basis of cellular decision-making. In Saccharomyces cerevisiae, entry into meiosis depends on multiple signals converging upon IME1, the master transcriptional activator of meiosis. Here we studied the regulation of the cis-acting regulatory element Upstream Activation Signal (UAS)ru, which resides within the IME1 promoter. Guided by our previous data acquired using a powerful high-throughput screening system, here we provide evidence that UASru is regulated by multiple stimuli that trigger distinct signal transduction pathways as follows: (i) The glucose signal inhibited UASru activity through the cyclic AMP (cAMP/protein kinase A (PKA) pathway, targeting the transcription factors (TFs), Com2 and Sko1; (ii) high osmolarity activated UASru through the Hog1/mitogen-activated protein kinase (MAPK) pathway and its corresponding TF Sko1; (iii) elevated temperature increased the activity of UASru through the cell wall integrity pathway and the TFs Swi4/Mpk1 and Swi4/Mlp1; (iv) the nitrogen source repressed UASru activity through Sum1; and (v) the absence of a nitrogen source was detected and transmitted to UASru by the Kss1 and Fus3 MAPK pathways through their respective downstream TFs, Ste12/Tec1 and Ste12/Ste12 as well as by their regulators Dig1/2. These signaling events were specific to UASru; they did not affect the mating and filamentation response elements that are regulated by MAPK pathways. The complex regulation of UASru through all the known vegetative MAPK pathways is unique to S. cerevisiae and is specific for IME1, likely because it is the master regulator of gametogenesis.

The molecular mechanism of a cis-regulatory adaptation in yeast

Despite recent advances in our ability to detect adaptive evolution involving the cis-regulation of gene expression, our knowledge of the molecular mechanisms underlying these adaptations has lagged far behind. Across all model organisms, the causal mutations have been discovered for only a handful of gene expression adaptations, and even for these, mechanistic details (e.g. the trans-regulatory factors involved) have not been determined. We previously reported a polygenic gene expression adaptation involving down-regulation of the ergosterol biosynthesis pathway in the budding yeast Saccharomyces cerevisiae. Here we investigate the molecular mechanism of a cis-acting mutation affecting a member of this pathway, ERG28. We show that the causal mutation is a two-base deletion in the promoter of ERG28 that strongly reduces the binding of two transcription factors, Sok2 and Mot3, thus abolishing their regulation of ERG28. This down-regulation increases resistance to a widely used antifungal drug targeting ergosterol, similar to mutations disrupting this pathway in clinical yeast isolates. The identification of the causal genetic variant revealed that the selection likely occurred after the deletion was already present at high frequency in the population, rather than when it was a new mutation. These results provide a detailed view of the molecular mechanism of a cis-regulatory adaptation, and underscore the importance of this view to our understanding of evolution at the molecular level.

Evolutionary adaptation is the process that has given rise to the ubiquitous, yet remarkable, fit between all living organisms and their environments. The molecular mechanisms of these adaptations have been a subject of great interest, but we still know very little about their mechanisms, particularly in the case of regulatory adaptations. In this work, we investigate the molecular mechanism of a regulatory adaptation that we previously identified in ERG28, a component of the ergosterol biosynthesis pathway in budding yeast. Ergosterol is an abundant lipid component of the fungal plasma membrane, and is of major biomedical importance, being targeted by numerous antifungal drugs. We identified the causal mutation underlying the ERG28 adaptation, a two-base deletion in its promoter which leads to lower abundance of its mRNA. This deletion acts via disrupting the binding of at least two transcription factors, Mot3 and Sok2, to the promoter. The deletion increases resistance to a widely used antifungal drug, Amphotericin B, which targets ergosterol. This effect is reminiscent of misregulation of the ergosterol pathway observed in clinical yeast isolates that have evolved resistance to Amphotericin B. Our results may therefore have medical implications, while also advancing our basic understanding of evolutionary mechanisms.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Rare variants in hypermutable genes underlie common morphology and growth traits in wild Saccharomyces paradoxus

Understanding the molecular basis of common traits is a primary challenge of modern genetics. One model holds that rare mutations in many genetic backgrounds may often phenocopy one another, together explaining the prevalence of the resulting trait in the population. For the vast majority of phenotypes, the role of rare variants and the evolutionary forces that underlie them are unknown. In this work, we use a population of Saccharomyces paradoxus yeast as a model system for the study of common trait variation. We observed an unusual, flocculation and invasive-growth phenotype in one-third of S. paradoxus strains, which were otherwise unrelated. In crosses with each strain in turn, these morphologies segregated as a recessive Mendelian phenotype, mapping either to IRA1 or to IRA2, yeast homologs of the hypermutable human neurofibromatosis gene NF1. The causal IRA1 and IRA2 haplotypes were of distinct evolutionary origin and, in addition to their morphological effects, associated with hundreds of stress-resistance and growth traits, both beneficial and disadvantageous, across S. paradoxus. Single-gene molecular genetic analyses confirmed variant IRA1 and IRA2 haplotypes as causal for these growth characteristics, many of which were independent of morphology. Our data make clear that common growth and morphology traits in yeast result from a suite of variants in master regulators, which function as a mutation-driven switch between phenotypic states.

Sporulation genes associated with sporulation efficiency in natural isolates of yeast

Yeast sporulation efficiency is a quantitative trait and is known to vary among experimental populations and natural isolates. Some studies have uncovered the genetic basis of this variation and have identified the role of sporulation genes (IME1, RME1) and sporulation-associated genes (FKH2, PMS1, RAS2, RSF1, SWS2), as well as non-sporulation pathway genes (MKT1, TAO3) in maintaining this variation. However, these studies have been done mostly in experimental populations. Sporulation is a response to nutrient deprivation. Unlike laboratory strains, natural isolates have likely undergone multiple selections for quick adaptation to varying nutrient conditions. As a result, sporulation efficiency in natural isolates may have different genetic factors contributing to phenotypic variation. Using Saccharomyces cerevisiae strains in the genetically and environmentally diverse SGRP collection, we have identified genetic loci associated with sporulation efficiency variation in a set of sporulation and sporulation-associated genes. Using two independent methods for association mapping and correcting for population structure biases, our analysis identified two linked clusters containing 4 non-synonymous mutations in genes - HOS4, MCK1, SET3, and SPO74. Five regulatory polymorphisms in five genes such as MLS1 and CDC10 were also identified as putative candidates. Our results provide candidate genes contributing to phenotypic variation in the sporulation efficiency of natural isolates of yeast.

A new system for comparative functional genomics of Saccharomyces yeasts

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.

GWAS meets TCGA to illuminate mechanisms of cancer predisposition

Genome-wide association studies (GWASs) have unraveled a large number of cancer risk alleles. Understanding how these allelic variants predispose to disease is a major bottleneck confronting translational application. In this issue, Li and colleagues combine GWASs with The Cancer Genome Atlas (TCGA) to disambiguate the contributions of germline and somatic variants to tumorigenic gene expression programs. They find that close to half of the known risk alleles for estrogen receptor (ER)-positive breast cancer are expression quantitative trait loci (eQTLs) acting upon major determinants of gene expression in tumors.

Extremely reduced levels of heterozygosity in the vertebrate pathogen Encephalitozoon cuniculi

The genomes of microsporidia in the genus Encephalitozoon have been extensively studied for their minimalistic features, but they have seldom been used to investigate basic characteristics of the biology of these organisms, such as their ploidy or their mode of reproduction. In the present study, we aimed to tackle this issue by mapping Illumina sequence reads against the genomes of four strains of E. cuniculi. This approach, combined with more conventional molecular biology techniques, resulted in the identification of heterozygosity in all strains investigated, a typical signature of a diploid nuclear state. In sharp contrast with similar studies recently performed on a distant microsporidian lineage (Nematocida spp.), the level of heterozygosity that we identified across the E. cuniculi genomes was found to be extremely low. This reductive intraindividual genetic variation could result from the long-term propagation of these strains under laboratory conditions, but we propose that it could also reflect an intrinsic capacity of these vertebrate pathogens to self-reproduce.

The genetic architecture of biofilm formation in a clinical isolate of Saccharomyces cerevisiae

Biofilms are microbial communities that form on surfaces. They are the primary form of microbial growth in nature and can have detrimental impacts on human health. Some strains of the budding yeast Saccharomyces cerevisiae form colony biofilms, and there is substantial variation in colony architecture between biofilm-forming strains. To identify the genetic basis of biofilm variation, we developed a novel version of quantitative trait locus mapping, which leverages cryptic variation in a clinical isolate of S. cerevisiae. We mapped 13 loci linked to heterogeneity in biofilm architecture and identified the gene most closely associated with each locus. Of these candidate genes, six are members of the cyclic AMP-protein kinase A pathway, an evolutionarily conserved cell signaling network. Principal among these is CYR1, which encodes the enzyme that catalyzes production of cAMP. Through a combination of gene expression measurements, cell signaling assays, and gene overexpression, we determined the functional effects of allelic variation at CYR1. We found that increased pathway activity resulting from protein coding and expression variation of CYR1 enhances the formation of colony biofilms. Four other candidate genes encode kinases and transcription factors that are targets of this pathway. The protein products of several of these genes together regulate expression of the sixth candidate, FLO11, which encodes a cell adhesion protein. Our results indicate that epistatic interactions between alleles with both positive and negative effects on cyclic AMP-protein kinase A signaling underlie much of the architectural variation we observe in colony biofilms. They are also among the first to demonstrate genetic variation acting at multiple levels of an integrated signaling and regulatory network. Based on these results, we propose a mechanistic model that relates genetic variation to gene network function and phenotypic outcomes.

Short read sequencing in studies of natural variation and adaptation

Today's high throughput sequencing approaches, coupled with equally revolutionary advances in bioinformatics, allow us to describe and analyze genomes in unprecedented detail. Short Read Sequencing (SRS) approaches have been especially instrumental in bringing genomic analysis to a wide range of questions and species in plant biology. We can now connect genotypes and phenotypes with greater efficiency, and investigate the molecular basis of natural variation and adaptation in a genomic framework. New and creative applications of SRS and other genomic approaches are not only reshaping how we study natural variation, but also our overall understanding of gene and genome evolution. Here we discuss examples of the application of SRS technologies to the characterization of genetic diversity, genome evolution and adaptation in plants.

Genotype to phenotype: lessons from model organisms for human genetics

To what extent can variation in phenotypic traits such as disease risk be accurately predicted in individuals? In this Review, I highlight recent studies in model organisms that are relevant both to the challenge of accurately predicting phenotypic variation from individual genome sequences ('whole-genome reverse genetics') and for understanding why, in many cases, this may be impossible. These studies argue that only by combining genetic knowledge with in vivo measurements of biological states will it be possible to make accurate genetic predictions for individual humans.