Multiple locus linkage analysis of genomewide expression in yeast.

With the ability to measure thousands of related phenotypes from a single biological sample, it is now feasible to genetically dissect systems-level biological phenomena. The genetics of transcriptional regulation and protein abundance are likely to be complex, meaning that genetic variation at multiple loci will influence these phenotypes. Several recent studies have investigated the role of genetic variation in transcription by applying traditional linkage analysis methods to genomewide expression data, where each gene expression level was treated as a quantitative trait and analyzed separately from one another. Here, we develop a new, computationally efficient method for simultaneously mapping multiple gene expression quantitative trait loci that directly uses all of the available data. Information shared across gene expression traits is captured in a way that makes minimal assumptions about the statistical properties of the data. The method produces easy-to-interpret measures of statistical significance for both individual loci and the overall joint significance of multiple loci selected for a given expression trait. We apply the new method to a cross between two strains of the budding yeast Saccharomyces cerevisiae, and estimate that at least 37% of all gene expression traits show two simultaneous linkages, where we have allowed for epistatic interactions. Pairs of jointly linking quantitative trait loci are identified with high confidence for 170 gene expression traits, where it is expected that both loci are true positives for at least 153 traits. In addition, we are able to show that epistatic interactions contribute to gene expression variation for at least 14% of all traits. We compare the proposed approach to an exhaustive two-dimensional scan over all pairs of loci. Surprisingly, we demonstrate that an exhaustive two-dimensional scan is less powerful than the sequential search used here. In addition, we show that a two-dimensional scan does not truly allow one to test for simultaneous linkage, and the statistical significance measured from this existing method cannot be interpreted among many traits

Multiparameter behavioral profiling reveals distinct thermal response regimes in Caenorhabditis elegans.

BackgroundResponding to noxious stimuli by invoking an appropriate escape response is critical for survival of an organism. The sensations of small and large changes in temperature in most organisms have been studied separately in the context of thermotaxis and nociception, respectively. Here we use the nematode C. elegans to address the neurogenetic basis of responses to thermal stimuli over a broad range of intensities.ResultsC. elegans responds to aversive temperature by eliciting a stereotypical behavioral sequence. Upon sensation of the noxious stimulus, it moves backwards, turns and resumes forward movement in a new direction. In order to study the response of C. elegans to a broad range of noxious thermal stimuli, we developed a novel assay that allows simultaneous characterization of multiple aspects of escape behavior elicited by thermal pulses of increasing amplitudes. We exposed the laboratory strain N2, as well as 47 strains with defects in various aspects of nervous system function, to thermal pulses ranging from ΔT = 0.4°C to 9.1°C and recorded the resulting behavioral profiles.ConclusionsThrough analysis of the multidimensional behavioral profiles, we found that the combinations of molecules shaping avoidance responses to a given thermal pulse are unique. At different intensities of aversive thermal stimuli, these distinct combinations of molecules converge onto qualitatively similar stereotyped behavioral sequences

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We use ceX-QTL to map a drug resistance locus with high resolution. We also map differences in gene expression in live worms and discovered that mutations in the co-chaperone sti-1 upregulate the transcription of HSP-90. Lastly, we use ceX-QTL to map loci that influence fitness genome-wide confirming previously reported causal variants and uncovering new fitness loci. ceX-QTL is fast, powerful and cost-effective, and will accelerate the study of complex traits in animals

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

Genetic Basis of Ammonium Toxicity Resistance in a Sake Strain of Yeast: A Mendelian Case.

High concentrations of ammonium at physiological concentrations of potassium are toxic for the standard laboratory strain of Saccharomyces cerevisiae In the original description of this metabolic phenotype, we focused on the standard laboratory strains of Saccharomyces In this study, we screened a large collection of S. cerevisiae natural isolates and identified one strain that is resistant to high concentrations of ammonium. This strain, K12, was isolated in sake breweries. When the K12 strain was crossed to the standard laboratory strain (FY4), the resulting tetrads displayed 2:2 segregation of the resistance phenotype, suggesting a single gene trait. Using a bulk segregant analysis strategy, we mapped this trait to a 150-kb region on chromosome X containing the TRK1 gene. This gene encodes a transporter required for high-affinity potassium transport in S. cerevisiae Data from reciprocal hemizygosity experiments with TRK1 deletion strains in K12 and BY backgrounds, as well as analysis of the deletion of this gene in the K12 strain, demonstrate that the K12 allele of TRK1 is responsible for ammonium toxicity resistance. Furthermore, we determined the minimal amount of potassium required for both the K12 and laboratory strain needed for growth. These results demonstrate that the gene encoded by the K12 allele of TRK1 has a greater affinity for potassium than the standard allele of TRK1 found in Saccharomyces strains. We hypothesize that this greater-affinity allele of the potassium transporter reduces the flux of ammonium into the yeast cells under conditions of ammonium toxicity. These findings further refine our understanding of ammonium toxicity in yeast and provide an example of using natural variation to understand cellular processes

Gene–Environment Interaction in Yeast Gene Expression

The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol as carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between BY and RM in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth-related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits