To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae. Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.

Many studies have identified quantitative trait loci (QTLs) that contribute to continuous variation in heritable traits of interest. However, general principles regarding the distribution of QTL numbers, effect sizes, and combined effects of multiple QTLs remain to be elucidated. Here, we characterize complex genetics underlying inheritance of thousands of transcript levels in a cross between two strains of Saccharomyces cerevisiae. Most detected QTLs have weak effects, with a median variance explained of 27% for highly heritable transcripts. Despite the high statistical power of the study, no QTLs were detected for 40% of highly heritable transcripts, indicating extensive genetic complexity. Modeling of QTL detection showed that only 3% of highly heritable transcripts are consistent with single-locus inheritance, 17-18% are consistent with control by one or two loci, and half require more than five loci under additive models. Strikingly, analysis of parent and progeny trait distributions showed that a majority of transcripts exhibit transgressive segregation. Sixteen percent of highly heritable transcripts exhibit evidence of interacting loci. Our results will aid design of future QTL mapping studies and may shed light on the evolution of quantitative traits.Beavis effect ͉ epistasis ͉ transgressive segregation M ost heritable traits show continuous variation in a population. Such quantitative traits have been a subject of intensive study (see refs. 1-4 for reviews). Identification of genetic polymorphisms underlying quantitative traits, known as quantitative trait loci or QTLs, is of interest in medical genetics, where they can provide insights into disease mechanisms and lead to new diagnostics and therapeutics, and in agricultural genetics, where they can aid breeding programs. Genetic factors underlying quantitative traits also play a crucial role in evolutionary theory. Most quantitative traits appear to be genetically complex, i.e., controlled by multiple QTLs (2).Linkage mapping of QTLs has been reported for thousands of quantitative traits. In a handful of cases, the DNA sequence polymorphisms underlying a quantitative trait have been identified (4-8). However, it has proven difficult to comprehensively identify the multiple QTLs that combine to determine the complex genetic architecture of a trait, largely because of limitations in the statistical power of mapping experiments (9). As a result, the principles that govern genetic complexity remain an area of active research. Are traits more likely to be controlled by a few loci of large effect or many loci of small effect (10, 11)? Are most QTL effects additive, or do QTLs often act in a nonadditive (epistatic) manner (12)? Does inheritance of alleles from a given parent at multiple QTLs usually affect a trait in the same direction, as predicted by certain evolutionary models (13)? In addition to elegant theoretical advances (1, 10, 11, 13), several studies have surveyed large numbers of traits empirically to identify genetic trends (14-16). But many que...

Natural genetic variation can cause significant differences in gene expression, but little is known about the polymorphisms that affect gene regulation. We analyzed regulatory variation in a cross between laboratory and wild strains of Saccharomyces cerevisiae. Clustering and linkage analysis defined groups of coregulated genes and the loci involved in their regulation. Most expression differences mapped to trans-acting loci. Positional cloning and functional assays showed that polymorphisms in GPA1 and AMN1 affect expression of genes involved in pheromone response and daughter cell separation, respectively. We also asked whether particular classes of genes were more likely to contain trans-regulatory polymorphisms. Notably, transcription factors showed no enrichment, and trans-regulatory variation seems to be broadly dispersed across classes of genes with different molecular functions.

A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.

Elucidating the connection between genotype, phenotype, and adaptation in wild populations is fundamental to the study of evolutionary biology, yet it remains an elusive goal, particularly for microscopic taxa, which comprise the majority of life. Even for microbes that can be reliably found in the wild, defining the boundaries of their populations and discovering ecologically relevant phenotypes has proved extremely difficult. Here, we have circumvented these issues in the microbial eukaryote Neurospora crassa by using a "reverse-ecology" population genomic approach that is free of a priori assumptions about candidate adaptive alleles. We performed Illumina whole-transcriptome sequencing of 48 individuals to identify single nucleotide polymorphisms. From these data, we discovered two cryptic and recently diverged populations, one in the tropical Caribbean basin and the other endemic to subtropical Louisiana. We conducted high-resolution scans for chromosomal regions of extreme divergence between these populations and found two such genomic "islands." Through growthrate assays, we found that the subtropical Louisiana population has a higher fitness at low temperature (10°C) and that several of the genes within these distinct regions have functions related to the response to cold temperature. These results suggest the divergence islands may be the result of local adaptation to the 9°C difference in average yearly minimum temperature between these two populations. Remarkably, another of the genes identified using this unbiased, whole-genome approach is the well-known circadian oscillator frequency, suggesting that the 2.4°-10.6°difference in latitude between the populations may be another important environmental parameter.ecological genomics | genome scan | fungi | circadian clock D iscovering the genetic basis behind adaptive phenotypes has long been considered the holy grail of evolutionary genetics. Although there are now several studies that have succeeded in identifying genes responsible for such phenotypes, the majority of them use a "forward-ecology" approach whereby candidate genes are identified on the basis of their having a function related to conspicuous traits, such as pigmentation (1-4). A paucity of obvious phenotypic traits has been a major impediment for studying adaptation in microbes because these organisms are, by nature, inconspicuous. However, next-generation sequencing technology has made it possible for individual laboratories to acquire whole-genome sequence information across populations. This innovation has enabled an unbiased "reverse-ecology" approach whereby genes with functions related to ecologically relevant traits can be identified by examining patterns of genetic diversity within and between populations and identifying candidate genes as those showing the signature of recent positive selection and/or divergent adaptation between populations (5).

SUMMARY Many genes that affect replicative lifespan (RLS) in the budding yeast Saccharomyces cerevisiae also affect aging in other organisms such as C. elegans and M. musculus. We performed a systematic analysis of yeast RLS in a set of 4,698 viable single-gene deletion strains. Multiple functional gene clusters were identified, and full genome-to-genome comparison demonstrated a significant conservation in longevity pathways between yeast and C. elegans. Among the mechanisms of aging identified, deletion of tRNA exporter LOS1 robustly extended lifespan. Dietary restriction (DR) and inhibition of mechanistic Target of Rapamycin (mTOR) exclude Los1 from the nucleus in a Rad53-dependent manner. Moreover, lifespan extension from deletion of LOS1 is non-additive with DR or mTOR inhibition, and results in Gcn4 transcription factor activation. Thus, the DNA damage response and mTOR converge on Los1-mediated nuclear tRNA export to regulate Gcn4 activity and aging.

Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs. Interacting loci have been identified in many organisms. However, the prevalence of interactions, and the nucleotide changes underlying them, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes--the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

Naturally occurring sequence variation that affects gene expression is an important source of phenotypic differences among individuals within a species. We and others have previously shown that such regulatory variation can occur both at the same locus as the gene whose expression it affects (local regulatory variation) and elsewhere in the genome at trans-acting factors. Here we present a detailed analysis of genome-wide local regulatory variation in Saccharomyces cerevisiae. We used genetic linkage analysis to show that nearly a quarter of all yeast genes contain local regulatory variation between two divergent strains. We measured allele-specific expression in a diploid hybrid of the two strains for 77 genes showing strong self-linkage and found that in 52%–78% of these genes, local regulatory variation acts directly in cis. We also experimentally confirmed one example in which local regulatory variation in the gene AMN1 acts in trans through a feedback loop. Genome-wide sequence analysis revealed that genes subject to local regulatory variation show increased polymorphism in the promoter regions, and that some but not all of this increase is due to polymorphisms in predicted transcription factor binding sites. Increased polymorphism was also found in the 3′ untranslated regions of these genes. These findings point to the importance of cis-acting variation, but also suggest that there is a diverse set of mechanisms through which local variation can affect gene expression levels.