Genetic interactions between transcription factors cause natural variation in yeast

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Experimental evolution and hybridization enhance the fermentative capacity of wild Saccharomyces eubayanus strains

Lager beer is traditionally fermented using Saccharomyces pastorianus. However, the limited availability of lager yeast strains restricts the potential range of beer profiles. Recently, Saccharomyces eubayanus strains showed the potential to impart novel aromas to beer, with slower fermentation rates than commercial strains. Here, we applied experimental evolution to nine S. eubayanus strains using three different selective conditions to generate improved strains to fermentative environments. We observed environment-dependent fitness changes across strains, with ethanol-enriched media resulting in the greatest fitness improvement. We identified subtelomeric genomic changes in a deficient fermentative strain underlying the greatest fitness improvement. Gene expression analysis and genome sequencing identified genes associated with oxidative stress, amino acid metabolism, sterol biosynthesis, and vacuole morphology underlying differences between evolved and the ancestral strain, revealing the cellular processes underlying fermentation improvement. A hybridization strategy between two evolved strains allowed us to expand the phenotypic space of the F2 segregants, obtaining strains with a 13.7% greater fermentative capacity relative to the best evolved parental strains. Our study highlights the potential of integrating experimental evolution and hybridization to enhance the fermentation capacity of wild yeast strains, offering strengthened solutions for industrial applications and highlighting the potential of Patagonian S. eubayanus in brewing.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

The role of longevity-related genetic variant interactions as predictors of survival after 85 years of age

Genome-wide association studies and candidate gene studies have identified several genetic variants that might play a role in achieving longevity. This study investigates interactions between pairs of those single nucleotide polymorphisms (SNPs) and their effect on survival above the age of 85 in a sample of 327 Croatian individuals. Although none of the SNPs individually showed a significant effect on survival in this sample, 14 of the 359 interactions tested (between SNPs not in LD) reached the level of nominal significance (p<0.05), showing a potential effect on late-life survival. Notably, SH2B3 rs3184504 interacted with different SNPs near TERC, TP53 rs1042522 with different SNPs located near the CDKN2B gene, and CDKN2B rs1333049 with different SNPs in FOXO3, as well as with LINC02227 rs2149954. The other interaction pairs with a possible effect on survival were FOXO3 rs2802292 and ERCC2 rs50871, IL6 rs1800795 and GHRHR rs2267723, LINC02227 rs2149954 and PARK7 rs225119, as well as PARK7 rs225119 and PTPN1 rs6067484. These interactions remained significant when tested together with a set of health-related variables that also had a significant effect on survival above 85 years. In conclusion, our results confirm the central role of genetic regulation of insulin signalling and cell cycle control in longevity.

A pair of RNA binding proteins inhibit ion transporter expression to maintain lifespan

Regulation of lifespan by transcription factors has been well established. More recently, a role for RNA binding proteins (RBPs) in regulating lifespan has also emerged. In both cases, a major challenge is to determine which regulatory targets are functionally responsible for the observed lifespan phenotype. We recently identified a pair of neuronal RBPs, exc-7/ELAVL and mbl-1/Muscleblind, which in Caenorhabditis elegans display synthetic (nonadditive) lifespan defects: single mutants do not affect lifespan, but exc-7; mbl-1 double mutants have strongly reduced lifespan. Such a strong synthetic phenotype represented an opportunity to use transcriptomics to search for potential causative targets that are synthetically regulated. Focus on such genes would allow us to narrow our target search by ignoring the hundreds of genes altered only in single mutants, and provide a shortlist of synthetically regulated candidate targets that might be responsible for the double mutant phenotype. We identified a small handful of genes synthetically dysregulated in double mutants and systematically tested each candidate gene for functional contribution to the exc-7; mbl-1 lifespan phenotype. We identified 1 such gene, the ion transporter nhx-6, which is highly upregulated in double mutants. Overexpression of nhx-6 causes reduced lifespan, and deletion of nhx-6 in an exc-7; mbl-1 background partially restores both lifespan and healthspan. Together, these results reveal that a pair of RBPs mediate lifespan in part by inhibiting expression of an ion transporter, and provide a template for how synthetic phenotypes (including lifespan) can be dissected at the transcriptomic level to reveal potential causative genes.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

A pair of RNA binding proteins inhibit ion transporter expression to maintain lifespan

Regulation of lifespan by transcription factors has been well established. More recently a role for RNA binding proteins (RBPs) in regulating lifespan has also emerged. In both cases, a major challenge is to determine which regulatory targets are functionally responsible for the observed lifespan phenotype. We recently identified a pair of RBPs, exc-7/ELAVL and mbl-1/Muscleblind, which display synthetic (non-additive) lifespan defects: single mutants do not affect lifespan, but exc-7; mbl-1 double mutants have strongly reduced lifespan. Such a strong synthetic phenotype represented an opportunity to use transcriptomics to search for potential causative targets that are synthetically regulated. Focus on such genes would allow us to narrow our target search by ignoring the hundreds of genes altered only in single mutants, and provide a shortlist of synthetically-regulated candidate targets that might be responsible for the double mutant phenotype. We identified a small handful of genes synthetically dysregulated in double mutants and systematically tested each candidate gene for functional contribution to the exc-7; mbl-1 lifespan phenotype. We identified one such gene, the ion transporter nhx-6, which is highly upregulated in double mutants. Overexpression of nhx-6 causes reduced lifespan, and deletion of nhx-6 in an exc-7; mbl-1 background partially restores both lifespan and healthspan. Together, these results reveal that a pair of RBPs mediate lifespan in part by inhibiting expression of an ion transporter, and provide a template for how synthetic phenotypes (including lifespan) can be dissected at the transcriptomic level to reveal potential causative genes.

Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains

Enhanced phenotypic diversity increases a population's likelihood of surviving catastrophic conditions. Hsp90, an essential molecular chaperone and a central network hub in eukaryotes, has been observed to suppress or enhance the effects of genetic variation on phenotypic diversity in response to environmental cues. Because many Hsp90-interacting genes are involved in signaling transduction pathways and transcriptional regulation, we tested how common Hsp90-dependent differential gene expression is in natural populations. Many genes exhibited Hsp90-dependent strain-specific differential expression in five diverse yeast strains. We further identified transcription factors (TFs) potentially contributing to variable expression. We found that on Hsp90 inhibition or environmental stress, activities or abundances of Hsp90-dependent TFs varied among strains, resulting in differential strain-specific expression of their target genes, which consequently led to phenotypic diversity. We provide evidence that individual strains can readily display specific Hsp90-dependent gene expression, suggesting that the evolutionary impacts of Hsp90 are widespread in nature.

Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding

Mendelian heredity is the cornerstone of plant breeding and has been used to develop new varieties of plants since the 19th century. However, there are several breeding cases, such as cytoplasmic inheritance, methylation, epigenetics, hybrid vigor, and loss of heterozygosity (LOH), where Mendelian heredity is not applicable, known as non-Mendelian heredity. This type of inheritance can be influenced by several factors besides the genetic architecture of the plant and its breeding potential. Therefore, exploring various non-Mendelian heredity mechanisms, their prevalence in plants, and the implications for plant breeding is of paramount importance to accelerate the pace of crop improvement. In this review, we examine the current understanding of non-Mendelian heredity in plants, including the mechanisms, inheritance patterns, and applications in plant breeding, provide an overview of the various forms of non-Mendelian inheritance (including epigenetic inheritance, cytoplasmic inheritance, hybrid vigor, and LOH), explore insight into the implications of non-Mendelian heredity in plant breeding, and the potential it holds for future research.

RNA-Mediated Regulation of Meiosis in Budding Yeast

Cells change their physiological state in response to environmental cues. In the absence of nutrients, unicellular fungi such as budding yeast exit mitotic proliferation and enter the meiotic cycle, leading to the production of haploid cells that are encased within spore walls. These cell state transitions are orchestrated in a developmentally coordinated manner. Execution of the meiotic cell cycle program in budding yeast, Saccharomyces cerevisiae, is regulated by the key transcription factor, Ime1. Recent developments have uncovered the role of non-coding RNA in the regulation of Ime1 and meiosis. In this review, we summarize the role of ncRNA-mediated and RNA homeostasis-based processes in the regulation of meiosis in Saccharomyces cerevisiae.

Matrix Metalloproteinases and Glaucoma

Matrix metalloproteinases (MMPs) are enzymes that decompose extracellular matrix (ECM) proteins. MMPs are thought to play important roles in cellular processes, such as cell proliferation, differentiation, angiogenesis, migration, apoptosis, and host defense. MMPs are distributed in almost all intraocular tissues and are involved in physiological and pathological mechanisms of the eye. MMPs are also associated with glaucoma, a progressive neurodegenerative disease of the eyes. MMP activity affects intraocular pressure control and apoptosis of retinal ganglion cells, which are the pathological mechanisms of glaucoma. It also affects the risk of glaucoma development based on genetic pleomorphism. In addition, MMPs may affect the treatment outcomes of glaucoma, including the success rate of surgical treatment and side effects on the ocular surface due to glaucoma medications. This review discusses the various relationships between MMP and glaucoma.

A Saccharomyces eubayanus haploid resource for research studies

Since its identification, Saccharomyces eubayanus has been recognized as the missing parent of the lager hybrid, S. pastorianus. This wild yeast has never been isolated from fermentation environments, thus representing an interesting candidate for evolutionary, ecological and genetic studies. However, it is imperative to develop additional molecular genetics tools to ease manipulation and thus facilitate future studies. With this in mind, we generated a collection of stable haploid strains representative of three main lineages described in S. eubayanus (PB-1, PB-2 and PB-3), by deleting the HO gene using CRISPR-Cas9 and tetrad micromanipulation. Phenotypic characterization under different conditions demonstrated that the haploid derivates were extremely similar to their parental strains. Genomic analysis in three strains highlighted a likely low frequency of off-targets, and sequencing of a single tetrad evidenced no structural variants in any of the haploid spores. Finally, we demonstrate the utilization of the haploid set by challenging the strains under mass-mating conditions. In this way, we found that S. eubayanus under liquid conditions has a preference to remain in a haploid state, unlike S. cerevisiae that mates rapidly. This haploid resource is a novel set of strains for future yeast molecular genetics studies.

Domestication reprogrammed the budding yeast life cycle

Domestication of plants and animals is the foundation for feeding the world human population but can profoundly alter the biology of the domesticated species. Here we investigated the effect of domestication on one of our prime model organisms, the yeast Saccharomyces cerevisiae, at a species-wide level. We tracked the capacity for sexual and asexual reproduction and the chronological life span across a global collection of 1,011 genome-sequenced yeast isolates and found a remarkable dichotomy between domesticated and wild strains. Domestication had systematically enhanced fermentative and reduced respiratory asexual growth, altered the tolerance to many stresses and abolished or impaired the sexual life cycle. The chronological life span remained largely unaffected by domestication and was instead dictated by clade-specific evolution. We traced the genetic origins of the yeast domestication syndrome using genome-wide association analysis and genetic engineering and disclosed causative effects of aneuploidy, gene presence/absence variations, copy number variations and single-nucleotide polymorphisms. Overall, we propose domestication to be the most dramatic event in budding yeast evolution, raising questions about how much domestication has distorted our understanding of the natural biology of this key model species.

Multiple epistatic DNA variants in a single gene affect gene expression in trans

DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.

Infection by dsRNA viruses is associated with enhanced sporulation efficiency in Saccharomyces cerevisiae

Upon starvation diploid cells of the facultative sexual yeast Saccharomyces cerevisiae undergo sporulation, forming four metabolically quiescent and robust haploid spores encased in a degradable ascus. All endosymbionts, whether they provide net benefits or costs, utilize host resources; in yeast, this should induce an earlier onset of sporulation. Here, we tested whether the presence of endosymbiotic dsRNA viruses (M satellite and L-A helper) correspond with higher sporulation rate of their host, S. cerevisiae. We find that S. cerevisiae hosting both the M and L-A viruses (so-called "killer yeasts") have significantly higher sporulation efficiency than those without. We also found that the removal of the M virus did not reduce sporulation frequency, possibly because the L-A virus still utilizes host resources with and without the M virus. Our findings indicate that either virulent resource use by endosymbionts induces sporulation, or that viruses are spread more frequently to sporulating strains. Further exploration is required to distinguish cause from effect.

Restoring fertility in yeast hybrids: Breeding and quantitative genetics of beneficial traits

Hybrids between species can harbor a combination of beneficial traits from each parent and may exhibit hybrid vigor, more readily adapting to new harsher environments. Interspecies hybrids are also sterile and therefore an evolutionary dead end unless fertility is restored, usually via auto-polyploidisation events. In the Saccharomyces genus, hybrids are readily found in nature and in industrial settings, where they have adapted to severe fermentative conditions. Due to their hybrid sterility, the development of new commercial yeast strains has so far been primarily conducted via selection methods rather than via further breeding. In this study, we overcame infertility by creating tetraploid intermediates of Saccharomyces interspecies hybrids to allow continuous multigenerational breeding. We incorporated nuclear and mitochondrial genetic diversity within each parental species, allowing for quantitative genetic analysis of traits exhibited by the hybrids and for nuclear-mitochondrial interactions to be assessed. Using pooled F12 generation segregants of different hybrids with extreme phenotype distributions, we identified quantitative trait loci (QTLs) for tolerance to high and low temperatures, high sugar concentration, high ethanol concentration, and acetic acid levels. We identified QTLs that are species specific, that are shared between species, as well as hybrid specific, in which the variants do not exhibit phenotypic differences in the original parental species. Moreover, we could distinguish between mitochondria-type-dependent and -independent traits. This study tackles the complexity of the genetic interactions and traits in hybrid species, bringing hybrids into the realm of full genetic analysis of diploid species, and paves the road for the biotechnological exploitation of yeast biodiversity.

Global distribution of IRC7 alleles in Saccharomyces cerevisiae populations: a genomic and phenotypic survey within the wine clade

The adaptation to the different biotic and abiotic factors of wine fermentation has led to the accumulation of numerous genomic hallmarks in Saccharomyces cerevisiae wine strains. IRC7, a gene encoding a cysteine-S-β-lyase enzyme related volatile thiols production in wines, has two alleles: a full-length allele (IRC7^F ) and a mutated one (IRC7^S ), harbouring a 38 bp-deletion. Interestingly, IRC7^S -encoding a less active enzyme - appears widespread amongst wine populations. Studying the global distribution of the IRC7^S allele in different yeast lineages, we confirmed its high prevalence in the Wine clade and demonstrated a minority presence in other domesticated clades (Wine-PDM, Beer and Bread) while it is completely missing in wild clades. Here, we show that IRC7^S -homozygous (HS) strains exhibited both fitness and competitive advantages compared with IRC7^F -homozygous (HF) strains. There are some pieces of evidence of the direct contribution of the IRC7^S allele to the outstanding behaviour of HS strains (i.e., improved response to oxidative stress conditions and higher tolerance to high copper levels); however, we also identified a set of sequence variants with significant co-occurrence patterns with the IRC7^S allele, which can be co-contributing to the fitness and competitive advantages of HS strains in wine fermentations.

Transcription levels of a noncoding RNA orchestrate opposing regulatory and cell fate outcomes in yeast

Transcription through noncoding regions of the genome is pervasive. How these transcription events regulate gene expression remains poorly understood. Here, we report that, in S. cerevisiae, the levels of transcription through a noncoding region, IRT2, located upstream in the promoter of the inducer of meiosis, IME1, regulate opposing chromatin and transcription states. At low levels, the act of IRT2 transcription promotes histone exchange, delivering acetylated histone H3 lysine 56 to chromatin locally. The subsequent open chromatin state directs transcription factor recruitment and induces downstream transcription to repress the IME1 promoter and meiotic entry. Conversely, increasing transcription turns IRT2 into a repressor by promoting transcription-coupled chromatin assembly. The two opposing functions of IRT2 transcription shape a regulatory circuit, which ensures a robust cell-type-specific control of IME1 expression and yeast meiosis. Our data illustrate how intergenic transcription levels are key to controlling local chromatin state, gene expression, and cell fate outcomes.

The budding yeast life cycle: More complex than anticipated?

The budding yeast, Saccharomyces cerevisiae, has served as a model for nearly a century to understand the principles of the eukaryotic life cycle. The canonical life cycle of S. cerevisiae comprises a regular alternation between haploid and diploid phases. Haploid gametes generated by sporulation are expected to quickly restore the diploid phase mainly through inbreeding via intratetrad mating or haploselfing, thereby promoting genome homozygotization. However, recent large population genomics data unveiled that heterozygosity and polyploidy are unexpectedly common. This raises the interesting paradox of a haplo-diplobiontic species being well-adapted to inbreeding and able to maintain high levels of heterozygosity and polyploidy, thereby suggesting an unanticipated complexity of the yeast life cycle. Here, we propose that unprogrammed mating type switching, heterothallism, reduced spore formation and viability, cell-cell fusion and dioecy could play key and uncharted contributions to generate and maintain heterozygosity through polyploidization.

A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits

The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

Molecular and evolutionary processes generating variation in gene expression

Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.

Saccharomyces paradoxus Transcriptional Alterations in Cells of Distinct Phenotype and Viral dsRNA Content

Killer yeasts are attractive antifungal agents with great potential applications in the food industry. Natural Saccharomyces paradoxus isolates provide new dsRNA-based killer systems available for investigation. The presence of viral dsRNA may alter transcriptional profile of S. paradoxus. To test this possibility, a high-throughput RNA sequencing was employed to compare the transcriptomes of S. paradoxus AML 15-66 K66 killer strains after curing them of either M-66 alone or both M-66 and L-A-66 dsRNA viruses. The S. paradoxus cells cured of viral dsRNA(s) showed respiration deficient or altered sporulation patterns. We have identified numerous changes in the transcription profile of genes including those linked to ribosomes and amino acid biosynthesis, as well as mitochondrial function. Our work advance studies of transcriptional adaptations of Saccharomyces spp. induced by changes in phenotype and set of dsRNA viruses, reported for the first time.

A yeast living ancestor reveals the origin of genomic introgressions

Genome introgressions drive evolution across the animal¹, plant² and fungal³ kingdoms. Introgressions initiate from archaic admixtures followed by repeated backcrossing to one parental species. However, how introgressions arise in reproductively isolated species, such as yeast⁴, has remained unclear. Here we identify a clonal descendant of the ancestral yeast hybrid that founded the extant Saccharomyces cerevisiae Alpechin lineage⁵, which carries abundant Saccharomyces paradoxus introgressions. We show that this clonal descendant, hereafter defined as a 'living ancestor', retained the ancestral genome structure of the first-generation hybrid with contiguous S. cerevisiae and S. paradoxus subgenomes. The ancestral first-generation hybrid underwent catastrophic genomic instability through more than a hundred mitotic recombination events, mainly manifesting as homozygous genome blocks generated by loss of heterozygosity. These homozygous sequence blocks rescue hybrid fertility by restoring meiotic recombination and are the direct origins of the introgressions present in the Alpechin lineage. We suggest a plausible route for introgression evolution through the reconstruction of extinct stages and propose that genome instability allows hybrids to overcome reproductive isolation and enables introgressions to emerge.

Wild Yeast for the Future: Exploring the Use of Wild Strains for Wine and Beer Fermentation

The continuous usage of single Saccharomyces cerevisiae strains as starter cultures in fermentation led to the domestication and propagation of highly specialized strains in fermentation, resulting in the standardization of wines and beers. In this way, hundreds of commercial strains have been developed to satisfy producers’ and consumers’ demands, including beverages with high/low ethanol content, nutrient deprivation tolerance, diverse aromatic profiles, and fast fermentations. However, studies in the last 20 years have demonstrated that the genetic and phenotypic diversity in commercial S. cerevisiae strains is low. This lack of diversity limits alternative wines and beers, stressing the need to explore new genetic resources to differentiate each fermentation product. In this sense, wild strains harbor a higher than thought genetic and phenotypic diversity, representing a feasible option to generate new fermentative beverages. Numerous recent studies have identified alleles in wild strains that could favor phenotypes of interest, such as nitrogen consumption, tolerance to cold or high temperatures, and the production of metabolites, such as glycerol and aroma compounds. Here, we review the recent literature on the use of commercial and wild S. cerevisiae strains in wine and beer fermentation, providing molecular evidence of the advantages of using wild strains for the generation of improved genetic stocks for the industry according to the product style.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10¹¹) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

QTL analysis of natural Saccharomyces cerevisiae isolates reveals unique alleles involved in lignocellulosic inhibitor tolerance

Decoding the genetic basis of lignocellulosic inhibitor tolerance in Saccharomyces cerevisiae is crucial for rational engineering of bioethanol strains with enhanced robustness. The genetic diversity of natural strains present an invaluable resource for the exploration of complex traits of industrial importance from a pan-genomic perspective to complement the limited range of specialised, tolerant industrial strains. Natural S. cerevisiae isolates have lately garnered interest as a promising toolbox for engineering novel, genetically encoded tolerance phenotypes into commercial strains. To this end, we investigated the genetic basis for lignocellulosic inhibitor tolerance of natural S. cerevisiae isolates. A total of 12 quantitative trait loci underpinning tolerance were identified by next-generation sequencing linked bulk-segregant analysis of superior interbred pools. Our findings corroborate the current perspective of lignocellulosic inhibitor tolerance as a multigenic, complex trait. Apart from a core set of genetic variants required for inhibitor tolerance, an additional genetic background-specific response was observed. Functional analyses of the identified genetic loci revealed the uncharacterised ORF, YGL176C and the bud-site selection XRN1/BUD13 as potentially beneficial alleles contributing to tolerance to a complex lignocellulosic inhibitor mixture. We present evidence for the consideration of both regulatory and coding sequence variants for strain improvement.

Toxo: a library for calculating penetrance tables of high-order epistasis models

Background

Epistasis is defined as the interaction between different genes when expressing a specific phenotype. The most common way to characterize an epistatic relationship is using a penetrance table, which contains the probability of expressing the phenotype under study given a particular allele combination. Available simulators can only create penetrance tables for well-known epistasis models involving a small number of genes and under a large number of limitations.

Results

Toxo is a MATLAB library designed to calculate penetrance tables of epistasis models of any interaction order which resemble real data more closely. The user specifies the desired heritability (or prevalence) and the program maximizes the table’s prevalence (or heritability) according to the input epistatic model boundaries.

Conclusions

Toxo extends the capabilities of existing simulators that define epistasis using penetrance tables. These tables can be directly used as input for software simulators such as GAMETES so that they are able to generate data samples with larger interactions and more realistic prevalences/heritabilities.

Mapping Ethanol Tolerance in Budding Yeast Reveals High Genetic Variation in a Wild Isolate

Ethanol tolerance, a polygenic trait of the yeast Saccharomyces cerevisiae, is the primary factor determining industrial bioethanol productivity. Until now, genomic elements affecting ethanol tolerance have been mapped only at low resolution, hindering their identification. Here, we explore the genetic architecture of ethanol tolerance, in the F6 generation of an Advanced Intercrossed Line (AIL) mapping population between two phylogenetically distinct, but phenotypically similar, S. cerevisiae strains (a common laboratory strain and a wild strain isolated from nature). Under ethanol stress, 51 quantitative trait loci (QTLs) affecting growth and 96 QTLs affecting survival, most of them novel, were identified, with high resolution, in some cases to single genes, using a High-Resolution Mapping Package of methodologies that provided high power and high resolution. We confirmed our results experimentally by showing the effects of the novel mapped genes: MOG1, MGS1, and YJR154W. The mapped QTLs explained 34% of phenotypic variation for growth and 72% for survival. High statistical power provided by our analysis allowed detection of many loci with small, but mappable effects, uncovering a novel “quasi-infinitesimal” genetic architecture. These results are striking demonstration of tremendous amounts of hidden genetic variation exposed in crosses between phylogenetically separated strains with similar phenotypes; as opposed to the more common design where strains with distinct phenotypes are crossed. Our findings suggest that ethanol tolerance is under natural evolutionary fitness-selection for an optimum phenotype that would tend to eliminate alleles of large effect. The study provides a platform for development of superior ethanol-tolerant strains using genome editing or selection.

Widespread Prion-Based Control of Growth and Differentiation Strategies in Saccharomyces cerevisiae

Theory and experiments suggest that organisms would benefit from pre-adaptation to future stressors based on reproducible environmental fluctuations experienced by their ancestors, but the mechanisms driving pre-adaptation remain enigmatic. We report that the [SMAUG⁺] prion allows yeast to anticipate nutrient repletion after periods of starvation, providing a strong selective advantage. By transforming the landscape of post-transcriptional gene expression, [SMAUG⁺] regulates the decision between two broad growth and survival strategies: mitotic proliferation or meiotic differentiation into a stress-resistant state. [SMAUG⁺] is common in laboratory yeast strains, where standard propagation practice produces regular cycles of nutrient scarcity followed by repletion. Distinct [SMAUG⁺] variants are also widespread in wild yeast isolates from multiple niches, establishing that prion polymorphs can be utilized in natural populations. Our data provide a striking example of how protein-based epigenetic switches, hidden in plain sight, can establish a transgenerational memory that integrates adaptive prediction into developmental decisions.

Multiple Changes Underlie Allelic Divergence of CUP2 Between Saccharomyces Species

Under the model of micromutationism, phenotypic divergence between species is caused by accumulation of many small-effect changes. While mapping the causal changes to single nucleotide resolution could be difficult for diverged species, genetic dissection via chimeric constructs allows us to evaluate whether a large-effect gene is composed of many small-effect nucleotide changes. In a previously described non-complementation screen, we found an allele difference of CUP2, a copper-binding transcription factor, underlies divergence in copper resistance between Saccharomyces cerevisiae and S. uvarum. Here, we tested whether the allele effect of CUP2 was caused by multiple nucleotide changes. By analyzing chimeric constructs containing four separate regions in the CUP2 gene, including its distal promoter, proximal promoter, DNA binding domain and transcriptional activation domain, we found that all four regions of the S. cerevisiae allele conferred copper resistance, with the proximal promoter showing the largest effect, and that both additive and epistatic effects are likely involved. These findings support a model of multiple changes underlying evolution and suggest an important role of both protein coding and cis-regulatory changes in evolution.

Genetic variants of TORC1 signaling pathway affect nitrogen consumption in Saccharomyces cerevisiae during alcoholic fermentation

In the alcoholic fermentation process, Saccharomyces cerevisiae strains present differences in their nitrogen consumption profiles, these phenotypic outcomes have complex genetic and molecular architectures. In this sense, variations in nitrogen signaling pathways regulated by TORC1 represent one of the main sources of phenotypic diversity in nitrogen consumption. This emphasizes the possible roles that allelic variants from the TORC1 pathway have in the nitrogen consumption differences observed in yeast during the alcoholic fermentation. Here, we studied the allelic diversity in the TORC1 pathway across four yeast strains and determined how these polymorphisms directly impact nitrogen consumption during alcoholic fermentation. Using a reciprocal hemizygosity approach combined with phenotyping under fermentative conditions, we found that allelic variants of GTR1, TOR2, SIT4, SAP185, EAP1, NPR1 and SCH9 underlie differences in the ammonium and amino acids consumption phenotypes. Among these, GTR1 alleles from the Wine/European and West African genetic backgrounds showed the greatest effects on ammonium and amino acid consumption, respectively. Furthermore, we identified allelic variants of SAP185, TOR2, SCH9 and NPR1 from an oak isolate that increased the amino acid consumption preference over ammonium; representing putative candidates coming from a non-domesticated strain that could be used for genetic improvement programs. In conclusion, our results demonstrated that a large number of allelic variants within the TORC1 pathway significantly impacts on regulatory mechanisms of nitrogen assimilation during alcoholic fermentation.

Genotypic trade-off between appetitive and aversive capacities in honeybees

Honey bees can learn both appetitive and aversive associations, using two olfactory conditioning protocols. Appetitive conditioning of the proboscis extension response (PER) involves associating an odor, the conditioned stimulus (CS) with a sucrose solution, the unconditioned stimulus (US). Conversely, aversive conditioning of the sting extension response (SER) involves associating the odor CS with an electric or thermal shock US. Here, we investigated the relationship between bees’ appetitive and aversive learning capacities at the individual level and the influence of bees’ genotype. As learning performance was shown to depend on an individuals’ sensitivity to the US, we systematically measured four different traits in each individual bee: sensitivity to sucrose, PER learning performance with a sucrose US, sensitivity to temperature, SER learning with a temperature US. First, we confirmed for both conditioning types that learning performance correlates with US responsiveness. Second, we found a trade-off between appetitive and aversive learning performances: bees that were better appetitive learners (and had a lower sucrose US threshold) learned less efficiently in the aversive conditioning (and had a higher temperature US threshold). Because the honey bee queen typically mates with 15–20 males, the workers from a honey bee hive belong to as many different patrilines, allowing for the search of the genetic determinism of cognitive abilities. Using microsatellite analysis, we show that a genetic determinism underlies the trade-off between appetitive and aversive capacities, with appetitively vs aversively biased patrilines. The honey bee hive thus appears as a genetically structured cognitive community.

A polyploid admixed origin of beer yeasts derived from European and Asian wine populations

Strains of Saccharomyces cerevisiae used to make beer, bread, and wine are genetically and phenotypically distinct from wild populations associated with trees. The origins of these domesticated populations are not always clear; human-associated migration and admixture with wild populations have had a strong impact on S. cerevisiae population structure. We examined the population genetic history of beer strains and found that ale strains and the S. cerevisiae portion of allotetraploid lager strains were derived from admixture between populations closely related to European grape wine strains and Asian rice wine strains. Similar to both lager and baking strains, ale strains are polyploid, providing them with a passive means of remaining isolated from other populations and providing us with a living relic of their ancestral hybridization. To reconstruct their polyploid origin, we phased the genomes of two ale strains and found ale haplotypes to both be recombinants between European and Asian alleles and to also contain novel alleles derived from extinct or as yet uncharacterized populations. We conclude that modern beer strains are the product of a historical melting pot of fermentation technology.

Population genomics reveals evolution and variation of Saccharomyces cerevisiae in the human and insects gut

The quest to discover the variety of ecological niches inhabited by Saccharomyces cerevisiae has led to research in areas as diverse as wineries, oak trees and insect guts. The discovery of fungal communities in the human gastrointestinal tract suggested the host's gut as a potential reservoir for yeast adaptation. Here, we report the existence of yeast populations associated with the human gut (HG) that differ from those isolated from other human body sites. Phylogenetic analysis on 12 microsatellite loci and 1715 combined CDSs from whole-genome sequencing revealed three subclusters of HG strains with further evidence of clonal colonization within the host's gut. The presence of such subclusters was supported by other genomic features, such as copy number variation, absence/introgressions of CDSs and relative polymorphism frequency. Functional analysis of CDSs specific of the different subclusters suggested possible alterations in cell wall composition and sporulation features. The phenotypic analysis combined with immunological profiling of these strains further showed that sporulation was related with strain-specific genomic characteristics in the immune recognition pattern. We conclude that both genetic and environmental factors involved in cell wall remodelling and sporulation are the main drivers of adaptation in S. cerevisiae populations in the human gut.

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.

GPD1 and ADH3 Natural Variants Underlie Glycerol Yield Differences in Wine Fermentation

Glycerol is one of the most important by-products of alcohol fermentation, and depending on its concentration it can contribute to wine flavor intensity and aroma volatility. Here, we evaluated the potential of utilizing the natural genetic variation of non-coding regions in budding yeast to identify allelic variants that could modulate glycerol phenotype during wine fermentation. For this we utilized four Saccharomyces cerevisiae strains (WE - Wine/European, SA – Sake, NA – North American, and WA – West African), which were previously profiled for genome-wide Allele Specific Expression (ASE) levels. The glycerol yields under Synthetic Wine Must (SWM) fermentations differed significantly between strains; WA produced the highest glycerol yields while SA produced the lowest yields. Subsequently, from our ASE database, we identified two candidate genes involved in alcoholic fermentation pathways, ADH3 and GPD1, exhibiting significant expression differences between strains. A reciprocal hemizygosity assay demonstrated that hemizygotes expressing GPD1^WA, GPD1^SA, ADH3^WA and ADH3^SA alleles had significantly greater glycerol yields compared to GPD1^WE and ADH3^WE. We further analyzed the gene expression profiles for each GPD1 variant under SWM, demonstrating that the expression of GPD1^WE occurred earlier and was greater compared to the other alleles. This result indicates that the level, timing, and condition of expression differ between regulatory regions in the various genetic backgrounds. Furthermore, promoter allele swapping demonstrated that these allele expression patterns were transposable across genetic backgrounds; however, glycerol yields did not differ between wild type and modified strains, suggesting a strong trans effect on GPD1 gene expression. In this line, Gpd1 protein levels in parental strains, particularly Gpd1p^WE, did not necessarily correlate with gene expression differences, but rather with glycerol yield where low Gpd1p^WE levels were detected. This suggests that GPD1^WE is influenced by recessive negative post-transcriptional regulation which is absent in the other genetic backgrounds. This dissection of regulatory mechanisms in GPD1 allelic variants demonstrates the potential to exploit natural alleles to improve glycerol production in wine fermentation and highlights the difficulties of trait improvement due to alternative trans-regulation and gene-gene interactions in the different genetic background.

The genetic component of human longevity: New insights from the analysis of pathway-based SNP-SNP interactions

In human longevity studies, single nucleotide polymorphism (SNP) analysis identified a large number of genetic variants with small effects, yet not easily replicable in different populations. New insights may come from the combined analysis of different SNPs, especially when grouped by metabolic pathway. We applied this approach to study the joint effect on longevity of SNPs belonging to three candidate pathways, the insulin/insulin‐like growth factor signalling (IIS), DNA repair and pro/antioxidant. We analysed data from 1,058 tagging SNPs in 140 genes, collected in 1825 subjects (1,089 unrelated nonagenarians from the Danish 1905 Birth Cohort Study and 736 Danish controls aged 46–55 years) for evaluating synergic interactions by SNPsyn. Synergies were further tested by the multidimensional reduction (MDR) approach, both intra‐ and interpathways. The best combinations (FDR<0.0001) resulted those encompassing IGF1R‐rs12437963 and PTPN1‐rs6067484, TP53‐rs2078486 and ERCC2‐rs50871, TXNRD1‐rs17202060 and TP53‐rs2078486, the latter two supporting a central role of TP53 in mediating the concerted activation of the DNA repair and pro‐antioxidant pathways in human longevity. Results were consistently replicated with both approaches, as well as a significant effect on longevity was found for the GHSR gene, which also interacts with partners belonging to both IIS and DNA repair pathways (PAPPA,PTPN1,PARK7, MRE11A). The combination GHSR‐MREA11, positively associated with longevity by MDR, was further found influencing longitudinal survival in nonagenarian females (p = .026). Results here presented highlight the validity of SNP‐SNP interactions analyses for investigating the genetics of human longevity, confirming previously identified markers but also pointing to novel genes as central nodes of additional networks involved in human longevity.

Common genetic variants in GAL, GAP43 and NRSN1 and interaction networks confer susceptibility to Hirschsprung disease

Hirschsprung disease (HSCR) is a severe multifactorial genetic disorder. Microarray studies indicated GAL,GAP43 and NRSN1 might contribute to the altered risk in HSCR. Thus, we focused on genetic variations in GAL,GAP43 and NRSN1, and the gene‐gene interactions involved in HSCR susceptibility. We recruited a strategy combining case‐control study and MassArray system with interaction network analysis. For GAL,GAP43 and NRSN1, a total of 18 polymorphisms were assessed in 104 subjects with sporadic HSCR and 151 controls of Han Chinese origin. We found statistically significant differences between HSCR and control groups at 5 genetic variants. For each gene, the haplotypes combining all polymorphisms were the most significant. Based on SNPsyn, MDR and GeneMANIA analyses, we observed significant gene‐gene interactions among GAL,GAP43,NRSN1 and our previous identified RELN,GABRG2 and PTCH1. Our study for the first time indicates that genetic variants within GAL,GAP43 and NRSN1 and related gene‐gene interaction networks might be involved in the altered susceptibility to HSCR in the Han Chinese population, which might shed more light on HSCR pathogenesis.

A regulatory circuit of two lncRNAs and a master regulator directs cell fate in yeast

Transcription of long noncoding RNAs (lncRNAs) regulates local gene expression in eukaryotes. Many examples of how a single lncRNA controls the expression of an adjacent or nearby protein-coding gene have been described. Here we examine the regulation of a locus consisting of two contiguous lncRNAs and the master regulator for entry into yeast meiosis, IME1. We find that the cluster of two lncRNAs together with several transcription factors form a regulatory circuit by which IME1 controls its own promoter and thereby promotes its own expression. Inhibition or stimulation of this unusual feedback circuit affects timing and rate of IME1 accumulation, and hence the ability for cells to enter meiosis. Our data demonstrate that orchestrated transcription through two contiguous lncRNAs promotes local gene expression and determines a critical cell fate decision.

Gene-gene and gene-environment interactions in complex traits in yeast

One of the fundamental questions in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that, in most cases, the effect of a genetic locus on the phenotype is context-dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the studies, in both model organisms and humans, that map the genetic regulation of phenotypic variation in complex traits primarily identify additive loci with independent effects. This does not reflect an absence of the contribution of genetic interactions to phenotypic variation, but instead is a consequence of the technical limitations in mapping gene-gene interactions (GGI) and gene-environment interactions (GEI). Yeast, with its detailed molecular understanding, diverse population genomics and ease of genetic manipulation, is a unique and powerful resource to study the contributions of GGI and GEI in the regulation of phenotypic variation. Here we review studies in yeast that have identified GGI and GEI that regulate phenotypic variation, and discuss the contribution of these findings in explaining missing heritability of complex traits, and how observations from these GGI and GEI studies enhance our understanding of the mechanisms underlying genetic robustness and adaptability that shape the architecture of the genotype-phenotype map.

Widespread epistasis regulates glucose homeostasis and gene expression

The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation.

Most complex traits and diseases are regulated by the combined influence of multiple genetic variants. However, it remains controversial whether these genetic variants independently influence complex traits, and therefore the impact of each variant could be simply added together (additivity), or whether the variants work together to influence trait variation, in which case the combined impact of multiple variants would differ from the summed impact of each individual variant (epistasis). In this study in mice, we discovered that the genetic regulation of blood sugar levels and gene expression in the liver were predominantly controlled by non-additive interactions, whereas body weight was predominantly controlled by additive interactions. Remarkably, the expression level of nearly 25% of all genes in the liver was controlled by non-additive interactions. The non-additive interactions typically acted to return trait values to the levels detected in control mice, thus contributing to a reduction in trait variation. We also demonstrated that not accounting for non-additive interactions significantly underestimated the phenotypic effect of a genetic variant on a particular genetic background, suggesting that many previously identified risk loci may have significantly larger effects on disease susceptibility in a subset of individuals. These studies highlight the importance of understanding interactions between genetic variants to better understand disease risk and personalize clinical care.

High-density genetic mapping identifies the genetic basis of a natural colony morphology mutant in the root rot pathogen Armillaria ostoyae

Filamentous fungi exhibit a broad spectrum of heritable growth patterns and morphological variations reflecting the adaptation of the different species to distinct ecological niches. But also within species, isolates show considerable variation in growth rates and other morphological characteristics. The genetic basis of this intraspecific variation in mycelial growth and morphology is currently poorly understood. By chance, a growth mutant in the root rot pathogen Armillaria ostoyae was discovered. The mutant phenotype was characterized by extremely compact and slow growth, as well as shorter aerial hyphae and hyphal compartments in comparison to the wildtype phenotype. Genetic analysis revealed that the abnormal phenotype is caused by a recessive mutation, which segregates asa single locus in sexual crosses. In order to identify the genetic basis of the mutant phenotype, we performed a quantitative trait locus (QTL) analysis. A mapping population of 198 haploid progeny was genotyped at 11,700 genome-wide single nucleotide polymorphisms (SNPs) making use of double digest restriction site associated DNA sequencing (ddRADseq). In accordance with the genetic analysis, a single significant QTL was identified for the abnormal growth phenotype. The QTL confidence interval spans a narrow, gene dense region of 87kb in the A. ostoyae genome which contains 37 genes. Overall, our study reports the first high-density genetic map for an Armillaria species and shows its successful application in forward genetics by resolving the genetic basis of a mutant phenotype with a severe defect in hyphal growth.

Mapping Quantitative Trait Loci in Yeast

Natural Saccharomyces strains isolated from the wild differ quantitatively in molecular and organismal phenotypes. Quantitative trait loci (QTL) mapping is a powerful approach for identifying sequence variants that alter gene function. In yeast, QTL mapping has been used in designed crosses to map functional polymorphisms. This approach, outlined here, is often the first step in understanding the molecular basis of quantitative traits. New large-scale sequencing surveys have the potential to directly associate genotypes with organismal phenotypes, providing a broader catalog of causative genetic variants. Additional analysis of intermediate phenotypes (e.g., RNA, protein, or metabolite levels) can produce a multilayered and integrated view of individual variation, producing a high-resolution view of the genotype-phenotype map.

Epistasis × environment interactions among Arabidopsis thaliana glucosinolate genes impact complex traits and fitness in the field

Despite the growing number of studies showing that genotype × environment and epistatic interactions control fitness, the influences of epistasis × environment interactions on adaptive trait evolution remain largely uncharacterized. Across three field trials, we quantified aliphatic glucosinolate (GSL) defense chemistry, leaf damage, and relative fitness using mutant lines of Arabidopsis thaliana varying at pairs of causal aliphatic GSL defense genes to test the impact of epistatic and epistasis × environment interactions on adaptive trait variation. We found that aliphatic GSL accumulation was primarily influenced by additive and epistatic genetic variation, leaf damage was primarily influenced by environmental variation and relative fitness was primarily influenced by epistasis and epistasis × environment interactions. Epistasis × environment interactions accounted for up to 48% of the relative fitness variation in the field. At a single field site, the impact of epistasis on relative fitness varied significantly over 2 yr, showing that epistasis × environment interactions within a location can be temporally dynamic. These results suggest that the environmental dependency of epistasis can profoundly influence the response to selection, shaping the adaptive trajectories of natural populations in complex ways, and deserves further consideration in future evolutionary studies.

Causal Genetic Variation Underlying Metabolome Differences

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81, cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype.

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the "MArginal ePIstasis Test", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.

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.