Epistasis: Searching for Interacting Genetic Variants Using Crosses

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the fields of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.

Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing

The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.

From QTL to gene: C. elegans facilitates discoveries of the genetic mechanisms underlying natural variation

Although many studies have examined quantitative trait variation across many species, only a small number of genes and thereby molecular mechanisms have been discovered. Without these data, we can only speculate about evolutionary processes that underlie trait variation. Here, we review how quantitative and molecular genetics in the nematode Caenorhabditis elegans led to the discovery and validation of 37 quantitative trait genes over the past 15 years. Using these data, we can start to make inferences about evolution from these quantitative trait genes, including the roles that coding versus noncoding variation, gene family expansion, common versus rare variants, pleiotropy, and epistasis play in trait variation across this species.

Mitochondrial-nuclear coadaptation revealed through mtDNA replacements in Saccharomyces cerevisiae

BACKGROUND

Mitochondrial function requires numerous genetic interactions between mitochondrial- and nuclear- encoded genes. While selection for optimal mitonuclear interactions should result in coevolution between both genomes, evidence for mitonuclear coadaptation is challenging to document. Genetic models where mitonuclear interactions can be explored are needed.

RESULTS

We systematically exchanged mtDNAs between 15 Saccharomyces cerevisiae isolates from a variety of ecological niches to create 225 unique mitochondrial-nuclear genotypes. Analysis of phenotypic profiles confirmed that environmentally-sensitive interactions between mitochondrial and nuclear genotype contributed to growth differences. Exchanges of mtDNAs between strains of the same or different clades were just as likely to demonstrate mitonuclear epistasis although epistatic effect sizes increased with genetic distances. Strains with their original mtDNAs were more fit than strains with synthetic mitonuclear combinations when grown in media that resembled isolation habitats.

CONCLUSIONS

This study shows that natural variation in mitonuclear interactions contributes to fitness landscapes. Multiple examples of coadapted mitochondrial-nuclear genotypes suggest that selection for mitonuclear interactions may play a role in helping yeasts adapt to novel environments and promote coevolution.

Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits

Gaussian process (GP)-based automatic relevance determination (ARD) is known to be an efficient technique for identifying determinants of gene-by-gene interactions important to trait variation. However, the estimation of GP models is feasible only for low-dimensional datasets (∼200 variables), which severely limits application of the GP-based ARD method for high-throughput sequencing data. In this paper, we provide a nonparametric prescreening method that preserves virtually all the major benefits of the GP-based ARD method and extends its scalability to the typical high-dimensional datasets used in practice. In several simulated test scenarios, the proposed method compared favorably with existing nonparametric dimension reduction/prescreening methods suitable for higher-order interaction searches. As a real-data example, the proposed method was applied to a high-throughput dataset downloaded from the cancer genome atlas (TCGA) with measured expression levels of 16,976 genes (after preprocessing) from patients diagnosed with acute myeloid leukemia.

Analysis of Epistasis in Natural Traits Using Model Organisms

The ability to detect and understand epistasis in natural populations is important for understanding how biological traits are influenced by genetic variation. However, identification and characterization of epistasis in natural populations remains difficult due to statistical issues that arise as a result of multiple comparisons, and the fact that most genetic variants segregate at low allele frequencies. In this review, we discuss how model organisms may be used to manipulate genotypic combinations to power the detection of epistasis as well as test interactions between specific genes. Findings from a number of species indicate that statistical epistasis is pervasive between natural genetic variants. However, the properties of experimental systems that enable analysis of epistasis also constrain extrapolation of these results back into natural populations.

Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Clinical Implications of Digenic Inheritance and Epistasis in Primary Immunodeficiency Disorders

The existence of epistasis in humans was first predicted by Bateson in 1909. Epistasis describes the non-linear, synergistic interaction of two or more genetic loci, which can substantially modify disease severity or result in entirely new phenotypes. The concept has remained controversial in human genetics because of the lack of well-characterized examples. In humans, it is only possible to demonstrate epistasis if two or more genes are mutated. In most cases of epistasis, the mutated gene products are likely to be constituents of the same physiological pathway leading to severe disruption of a cellular function such as antibody production. We have recently described a digenic family, who carry mutations of TNFRSF13B/TACI as well as TCF3 genes. Both genes lie in tandem along the immunoglobulin isotype switching and secretion pathway. We have shown they interact in an epistatic way causing severe immunodeficiency and autoimmunity in the digenic proband. With the advent of next generation sequencing, it is likely other families with digenic inheritance will be identified. Since digenic inheritance does not always cause epistasis, we propose an epistasis index which may help quantify the effects of the two mutations. We also discuss the clinical implications of digenic inheritance and epistasis in humans with primary immunodeficiency disorders.