Epistasis: Searching for Interacting Genetic Variants Using Crosses

Discovery of malathion resistance QTL in Drosophila melanogaster using a bulked phenotyping approach

Drosophila melanogaster has proved an effective system with which to understand the evolutionary genetics and molecular mechanisms of insecticide resistance. Insecticide use has left signatures of selection in the fly genome, and both functional and quantitative genetic studies in the system have identified genes and variants associated with resistance. Here, we use D. melanogaster and leverage a bulk phenotyping and pooled sequencing "extreme quantitative trait loci" approach to genetically dissect variation in resistance to malathion, an organophosphate insecticide. We resolve 2 quantitative trait loci, one of which implicates allelic variation at the cytochrome P450 gene Cyp6g1, a strong candidate based on previous work. The second shows no overlap with hits from a previous genome-wide association study for malathion resistance, recapitulating other studies showing that different strategies for complex trait dissection in flies can yield apparently different architectures. Notably, we see no genetic signal at the Ace gene. Ace encodes the target of organophosphate insecticide inhibition, and genome-wide association studies have identified strong Ace-linked associations with resistance in flies. The absence of quantitative trait locus implicating Ace here is most likely because our mapping population does not segregate for several of the known functional polymorphisms impacting resistance at Ace, perhaps because our population is derived from flies collected prior to the widespread use of organophosphate insecticides. Our fundamental approach can be an efficient, powerful strategy to dissect genetic variation in resistance traits. Nonetheless, studies seeking to interrogate contemporary insecticide resistance variation may benefit from deriving mapping populations from more recently collected strains.

The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross

In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids.

Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.

Genetic basis of a spontaneous mutation’s expressivity

Genetic background often influences the phenotypic consequences of mutations, resulting in variable expressivity. How standing genetic variants collectively cause this phenomenon is not fully understood. Here, we comprehensively identify loci in a budding yeast cross that impact the growth of individuals carrying a spontaneous missense mutation in the nuclear-encoded mitochondrial ribosomal gene MRP20. Initial results suggested that a single large effect locus influences the mutation’s expressivity, with one allele causing inviability in mutants. However, further experiments revealed this simplicity was an illusion. In fact, many additional loci shape the mutation’s expressivity, collectively leading to a wide spectrum of mutational responses. These results exemplify how complex combinations of alleles can produce a diversity of qualitative and quantitative responses to the same mutation.

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method's high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework's versatility and broad practical relevance.

MeSCoT: The tool for quantitative trait simulation through the mechanistic modelling of genes' regulatory interactions

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait’s realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes’ regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.

The complex role of genetic background in shaping the effects of spontaneous and induced mutations

Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects.

A large fraction of mutations show different effects in distinct individuals.

These background effects are mainly caused by epistasis with segregating loci.

Mapping studies show a diversity of genetic architectures can be involved.

Genetically complex changes in gene expression are often, but not always, causative.

Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis"

Background

The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

Results

We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

Conclusions

Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

Many statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.

Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

Complex nutrient channel phenotypes despite Mendelian inheritance in a Plasmodium falciparum genetic cross

Malaria parasites activate a broad-selectivity ion channel on their host erythrocyte membrane to obtain essential nutrients from the bloodstream. This conserved channel, known as the plasmodial surface anion channel (PSAC), has been linked to parasite clag3 genes in P. falciparum, but epigenetic switching between the two copies of this gene hinders clear understanding of how the encoded protein determines PSAC activity. Here, we used linkage analysis in a P. falciparum cross where one parent carries a single clag3 gene to overcome the effects of switching and confirm a primary role of the clag3 product with high confidence. Despite Mendelian inheritance, CLAG3 conditional knockdown revealed remarkably preserved nutrient and solute uptake. Even more surprisingly, transport remained sensitive to a CLAG3 isoform-specific inhibitor despite quantitative knockdown, indicating that low doses of the CLAG3 transgene are sufficient to confer block. We then produced a complete CLAG3 knockout line and found it exhibits an incomplete loss of transport activity, in contrast to rhoph2 and rhoph3, two PSAC-associated genes that cannot be disrupted because nutrient uptake is abolished in their absence. Although the CLAG3 knockout did not incur a fitness cost under standard nutrient-rich culture conditions, this parasite could not be propagated in a modified medium that more closely resembles human plasma. These studies implicate oligomerization of CLAG paralogs encoded by various chromosomes in channel formation. They also reveal that CLAG3 is dispensable under standard in vitro conditions but required for propagation under physiological conditions.

Why and how to study genetic changes with context-dependent effects

The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.

Myopia genetics in genome-wide association and post-genome-wide association study era

Genome-wide association studies (GWAS) of myopia and refractive error have generated exciting results and identified novel risk-associated loci. However, the interpretation of the findings of GWAS of complex diseases is not straightforward and has remained challenging. This review provides a brief summary of the main focus on the advantages and limitations of GWAS of myopia, with potential strategies that may contribute to further insight into the genetics of myopia in the post-GWAS or omics era.

Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species

Global climate is rapidly changing, and the ability for tree species to adapt is dependent on standing genomic variation; however, the distribution and abundance of functional and adaptive variants are poorly understood in natural systems. We test key hypotheses regarding the genetics of adaptive variation in a foundation tree: genomic variation is associated with climate, and genomic variation is more likely to be associated with temperature than precipitation or aridity. To test these hypotheses, we used 9,593 independent, genomic single-nucleotide polymorphisms (SNPs) from 270 individuals sampled from Corymbia calophylla's entire distribution in south-western Western Australia, spanning orthogonal temperature and precipitation gradients. Environmental association analyses returned 537 unique SNPs putatively adaptive to climate. We identified SNPs associated with climatic variation (i.e., temperature [458], precipitation [75] and aridity [78]) across the landscape. Of these, 78 SNPs were nonsynonymous (NS), while 26 SNPs were found within gene regulatory regions. The NS and regulatory candidate SNPs associated with temperature explained more deviance (27.35%) than precipitation (5.93%) and aridity (4.77%), suggesting that temperature provides stronger adaptive signals than precipitation. Genes associated with adaptive variants include functions important in stress responses to temperature and precipitation. Patterns of allelic turnover of NS and regulatory SNPs show small patterns of change through climate space with the exception of an aldehyde dehydrogenase gene variant with 80% allelic turnover with temperature. Together, these findings provide evidence for the presence of adaptive variation to climate in a foundation species and provide critical information to guide adaptive management practices.

Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions

Mixed models can be considered as a type of penalized regression and are everyday tools in statistical genetics. The standard mixed model for whole genome regression (WGR) is ridge regression best linear unbiased prediction (RRBLUP) which is based on an additive marker effect model. Many publications have extended the additive WGR approach by incorporating interactions between loci or between genes and environment. In this context of penalized regressions with interactions, it has been reported that translating the coding of single nucleotide polymorphisms -for instance from -1,0,1 to 0,1,2- has an impact on the prediction of genetic values and interaction effects. In this work, we identify the reason for the relevance of variable coding in the general context of penalized polynomial regression. We show that in many cases, predictions of the genetic values are not invariant to translations of the variable coding, with an exception when only the sizes of the coefficients of monomials of highest total degree are penalized. The invariance of RRBLUP can be considered as a special case of this setting, with a polynomial of total degree 1, penalizing additive effects (total degree 1) but not the fixed effect (total degree 0). The extended RRBLUP (eRRBLUP), which includes interactions, is not invariant to translations because it does not only penalize interactions (total degree 2), but also additive effects (total degree 1). This observation implies that translation-invariance can be maintained in a pair-wise epistatic WGR if only interaction effects are penalized, but not the additive effects. In this regard, approaches of pre-selecting loci may not only reduce computation time, but can also help to avoid the variable coding issue. To illustrate the practical relevance, we compare different regressions on a publicly available wheat data set. We show that for an eRRBLUP, the relevance of the marker coding for interaction effect estimates increases with the number of variables included in the model. A biological interpretation of estimated interaction effects may therefore become more difficult. Consequently, comparing reproducing kernel Hilbert space (RKHS) approaches to WGR approaches modeling effects explicitly, the supposed advantage of an increased interpretability of the latter may not be real. Our theoretical results are generally valid for penalized regressions, for instance also for the least absolute shrinkage and selection operator (LASSO). Moreover, they apply to any type of interaction modeled by products of predictor variables in a penalized regression approach or by Hadamard products of covariance matrices in a mixed model.

Layers of Cryptic Genetic Variation Underlie a Yeast Complex Trait

To better understand cryptic genetic variation, Lee et al. comprehensively map the genetic basis of a trait that is typically suppressed in a yeast cross. By determining how three different genetic perturbations give rise...

Cryptic genetic variation may be an important contributor to heritable traits, but its extent and regulation are not fully understood. Here, we investigate the cryptic genetic variation underlying a Saccharomyces cerevisiae colony phenotype that is typically suppressed in a cross of the laboratory strain BY4716 (BY) and a derivative of the clinical isolate 322134S (3S). To do this, we comprehensively dissect the trait’s genetic basis in the BYx3S cross in the presence of three different genetic perturbations that enable its expression. This allows us to detect and compare the specific loci that interact with each perturbation to produce the trait. In total, we identify 21 loci, all but one of which interact with just a subset of the perturbations. Beyond impacting which loci contribute to the trait, the genetic perturbations also alter the extent of additivity, epistasis, and genotype–environment interaction among the detected loci. Additionally, we show that the single locus interacting with all three perturbations corresponds to the coding region of the cell surface gene FLO11. While nearly all of the other remaining loci influence FLO11 transcription in cis or trans, the perturbations tend to interact with loci in different pathways and subpathways. Our work shows how layers of cryptic genetic variation can influence complex traits. Here, these layers mainly represent different regulatory inputs into the transcription of a single key gene.

The Genetic Architecture of Intra-Species Hybrid Mito-Nuclear Epistasis

Genetic variants that are neutral within, but deleterious between, populations (Dobzhansky-Muller Incompatibilities) are thought to initiate hybrid dysfunction and then to accumulate and complete the speciation process. To identify the types of genetic differences that might initiate speciation, it is useful to study inter-population (intra-species) hybrids that exhibit reduced fitness. In Caenorhabditis briggsae, a close relative of the nematode C. elegans, such minor genetic incompatibilities have been identified. One incompatibility between the mitochondrial and nuclear genomes reduces the fitness of some hybrids. To understand the nuclear genetic architecture of this epistatic interaction, we constructed two sets of recombinant inbred lines by hybridizing two genetically diverse wild populations. In such lines, selection is able to eliminate deleterious combinations of alleles derived from the two parental populations. The genotypes of surviving hybrid lines thus reveal favorable allele combinations at loci experiencing selection. Our genotype data from the resulting lines are consistent with the interpretation that the X alleles participate in epistatic interactions with autosomes and the mitochondrial genome. We evaluate this possibility given predictions that mitochondria-X epistasis should be more prevalent than between mitochondria and autosomes. Our empirical identification of inter-genomic linkage disequilibrium supports the body of literature indicating that the accumulation of mito-nuclear genetic incompatibilities might initiate the speciation process through the generation of less-fit inter-population hybrids.

The complex underpinnings of genetic background effects

Genetic interactions between mutations and standing polymorphisms can cause mutations to show distinct phenotypic effects in different individuals. To characterize the genetic architecture of these so-called background effects, we genotype 1411 wild-type and mutant yeast cross progeny and measure their growth in 10 environments. Using these data, we map 1086 interactions between segregating loci and 7 different gene knockouts. Each knockout exhibits between 73 and 543 interactions, with 89% of all interactions involving higher-order epistasis between a knockout and multiple loci. Identified loci interact with as few as one knockout and as many as all seven knockouts. In mutants, loci interacting with fewer and more knockouts tend to show enhanced and reduced phenotypic effects, respectively. Cross–environment analysis reveals that most interactions between the knockouts and segregating loci also involve the environment. These results illustrate the complicated interactions between mutations, standing polymorphisms, and the environment that cause background effects.

Mutations often show distinct phenotypic effects across different genetic backgrounds. Here the authors describe the genetic basis of these so-called background effects using data on genotype and growth in 10 environments from 1411 segregants from a cross of two strains of budding yeast.

Epistasis confers resistance to Bt toxin Cry1Ac in the cotton bollworm

Evolution of resistance by insect pests reduces the benefits of extensively cultivated transgenic crops that produce insecticidal proteins from Bacillus thuringiensis (Bt). Previous work showed that resistance to Bt toxin Cry1Ac, which is produced by transgenic cotton, can be conferred by mutations disrupting a cadherin protein that binds this Bt toxin in the larval midgut. However, the potential for epistatic interactions between the cadherin gene and other genes has received little attention. Here, we report evidence of epistasis conferring resistance to Cry1Ac in the cotton bollworm, Helicoverpa armigera, one of the world's most devastating crop pests. Resistance to Cry1Ac in strain LF256 originated from a field-captured male and was autosomal, recessive, and 220-fold relative to susceptible strain SCD. We conducted complementation tests for allelism by crossing LF256 with a strain in which resistance to Cry1Ac is conferred by a recessive allele at the cadherin locus HaCad. The resulting F1 offspring were resistant, suggesting that resistance to Cry1Ac in LF256 is also conferred by resistance alleles at this locus. However, the HaCad amino acid sequence in LF256 lacked insertions and deletions, and did not differ consistently between LF256 and a susceptible strain. In addition, most of the cadherin alleles in LF256 were not derived from the field-captured male. Moreover, Cry1Ac resistance was not genetically linked with the HaCad locus in LF256. Furthermore, LF256 and the susceptible strain were similar in levels of HaCad transcript, cadherin protein, and binding of Cry1Ac to cadherin. Overall, the results imply that epistasis between HaCad and an unknown second locus in LF256 yielded the observed resistance in the F1 progeny from the complementation test. The observed epistasis has important implications for interpreting results of the F1 screen used widely to monitor and analyze resistance, as well as the potential to accelerate evolution of resistance.

Genetic background effects in quantitative genetics: gene-by-system interactions

Proper cell function depends on networks of proteins that interact physically and functionally to carry out physiological processes. Thus, it seems logical that the impact of sequence variation in one protein could be significantly influenced by genetic variants at other loci in a genome. Nonetheless, the importance of such genetic interactions, known as epistasis, in explaining phenotypic variation remains a matter of debate in genetics. Recent work from our lab revealed that genes implicated from an association study of toxin tolerance in Saccharomyces cerevisiae show extensive interactions with the genetic background: most implicated genes, regardless of allele, are important for toxin tolerance in only one of two tested strains. The prevalence of background effects in our study adds to other reports of widespread genetic-background interactions in model organisms. We suggest that these effects represent many-way interactions with myriad features of the cellular system that vary across classes of individuals. Such gene-by-system interactions may influence diverse traits and require new modeling approaches to accurately represent genotype-phenotype relationships across individuals.

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population

Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait.