Epistasis for quantitative traits in Drosophila

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

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.

Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies

Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.

The effects of the sex chromosomes on the inheritance of species-specific traits of the copulatory organ shape in Drosophila virilis and Drosophila lummei

The shape of the male genitalia in many taxa is the most rapidly evolving morphological structure, often driving reproductive isolation, and is therefore widely used in systematics as a key character to distinguish between sibling species. However, only a few studies have used the genital arch of the male copulatory organ as a model to study the genetic basis of species-specific differences in the Drosophila copulatory system. Moreover, almost nothing is known about the effects of the sex chromosomes on the shape of the male mating organ. In our study, we used a set of crosses between D. virilis and D. lummei and applied the methods of quantitative genetics to assess the variability of the shape of the male copulatory organ and the effects of the sex chromosomes and autosomes on its variance. Our results showed that the male genital shape depends on the species composition of the sex chromosomes and autosomes. Epistatic interactions of the sex chromosomes with autosomes and the species origin of the Y-chromosome in a male in interspecific crosses also influenced the expression of species-specific traits in the shape of the male copulatory system. Overall, the effects of sex chromosomes were comparable to the effects of autosomes despite the great differences in gene numbers between them. It may be reasonably considered that sexual selection for specific genes associated with the shape of the male mating organ prevents the demasculinization of the X chromosome.

The environmental toxicant ziram enhances neurotransmitter release and increases neuronal excitability via the EAG family of potassium channels

Environmental toxicants have the potential to contribute to the pathophysiology of multiple complex diseases, but the underlying mechanisms remain obscure. One such toxicant is the widely used fungicide ziram, a dithiocarbamate known to have neurotoxic effects and to increase the risk of Parkinson's disease. We have used Drosophila melanogaster as an unbiased discovery tool to identify novel molecular pathways by which ziram may disrupt neuronal function. Consistent with previous results in mammalian cells, we find that ziram increases the probability of synaptic vesicle release by dysregulation of the ubiquitin signaling system. In addition, we find that ziram increases neuronal excitability. Using a combination of live imaging and electrophysiology, we find that ziram increases excitability in both aminergic and glutamatergic neurons. This increased excitability is phenocopied and occluded by null mutant animals of the ether a-go-go (eag) potassium channel. A pharmacological inhibitor of the temperature sensitive hERG (human ether-a-go-go related gene) phenocopies the excitability effects of ziram but only at elevated temperatures. seizure (sei), a fly ortholog of hERG, is thus another candidate target of ziram. Taken together, the eag family of potassium channels emerges as a candidate for mediating some of the toxic effects of ziram. We propose that ziram may contribute to the risk of complex human diseases by blockade of human eag and sei orthologs, such as hERG.

Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives

Thought to be directly and uniquely dependent from genotypes, the ontogeny of individual phenotypes is much more complicated. Individual genetics, environmental exposures, and their interaction are the three main determinants of individual's phenotype. This picture has been further complicated a decade ago when the Lamarckian theory of acquired inheritance has been rekindled with the discovery of epigenetic inheritance, according to which acquired phenotypes can be transmitted through fertilization and affect phenotypes across generations. The results of Genome-Wide Association Studies have also highlighted a big degree of missing heritability in genetics and have provided hints that not only acquired phenotypes, but also individual's genotypes affect phenotypes intergenerationally through indirect genetic effects. Here, we review available examples of indirect genetic effects in mammals, what is known of the underlying molecular mechanisms and their potential impact for our understanding of missing heritability, phenotypic variation. and individual disease risk.

The Impact of Non-additive Effects on the Genetic Correlation Between Populations

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.

Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

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.

Polygenic Basis and Variable Genetic Architectures Contribute to the Complex Nature of Body Weight -A Genome-Wide Study in Four Chinese Indigenous Chicken Breeds

Body weight (BW) is one of the most important economic traits for animal production and breeding, and it has been studied extensively for its phenotype–genotype associations. While mapping studies have mostly aimed at finding as many loci as possible that contributed to the variation in BW, the role of other factors in its genetic architecture, including their frequencies in the population and their interactions, have been largely overlooked. To comprehensively characterized the genetic architecture of BW, we performed a genome-wide association study (GWAS) both at the single-marker and haplotype level on birds from four indigenous Chinese chicken breeds (Chahua, Silkie, Langshan, and Beard), rather than studying crosses between two founder lines. Additionally, samples from two more breeds (Red Junglefowl and Recessive White) were included to better reflect variable genetic characteristics across populations. Six loci were mapped in this study, revealing the polygenic basis underlying BW. Moreover, by further examining the frequencies of the significantly associated haplotypes in each subpopulation and their effect sizes, most of the loci were found to affect BW in the Beard chicken breed alone. Two loci in GGA9 and GGA27, however, had a common effect on BW across subpopulations, showing that different underlying genetic mechanisms contribute to the phenotypic variability. These findings, particularly the variable genetic architectures found in different loci, improve our understanding of the overall genetic contributions to the large variability in BW among Chinese indigenous chicken breeds. These findings thus will have important implications for future chicken breeding.

The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation

One of the primary goals in this era of precision medicine is to understand the biology of human diseases and their treatment, such that each individual patient receives the best possible treatment for their disease based on their genetic and environmental exposures. One way to work towards achieving this goal is to identify the environmental exposures and genetic variants that are relevant to each disease in question, as well as the complex interplay between genes and environment. Genome-wide association studies (GWAS) have allowed for a greater understanding of the genetic component of many complex traits. However, these genetic effects are largely small and thus, our ability to use these GWAS finding for precision medicine is limited. As more and more GWAS have been performed, rather than focusing only on common single nucleotide polymorphisms (SNPs) and additive genetic models, many researchers have begun to explore alternative heritable components of complex traits including rare variants, structural variants, epigenetics, and genetic interactions. While genetic interactions are a plausible reality that could explain some of the heritabliy that has not yet been identified, especially when one considers the identification of genetic interactions in model organisms as well as our understanding of biological complexity, still there are significant challenges and considerations in identifying these genetic interactions. Broadly, these can be summarized in three categories: abundance of methods, practical considerations, and biological interpretation. In this review, we will discuss these important elements in the search for genetic interactions along with some potential solutions. While genetic interactions are theoretically understood to be important for complex human disease, the body of evidence is still building to support this component of the underlying genetic architecture of complex human traits. Our hope is that more sophisticated modeling approaches and more robust computational techniques will enable the community to identify these important genetic interactions and improve our ability to implement precision medicine in the future.

Another Round of "Clue" to Uncover the Mystery of Complex Traits

A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.

From Peas to Disease: Modifier Genes, Network Resilience, and the Genetics of Health

Phenotypes are rarely consistent across genetic backgrounds and environments, but instead vary in many ways depending on allelic variants, unlinked genes, epigenetic factors, and environmental exposures. In the extreme, individuals carrying the same causal DNA sequence variant but on different backgrounds can be classified as having distinct conditions. Similarly, some individuals that carry disease alleles are nevertheless healthy despite affected family members in the same environment. These genetic background effects often result from the action of so-called "modifier genes" that modulate the phenotypic manifestation of target genes in an epistatic manner. While complicating the prospects for gene discovery and the feasibility of mechanistic studies, such effects are opportunities to gain a deeper understanding of gene interaction networks that provide organismal form and function as well as resilience to perturbation. Here, we review the principles of modifier genetics and assess progress in studies of modifier genes and their targets in both simple and complex traits. We propose that modifier effects emerge from gene interaction networks whose structure and function vary with genetic background and argue that these effects can be exploited as safe and effective ways to prevent, stabilize, and reverse disease and dysfunction.

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

BACKGROUND

The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG).

RESULTS

Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing.

CONCLUSIONS

These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

Bayesian reversible-jump for epistasis analysis in genomic studies

Background

The large amount of data used in genomic analysis has allowed geneticists to achieve some understanding of the genetic architecture of complex traits. Although the information gathered by molecular markers has permitted gains in predictive accuracy and gene discovery, epistatic effects have been ignored based on exhaustive searches requesting estimates of its effects on the whole genome. In this work, we propose the reversible-jump technique to estimate epistasis in the genome without drastically altering the model dimension. To this end, we used a real maize dataset based on 256 F_2:3 progenies plus a simulation data set based on 300 F₂ individuals. In the simulation scenario, six QTL presenting main effects (additive and dominance) were combined with seven other epistatic effects totaling 13 QTL controlling the trait.

Results

Our model explored 18,624 candidate epistases, but even in this vast space, only one spurious interaction was found. The three epistases selected by our model, named here as 18x26, 56x68 and 59x93, were very close to simulated ones (19x25, 54x72, 59x91 and 59x94). In the real dataset, we estimate 33,024 epistatic effects, and several minor epistatic combinations were found to explain a significant proportion of the genetic variance. The broad participation of epistasis in the real dataset may indicate the presence of pervasive epistasis acting on maize grain yield.

Conclusions

The power of selecting true epistasis in thousands of possible combinations suggests the attractiveness of our model to handle genomic data

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3342-6) contains supplementary material, which is available to authorized users.

Genetic and Genomic Response to Selection for Food Consumption in Drosophila melanogaster

Food consumption is an essential component of animal fitness; however, excessive food intake in humans increases risk for many diseases. The roles of neuroendocrine feedback loops, food sensing modalities, and physiological state in regulating food intake are well understood, but not the genetic basis underlying variation in food consumption. Here, we applied ten generations of artificial selection for high and low food consumption in replicate populations of Drosophila melanogaster. The phenotypic response to selection was highly asymmetric, with significant responses only for increased food consumption and minimal correlated responses in body mass and composition. We assessed the molecular correlates of selection responses by DNA and RNA sequencing of the selection lines. The high and low selection lines had variants with significantly divergent allele frequencies within or near 2081 genes and 3526 differentially expressed genes in one or both sexes. A total of 519 genes were both genetically divergent and differentially expressed between the divergent selection lines. We performed functional analyses of the effects of RNAi suppression of gene expression and induced mutations for 27 of these candidate genes that have human orthologs and the strongest statistical support, and confirmed that 25 (93 %) affected the mean and/or variance of food consumption.