More than the sum of its parts: a complex epistatic network underlies natural variation in thermal preference behavior in Caenorhabditis elegans

Life history in Caenorhabditis elegans: from molecular genetics to evolutionary ecology

Life history is defined by traits that reflect key components of fitness, especially those relating to reproduction and survival. Research in life history seeks to unravel the relationships among these traits and understand how life history strategies evolve to maximize fitness. As such, life history research integrates the study of the genetic and developmental mechanisms underlying trait determination with the evolutionary and ecological context of Darwinian fitness. As a leading model organism for molecular and developmental genetics, Caenorhabditis elegans is unmatched in the characterization of life history-related processes, including developmental timing and plasticity, reproductive behaviors, sex determination, stress tolerance, and aging. Building on recent studies of natural populations and ecology, the combination of C. elegans' historical research strengths with new insights into trait variation now positions it as a uniquely valuable model for life history research. In this review, we summarize the contributions of C. elegans and related species to life history and its evolution. We begin by reviewing the key characteristics of C. elegans life history, with an emphasis on its distinctive reproductive strategies and notable life cycle plasticity. Next, we explore intraspecific variation in life history traits and its underlying genetic architecture. Finally, we provide an overview of how C. elegans has guided research on major life history transitions both within the genus Caenorhabditis and across the broader phylum Nematoda. While C. elegans is relatively new to life history research, significant progress has been made by leveraging its distinctive biological traits, establishing it as a highly cross-disciplinary system for life history studies.

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.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

Identification of a spontaneously arising variant affecting thermotaxis behavior in a recombinant inbred Caenorhabditis elegans line

Analyses of the contributions of genetic variants in wild strains to phenotypic differences have led to a more complete description of the pathways underlying cellular functions. Causal loci are typically identified via interbreeding of strains with distinct phenotypes in order to establish recombinant inbred lines (RILs). Since the generation of RILs requires growth for multiple generations, their genomes may contain not only different combinations of parental alleles but also genetic changes that arose de novo during the establishment of these lines. Here, we report that in the course of generating RILs between Caenorhabditis elegans strains that exhibit distinct thermotaxis behavioral phenotypes, we identified spontaneously arising variants in the ttx-1 locus. ttx-1 encodes the terminal selector factor for the AFD thermosensory neurons, and loss-of-function mutations in ttx-1 abolish thermotaxis behaviors. The identified genetic changes in ttx-1 in the RIL are predicted to decrease ttx-1 function in part via specifically affecting a subset of AFD-expressed ttx-1 isoforms. Introduction of the relevant missense mutation in the laboratory C. elegans strain via gene editing recapitulates the thermotaxis behavioral defects of the RIL. Our results suggest that spontaneously occurring genomic changes in RILs may complicate identification of loci contributing to phenotypic variation, but that these mutations may nevertheless lead to the identification of important causal molecules and mechanisms.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

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.

Variation in mutational (co)variances

Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode Caenorhabditis elegans that accumulated mutations in a nearly neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of C. elegans genotypes allows selection on new phenotypic dimensions of locomotion behavior.

Population bottleneck has only marginal effect on fitness evolution and its repeatability in dioecious Caenorhabditis elegans

The predictability of evolution is expected to depend on the relative contribution of deterministic and stochastic processes. This ratio is modulated by effective population size. Smaller effective populations harbor less genetic diversity and stochastic processes are generally expected to play a larger role, leading to less repeatable evolutionary trajectories. Empirical insight into the relationship between effective population size and repeatability is limited and focused mostly on asexual organisms. Here, we tested whether fitness evolution was less repeatable after a population bottleneck in obligately outcrossing populations of Caenorhabditis elegans. Replicated populations founded by 500, 50, or five individuals (no/moderate/strong bottleneck) were exposed to a novel environment with a different bacterial prey. As a proxy for fitness, population size was measured after one week of growth before and after 15 weeks of evolution. Surprisingly, we found no significant differences among treatments in their fitness evolution. Even though the strong bottleneck reduced the relative contribution of selection to fitness variation, this did not translate to a significant reduction in the repeatability of fitness evolution. Thus, although a bottleneck reduced the contribution of deterministic processes, we conclude that the predictability of evolution may not universally depend on effective population size, especially in sexual organisms.

Reconstruction of the High Stigma Exsertion Rate Trait in Rice by Pyramiding Multiple QTLs

Asian cultivated rice is a self-pollinating crop, which has already lost some traits of natural outcrossing in the process of domestication. However, male sterility lines (MSLs) need to have a strong outcrossing ability to produce hybrid seeds by outcrossing with restorer lines of male parents in hybrid rice seed production. Stigma exsertion rate (SER) is a trait related to outcrossing ability. Reconstruction of the high-SER trait is essential in the MSL breeding of rice. In previous studies, we detected eighteen quantitative trait loci (QTLs) for SER from Oryza sativa, Oryza glaberrima, and Oryza glumaepatula using single-segment substitution lines (SSSLs) in the genetic background of Huajingxian 74 (HJX74). In this study, eleven of the QTLs were used to develop pyramiding lines. A total of 29 pyramiding lines with 2-6 QTLs were developed from 10 SSSLs carrying QTLs for SER in the HJX74 genetic background. The results showed that the SER increased with increasing QTLs in the pyramiding lines. The SER in the lines with 5-6 QTLs was as high as wild rice with strong outcrossing ability. The epistasis of additive by additive interaction between QTLs in the pyramiding lines was less-than-additive or negative effect. One QTL, qSER3a-sat, showed minor-effect epistasis and increased higher SER than other QTLs in pyramiding lines. The detection of epistasis of QTLs on SER uncovered the genetic architecture of SER, which provides a basis for using these QTLs to improve SER levels in MSL breeding. The reconstruction of the high-SER trait will help to develop the MSLs with strong outcrossing ability in rice.

Analytical and numerical comparisons of two methods of estimation of additive × additive × additive interaction of QTL effects

This paper presents the analytical and numerical comparison of two methods of estimation of additive × additive × additive (aaa) interaction of QTL effects. The first method takes into account only the plant phenotype, while in the second we also included genotypic information from molecular marker observation. Analysis was made on 150 doubled haploid (DH) lines of barley derived from cross Steptoe × Morex and 145 DH lines from Harrington × TR306 cross. In total, 153 sets of observation was analyzed. In most cases, aaa interactions were found with an exert effect on QTL. Results also show that with molecular marker observations, obtained estimators had smaller absolute values than phenotypic estimators.

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.

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.

Natural variation in the sequestosome-related gene, sqst-5, underlies zinc homeostasis in Caenorhabditis elegans

Zinc is an essential trace element that acts as a co-factor for many enzymes and transcription factors required for cellular growth and development. Altering intracellular zinc levels can produce dramatic effects ranging from cell proliferation to cell death. To avoid such fates, cells have evolved mechanisms to handle both an excess and a deficiency of zinc. Zinc homeostasis is largely maintained via zinc transporters, permeable channels, and other zinc-binding proteins. Variation in these proteins might affect their ability to interact with zinc, leading to either increased sensitivity or resistance to natural zinc fluctuations in the environment. We can leverage the power of the roundworm nematode Caenorhabditis elegans as a tractable metazoan model for quantitative genetics to identify genes that could underlie variation in responses to zinc. We found that the laboratory-adapted strain (N2) is resistant and a natural isolate from Hawaii (CB4856) is sensitive to micromolar amounts of exogenous zinc supplementation. Using a panel of recombinant inbred lines, we identified two large-effect quantitative trait loci (QTL) on the left arm of chromosome III and the center of chromosome V that are associated with zinc responses. We validated and refined both QTL using near-isogenic lines (NILs) and identified a naturally occurring deletion in sqst-5, a sequestosome-related gene, that is associated with resistance to high exogenous zinc. We found that this deletion is relatively common across strains within the species and that variation in sqst-5 is associated with zinc resistance. Our results offer a possible mechanism for how organisms can respond to naturally high levels of zinc in the environment and how zinc homeostasis varies among individuals.

Dissecting the eQTL Micro-Architecture in Caenorhabditis elegans

The study of expression quantitative trait loci (eQTL) using natural variation in inbred populations has yielded detailed information about the transcriptional regulation of complex traits. Studies on eQTL using recombinant inbred lines (RILs) led to insights on cis and trans regulatory loci of transcript abundance. However, determining the underlying causal polymorphic genes or variants is difficult, but ultimately essential for the understanding of regulatory networks of complex traits. This requires insight into whether associated loci are single eQTL or a combination of closely linked eQTL, and how this QTL micro-architecture depends on the environment. We addressed these questions by testing for independent replication of previously mapped eQTL in Caenorhabditis elegans using new data from introgression lines (ILs). Both populations indicate that the overall heritability of gene expression, number, and position of eQTL differed among environments. Across environments we were able to replicate 70% of the cis- and 40% of the trans-eQTL using the ILs. Testing eight different simulation models, we suggest that additive effects explain up to 60-93% of RIL/IL heritability for all three environments. Closely linked eQTL explained up to 40% of RIL/IL heritability in the control environment whereas only 7% in the heat-stress and recovery environments. In conclusion, we show that reproducibility of eQTL was higher for cis vs. trans eQTL and that the environment affects the eQTL micro-architecture.

The Ancestral Caenorhabditis elegans Cuticle Suppresses rol-1

Genetic background commonly modifies the effects of mutations. We discovered that worms mutant for the canonical rol-1 gene, identified by Brenner in 1974, do not roll in the genetic background of the wild strain CB4856. Using linkage mapping, association analysis and gene editing, we determined that N2 carries an insertion in the collagen gene col-182 that acts as a recessive enhancer of rol-1 rolling. From population and comparative genomics, we infer the insertion is derived in N2 and related laboratory lines, likely arising during the domestication of Caenorhabditis elegans, and breaking a conserved protein. The ancestral version of col-182 also modifies the phenotypes of four other classical cuticle mutant alleles, and the effects of natural genetic variation on worm shape and locomotion. These results underscore the importance of genetic background and the serendipity of Brenner's choice of strain.

Dissecting Adaptive Traits with Nested Association Mapping: Genetic Architecture of Inflorescence Morphology in Sorghum

In the cereal crop sorghum (Sorghum bicolor) inflorescence morphology variation underlies yield variation and confers adaptation across precipitation gradients, but its genetic basis is poorly understood. We characterized the genetic architecture of sorghum inflorescence morphology using a global nested association mapping (NAM) population (2200 recombinant inbred lines) and 198,000 phenotypic observations from multi-environment trials for four inflorescence morphology traits (upper branch length, lower branch length, rachis length, and rachis diameter). Trait correlations suggest that lower and upper branch length are under somewhat independent control, while lower branch length and rachis diameter are highly pleiotropic. Joint linkage and genome-wide association mapping revealed an oligogenic architecture with 1–22 QTL per trait, each explaining 0.1–5.0% of variation across the entire NAM population. There is a significant enrichment (2.twofold) of QTL colocalizing with grass inflorescence gene homologs, notably with orthologs of maize Ramosa2 and rice Aberrant Panicle Organization1 and TAWAWA1. Still, many QTL do not colocalize with inflorescence gene homologs. In global georeferenced germplasm, allelic variation at the major inflorescence QTL is geographically patterned but only weakly associated with the gradient of annual precipitation. Comparison of NAM with diversity panel association suggests that naive association models may capture some true associations not identified by mixed linear models. Overall, the findings suggest that global inflorescence diversity in sorghum is largely controlled by oligogenic, epistatic, and pleiotropic variation in ancestral regulatory networks. The findings also provide a basis for genomics-enabled breeding of locally-adapted inflorescence morphology.

How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases

Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.

Evolution of Epistatic Networks and the Genetic Basis of Innate Behaviors

Instinctive behaviors are genetically programmed behaviors that occur independent of experience. How genetic programs that give rise to the manifestation of such behaviors evolve remains an unresolved question. I propose that evolution of species-specific innate behaviors is accomplished through progressive modifications of pre-existing genetic networks composed of allelic variants. I hypothesize that changes in frequencies of one or more constituent allelic variants within the network leads to changes in gene network connectivity and the emergence of a reorganized network that can support the emergence of a novel behavioral phenotype and becomes stabilized when key allelic variants are driven to fixation.

WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans

Quantitative genetics provides the tools for linking polymorphic loci to trait variation. Linkage analysis of gene expression is an established and widely applied method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL detection facilitates the identification and understanding of the underlying molecular components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here, we present WormQTL2, a database and platform for comparative investigations and meta-analyses of published (e)QTL data sets in the model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 conditions as well as over 1000 traits from 32 studies and allows experimental results to be compared, reused and extended upon to guide further experiments and conduct systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs that could be linked to variation in other traits, detect gene-by-environment interactions by comparing eQTLs under different conditions, or find correlations between QTL profiles of classical traits and gene expression. WormQTL2 makes data on natural variation in C. elegans and the identified QTLs interactively accessible, allowing studies beyond the original publications. Database URL: www.bioinformatics.nl/WormQTL2/.

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation in C. elegans

Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population-based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near-isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome-wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL-like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large-effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.

Partial Selfing Can Reduce Genetic Loads While Maintaining Diversity During Experimental Evolution

Partial selfing, whereby self- and cross- fertilization occur in populations at intermediate frequencies, is generally thought to be evolutionarily unstable. Yet, it is found in natural populations. This could be explained if populations with partial selfing are able to reduce genetic loads and the possibility for inbreeding depression while keeping genetic diversity that may be important for future adaptation. To address this hypothesis, we compare the experimental evolution of Caenorhabditis elegans populations under partial selfing, exclusive selfing or predominant outcrossing, while they adapt to osmotically challenging conditions. We find that the ancestral genetic load, as measured by the risk of extinction upon inbreeding by selfing, is maintained as long as outcrossing is the main reproductive mode, but becomes reduced otherwise. Analysis of genome-wide single-nucleotide polymorphisms (SNPs) during experimental evolution and among the inbred lines that survived enforced inbreeding indicates that populations with predominant outcrossing or partial selfing maintained more genetic diversity than expected with neutrality or purifying selection. We discuss the conditions under which this could be explained by the presence of recessive deleterious alleles and/or overdominant loci. Taken together, our observations suggest that populations evolving under partial selfing can gain some of the benefits of eliminating unlinked deleterious recessive alleles and also the benefits of maintaining genetic diversity at partially dominant or overdominant loci that become associated due to variance of inbreeding levels.

Genetically Distinct Behavioral Modules Underlie Natural Variation in Thermal Performance Curves

Thermal reaction norms pervade organismal traits as stereotyped responses to temperature, a fundamental environmental input into sensory and physiological systems. Locomotory behavior represents an especially plastic read-out of animal response, with its dynamic dependence on environmental stimuli presenting a challenge for analysis and for understanding the genomic architecture of heritable variation. Here we characterize behavioral reaction norms as thermal performance curves for the nematode Caenorhabditis briggsae, using a collection of 23 wild isolate genotypes and 153 recombinant inbred lines to quantify the extent of genetic and plastic variation in locomotory behavior to temperature changes. By reducing the dimensionality of the multivariate phenotypic response with a function-valued trait framework, we identified genetically distinct behavioral modules that contribute to the heritable variation in the emergent overall behavioral thermal performance curve. Quantitative trait locus mapping isolated regions on Chromosome II associated with locomotory activity at benign temperatures and Chromosome V loci related to distinct aspects of sensitivity to high temperatures, with each quantitative trait locus explaining up to 28% of trait variation. These findings highlight how behavioral responses to environmental inputs as thermal reaction norms can evolve through independent changes to genetically distinct modular components of such complex phenotypes.

A multi-parent recombinant inbred line population of C. elegans allows identification of novel QTLs for complex life history traits

BACKGROUND

The nematode Caenorhabditis elegans has been extensively used to explore the relationships between complex traits, genotypes, and environments. Complex traits can vary across different genotypes of a species, and the genetic regulators of trait variation can be mapped on the genome using quantitative trait locus (QTL) analysis of recombinant inbred lines (RILs) derived from genetically and phenotypically divergent parents. Most RILs have been derived from crossing two parents from globally distant locations. However, the genetic diversity between local C. elegans populations can be as diverse as between global populations and could thus provide means of identifying genetic variation associated with complex traits relevant on a broader scale.

RESULTS

To investigate the effect of local genetic variation on heritable traits, we developed a new RIL population derived from 4 parental wild isolates collected from 2 closely located sites in France: Orsay and Santeuil. We crossed these 4 genetically diverse parental isolates to generate a population of 200 multi-parental RILs and used RNA-seq to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the 4 genotypes with an average spacing of 11 kb, doubling the mapping resolution relative to currently available RIL panels for many loci. The SNPs were used to construct a genetic map to facilitate QTL analysis. We measured life history traits such as lifespan, stress resistance, developmental speed, and population growth in different environments, and found substantial variation for most traits. We detected multiple QTLs for most traits, including novel QTLs not found in previous QTL analysis, including those for lifespan and pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping.

CONCLUSION

Taken together, we show that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates facilitates the detection of QTLs. The use of multi-parent RIL populations can further enhance our understanding of local adaptation and life history trade-offs.

Polygenicity and Epistasis Underlie Fitness-Proximal Traits in the Caenorhabditis elegans Multiparental Experimental Evolution (CeMEE) Panel

Understanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity, and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty can be problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here, we report an advanced recombinant inbred line (RIL) quantitative trait locus mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs at present, was created by hybridization of 16 wild isolates, experimental evolution for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of single-nucleotide polymorphisms known to segregate in natural populations, and complements existing C. elegans mapping resources by providing fine resolution and high nucleotide diversity across > 95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad-sense heritability in the CeMEE. While simulations show that we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits do not feature such alleles. We instead find that a significant fraction of trait variance, approaching 40% for fertility, can be explained by sign epistasis with main effects below the detection limit. In congruence, phenotype prediction from genomic similarity, while generally poor ([Formula: see text]), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms.

Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.

Forward and reverse genetics approaches to uncover metabolic aging pathways in Caenorhabditis elegans

The biological mechanisms of aging have been studied in depth and prominent findings in this field promote the development of new therapies for age-associated disorders. Various model organisms are used for research on aging; among these, the nematode Caenorhabditis elegans has been widely used and has provided valuable knowledge in determining the regulatory mechanisms driving the aging process. Many genes involved in lifespan regulation are associated with metabolic pathways and are influenced by genetic and environmental factors. In line with this, C. elegans provides a promising platform to study such gene by environment interactions, in either a reverse or forward genetics approach. In this review, we discuss longevity mechanisms related to metabolic networks that have been discovered in C. elegans. We also highlight the use of wild populations to study the complex genetic basis of natural variation for quantitative traits that mediate longevity.

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.

Contribution of trans regulatory eQTL to cryptic genetic variation in C. elegans

Background

Cryptic genetic variation (CGV) is the hidden genetic variation that can be unlocked by perturbing normal conditions. CGV can drive the emergence of novel complex phenotypes through changes in gene expression. Although our theoretical understanding of CGV has thoroughly increased over the past decade, insight into polymorphic gene expression regulation underlying CGV is scarce. Here we investigated the transcriptional architecture of CGV in response to rapid temperature changes in the nematode Caenorhabditis elegans. We analyzed regulatory variation in gene expression (and mapped eQTL) across the course of a heat stress and recovery response in a recombinant inbred population.

Results

We measured gene expression over three temperature treatments: i) control, ii) heat stress, and iii) recovery from heat stress. Compared to control, exposure to heat stress affected the transcription of 3305 genes, whereas 942 were affected in recovering animals. These affected genes were mainly involved in metabolism and reproduction. The gene expression pattern in recovering animals resembled both the control and the heat-stress treatment. We mapped eQTL using the genetic variation of the recombinant inbred population and detected 2626 genes with an eQTL in the heat-stress treatment, 1797 in the control, and 1880 in the recovery. The cis-eQTL were highly shared across treatments. A considerable fraction of the trans-eQTL (40–57%) mapped to 19 treatment specific trans-bands. In contrast to cis-eQTL, trans-eQTL were highly environment specific and thus cryptic. Approximately 67% of the trans-eQTL were only induced in a single treatment, with heat-stress showing the most unique trans-eQTL.

Conclusions

These results illustrate the highly dynamic pattern of CGV across three different environmental conditions that can be evoked by a stress response over a relatively short time-span (2 h) and that CGV is mainly determined by response related trans regulatory eQTL.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3899-8) contains supplementary material, which is available to authorized users.

Experimental Evolution with Caenorhabditis Nematodes

The hermaphroditic nematode Caenorhabditis elegans has been one of the primary model systems in biology since the 1970s, but only within the last two decades has this nematode also become a useful model for experimental evolution. Here, we outline the goals and major foci of experimental evolution with C. elegans and related species, such as C. briggsae and C. remanei, by discussing the principles of experimental design, and highlighting the strengths and limitations of Caenorhabditis as model systems. We then review three exemplars of Caenorhabditis experimental evolution studies, underlining representative evolution experiments that have addressed the: (1) maintenance of genetic variation; (2) role of natural selection during transitions from outcrossing to selfing, as well as the maintenance of mixed breeding modes during evolution; and (3) evolution of phenotypic plasticity and its role in adaptation to variable environments, including host-pathogen coevolution. We conclude by suggesting some future directions for which experimental evolution with Caenorhabditis would be particularly informative.

Genetic variation in neurodegenerative diseases and its accessibility in the model organism Caenorhabditis elegans

BACKGROUND

Neurodegenerative diseases (NGDs) such as Alzheimer's and Parkinson's are debilitating and largely untreatable conditions strongly linked to age. The clinical, neuropathological, and genetic components of NGDs indicate that neurodegeneration is a complex trait determined by multiple genes and by the environment.

MAIN BODY

The symptoms of NGDs differ among individuals due to their genetic background, and this variation affects the onset and progression of NGD and NGD-like states. Such genetic variation affects the molecular and cellular processes underlying NGDs, leading to differential clinical phenotypes. So far, we have a limited understanding of the mechanisms of individual background variation. Here, we consider how variation between genetic backgrounds affects the mechanisms of aging and proteostasis in NGD phenotypes. We discuss how the nematode Caenorhabditis elegans can be used to identify the role of variation between genetic backgrounds. Additionally, we review advances in C. elegans methods that can facilitate the identification of NGD regulators and/or networks.

CONCLUSION

Genetic variation both in disease genes and in regulatory factors that modulate onset and progression of NGDs are incompletely understood. The nematode C. elegans represents a valuable system in which to address such questions.

Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated Caenorhabditis elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute and is instead dependent upon multiple factors such as the age and genetic background of an organism. In order to understand the mechanistic basis of these changes, we characterized heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused in part by a mutation in a component of the NURF chromatin remodeling complex, that regulates reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animal’s reproductive lifespan. Here, we use a previously published macroscale model of the egg-laying rate in C. elegans to show that time-dependent effect-size is explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate key predictions of this model with controlled mating experiments and quantification of oogenesis and sperm use. Incorporation of this model into QTL mapping allows us to identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of the unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.

Complex traits are influenced by the individual effects of genetic variants in addition to the interactions of the variants with the environment, age, and each other. While complex genetic architectures are ubiquitous in natural traits, little is known about the causal mechanisms that create their complex genetic architectures. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. We use computational modeling to demonstrate how age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how antagonistic pleiotropy can emerge from multicellular feedback systems.

REVIEW: Epistasis and dominance in the emergence of catalytic function as exemplified by the evolution of plant terpene synthases

Epistasis, the interaction between mutations and the genetic background, is a pervasive force in evolution that is difficult to predict yet derives from a simple principle - biological systems are interconnected. Therefore, one effect may be intimately linked to another, hence interdependent. Untangling epistatic interactions between and within genes is a vibrant area of research. Deriving a mechanistic understanding of epistasis is a major challenge. Particularly, elucidating how epistasis can attenuate the effects of otherwise dominant mutations that control phenotypes. Using the emergence of terpene cyclization in specialized metabolism as an excellent example, this review describes the process of discovery and interpretation of dominance and epistasis in relation to current efforts. Specifically, we outline experimental approaches to isolating epistatic networks of mutations in protein structure, formally quantifying epistatic interactions, then building biochemical models with chemical mechanisms in efforts to achieve an understanding of the physical basis for epistasis. From these models we describe informed conjectures about past evolutionary events that underlie the emergence, divergence and specialization of terpene synthases to illustrate key principles of the constraining forces of epistasis in enzyme function.

Natural Genetic Variation Differentially Affects the Proteome and Transcriptome in Caenorhabditis elegans

Natural genetic variation is the raw material of evolution and influences disease development and progression. An important question is how this genetic variation translates into variation in protein abundance. To analyze the effects of the genetic background on gene and protein expression in the nematode Caenorhabditis elegans, we quantitatively compared the two genetically highly divergent wild-type strains N2 and CB4856. Gene expression was analyzed by microarray assays, and proteins were quantified using stable isotope labeling by amino acids in cell culture. Among all transcribed genes, we found 1,532 genes to be differentially transcribed between the two wild types. Of the total 3,238 quantified proteins, 129 proteins were significantly differentially expressed between N2 and CB4856. The differentially expressed proteins were enriched for genes that function in insulin-signaling and stress-response pathways, underlining strong divergence of these pathways in nematodes. The protein abundance of the two wild-type strains correlates more strongly than protein abundance versus transcript abundance within each wild type. Our findings indicate that in C. elegans only a fraction of the changes in protein abundance can be explained by the changes in mRNA abundance. These findings corroborate with the observations made across species.

Armour GE, Mackay TFC, Anholt RRH. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In this study, we implemented a powerful screen to quantify the extent of epistasis as well as identify candidate interacting variants using 203 inbred wild-derived lines with sequenced genomes of the Drosophila melanogaster Genetic Reference Panel (DGRP). We crossed the DGRP lines to P[GT1]-element insertion mutants in Sema-5c and neuralized (neur), two neurodevelopmental loci which affect olfactory behavior, and to their coisogenic wild-type control. We observed significant variation in olfactory responses to benzaldehyde among F1 genotypes and for the DGRP line by mutant genotype interactions for both loci, showing extensive nonadditive genetic variation. We performed genome-wide association analyses to identify the candidate modifier loci. None of these polymorphisms were in or near the focal genes; therefore, epistasis is the cause of the nonadditive genetic variance. Candidate genes could be placed in interaction networks. Several candidate modifiers are associated with neural development. Analyses of mutants of candidate epistatic partners with neur (merry-go-round (mgr), prospero (pros), CG10098, Alhambra (Alh) and CG12535) and Sema-5c (CG42540 and bruchpilot (brp)) showed aberrant olfactory responses compared with coisogenic controls. Thus, integrating genome-wide analyses of natural variants with mutations at defined genomic locations in a common coisogenic background can unmask specific epistatic modifiers of behavioral phenotypes.

Experience Modulates the Reproductive Response to Heat Stress in C. elegans via Multiple Physiological Processes

Natural environments are considerably more variable than laboratory settings and often involve transient exposure to stressful conditions. To fully understand how organisms have evolved to respond to any given stress, prior experience must therefore be considered. We investigated the effects of individual and ancestral experience on C. elegans reproduction. We documented ways in which cultivation at 15°C or 25°C affects developmental time, lifetime fecundity, and reproductive performance after severe heat stress that exceeds the fertile range of the organism but is compatible with survival and future fecundity. We found that experience modulates multiple aspects of reproductive physiology, including the male and female germ lines and the interaction between them. These responses vary in their environmental sensitivity, suggesting the existence of complex mechanisms for coping with unpredictable and stressful environments.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

This study uses a large number of crosses between a common lab strain and vineyard-isolated strain of yeast, and estimates the phenotypic variance for various quantitative traits. Using this data set, the authors show additive quantitative trait loci (QTL) and QTL–QTL interactions to be on average 43% and 9%, respectively.

Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests

Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.

The laboratory domestication of Caenorhabditis elegans

Model organisms are of great importance to our understanding of basic biology and to making advances in biomedical research. However, the influence of laboratory cultivation on these organisms is underappreciated, and especially how that environment can affect research outcomes. Recent experiments led to insights into how the widely used laboratory reference strain of the nematode Caenorhabditis elegans compares with natural strains. Here we describe potential selective pressures that led to the fixation of laboratory-derived alleles for the genes npr-1, glb-5, and nath-10. These alleles influence a large number of traits, resulting in behaviors that affect experimental interpretations. Furthermore, strong phenotypic effects caused by these laboratory-derived alleles hinder the discovery of natural alleles. We highlight strategies to reduce the influence of laboratory-derived alleles and to harness the full power of C. elegans.

A Powerful New Quantitative Genetics Platform, Combining Caenorhabditis elegans High-Throughput Fitness Assays with a Large Collection of Recombinant Strains

The genetic variants underlying complex traits are often elusive even in powerful model organisms such as Caenorhabditis elegans with controlled genetic backgrounds and environmental conditions. Two major contributing factors are: (1) the lack of statistical power from measuring the phenotypes of small numbers of individuals, and (2) the use of phenotyping platforms that do not scale to hundreds of individuals and are prone to noisy measurements. Here, we generated a new resource of 359 recombinant inbred strains that augments the existing C. elegans N2xCB4856 recombinant inbred advanced intercross line population. This new strain collection removes variation in the neuropeptide receptor gene npr-1, known to have large physiological and behavioral effects on C. elegans and mitigates the hybrid strain incompatibility caused by zeel-1 and peel-1, allowing for identification of quantitative trait loci that otherwise would have been masked by those effects. Additionally, we optimized highly scalable and accurate high-throughput assays of fecundity and body size using the COPAS BIOSORT large particle nematode sorter. Using these assays, we identified quantitative trait loci involved in fecundity and growth under normal growth conditions and after exposure to the herbicide paraquat, including independent genetic loci that regulate different stages of larval growth. Our results offer a powerful platform for the discovery of the genetic variants that control differences in responses to drugs, other aqueous compounds, bacterial foods, and pathogenic stresses.

A future of the model organism model

Changes in technology are fundamentally reframing our concept of what constitutes a model organism. Nevertheless, research advances in the more traditional model organisms have enabled fresh and exciting opportunities for young scientists to establish new careers and offer the hope of comprehensive understanding of fundamental processes in life. New advances in translational research can be expected to heighten the importance of basic research in model organisms and expand opportunities. However, researchers must take special care and implement new resources to enable the newest members of the community to engage fully with the remarkable legacy of information in these fields.

Higher-order genetic interactions and their contribution to complex traits

The contribution of genetic interactions involving three or more loci to complex traits is poorly understood. These higher-order genetic interactions (HGIs) are difficult to detect in genetic mapping studies, therefore, few examples of them have been described. However, the lack of data on HGIs should not be misconstrued as proof that this class of genetic effect is unimportant. To the contrary, evidence from model organisms suggests that HGIs frequently influence genetic studies and contribute to many complex traits. Here, we review the growing literature on HGIs and discuss the future of research on this topic.

Cryptic genetic variation: evolution's hidden substrate

Cryptic genetic variation (CGV) is invisible under normal conditions, but it can fuel evolution when circumstances change. In theory, CGV can represent a massive cache of adaptive potential or a pool of deleterious alleles that are in need of constant suppression. CGV emerges from both neutral and selective processes, and it may inform about how human populations respond to change. CGV facilitates adaptation in experimental settings, but does it have an important role in the real world? Here, we review the empirical support for widespread CGV in natural populations, including its potential role in emerging human diseases and the growing evidence of its contribution to evolution.

Worm variation made accessible: Take your shopping cart to store, link, and investigate!

In Caenorhabditis elegans, the recent advances in high-throughput quantitative analyses of natural genetic and phenotypic variation have led to a wealth of data on genotype phenotype relations. This data has resulted in the discovery of genes with major allelic effects and insights in the effect of natural genetic variation on a whole range of complex traits as well as how this variation is distributed across the genome. Regardless of the advances presented in specific studies, the majority of the data generated in these studies had yet to be made easily accessible, allowing for meta-analysis. Not only data in figures or tables but meta-data should be accessible for further investigation and comparison between studies. A platform was created where all the data, phenotypic measurements, genotypes, and mappings can be stored, compared, and new linkages within and between published studies can be discovered. WormQTL focuses on quantitative genetics in Caenorhabditis and other nematode species, whereas WormQTL^HD quantitatively links gene expression quantitative trait loci (eQTL) in C. elegans to gene–disease associations in humans.

A variant in the neuropeptide receptor npr-1 is a major determinant of Caenorhabditis elegans growth and physiology

The mechanistic basis for how genetic variants cause differences in phenotypic traits is often elusive. We identified a quantitative trait locus in Caenorhabditis elegans that affects three seemingly unrelated phenotypic traits: lifetime fecundity, adult body size, and susceptibility to the human pathogen Staphyloccus aureus. We found a QTL for all three traits arises from variation in the neuropeptide receptor gene npr-1. Moreover, we found that variation in npr-1 is also responsible for differences in 247 gene expression traits. Variation in npr-1 is known to determine whether animals disperse throughout a bacterial lawn or aggregate at the edges of the lawn. We found that the allele that leads to aggregation is associated with reduced growth and reproductive output. The altered gene expression pattern caused by this allele suggests that the aggregation behavior might cause a weak starvation state, which is known to reduce growth rate and fecundity. Importantly, we show that variation in npr-1 causes each of these phenotypic differences through behavioral avoidance of ambient oxygen concentrations. These results suggest that variation in npr-1 has broad pleiotropic effects mediated by altered exposure to bacterial food.

Using the nematode roundworm Caenorhabditis elegans, we identified differences in lifetime fecundity, adult body size, and susceptibility to the human pathogen Staphyloccus aureus between the laboratory strain (N2) from Bristol, England and a wild strain (CB4856) from Hawaii, USA. Using linkage mapping and other genetic tests, we found a QTL for all three traits arises from variation in the neuropeptide receptor gene npr-1. Moreover, we found that variation in npr-1 is also responsive for differences in 247 gene expression traits. Variation in npr-1 is known to determine whether animals disperse throughout a bacterial lawn or aggregate at the edges of the lawn. We found that the allele that leads to aggregation is associated with reduced growth and reproductive output likely caused by a weak chronic starvation state. These results suggest that variation in npr-1 has broad effects on the phenotype of an organism mediated by altered exposure to bacterial food.

Multigenic natural variation underlies Caenorhabditis elegans olfactory preference for the bacterial pathogen Serratia marcescens

The nematode Caenorhabditis elegans can use olfaction to discriminate among different kinds of bacteria, its major food source. We asked how natural genetic variation contributes to choice behavior, focusing on differences in olfactory preference behavior between two wild-type C. elegans strains. The laboratory strain N2 strongly prefers the odor of Serratia marcescens, a soil bacterium that is pathogenic to C. elegans, to the odor of Escherichia coli, a commonly used laboratory food source. The divergent Hawaiian strain CB4856 has a weaker attraction to Serratia than the N2 strain, and this behavioral difference has a complex genetic basis. At least three quantitative trait loci (QTLs) from the CB4856 Hawaii strain (HW) with large effect sizes lead to reduced Serratia preference when introgressed into an N2 genetic background. These loci interact and have epistatic interactions with at least two antagonistic QTLs from HW that increase Serratia preference. The complex genetic architecture of this C. elegans trait is reminiscent of the architecture of mammalian metabolic and behavioral traits.

Identity-by-descent genomic selection using selective and sparse genotyping

BACKGROUND

Genomic selection methods require dense and widespread genotyping data, posing a particular challenge if both sexes are subject to intense selection (e.g., aquaculture species). This study focuses on alternative low-cost genomic selection methods (IBD-GS) that use selective genotyping with sparse marker panels to estimate identity-by-descent relationships through linkage analysis. Our aim was to evaluate the potential of these methods in selection programs for continuous traits measured on sibs of selection candidates in a typical aquaculture breeding population.

METHODS

Phenotypic and genomic data were generated by stochastic simulation, assuming low to moderate heritabilities (0.10 to 0.30) for a Gaussian trait measured on sibs of the selection candidates in a typical aquaculture breeding population that consisted of 100 families (100 training animals and 20 selection candidates per family). Low-density marker genotype data (~ 40 markers per Morgan) were used to trace genomic identity-by-descent relationships. Genotyping was restricted to selection candidates from 30 phenotypically top-ranking families and varying fractions of their phenotypically extreme training sibs. All phenotypes were included in the genetic analyses. Classical pedigree-based and IBD-GS models were compared based on realized genetic gain over one generation of selection.

RESULTS

Genetic gain increased substantially (13 to 32%) with IBD-GS compared to classical selection and was greatest with higher heritability. Most of the extra gain from IBD-GS was obtained already by genotyping the 5% phenotypically most extreme sibs within the pre-selected families. Additional genotyping further increased genetic gains, but these were small when going from genotyping 20% of the extremes to all phenotyped sibs. The success of IBD-GS with sparse and selective genotyping can be explained by the fact that within-family haplotype blocks are accurately traced even with low-marker densities and that most of the within-family variance for normally distributed traits is captured by a small proportion of the phenotypically extreme sibs.

CONCLUSIONS

IBD-GS was substantially more effective than classical selection, even when based on very few markers and combined with selective genotyping of small fractions of the population. The study shows that low-cost GS programs can be successful by combining sparse and selective genotyping with pedigree and linkage information.

Epistasis and quantitative traits: using model organisms to study gene-gene interactions

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.