Evaluating the power and limitations of genome-wide association studies in Caenorhabditis elegans

Glial expression of a steroidogenic enzyme underlies natural variation in hitchhiking behavior

Phoresy is an interspecies interaction that facilitates spatial dispersal by attaching to a more mobile species. Hitchhiking species have evolved specific traits for physical contact and successful phoresy, but the regulatory mechanisms involved in such traits and their evolution are largely unexplored. The nematode Caenorhabditis elegans displays a hitchhiking behavior known as nictation during its stress-induced developmental stage. Dauer-specific nictation behavior has an important role in natural C. elegans populations, which experience boom-and-bust population dynamics. In this study, we investigated the nictation behavior of 137 wild C. elegans strains sampled throughout the world. We identified species-wide natural variation in nictation and performed a genome-wide association mapping. We show that the variants in the promoter of nta-1, encoding a putative steroidogenic enzyme, underlie differences in nictation. This difference is due to the changes in nta-1 expression in glial cells, which implies that glial steroid metabolism regulates phoretic behavior. Population genetic analysis and geographic distribution patterns suggest that balancing selection maintained two nta-1 haplotypes that existed in ancestral C. elegans populations. Our findings contribute to further understanding of the molecular mechanism of species interaction and the maintenance of genetic diversity within natural populations.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Natural variation in the Caenorhabditis elegans egg-laying circuit modulates an intergenerational fitness trade-off

Evolutionary transitions from egg laying (oviparity) to live birth (viviparity) are common across various taxa. Many species also exhibit genetic variation in egg-laying mode or display an intermediate mode with laid eggs containing embryos at various stages of development. Understanding the mechanistic basis and fitness consequences of such variation remains experimentally challenging. Here, we report highly variable intra-uterine egg retention across 316 Caenorhabditis elegans wild strains, some exhibiting strong retention, followed by internal hatching. We identify multiple evolutionary origins of such phenotypic extremes and pinpoint underlying candidate loci. Behavioral analysis and genetic manipulation indicates that this variation arises from genetic differences in the neuromodulatory architecture of the egg-laying circuitry. We provide experimental evidence that while strong egg retention can decrease maternal fitness due to in utero hatching, it may enhance offspring protection and confer a competitive advantage. Therefore, natural variation in C. elegans egg-laying behaviour can alter an apparent trade-off between different fitness components across generations. Our findings highlight underappreciated diversity in C. elegans egg-laying behavior and shed light on its fitness consequences. This behavioral variation offers a promising model to elucidate the molecular changes in a simple neural circuit underlying evolutionary shifts between alternative egg-laying modes in invertebrates.

CaeNDR, the Caenorhabditis Natural Diversity Resource

Studies of model organisms have provided important insights into how natural genetic differences shape trait variation. These discoveries are driven by the growing availability of genomes and the expansive experimental toolkits afforded to researchers using these species. For example, Caenorhabditis elegans is increasingly being used to identify and measure the effects of natural genetic variants on traits using quantitative genetics. Since 2016, the C. elegans Natural Diversity Resource (CeNDR) has facilitated many of these studies by providing an archive of wild strains, genome-wide sequence and variant data for each strain, and a genome-wide association (GWA) mapping portal for the C. elegans community. Here, we present an updated platform, the Caenorhabditis Natural Diversity Resource (CaeNDR), that enables quantitative genetics and genomics studies across the three Caenorhabditis species: C. elegans, C. briggsae and C. tropicalis. The CaeNDR platform hosts several databases that are continually updated by the addition of new strains, whole-genome sequence data and annotated variants. Additionally, CaeNDR provides new interactive tools to explore natural variation and enable GWA mappings. All CaeNDR data and tools are accessible through a freely available web portal located at caendr.org.

Integrating External Controls by Regression Calibration for Genome-Wide Association Study

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Natural genetic variation in the pheromone production of C. elegans

From bacterial quorum sensing to human language, communication is essential for social interactions. Nematodes produce and sense pheromones to communicate among individuals and respond to environmental changes. These signals are encoded by different types and mixtures of ascarosides, whose modular structures further enhance the diversity of this nematode pheromone language. Interspecific and intraspecific differences in this ascaroside pheromone language have been described previously, but the genetic basis and molecular mechanisms underlying the variation remain largely unknown. Here, we analyzed natural variation in the production of 44 ascarosides across 95 wild Caenorhabditis elegans strains using high-performance liquid chromatography coupled to high-resolution mass spectrometry. We discovered wild strains defective in the production of specific subsets of ascarosides (e.g., the aggregation pheromone icas#9) or short- and medium-chain ascarosides, as well as inversely correlated patterns between the production of two major classes of ascarosides. We investigated genetic variants that are significantly associated with the natural differences in the composition of the pheromone bouquet, including rare genetic variants in key enzymes participating in ascaroside biosynthesis, such as the peroxisomal 3-ketoacyl-CoA thiolase, daf-22, and the carboxylesterase cest-3. Genome-wide association mappings revealed genomic loci harboring common variants that affect ascaroside profiles. Our study yields a valuable dataset for investigating the genetic mechanisms underlying the evolution of chemical communication.

Repurposing degradation pathways for modular metabolite biosynthesis in nematodes

Recent studies have revealed that Caenorhabditis elegans and other nematodes repurpose products from biochemical degradation pathways for the combinatorial assembly of complex modular structures that serve diverse signaling functions. Building blocks from neurotransmitter, amino acid, nucleoside and fatty acid metabolism are attached to scaffolds based on the dideoxyhexose ascarylose or glucose, resulting in hundreds of modular ascarosides and glucosides. Genome-wide association studies have identified carboxylesterases as the key enzymes mediating modular assembly, enabling rapid compound discovery via untargeted metabolomics and suggesting that modular metabolite biosynthesis originates from the 'hijacking' of conserved detoxification mechanisms. Modular metabolites thus represent a distinct biosynthetic strategy for generating structural and functional diversity in nematodes, complementing the primarily polyketide synthase- and nonribosomal peptide synthetase-derived universe of microbial natural products. Although many aspects of modular metabolite biosynthesis and function remain to be elucidated, their identification demonstrates how phenotype-driven compound discovery, untargeted metabolomics and genomic approaches can synergize to facilitate the annotation of metabolic dark matter.

C. elegans toxicant responses vary among genetically diverse individuals

The genetic variability of toxicant responses among indisviduals in humans and mammalian models requires practically untenable sample sizes to create comprehensive chemical hazard risk evaluations. To address this need, tractable model systems enable reproducible and efficient experimental workflows to collect high-replication measurements of exposure cohorts. Caenorhabditis elegans is a premier toxicology model that has revolutionized our understanding of cellular responses to environmental pollutants and boasts robust genomic resources and high levels of genetic variation across the species. In this study, we performed dose-response analysis across 23 environmental toxicants using eight C. elegans strains representative of species-wide genetic diversity. We observed substantial variation in EC10 estimates and slope parameter estimates of dose-response curves of different strains, demonstrating that genetic background is a significant driver of differential toxicant susceptibility. We also showed that, across all toxicants, at least one C. elegans strain exhibited a significantly different EC10 or slope estimate compared to the reference strain, N2 (PD1074), indicating that population-wide differences among strains are necessary to understand responses to toxicants. Moreover, we quantified the heritability of responses (phenotypic variance attributable to genetic differences between individuals) to each toxicant exposure and observed a correlation between the exposure closest to the species-agnostic EC10 estimate and the exposure that exhibited the most heritable response. At least 20% of the variance in susceptibility to at least one exposure level of each compound was explained by genetic differences among the eight C. elegans strains. Taken together, these results provide robust evidence that heritable genetic variation explains differential susceptibility across an array of environmental pollutants and that genetically diverse C. elegans strains should be deployed to aid high-throughput toxicological screening efforts.

The impact of species-wide gene expression variation on Caenorhabditis elegans complex traits

Phenotypic variation in organism-level traits has been studied in Caenorhabditis elegans wild strains, but the impacts of differences in gene expression and the underlying regulatory mechanisms are largely unknown. Here, we use natural variation in gene expression to connect genetic variants to differences in organismal-level traits, including drug and toxicant responses. We perform transcriptomic analyses on 207 genetically distinct C. elegans wild strains to study natural regulatory variation of gene expression. Using this massive dataset, we perform genome-wide association mappings to investigate the genetic basis underlying gene expression variation and reveal complex genetic architectures. We find a large collection of hotspots enriched for expression quantitative trait loci across the genome. We further use mediation analysis to understand how gene expression variation could underlie organism-level phenotypic variation for a variety of complex traits. These results reveal the natural diversity in gene expression and possible regulatory mechanisms in this keystone model organism, highlighting the promise of using gene expression variation to understand how phenotypic diversity is generated.