Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

Abstract

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.

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

“…Nine of 15 intervals, each ß95 kb on average, had significant effects on the phenotypes. The focal region of the X chromosome is not particularly noteworthy with regard to trait variation as a whole in these strains Evans et al 2018). Its SNP density is similar to the X chromosome arms as a whole, and the X chromosome arms are considerably less SNP-dense and indel-dense than the autosome arms (Thompson et al 2015).…”

“…; Evans et al. ). Its SNP density is similar to the X chromosome arms as a whole, and the X chromosome arms are considerably less SNP‐dense and indel‐dense than the autosome arms (Thompson et al.…”

“…These features allow for relatively quick construction of recombinant panels and high-throughput assays in 96-well plates . Although many traits in C. elegans have a simple genetic basis (e.g., Palopoli et al 2008;Ghosh et al 2012;Noble et al 2015;Zdraljevic et al 2017), complex traits in C. elegans often have polygenic or otherwise complex architectures (Green et al 2013;Glater et al 2014;Andersen et al 2015;Greene et al 2016;Evans et al 2018;Noble et al 2017).…”

A single locus underlies variation inCaenorhabditis eleganschemotherapeutic responses

“…The nematode Caenorhabditis elegans provides a tractable metazoan model to identify and study pleiotropic QTL (Paaby and Rockman 2013) . A large panel of recombinant inbred advanced intercross lines (RIAILs) derived from two divergent strains, N2 and CB4856 (Rockman and Kruglyak 2009;Andersen et al 2015) , has been leveraged in several linkage mapping analyses (McGrath et al 2009;Lee et al 2017;Singh et al 2016;Zdraljevic et al 2019;Brady et al 2019;Zdraljevic et al 2017;Evans et al 2018;Andersen et al 2014;Viñuela et al 2010;Doroszuk et al 2009;Snoek et al 2014;Rodriguez et al 2012;Glater, Rockman, and Bargmann 2014;Rockman, Skrovanek, and Kruglyak 2010;Zamanian et al 2018a;Bendesky et al , 2012Schmid et al 2015;Balla et al 2015;Kammenga et al 2007;Gutteling, Doroszuk, et al 2007;Li et al 2006;Reddy et al 2009;Seidel et al 2011;Seidel, Rockman, and Kruglyak 2008) . Quantitative genetic analysis using these panels and a high-throughput phenotyping assay has facilitated the discovery of numerous QTL (Zamanian et al 2018b) , several quantitative trait genes (QTG) (Brady et al 2019) and quantitative trait nucleotides (QTN) (Zdraljevic et al 2019(Zdraljevic et al , 2017 underlying fitness-related traits in the nematode.…”

“…Quantitative genetic analysis using these panels and a high-throughput phenotyping assay has facilitated the discovery of numerous QTL (Zamanian et al 2018b) , several quantitative trait genes (QTG) (Brady et al 2019) and quantitative trait nucleotides (QTN) (Zdraljevic et al 2019(Zdraljevic et al , 2017 underlying fitness-related traits in the nematode. Additionally, three pleiotropic genomic regions were recently found to influence responses to a diverse group of toxins (Evans et al 2018) . However, overlapping genomic regions might not represent true pleiotropy but could demonstrate the co-existence of tightly linked loci (Paaby and Rockman 2013) .…”