Noble, L.M., Chelo, I., Guzella, T., Afonso, B., Riccardi, D.D., Ammerman, P., Dayarian, A., Carvalho, S., Crist, A., Pino-Querido, A., Shraiman, B., Rockman, M.V. & Teotónio, H. (2017) Polygenicity and epistasis underlie fitness-proximal traits in the caenorhabditis elegans multiparental experimental evolution (CeMEE) panel.Genetics, Online early, . DOI:10.1534/genetics.117.300406 (IF2016 4,556; Q1 Genetics & Heredity) NON-cE3c affiliated
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 (QTL) 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 we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits does 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 (r 2 < 10%), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms.