Capturing the natural genetic complexity of a simple neural circuit

forthcoming

The egg-laying circuit of the nematode Caenorhabditis elegans is one of the best-understood model neural circuits. Yet, how genetic changes in the circuit translate into natural behavioral variation within the species remains limited. Characterizing standing genetic variation—an inherent feature of most animal populations and essential for evolution—is a fundamental aspect of understanding any model system. I will study the genetic diversity underlying C. elegans egg-laying behavior by focusing on an ecologically important phenotype: matricidal hatching (MH). MH occurs when egg laying is so limited that larvae are forced to hatch inside the parental body. While usually a plastic response to poor environments, MH is constitutive (CMH) in multiple divergent lineages: this represents a chance to study how different molecular pathways converge on the same phenotype. I will identify the molecular causes of CMH in different natural strains and I will further study how CMH can emerge through novel genetic interactions in hybrid strains derived from crosses between non-CMH parents. Traditionally, genome-wide association (GWA) and linkage mapping in C. elegans are complicated by the species’ strong linkage disequilibrium and the labor-intensive construction of mapping panels, respectively. To overcome this, I will employ state-of-the-art methods and novel resources to identify Quantitative Trait Loci (QTL) and their underlying molecular variants that lead to CMH. This project will allow me to learn the handling of a new model organism, broaden my molecular biology and bioinformatics skills, and grow as a scientist by expanding my experience in genotype-phenotype analysis from single proteins to genome-wide studies of a complex phenotype. As the first study to examine the effects of genetic diversity on the nematode egg-laying circuit, my results will deepen our knowledge of this model circuit and deepen our general understanding of within-species behavioral variation.