Cornell University

Department of Neurobiology and Behavior

Mudd hall, 215 Tower Rd, Ithaca, NY


Quantitative Genetics of Song and Preference Behavior

Quantitative genetics is the study of the genetic variation in continuously varying traits. It provides a powerful statistical framework that allows us to ask questions about the genetic basis of a variety of complex traits, such as biomass and height of plants and animals, communication behavior, and human intelligence.


I use quantitative genetics for two main purposes: to predict (co)evolutionary dynamics of song and preference behavior and, in combination with molecular techinques, to identify the location, number, and effect of the genetic regions that contribute to natural variation in these behaviors (quantitative trait locus - QTL - mapping). The song of the male cricket varies in different, correlated traits and it is important to take into account the interdependence of different measurements of the song when thinking about song divergence. For example, due to genetic or physiological correlations, males that sing at a faster rhythm, might also sing at higher tone frequencies.


Although female preference might intuitively sound like a discrete rather than continuous variable, preference behavior (of any kind, not limited to sexual or acoustic preferences) is continuous: an individual will show variation in the response strength to different values of a quantitative trait. These varying preference can be described by a preference function, which is typically bell-shaped curve with lower preference for extreme (low and high) values and high preference close to the population mean. Combining preferences across traits creates a preference landscape, which can be usefull to predict the strength and direction of change in a sexually selected trait.


For any quantitative trait, variation in natural populations or laboratory crosses can be coupled to variation in genetic markers, for example those obtained from large DNA sequencing projects. In association analyses or QTL mapping experiments, these combined data are extremely powerful in localizing the regions in the genome where variation in the trait of interest is genetically anchored and can even be used to obtain a set of candidate genes.


During my PhD I worked on several topics, including a phenotypic approach to unraveling the genetic architecture of song and preference behaviour using a closely related species pair of Gryllus crickets, a decomposition of multivariate song variation to compare evolutionary trajectories among a variety of cricket species, and a quantitative trait locus (QTL) study (in preparation). Read more about my current project with the Hawaiian sword-tail cricket genus Laupala here. One of my major interests and a field that us likely to develop strongly in the near future is the integration of quantitive genetics research with large scale population genetic data sets generated using next-generation sequencing.

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