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Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation


Conith, Andrew; Hope, Sylvie; Chhouk, Brian; Albertson, Craig (2021), Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation, Dryad, Dataset,


Phenotypic integration is an important metric that describes the degree of covariation among traits in a population, and is hypothesized to arise due to selection for shared functional processes. Our ability to identify the genetic and/or developmental underpinnings of integration is marred by temporally overlapping cell-, tissue-, and structure-level processes that serve to continually ‘overwrite’ the structure of covariation among traits through ontogeny. Here we examine whether traits that are integrated at the phenotypic level, also exhibit a shared genetic basis (e.g., pleiotropy). We micro-CT scanned two hard tissue traits, and two soft tissue traits (mandible, pectoral girdle, atrium, and ventricle respectively) from an F5 hybrid population of Lake Malawi cichlids, and used geometric morphometrics to extract 3D shape information from each trait. Given the large degree of asymmetric variation that may reflect developmental instability, we separated symmetric- from asymmetric-components of shape variation. We then performed quantitative trait loci (QTL) analysis to determine the degree of genetic overlap between shapes. While we found ubiquitous associations among traits at the phenotypic level, except for a handful of notable exceptions, our QTL analysis revealed few overlapping genetic regions. Taken together, this indicates developmental interactions can play a large role in determining the degree of phenotypic integration among traits, and likely obfuscate the genotype to phenotype map, limiting our ability to gain a comprehensive picture of the genetic contributors responsible for phenotypic divergence.

Usage Notes

Files with the .tps extension contain landmark coordinate data and can be viewed in any text editor (e.g., TextEdit, Notepad, NotePad++, BBEdit, etc.). All analyses conducted with the .tps files were performed using the R environment for statistical computing. Files with the .R extension are scripts that can be run natively within R, and demonstrate exactly how we obtained the results presented in our manuscript.


National Institutes of Health, Award: R01DE026446