QPCR‐based molecular sexing by copy number variation in rRNA genes and its utility for sex identification in soft‐shell turtles. Issue 9 (25th July 2014) - Literman, Robert; Badenhorst, Daleen; Valenzuela, Nicole; Bunce, Michael
Summary Sex diagnosis is important in ecology, evolution, conservation biology, medicine, and food production. However, sex diagnosis is difficult in species without conspicuous sexual dimorphism or at life stages before such differences develop. This problem is exacerbated when the diagnostic trait is a continuous (non‐discrete) variable to which general analytical methods are not commonly applied. Here we demonstrate the use of copy‐number variation between males and females of the nucleolar organizing region (NOR) in the genome of Apalone spinifera softshell turtles, which we quantify by real‐time PCR. We analyze these continuous data using mixture models that can be applied either in discriminant analysis when a subset of individuals of known sex is used as a training set, or in clustering procedures when all individuals are of unknown sex. Using individuals of known sex, the discriminant analysis exhibited 100% accurate classification rate for both the training set and the test set. Classification rates were also 100% when using the clustering procedure to identify groups and classify individuals in the absence of sex information. Standard curves using only male DNA provided better discrimination than using mixed‐sex DNA during qPCR. NOR copy number is an effective sex diagnostic for A. spinifera turtles. Our sexing approach using qPCR of 18S genes should prove useful for other taxa that also possess dimorphic NORs, as is known in some vertebrates and insects. While the 18S copy numbers in our dataset exhibited a non‐overlapping binomial distribution, this may not always be the case in future studies of A. spinifera or for other taxa. Importantly however, our sex‐typing approach using mixture models provides an attractive alternative under overlapping distributions of these and of other continuous data such as hormone levels, gene expression levels, shape or behavior. We present an example using overlapping distributions of hormone levels in Chelydra serpentina turtles, to demonstrate the broader utility of mixture models for sex‐typing, and obtain a high correct classification of 90%.