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Data from: Sex matters in massive parallel sequencing: Evidence for biases in genetic parameter estimation and investigation of sex determination systems

Citation

Benestan, Laura et al. (2017), Data from: Sex matters in massive parallel sequencing: Evidence for biases in genetic parameter estimation and investigation of sex determination systems, Dryad, Dataset, https://doi.org/10.5061/dryad.7dk42

Abstract

Using massively parallel sequencing data from two species with different life history traits, American lobster (Homarus americanus) and Arctic Char (Salvelinus alpinus), we highlight how an unbalanced sex ratio in the samples and a few sex-linked markers may lead to false interpretations of population structure and thus to potentially erroneous management recommendations. Here, multivariate analyses revealed two genetic clusters separating samples by sex instead of by expected spatial variation; inshore and offshore locations in lobster, or east and west locations in Arctic Char. To further investigate this, we created several subsamples artificially varying the sex ratio in the inshore/offshore and east/west groups, and then demonstrated that significant genetic differentiation could be observed despite panmixia in lobster, and that Fst values were overestimated in Arctic Char. This pattern was due to 12 and 94 sex-linked markers driving differentiation for lobster and Arctic Char, respectively. Removing sex-linked markers led to non-significant genetic structure in lobster and a more accurate estimation of Fst in Arctic Char. The locations of these markers and putative identities of genes containing, or nearby the markers were determined using available transcriptomic and genomic data, and this provided new information related to sex determination in both species. Given that only 9.6% of all marine/diadromous population genomic studies to date have reported sex information, we urge researchers to collect and consider individual sex information. Sex information is therefore relevant for avoiding unexpected biases due to sex-linked markers as well as for improving our knowledge of sex determination systems in non-model species.

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