A probabilistic method for identifying sex-linked genes using RNA-Seq-derived genotyping data

The genetic basis of sex determination remains unknown for the vast majority of organisms with separate sexes. A key question is whether a species has sex chromosomes (SC). SC presence indicates genetic sex determination, and their sequencing may help identifying the sex-determining genes and understanding the molecular mechanisms of sex determination. Identifying SC, especially homomorphic SC, can be difficult. Sequencing SC is also very challenging, in particular the repeat-rich non-recombining regions.

A novel approach for identifying sex-linked genes and SC consisting of using RNA-seq to genotype male and female individuals and study sex-linkage has recently been proposed. This approach entails a modest sequencing effort and does not require prior genomic or genetic resources, and is thus particularly suited to study non-model organisms. Applying this approach to many organisms is, however, difficult due to the lack of an appropriate statistically-grounded pipeline to analyse the data.

Here researchers from the Laboratory of Biometry and Evolutionary Biology UMR 5558 propose a model-based method to infer sex-linkage using a maximum likelihood framework and genotyping data from a full-sib family, which can be obtained for most organisms that can be grown in the lab and for economically important animals/plants. This method works on any type of SC (XY, ZW, UV) and has been embedded in a pipeline that includes a genotyper specifically developed for RNA-seq data. Validation on empirical and simulated data indicates that our pipeline is particularly relevant to study SC of recent or intermediate age but can return useful information in old systems as well.


Schematic steps of our pipeline (A) and examples of family genotypes for the three types of segregation types in an X/Y system (B).

Availability – the pipeline is available as a Galaxy workflow. See user guide and source codes at http://lbbe.univ-lyon1.fr/-SEXDETector-.html

Muyle A, Käfer J, Zemp N, Mousset S, Picard F, Marais GAB. (2015) A probabilistic method for identifying sex-linked genes using RNA-seq-derived genotyping data. bioRxiv [Epub ahead of print]. [article]

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