Molecular Ecology ResourcesGenomicsPeer-reviewed journal article
SLRfinder: a method to detect candidate sex-linked regions with linkage disequilibrium clustering
Abstract
Despite their critical roles in genetic sex determination, sex chromosomes remain unknown in many non-model organisms. In contrast to conserved sex chromosomes in mammals and birds, studies of fish, amphibians, and reptiles have found highly labile sex chromosomes with newly evolved sex-linked regions (SLRs). These labile sex chromosomes are important for understanding early sex chromosome evolution but are difficult to identify due to the lack of Y/W degeneration and SLRs limited to small genomic regions. Here we present SLRfinder, a method to identify candidate SLRs and labile sex chromosomes using linkage disequilibrium (LD) clustering, patterns of heterozygosity, and genetic divergence. SLRfinder does not rely on specific sequencing methods or reference genomes and does not require phenotypic sexes which may be unknown from population sampling, although sex information can be incorporated to provide additional inference on candidate SLRs. We tested SLRfinder using various published datasets and compared it to SATC, a method that identifies sex chromosomes based on the depth of coverage and also does not require phenotypic sex. Results show that SATC works better on conserved sex chromosomes (e.g., in African leopards), whereas SLRfinder outperforms SATC in analyzing labile sex chromosomes (e.g., in nine-spined sticklebacks and chum salmon). Since SLRfinder primarily relies on LD clusters, it is expected to be most sensitive to the SLRs harboring structural variants (e.g., inversion) due to strongly reduced recombination rates in heterozygotes. SLRfinder provides a novel and complementary approach for identifying SLRs and uncovering additional sex chromosome diversity in nature.
- assay: GWAS summary statistics
- research focus: benchmarking
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 2
- FWCI
- 0.53
- Field percentile
- top 39%
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