Abstract

At present, public databases house an extensive repository of transcriptome data, with the volume continuing to grow at an accelerated pace. Utilizing these data effectively is a shared interest within the scientific community. In this study, we introduced a novel strategy that harnesses SNPs and InDels identified from transcriptome data, combined with sample metadata from databases, to effectively screen for molecular markers correlated with traits. We utilized 228 transcriptome datasets of Eriocheir sinensis from the NCBI database and employed the Genome Analysis Toolkit software to identify 96 388 SNPs and 20 645 InDels. Employing the genome-wide association study analysis, in conjunction with the gender information from databases, we identified 3456 sex-biased SNPs and 639 sex-biased InDels. The KOG and KEGG annotations of the sex-biased SNPs and InDels revealed that these genes were primarily involved in the metabolic processes of E. sinensis. Combined with SnpEff annotation and PCR experimental validation, a highly sex-biased SNP located in the Kelch domain containing 4 (Klhdc4) gene, CHR67-6415071, was found to alter the splicing sites of Klhdc4, generating two splice variants, Klhdc4_a and Klhdc4_b. Additionally, Klhdc4 exhibited robust expression across the ovaries, testes, and accessory glands. The sex-biased SNPs and InDels identified in this study are conducive to the development of unisexual cultivation methods for E. sinensis, and the alternative splicing event caused by the sex-biased SNP in Klhdc4 may serve as a potential mechanism for sex regulation in E. sinensis. The analysis strategy employed in this study represents a new direction for the rational exploitation and utilization of transcriptome data in public databases.

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