Identification of the genetic background of laboratory rats through amplicon-based next-generation sequencing for single-nucleotide polymorphism genotyping.

Abstract

Background: Laboratory rats, as model animals, have been extensively used in the fields of life science and medicine. It is crucial to routinely monitor the genetic background of laboratory rats. The conventional approach relies on gel electrophoresis and capillary electrophoresis (CE) technologies. However, the experimental and data analysis procedures for both of these methods are time consuming and costly.

Results: We established a single-nucleotide polymorphism (SNP) typing scheme using multiplex polymerase chain reaction (PCR) and next-generation sequencing (NGS) to address the genetic background ambiguity in laboratory rats. This methodology involved three rounds of PCR and two rounds of magnetic bead selection to improve the quality of the sequencing data. We simultaneously analysed 100 laboratory rats (including rats of 5 inbred strains and 2 in-house closed colonies), and the sequencing depth varied from an average of 108.25 to 5189.89, with sample uniformity ranging from 82.5 to 97.5%. A total of 98.9% of the amplicons were successfully genotyped (≥ 30 reads). Genetic background analysis revealed that all 38 experimental rats from the 5 inbred strains were successfully identified (without a heterozygous allele). For the 2 in-house closed colonies, the average heterozygosity (0.162 and 0.169) deviated from the typical range of 0.5-0.7, indicating a departure from the ideal heterozygosity level. Additionally, we employed multiplex PCR-CE to validate the NGS-based method, which yielded consistent results for all the rat strains. These results demonstrated that this approach significantly improves efficiency, saves time, reduces costs and ensures accuracy.

Conclusion: By utilizing NGS technology, our developed method leverages SNP genotyping for genetic background identification in laboratory rats, demonstrating advantages in terms of labour efficiency and cost-effectiveness, thereby rendering it well suited for projects involving extensive sample cohorts.