Accurate detections of the heterozygous SNPs with rice genomic data and prediction of de novo spontaneous mutation rate.

Background: The use of Illumina sequencing technologies has enabled the identification and removal of mutations in various plant species. However, the Illumina sequencing method requires a considerable amount of data to ensure its integrity and quality due to the enormous number of false positives. This study aimed to explore an effective genomic data analysis for the detection of heterozygous variant (HV) in rice varieties.

Results: We compared the accuracy of four combinations of mapping tools and variant calling pipelines and selected BWA-MEM2 with GATK4.3 HaplotypeCaller. To detect heterozygous de novo polymorphisms such as HVs in the three different rice varieties (Nipponbare, Kitaake, and Hinohikari), we adopted the following cost-saving procedures; secondary references were created in Nipponbare and Kitaake, and generation-based comparison was performed in Hinohikari. The similar HVs were estimated by the three varieties to range from 2.55814 × 10^-8 to 4.41860 × 10^-8, with an average of 3.10278 × 10^-8 per nucleotide in a single rice plant, a rate consistent with observations in other organisms. Of 107 HVs identified in all eight plant samples, nine were found to be non-synonymous, resulting in an average of one non-synonymous HV per plant in a single generation.

Conclusions: We have developed a methodology for the detection of true positive HVs within Illumina sequencing techniques. This system removed false positive HVs, allowing for the estimation of true positive HVs and, consequently, the estimation of the mutation rate. The study outlines a clear, step-by-step procedure that can be employed to detect true HVs in different organisms.