Research on the Detection of Trace DNA Using SNP Chip Data Genealogy Inference Technology

Forensic SNP genealogy can infer distant kinship based on SNP chip data. In order to clarify the ability of genealogical inference technology based on SNP chip data to detect trace DNA in forensic feld, in this study Illumina CGA microarray was used to detect the samples. The samples were preliminarily evaluated based on the DNA input, detection rate, sample heterozygosity and other indicators, then the IBS and IBD algorithms were used for forensic SNP genealogy. The classification consistency was compared with the reference samples, and the factors affecting the prediction accuracy were analyzed, the detection ability of the technology system for different input amounts of DNA were determined, and fnally the accurate SNP typing data in the low-quality data were screened based on the signal ratio and other indicators, so as to improve the use value of the trace DNA detection data. The results show that when the DNA input was higher than 1.95 ng, the IBS algorithm had an average confdence interval accuracy of 94.33% and 91.96% for IBD, and when the input was 488-781 pg, the IBS algorithm had an accuracy of 23.11% for the average confdence interval for 1-5 kinship, while the IBD algorithm reached 30.13%. When the DNA input is less than 488 pg, both the IBS and IBD algorithms are unable to make genealogical inferences. Allele insertion is a major factor affecting the accuracy of pedigree inference, and when the homozygous error reaches 22.5%, the sample cannot be used for pedigree inference. By screening the signal ratio, the heterozygous SNP loci with a signal ratio greater than 1.5 can be removed, which can improve the genealogical inference ability of low-input samples. Based on the real family data of Illumina CGA chips, this study analyzed the infuence of sample input on the accuracy of genealogical inference, and optimized the SNP data by signal ratio, so as to improve the application value of low-input samples in genealogical inference.