Strategies to deal with genetic analyzer‐specific DNA methylation measurements

Abstract Targeted bisulfite sequencing using single‐base extension (SBE) can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers ( ELOVL2 , FHL2 , KLF14 , MIR29B2CHG/C1orf132 , and TRIM59 ). New equations were generated for semen DNA age prediction markers ( TTC7B , LOC401324/cg12837463 , and LOC729960/NOX4 ) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer‐independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer‐specific models showed the highest accuracy. However, genetic analyzer‐independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.