How often have X- and autosomal-STRs mutations equivocal parental origin been assigned?

Short tandem repeat markers (STRs) are widely applied in population, evolutionary, and forensic genetics, due to extensive polymorphism in the number of repetitive motifs. The primary mutational mechanism leading to changes in the length of STRs is thought to be polymerase template slippage. Mutation rates in STRs and corresponding parental assignment are usually assessed through the number of Mendelian incompatibilities observed in one-generational, parent(s)-child, pedigrees, and paternal mutations have been assumed to be preponderant over maternal ones. Notwithstanding, diploid and haplodiploid modes of genetic transmission may not allow for the unequivocal assigning of the mutation to the correct parental origin (either paternal or maternal), especially when genotyping methodologies of fragment length determination are employed. In this work, the frequency under which a mutation might be assigned to the wrong parental origin or be interpreted as having an ambiguous origin is analyzed for both diploid and haplodiploid modes of genetic transmission. Genotypic configurations were generated with Python™ programming language, considering parents-child trios for autosomal transmission, and parents-daughter trios for the X chromosomal one. One single-, one two- or one three-step mutation was simulated in each familial constellation, and the resulting genotypic configuration was analyzed regarding the parental assignment of the mutation. When considering autosomal transmission, the meiosis suffering mutation was randomly selected. Contrarily, differential analyses were performed for paternal and maternal mutations for X-chromosomal transmission. In this work, we show that the biases in the rates between paternal and maternal mutations differ for autosomal and X-chromosomal modes of transmission. In the differential analysis performed for the X-chromosomal STRs, it is possible to ascertain that the maternal and paternal meioses are subject to different biases, the latter being better estimated than the first. This work shows that simulated data, along with reliable and properly communicated real one, may be crucial for the correct modeling of biological processes, such as the mutation in STRs.