RESUMO
BACKGROUND: When the mother's DNA profile is not available for paternity testing, there is a smaller probability that a locus will exclude an alleged father. This study aims to evaluate the risk of potential false paternity inclusions in motherless cases. STUDY DESIGN AND METHODS: More than 20 000 duos were generated by removing the maternal genotypes from exclusion trios. After recalculating paternity in these duos, any found inclusions would be false. RESULTS: The use of an appropriate number of loci, mutation model, and mutation rates to analyze motherless paternity cases was robust against false inclusions. A single potential false inclusion was observed in a case wherein kinship plays a role. This result highlights the importance of testing the mother when available and of obtaining information on family circumstances for the proper handling of cases involving related individuals. CONCLUSION: The guidelines we used here were sufficient to avoid false inclusions in a data set of more than 20 000 motherless cases.
Assuntos
Loci Gênicos , Repetições de Microssatélites , Alelos , Simulação por Computador , Feminino , Genótipo , Humanos , Masculino , Mães , Mutação , Paternidade , ProbabilidadeRESUMO
We have reported the allele frequencies of 15 STR loci, including the original 13 CODIS core loci, in over 100,000 Brazilian individuals. A new CODIS core loci has been proposed, but the recently established Brazilian Integrated Network of DNA Databases made a decision in 2010 to postpone the implementation of this new set of loci due to the lack of allele frequency data for the Brazilian population. We aimed to report allele frequencies of 20 loci, estimated from 96,400 Brazilian individuals undergoing paternity testing during 2011-2013. The percentage of missing data was less than 0.6% for all loci, except for CSF1PO (3.15%) and D7S820 (2.5%). The dropout rates estimated by the MicroDrop software were 0.013 for CSF1PO, 0.000037 for D7S820 and less than 0.000001 for other loci. Small missing data percentages and dropout rates reflect the high quality of the data.