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Stutter analysis of complex STR MPS data.

Søren B Vilsen1, Torben Tvedebrink1, Poul Svante Eriksen1

  • 1Department of Mathematical Sciences, Aalborg University, Denmark.

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|April 28, 2018
PubMed
Summary
This summary is machine-generated.

Stutter artifacts in short tandem repeat (STR) analysis can be better understood by analyzing all DNA blocks, not just the longest uninterrupted stretch (LUS). A new metric, block length of the missing motif (BLMM), improves stutter ratio prediction, especially for complex genetic markers.

Keywords:
BLMMBlocksLUSMPSMissing motifStutter

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Area of Science:

  • Genetics
  • Forensic Science
  • Molecular Biology

Background:

  • Stutters are common artifacts in short tandem repeat (STR) analysis during polymerase chain reaction (PCR).
  • Accurate stutter analysis is crucial for samples with low DNA quantity or multiple contributors.
  • Existing models using the longest uninterrupted stretch (LUS) are insufficient with massively parallel sequencing (MPS) revealing diverse stutter patterns.

Purpose of the Study:

  • To refine the understanding of stutter formation mechanisms in STR analysis.
  • To develop a more accurate method for predicting stutter ratios beyond the LUS concept.
  • To improve the analysis of complex and compound autosomal STR markers.

Main Methods:

  • Analysis of all uninterrupted DNA stretches (blocks) within STR alleles.
  • Identification of the specific block responsible for stutter formation.
  • Definition and application of the block length of the missing motif (BLMM) metric.
  • Development of a system of recurrence relations to model stutter ratios.

Main Results:

  • A linear relationship was established between the stutter ratio and the block length of the missing motif (BLMM).
  • The BLMM metric demonstrated improved accuracy in predicting stutter ratios compared to LUS.
  • Mean square error was reduced by up to 17.5 times for compound and complex autosomal markers using BLMM.

Conclusions:

  • The block length of the missing motif (BLMM) offers a more refined approach to modeling stutter ratios in STR analysis.
  • BLMM enhances the accuracy of genetic data interpretation, particularly with MPS data and complex markers.
  • This refined method has significant implications for forensic science and genetic research involving low-template or mixed DNA samples.