Pedigree-based relationship inference from complex DNA mixtures


We present a general method for analysing DNA mixtures involving relatives that accounts for dropout and drop-in, mutations, silent alleles and population substructure. Whether the aim is to identify the contributors to a mixture who may be related, or to determine the relationship between individuals based on a DNA mixture, both types of problems can be handled by the method and software presented here. We focus on the latter scenario, motivated by non-invasive prenatal paternity testing where the profile of the child is available only in the form of a mixture with the mother’s profile. Relationships are represented by pedigrees and can include kinship between more than two individuals. The software is freely available as a graphical user interface in the R package relMix.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    Ashoor G, Poon L, Syngelaki A, Mosimann B, Nicolaides KH (2012) Fetal fraction in maternal plasma cell-Free DNA at 11-13 weeks’ gestation: effect of maternal and fetal factors. Fetal Diagn Ther 31(4):237–243. doi:10.1159/000337373

    Article  PubMed  Google Scholar 

  2. 2.

    Egeland T, Kling D, Mostad P (2016) Relationship inference with familias and R: statistical methods in forensic genetics Academic Press

  3. 3.

    Egeland T, Dørum G, Vigeland MD, Sheehan NA (2014) Mixtures with relatives: a pedigree perspective. Forensic Sci Int Genet 10:49–54. doi:10.1016/j.fsigen.2014.01.007

    Article  PubMed  Google Scholar 

  4. 4.

    Fung WK, Hu YQ (2008) Statistical DNA forensics theory, methods and computation. Wiley, England

    Google Scholar 

  5. 5.

    Gysi M, Arora N, Sulzer A, Voegeli P, Kratzer A (2015) Non-invasive prenatal paternity testing with STRs: a pilot study. Forensic Science International: Genetics Supplement Series 5:e291 – e292. doi:10.1016/j.fsigss.2015.09.115

    Google Scholar 

  6. 6.

    Haned H, Slooten K, Gill P (2012) Exploratory data analysis for the interpretation of low template DNA mixtures. Forensic Sci Int Genet 6(6):762 – 774. doi:10.1016/j.fsigen.2012.08.008

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Hill CR, Butler JM, Coble MD (2006) Allele frequencies for 26 MiniSTR loci with U.S. Caucasian, African American, and Hispanic populations.

  8. 8.

    Hill CR, Duewer DL, Kline MC, Coble MD, Butler JM (2013) U.S. population data for 29 autosomal STR loci. Forensic Sci Int Genet 7(3):e82 – e83. doi:10.1016/j.fsigen.2012.12.004

    Article  PubMed  Google Scholar 

  9. 9.

    Kaur N, Bouzga MM, Dørum G, Egeland T (2015) Relationship inference based on DNA mixtures. Int J Legal Med 2:323–329. doi:10.1007/s00414-015-1276-1

    Google Scholar 

  10. 10.

    Kruijver M (2015) Efficient computations with the likelihood ratio distribution. Forensic Sci Int Genet 14:116 – 124. doi:10.1016/j.fsigen.2014.09.018

    Article  PubMed  Google Scholar 

  11. 11.

    Lo YMD, Tein MS, Lau TK, Haines CJ, Leung TN, Poon PM, Wainscoat JS, Johnson PJ, Chang AM, Hjelm NM (1998) Quantitative analysis of fetal DNA in maternal plasma and serum: implications for noninvasive prenatal diagnosis. Am J Hum Genet 62(4):768 – 775. doi:10.1086/301800

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Mortera J, Vecchiotti C, Zoppis S, Merigioli S (2016) Paternity testing that involves a DNA mixture. Forensic Sci Int Genet 23:50–54. doi:10.1016/j.fsigen.2016.02.014

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Slooten K (2016) Distinguishing between donors and their relatives in complex DNA mixtures with binary models. Forensic Sci Int Genet 21:95 – 109. doi:10.1016/j.fsigen.2015.12.001

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Taylor D, Bright J-A, Buckleton J (2014) Considering relatives when assessing the evidential strength of mixed DNA profiles. Forensic Sci Int Genet 13:259–263

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Tvedebrink T, Eriksen PS, Mogensen HS, Morling N (2009) Estimating the probability of allelic drop-out of STR alleles in forensic genetics. Forensic Sci Int Genet 3(4):222 – 226. doi:10.1016/j.fsigen.2009.02.002

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Tvedebrink T, Eriksen PS, Asplund M, Mogensen HS, Morling N (2012) Allelic drop-out probabilities estimated by logistic regression - Further considerations and practical implementation. Forensic Sci Int Genet 6 (2):263 – 267. doi:10.1016/j.fsigen.2011.06.004

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    van der Gaag KJ, de Leeuw KJ, Hoogenboom J, Patel J, Storts DR, Laros JF, de Knijff P (2016) Massively parallel sequencing of short tandem repeats—population data and mixture analysis results for the PowerSeq system. Forensic Sci Int Genet 24:86 – 96. doi:10.1016/j.fsigen.2016.05.016

Download references


GD has received funding support from the European Union Seventh Framework Programme, EUROFORGEN-NoE (FP7/2007-2013) under grant agreement no. 285487. The authors would like to thank Thore Egeland for helpful comments and two anonymous reviewers who helped improve the paper.

Author information



Corresponding author

Correspondence to Guro Dørum.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dørum, G., Kaur, N. & Gysi, M. Pedigree-based relationship inference from complex DNA mixtures. Int J Legal Med 131, 629–641 (2017).

Download citation


  • DNA mixtures
  • Kinship
  • Likelihood ratio
  • Dropout
  • Drop-in
  • Mutations
  • Non-invasive prenatal paternity testing
  • NGS