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We present methods for inference about relationships between contributors toa DNA mixture and other individuals of known genotype: a basic example would betesting whether a contributor to a mixture is the father of a child of knowngenotype. The evidence for such a relationship is evaluated as the likelihoodratio for the specified relationship versus the alternative that there is nosuch relationship. We analyse real casework examples from a criminal case and adisputed paternity case; in both examples part of the evidence was from a DNAmixture. DNA samples are of varying quality and therefore present challengingproblems in interpretation. Our methods are based on a recent statistical modelfor DNA mixtures, in which a Bayesian network (BN) is used as a computationaldevice; the present work builds on that approach, but makes more explicit useof the BN in the modelling. The R code for the analyses presented is freelyavailable as supplementary material.  We show how additional information of specific genotypes relevant to therelationship under analysis greatly strengthens the resulting inference. Wefind that taking full account of the uncertainty inherent in a DNA mixture canyield likelihood ratios very close to what one would obtain if we had a singlesource DNA profile. Furthermore, the methods can be readily extended to analysedifferent scenarios as our methods are not limited to the particular genotypingkits used in the examples, to the allele frequency databases used, to thenumbers of contributors assumed, to the number of traces analysedsimultaneously, nor to the specific hypotheses tested.

Paternity testing and other inference about relationships from DNA mixtures