Using all available evidence to solve kinship cases.

Kinship cases, ranging from standard paternity tests to complex disaster victim identifications, are typically evaluated using likelihood ratios (LR) based on forensic genetic markers. However, in some contexts, genetic information alone is not enough to reach conclusive results. This is common when establishing distant familial connections using large DNA-databases, or even in simple cases such as determining which individual is the parent and which is the child in a relationship pair. Although forensic practitioners frequently incorporate supplementary evidence (SE), such as age, biological sex, or phenotypic traits, in these cases, this integration typically occurs informally, without systematic quantitative assessment, compromising procedural transparency and reliability. Here, we present a framework that synthesizes forensic DNA evidence (FDE) with SE through Markov chain models and customized transition matrices designed for various biological traits. This approach generates combined likelihood assessments expressed as LRs or posterior probabilities. Validation through simulated and real-world case studies demonstrates that systematic incorporation of SE improves resolution accuracy in kinship determinations. To facilitate applications, we have implemented this methodology in mispitools, an open-source R package.