Systematic bias in malaria parasite relatedness estimation

Genetic studies of malaria parasites increasingly feature estimates of relatedness. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, estimates of relatedness based on whole-genome-sequence (WGS) data often exceed those based on more sparse data types. We explore systematic bias in relatedness estimation using theoretical, numerical and empirical approaches. Specifically, we use a non-ancestral model of pairwise relatedness to derive theoretical results; a simulation model of ancestry to independently verify and expand our theoretical results; and data on parasites sampled from Guyana to explore how theoretical and numerical results translate empirically. We show that allele frequencies encode, locus-by-locus, relatedness averaged over the set of sampled parasites used to compute them. These sample allele frequencies are typically plugged into the models used to estimate pairwise relatedness. Consequently, models of pairwise relatedness are misspecified and pairwise relatedness values are systematically underestimated. However, systematic underestimation can be viewed as population-relatedness calibration, i.e., a way of generating measures of relative relatedness. Systematic underestimation is unavoidable when relatedness is estimated assuming independence between genetic markers. It is mitigated when estimated using WGS data under a hidden Markov model (HMM), which exploits linkage between proximal markers. Estimates of absolute relatedness generated under a HMM using relatively sparse data should be treated with caution because the extent to which underestimation is mitigated is unknowable. That said, analyses dependent on absolute values and high relatedness thresholds are relatively robust. In summary, practitioners have two options: resolve to use relative relatedness estimated under independence or try to estimate absolute relatedness under a HMM. We propose various practical tools to help practitioners evaluate their situation on a case-by-case basis. ### Competing Interest Statement The authors have declared no competing interest.