Dcifer: an IBD-based method to calculate genetic distance between polyclonal infections

Abstract

An essential step toward reconstructing pathogen transmission and answering epidemiologically relevant questions from genomic data is obtaining pairwise genetic distance between infections. For recombining organisms such as malaria parasites, relatedness measures quantifying recent shared ancestry would provide a meaningful distance, suggesting methods based on identity by descent (IBD). While the concept of relatedness and consequently an IBD approach is fairly straightforward for individual parasites, the distance between polyclonal infections, which are prevalent in malaria, presents specific challenges and awaits a general solution that could be applied to infections of any clonality and accommodate multiallelic (e.g. microsatellite or microhaplotype) and biallelic (SNP) data. Filling this methodological gap, we present Dcifer (Distance for complex infections: fast estimation of relatedness), a method for calculating genetic distance between polyclonal infections, which is designed for unphased data, explicitly accounts for population allele frequencies and complexity of infection, and provides reliable inference. Dcifer’s IBD-based framework allows us to define model parameters that represent interhost relatedness and to propose corresponding estimators with attractive statistical properties. By using combinatorics to account for unobserved phased haplotypes, Dcifer is able to quickly process large datasets and estimate pairwise relatedness along with measures of uncertainty. We show that Dcifer delivers accurate and interpretable results and detects related infections with statistical power that is 2-4 times greater than that of approaches based on identity by state. Applications to real data indicate that relatedness structure aligns with geographic locations. Dcifer is implemented in a comprehensive publicly available software package.

Unravelling var complexity: Relationship between DBLα types and var genes in Plasmodium falciparum

“…The finding of predominantly 1-to-1 relationships for non-upsA DBLa types in high transmission strengthens the strategic assumption that counts of unique non-upsA DBLa types are representative of actual counts of unique var genes in an isolate. This approach presents an alternative for estimating MOI in highly-multiclonal isolates (i.e., MOI > 5), for which current methods using SNP and microsatellite data are limited (Chang et al, 2017;Gerlovina et al, 2022).…”

Genomic epidemiology demonstrates spatially clustered, local transmission ofPlasmodium falciparumin forest-going populations in southern Lao PDR

“…To estimate between-host relatedness, we applied the Dcifer v1.2.0 R package, a method to calculate pairwise genetic distance as identity by descent (IBD) between infections, including those which are polyclonal [22]. By inputting the MOI and allele frequency estimated from MOIRE, each pair obtained an estimation of relatedness ranging from 0 to 1 and a corresponding statistical significance (p-value).…”

Comparison of molecular surveillance methods to assess changes in the population genetics of Plasmodium falciparum in high transmission

“…More recently with the development of newer genetic panels composed of a larger number of biallelic SNPs (Jacob et al, 2021;LaVerriere et al, 2022) or multiallelic microhaplotypes (Tessema et al, 2020) (≥ 2 SNPs within a DNA segment unbroken by recombination (Baetscher et al, 2018)), statistical packages (e.g., DEploid (Zhu et al, 2018), DEploidIBD (Zhu et al, 2019), and Dcifer (Gerlovina et al, 2022)) have been developed using identityby-decent (IBD) based methods to estimate relatedness for phased or unphased multiclonal infections. Although promising, they have been developed on limited datasets from high transmission showing greater error for isolates with greater than five clones and are yet to be tested in the field.…”