Maerkle, H. | Search bioRxiv

bioRxiv2019

Cross-Species association statistics for genome-wide studies of host and parasite polymorphism data

Uncovering the genes governing host-parasite coevolutionary interactions is of importance for disease management. The increasing availability of host and parasite full genome-data allows for cross-species genome-wide association studies based on the genomic data of in-fected hosts and their associated parasite strains sampled from natural populations (i.e. natural co-GWAs). Such studies focus on searching for cross-species allelic associations between pairs of host and parasite SNPs. We aim to quantify the power of natural co-GWAs to pinpoint loci under coevolution with respect to the intrinsic complexities of coevolutionary systems, such as the genetic specificity of the interaction and the temporal allele frequency changes resulting from the interaction. Therefore, we develop the cross-species association (CSA) and the cross-species prevalence (CSP) indices, the latter additionally incorporating genomic data from uninfected hosts. To provide an assessment of the statistical power of these indices, we analytically derive their genome-wide False Discovery Rates (FDR) based on the neutral site-frequency spectrum of the host and the parasite population. Using two coevolutionary models, we investigate under which parameter regimes these indices pin-point the coevolving loci. Under trench warfare dynamics, CSA and CSP are very accurate in pinpointing the loci under coevolution, while under arms race dynamics the power is limited especially for gene-for-gene interactions. Furthermore, we reveal that the combination of both indices across time samples is an indicator for the specificity of the interaction. Our results provide novel insights into the power and biological interpretation of natural cross-species association studies.

evolutionary biology↗

bioRxiv2019

Inference of coevolutionary dynamics and parameters from host and parasite polymorphism data of repeated experiments

There is a long-standing interest in understanding host-parasite coevolutionary dynamics and associated fitness effects. Increasing amounts of genomic data for both interacting species offer a promising source to identify candidate loci and to infer the main parameters of the past coevolutionary history. However, so far no method exists to do so. By coupling a gene-for-gene model with coalescent simulations, we first show that three types of biological cost, resistance, infectivity and infection, define the allele frequencies at the internal equilibrium point of the coevolution model, which in return determine the strength of the selective signatures signatures at host and parasite loci. We apply an Approximate Bayesian Computation (ABC) approach on simulated datasets to infer these costs by jointly integrating host and parasite polymorphism data at the coevolving loci. To control for the effect of genetic drift on coevolutionary dynamics, we assume that 10 or 30 repetitions are available from controlled experiments or several natural populations. We study two scenarios: 1) the cost of infection and population sizes (host and parasite) are unknown while costs of infectivity and resistance are known, and 2) all three costs are unknown while populations sizes are known. Using the ABC model choice procedure, we show that for both scenarios, we can distinguish with high accuracy pairs of loci from host and parasite under coevolution from neutrally evolving loci, though the statistical power decreases with higher cost of infection. The accuracy of parameter inference is also very high under both scenarios especially when using both host and parasite data because parasite polymorphism data do inform on host costs and vice-versa. As the false positive rate to detect genes under coevolution is small, we suggest to use our method to identify host and parasite candidate loci for further functional studies.\n\nAuthor summaryIt is of importance for agriculture and medicine to understand host-parasite antagonistic coevolutionary dynamics and the deleterious associated fitness effects, as well as to reveal the genes underpinning these interactions. The increasing amount of genomic data for hosts and parasites offer a promising source to identify such candidate loci, but also to use statistical inference methods to reconstruct the past coevolutionary history. In our study we attempt to draw inference of the past coevolutionary history at key host and parasites loci using sequence data from several individuals and across several experimental replicates. We demonstrate that using a Bayesian statistical method, it is possible to estimate the parameters driving the interaction of hosts and parasites at these loci for thousands of generations. The main parameter that can be estimated is the fitness loss by hosts upon infection. Our method and results can be applied to experimental coevolution data with sequences at the key candidate loci providing enough repetitions and large enough population sizes. As a proof of principle, our results open the door to reconstruct past coevolutionary dynamics using sequence data of interacting species.

evolutionary biology↗