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Sex differences in parasite load and immune responses are found across a wide range of animals, with females generally having lower parasite loads and stronger immune responses than males. Intrigued by these general patterns, we investigated if there was any sign of sex-specific selection on an essential component of adaptive immunity that is known to affect fitness, the major histocompatibility complex class I (MHC-I) genes, in a 20-year study of great reed warblers. Our analyses on fitness related to MHC-I diversity showed a highly significant interaction between MHC-I diversity and sex, where males with higher, and females with lower, MHC-I diversity were more successful in recruiting offspring. Importantly, mean MHC-I diversity did not differ between males and females, and consequently neither sex reached its MHC-I fitness optimum. Thus, there is an unresolved genetic sexual conflict over MHC-I diversity in great reed warblers. Selection from pathogens is known to maintain MHC diversity, but previous theory ignores that the immune environments are considerably different in males and females. Our results suggest that sexually antagonistic selection is an important, previously neglected, force in the evolution of vertebrate adaptive immunity, and have implications for evolutionary understanding of costs of immune responses and autoimmune diseases.

Evolutionary genomics has recently entered a new era in the study of host-pathogen interactions. A variety of novel genomic techniques has transformed the identification, detection and classification of both hosts and pathogens, allowing a greater resolution that helps decipher their underlying dynamics and provides novel insights into their environmental context. Nevertheless, many challenges to a general understanding of host-pathogen interactions remain, in particular in the synthesis and integration of concepts and findings across a variety of systems and different spatiotemporal and ecological scales. In this perspective we aim to highlight some of the commonalities and complexities across diverse studies of host-pathogen interactions, with a focus on ecological, spatiotemporal variation, and the choice of genomic methods used. We performed a quantitative review of recent literature to investigate links, patterns and potential tradeoffs between the complexity of genomic, ecological and spatiotemporal scales undertaken in individual host-pathogen studies. We found that the majority of studies used whole genome resolution to address their research objectives across a broad range of ecological scales, especially when focusing on the pathogen side of the interaction. Nevertheless, genomic studies conducted in a complex spatiotemporal context are currently rare in the literature. Because processes of host-pathogen interactions can be understood at multiple scales, from molecular-, cellular-, and physiological-scales to the levels of populations and ecosystems, we conclude that a major obstacle for synthesis across diverse host-pathogen systems is that data are collected on widely diverging scales with different degrees of resolution. This disparity not only hampers effective infrastructural organization of the data but also data granularity and accessibility. Comprehensive metadata deposited in association with genomic data in easily accessible databases will allow greater inference across systems in the future, especially when combined with open data standards and practices. The standardization and comparability of such data will facilitate early detection of emerging infectious diseases as well as studies of the impact of anthropogenic stressors, such as climate change, on disease dynamics in humans and wildlife.

Major histocompatibility complex (MHC) genes play a central role for pathogen recognition by the adaptive immune system. The MHC genes are often duplicated and tightly linked within a small genomic region. This structural organization suggests that natural selection acts on the combined property of multiple MHC gene copies in segregating haplotypes, rather than on single MHC genes. This may have important implications for analyses of patterns of selection on MHC genes. Here, we present a computer-assisted protocol to infer segregating MHC haplotypes from family data, based on functions in the R package MHCtools. We employed this method to identify 107 unique MHC class I (MHC-I) haplotypes in 116 families of wild great reed warblers (Acrocephalus arundinaceus). In our data, the MHC-I genes were tightly linked in haplotypes and inherited as single units, with only two observed recombination events among 334 offspring. We found substantial variation in the number of different MHC-I alleles per haplotype, and the divergence between alleles in MHC-I haplotypes was significantly higher than between randomly assigned alleles in simulated haplotypes. This suggests that selection has favored non-random associations of divergent MHC-I alleles in haplotypes to increase the range of pathogens that can be recognized by the adaptive immune system. Further studies of selection on MHC haplotypes in natural populations is an interesting avenue for future research. Moreover, inference and analysis of MHC haplotypes offers important insights into the structural organization of MHC genes, and may improve the accuracy of the MHC region in de novo genome assemblies.

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