Spatial heterogeneity influences the distribution, prevalence, and diversity of haemosporidian parasites. Previous studies have found complex patterns of prevalence with respect to habitat characteristics and parasite genotype, and their interactions, but there is little information regarding how parasitemia intensity and the prevalence of co-infections may vary in space. Here, using both molecular methods and microscopy, we report an analysis of the variation of parasitemia intensity and co-infections of avian haemosporidian parasites ( Plasmodium and Haemoproteus species) in 2 common African birds species, the yellow-whiskered greenbul ( Andropadus latirostris ) and the olive sunbird ( Cyanomitra olivacea ), at 3 sites with distinct habitat characteristics in Ghana. First, we found an interaction between the site and host species for the prevalence of Plasmodium spp. and Haemoproteus spp. For the olive sunbird, the prevalence of Plasmodium spp., as well as the number of individuals with co-infections, varied significantly among the sites, but these measures remained constant for the yellow-whiskered greenbul. In addition, yellow-whiskered greenbuls infected with Haemoproteus spp. were found only at 1 site. Furthermore, for both bird species, the parasitemia intensity of Plasmodium spp. varied significantly among the 3 sites, but with opposing trends. These results suggest that spatial heterogeneity differently affects haemosporidian infection parameters in these vertebrate-hosts. Environmental conditions here can either favor or reduce parasite infection. We discuss the implications of these discrepancies for conservation and ecological studies of infectious diseases in natural populations.

While thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further ‘fine-mapping’ step. We review the standard fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognised as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region, and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium structures across ancestries, or borrowing information between distinct but related traits. Finally, we look to the future, and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by strong linkage disequilibrium. To determine whether causal variants could be identified from their functional effects, we adapted a massively parallel reporter assay for use in primary CD4 T cells, the cell type whose regulatory DNA is most enriched for immune-mediated disease SNPs. This enabled the effects of candidate SNPs to be examined in a relevant cellular context and generated testable hypotheses into disease mechanisms. To illustrate the power of this approach, we investigated a locus that has been linked to six immunemediated diseases but cannot be fine-mapped. By studying the lead expression-modulating SNP, we uncovered an NF-jB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates TNFAIP3 (A20), a key NF-jB inhibitor. In activated T cells, this feedback circuit is disrupted-and super-enhancer formation prevented-by the risk variant at the lead SNP, leading to unrestrained T-cell activation via a molecular mechanism that appears to broadly predispose to human autoimmunity.

Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to reestimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the "adjusted coverage estimate". This is extended to find "adjusted credible sets", which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants-a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.

AbstractGenome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some “coverage probability”.Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the “corrected coverage estimate”. This is extended to find “corrected credible sets”, which are the smallest set of variants such that their corrected coverage estimate meets the target coverage.We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants – a pattern matched in simulations of well powered GWAS.Crucially, our correction method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.Author summaryPinpointing specific genetic variants within the genome that are causal for human diseases is difficult due to complex correlation patterns existing between variants. Consequently, researchers typically prioritise a set of plausible causal variants for functional validation - these sets of putative causal variants are called “credible sets”. We find that the probabilistic interpretation that these credible sets do indeed contain the true causal variant is variable, in that the reported probabilities often underestimate the true coverage of the causal variant in the credible set. We have developed a method to provide researchers with a “corrected coverage estimate” that the true causal variant appears in the credible set, and this has been extended to find “corrected credible sets”, allowing for more efficient allocation of resources in the expensive follow-up laboratory experiments. We used our method to reduce the number of genetic variants to consider as causal candidates for follow-up in 27 genomic regions that are associated with type 1 diabetes.