Genomic dissection of the microevolution of Australian epidemic Bordetella pertussis

Whooping cough (pertussis) is a highly contagious respiratory disease caused by the bacterium Bordetella pertussis. Despite high vaccine coverage, pertussis has re-emerged in many countries and caused two large epidemics in Australia since 2007. Here, we undertook a genomic and phylogeographic study of 385 Australian B. pertussis isolates collected from 2008 to 2017. The Australian B. pertussis population was found to be composed of mostly ptxP3 strains carrying different fim3 alleles, with ptxP3-fim3A genotype expanded far more than ptxP3-fim3B. Within the former, there were six co-circulating epidemic lineages (EL1 to EL6). The multiple ELs emerged, expanded, and then declined at different time points over the two epidemics, likely driven by immune selection from pertussis vaccination and natural infection in addition to local and global transmission events. Both hard and soft selective sweeps through vaccine selection pressures determined the current B. pertussis population dynamics. Relative risk analysis found that once a new B. pertussis lineage emerged, it was more likely to spread locally within the first 1.5 years. However, after 1.5 years, any new lineage was likely to expand to a wider region and became no longer spatially structured across the country. Phylogenetic analysis revealed the expansion of ptxP3 strains was also associated with replacement of the type III secretion system allele bscI1 with bscI3. This study advanced our understanding of the epidemic population structure and spatial and temporal dynamics of B. pertussis in a highly immunised population.

Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population ofDrosophila melanogaster(DGRP) allowed us to rediscover three known cases of adaptation at the lociAce,Cyp6g1, andCHKov1known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harriset al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps inD.melanogasterare first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.

Dominance shifts increase the likelihood of soft selective sweeps

Genetic models of adaptation to a new environment have typically assumed that the alleles involved maintain a constant fitness dominance across the old and new environments. However, theories of dominance suggest that this should often not be the case. Instead, the alleles involved should frequently shift from recessive deleterious in the old environment to dominant beneficial in the new environment. Here, we study the consequences of these expected dominance shifts for the genetics of adaptation to a new environment. We find that dominance shifts increase the likelihood that adaptation occurs from the standing variation, and that multiple alleles from the standing variation are involved (a soft selective sweep). Furthermore, we find that expected dominance shifts increase the haplotypic diversity of selective sweeps, rendering soft sweeps more detectable in small genomic samples. In cases where an environmental change threatens the viability of the population, we show that expected dominance shifts of newly beneficial alleles increase the likelihood of evolutionary rescue and the number of alleles involved. Finally, we apply our results to a well-studied case of adaptation to a new environment: the evolution of pesticide resistance at the Ace locus in Drosophila melanogaster. We show that, under reasonable demographic assumptions, the expected dominance shift of resistant alleles causes soft sweeps to be the most frequent outcome in this case, with the primary source of these soft sweeps being the standing variation at the onset of pesticide use, rather than recurrent mutation thereafter.

Multiple mechanisms drive genomic adaptation to extreme O2 levels in Drosophila melanogaster

To detect the genomic mechanisms underlying evolutionary dynamics of adaptation in sexually reproducing organisms, we analyze multigenerational whole genome sequences of Drosophila melanogaster adapting to extreme O2 conditions over an experiment conducted for nearly two decades. We develop methods to analyze time-series genomics data and predict adaptive mechanisms. Here, we report a remarkable level of synchronicity in both hard and soft selective sweeps in replicate populations as well as the arrival of favorable de novo mutations that constitute a few asynchronized sweeps. We additionally make direct experimental observations of rare recombination events that combine multiple alleles on to a single, better-adapted haplotype. Based on the analyses of the genes in genomic intervals, we provide a deeper insight into the mechanisms of genome adaptation that allow complex organisms to survive harsh environments.

Natural Selection in Large Populations

This chapter includes selection in haploid and diploid organisms, hard and soft selective sweeps, background selection, and the probability of ultimate survival of a new favorable mutation in a large population. It considers overdominance and heterozygote inferiority in detail as well as different types of equilibria and the fundamental theorem of natural selection. Various types of balancing selection are examined including mutation–selection balance, migration–selection balance, meiotic drive and gametic selection, and the theory of CRISPR-mediated gene drive to control natural populations. It closes with a discussion of other modes of selection and their implications.

Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.

On the unfounded enthusiasm for soft selective sweeps II: examining recent evidence from humans, flies, and viruses

ABSTRACTSince the initial description of the genomic patterns expected under models of positive selection acting on standing genetic variation and on multiple beneficial mutations—so-called soft selective sweeps—researchers have sought to identify these patterns in natural population data. Indeed, over the past two years, large-scale data analyses have argued that soft sweeps are pervasive across organisms of very different effective population size and mutation rate—humans, Drosophila, and HIV. Yet, others have evaluated the relevance of these models to natural populations, as well as the identifiability of the models relative to other known population-level processes, arguing that soft sweeps are likely to be rare. Here, we look to reconcile these opposing results by carefully evaluating three recent studies and their underlying methodologies. Using population genetic theory, as well as extensive simulation, we find that all three examples are prone to extremely high false-positive rates, incorrectly identifying soft sweeps under both hard sweep and neutral models. Furthermore, we demonstrate that well-fit demographic histories combined with rare hard sweeps serve as the more parsimonious explanation. These findings represent a necessary response to the growing tendency of invoking parameter-heavy, assumption-laden models of pervasive positive selection, and neglecting best practices regarding the construction of proper demographic null models.

Architecture of parallel adaptation to freshwater in multiple populations of threespine stickleback

Threespine sticklebacks adapted to freshwater environments all over the Northern Hemisphere. This adaptation involved parallel recruitment of freshwater alleles in clusters of closely linked sites, or divergence islands (DIs). However, it is unclear to what extent the DIs involved in adaptation and the alleles within them coincide between populations adapting to similar environments. Here, we examine 10 freshwater populations of similar ages from the White Sea basin, and study the repeatability of patterns of adaptation in them. Overall, the 65 detected DIs tend to reside in regions of low recombination, underlining the role of reduced recombination in their establishment. Moreover, the DIs are clustered in the genome to the extent that is not explainable by the recombination rate alone, consistent with the divergence hitchhiking model. 21 out of the 65 DIs are universal; i.e., the frequency of freshwater alleles in them is increased in all analyzed populations. Universal DIs tend to have longer core region shared between populations, and the divergence between the marine and the freshwater haplotypes in them is higher, implying that they are older, also consistently with divergence hitchhiking. Within most DIs, the same set of sites distinguished the marine and the freshwater haplotypes in all populations; however, in some of the DIs, the genetic architecture of the freshwater haplotype differed between populations, suggesting that they could have been established by soft selective sweeps.

diploS/HIC: an updated approach to classifying selective sweeps

Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes