Predicting Environmental and Ecological Drivers of Human Population Structure.

Landscape, climate, and culture can all structure human populations, but few existing methods are designed to simultaneously disentangle among a large number of variables in explaining genetic patterns. We developed a machine learning method for identifying the variables which best explain migration rates, as measured by the coalescent-based program MAPS that uses shared identical by descent tracts to infer spatial migration across a region of interest. We applied our method to 30 human populations in eastern Africa with high-density single nucleotide polymorphism array data. The remarkable diversity of ethnicities, languages, and environments in this region offers a unique opportunity to explore the variables that shape migration and genetic structure. We explored more than 20 spatial variables relating to landscape, climate, and presence of tsetse flies. The full model explained ∼40% of the variance in migration rate over the past 56 generations. Precipitation, minimum temperature of the coldest month, and elevation were the variables with the highest impact. Among the three groups of tsetse flies, the most impactful was fusca which transmits livestock trypanosomiasis. We also tested for adaptation to high elevation among Ethiopian populations. We did not identify well-known genes related to high elevation, but we did find signatures of positive selection related to metabolism and disease. We conclude that the environment has influenced the migration and adaptation of human populations in eastern Africa; the remaining variance in structure is likely due in part to cultural or other factors not captured in our model.

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza-Edwards distance (CSE) performs better than linearized FST The correlation (R) between the model's predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti's range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Evidence for serial founder events during the colonization of North America by the yellow fever mosquito, Aedes aegypti.

The Aedes aegypti mosquito first invaded the Americas about 500 years ago and today is a widely distributed invasive species and the primary vector for viruses causing dengue, chikungunya, Zika, and yellow fever. Here, we test the hypothesis that the North American colonization by Ae. aegypti occurred via a series of founder events. We present findings on genetic diversity, structure, and demographic history using data from 70 Ae. aegypti populations in North America that were genotyped at 12 microsatellite loci and/or ~20,000 single nucleotide polymorphisms, the largest genetic study of the region to date. We find evidence consistent with colonization driven by serial founder effect (SFE), with Florida as the putative source for a series of westward invasions. This scenario was supported by (1) a decrease in the genetic diversity of Ae. aegypti populations moving west, (2) a correlation between pairwise genetic and geographic distances, and (3) demographic analysis based on allele frequencies. A few Ae. aegypti populations on the west coast do not follow the general trend, likely due to a recent and distinct invasion history. We argue that SFE provides a helpful albeit simplified model for the movement of Ae. aegypti across North America, with outlier populations warranting further investigation.

The recombination landscape of the Khoe-San likely represents the upper limits of recombination divergence in humans.

BACKGROUND: Recombination maps are important resources for epidemiological and evolutionary analyses; however, there are currently no recombination maps representing any African population outside of those with West African ancestry. We infer the demographic history for the Nama, an indigenous Khoe-San population of southern Africa, and derive a novel, population-specific recombination map from the whole genome sequencing of 54 Nama individuals. We hypothesise that there are no publicly available recombination maps representative of the Nama, considering the deep population divergence and subsequent isolation of the Khoe-San from other African groups. RESULTS: We show that the recombination landscape of the Nama does not cluster with any continental groups with publicly available representative recombination maps. Finally, we use selection scans as an example of how fine-scale differences between the Nama recombination map and the combined Phase II HapMap recombination map can impact the outcome of selection scans. CONCLUSIONS: Fine-scale differences in recombination can meaningfully alter the results of a selection scan. The recombination map we infer likely represents an upper bound on the extent of divergence we expect to see for a recombination map in humans and would be of interest to any researcher that wants to test the sensitivity of population genetic or GWAS analysis to recombination map input.

Origins of high latitude introductions of Aedes aegypti to Nebraska and Utah during 2019.

Aedes aegypti (L.), the yellow fever mosquito, is also an important vector of dengue and Zika viruses, and an invasive species in North America. Aedes aegypti inhabits tropical and sub-tropical areas of the world and in North America is primarily distributed throughout the southern US states and Mexico. The northern range of Ae. aegypti is limited by cold winter months and establishment in these areas has been mostly unsuccessful. However, frequent introductions of Ae. aegypti to temperate, non-endemic areas during the warmer months can lead to seasonal activity and disease outbreaks. Two Ae. aegypti incursions were reported in the late summer of 2019 into York, Nebraska and Moab, Utah. These states had no history of established populations of this mosquito and no evidence of previous seasonal activity. We genotyped a subset of individuals from each location at 12 microsatellite loci and ~ 14,000 single nucleotide polymorphic markers to determine their genetic affinities to other populations worldwide and investigate their potential source of introduction. Our results support a single origin for each of the introductions from different sources. Aedes aegypti from Utah likely derived from Tucson, Arizona, or a nearby location. Nebraska specimen results were not as conclusive, but point to an origin from southcentral or southeastern US. In addition to an effective, efficient, and sustainable control of invasive mosquitoes, such as Ae. aegypti, identifying the potential routes of introduction will be key to prevent future incursions and assess their potential health threat based on the ability of the source population to transmit a particular virus and its insecticide resistance profile, which may complicate vector control.

A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning.

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.

Sunshine versus gold: The effect of population age on genetic structure of an invasive mosquito.

The genetic diversity and structure of invasive species are affected by the time since invasion, but it is not well understood how. We compare likely the oldest populations of Aedes aegypti in continental North America with some of the newest to illuminate the range of genetic diversity and structure that can be found within the invasive range of this important disease vector. Aedes aegypti populations in Florida have probably persisted since the 1600-1700s, while populations in southern California derive from new invasions that occurred in the last 10 years. For this comparison, we genotyped 1,193 individuals from 28 sites at 12 highly variable microsatellites and a subset of these individuals at 23,961 single nucleotide polymorphisms (SNPs). This is the largest sample analyzed for genetic structure for either region, and it doubles the number of southern California populations previously analyzed. As predicted, the older populations (Florida) showed fewer indicators of recent founder effect and bottlenecks; in particular, these populations have dramatically higher genetic diversity and lower genetic structure. Geographic distance and driving distance were not good predictors of genetic distance in either region, especially southern California. Additionally, southern California had higher levels of genetic differentiation than any comparably sized documented region throughout the worldwide distribution of the species. Although population age and demographic history are likely driving these differences, differences in climate and transportation practices could also play a role.

Origin of Aedes aegypti In Clark County, Nevada.

Aedesaegypti is the primary vector for serious diseases, including those caused by chikungunya, dengue, and Zika viruses. In 2017, the Southern Nevada Health District first detected this invasive species in Clark County, NV, including in the city of Las Vegas. We analyzed Ae. aegypti from the city of North Las Vegas to determine the likely source of the invasion. We genotyped a sample of Ae. aegypti at 12 highly variable microsatellites and analyzed the data in reference to published data from 25 sites in the southern USA. We found that the Ae. aegypti in Las Vegas most likely invaded from southern California. Knowing the source of new invasions may provide information about the invading population (e.g., previous insecticide exposure) and can help prevent future invasions from the region.

Multiple introductions of the dengue vector, Aedes aegypti, into California.

The yellow fever mosquito Aedes aegypti inhabits much of the tropical and subtropical world and is a primary vector of dengue, Zika, and chikungunya viruses. Breeding populations of A. aegypti were first reported in California (CA) in 2013. Initial genetic analyses using 12 microsatellites on collections from Northern CA in 2013 indicated the South Central US region as the likely source of the introduction. We expanded genetic analyses of CA A. aegypti by: (a) examining additional Northern CA samples and including samples from Southern CA, (b) including more southern US populations for comparison, and (c) genotyping a subset of samples at 15,698 SNPs. Major results are: (1) Northern and Southern CA populations are distinct. (2) Northern populations are more genetically diverse than Southern CA populations. (3) Northern and Southern CA groups were likely founded by two independent introductions which came from the South Central US and Southwest US/northern Mexico regions respectively. (4) Our genetic data suggest that the founding events giving rise to the Northern CA and Southern CA populations likely occurred before the populations were first recognized in 2013 and 2014, respectively. (5) A Northern CA population analyzed at multiple time-points (two years apart) is genetically stable, consistent with permanent in situ breeding. These results expand previous work on the origin of California A. aegypti with the novel finding that this species entered California on multiple occasions, likely some years before its initial detection. This work has implications for mosquito surveillance and vector control activities not only in California but also in other regions where the distribution of this invasive mosquito is expanding.

Effective population sizes of a major vector of human diseases, Aedes aegypti.

The effective population size (Ne ) is a fundamental parameter in population genetics that determines the relative strength of selection and random genetic drift, the effect of migration, levels of inbreeding, and linkage disequilibrium. In many cases where it has been estimated in animals, Ne is on the order of 10%-20% of the census size. In this study, we use 12 microsatellite markers and 14,888 single nucleotide polymorphisms (SNPs) to empirically estimate Ne in Aedes aegypti, the major vector of yellow fever, dengue, chikungunya, and Zika viruses. We used the method of temporal sampling to estimate Ne on a global dataset made up of 46 samples of Ae. aegypti that included multiple time points from 17 widely distributed geographic localities. Our Ne estimates for Ae. aegypti fell within a broad range (~25-3,000) and averaged between 400 and 600 across all localities and time points sampled. Adult census size (Nc) estimates for this species range between one and five thousand, so the Ne /Nc ratio is about the same as for most animals. These Ne values are lower than estimates available for other insects and have important implications for the design of genetic control strategies to reduce the impact of this species of mosquito on human health.

How elephants beat cancer.

Elephants have significantly reduced their risk of cancer by duplicating an important gene called TP53.

Interactions Increase Forager Availability and Activity in Harvester Ants.

Social insect colonies use interactions among workers to regulate collective behavior. Harvester ant foragers interact in a chamber just inside the nest entrance, here called the 'entrance chamber'. Previous studies of the activation of foragers in red harvester ants show that an outgoing forager inside the nest experiences an increase in brief antennal contacts before it leaves the nest to forage. Here we compare the interaction rate experienced by foragers that left the nest and ants that did not. We found that ants in the entrance chamber that leave the nest to forage experienced more interactions than ants that descend to the deeper nest without foraging. Additionally, we found that the availability of foragers in the entrance chamber is associated with the rate of forager return. An increase in the rate of forager return leads to an increase in the rate at which ants descend to the deeper nest, which then stimulates more ants to ascend into the entrance chamber. Thus a higher rate of forager return leads to more available foragers in the entrance chamber. The highest density of interactions occurs near the nest entrance and the entrances of the tunnels from the entrance chamber to the deeper nest. Local interactions with returning foragers regulate both the activation of waiting foragers and the number of foragers available to be activated.