Human Population Patterns Research Articles

AncestryGrapher toolkit: Python command-line pipelines to visualize global- and local- ancestry inferences from the RFMIX version 2 software.

Admixture is a fundamental process that has shaped levels and patterns of genetic variation in human populations. RFMIX version 2 (RFMIX2) utilizes a robust modeling approach to identify the genetic ancestries in admixed populations. However, this software does not have a built-in method to visually summarize the results of analyses. Here, we introduce the AncestryGrapher toolkit, which converts the numerical output of RFMIX2 into graphical representations of global and local ancestry (i.e., the per-individual ancestry components and the genetic ancestry along chromosomes, respectively). To demonstrate the utility of our methods, we applied the AncestryGrapher toolkit to visualize the global and local ancestry of individuals in the North African Mozabite Berber population from the Human Genome Diversity Panel (HGDP). Our results showed that the Mozabite Berbers derived their ancestry from the Middle East, Europe, and sub-Saharan Africa (global ancestry). We also found that the population origin of ancestry varied considerably along chromosomes (local ancestry). For example, we observed variance in local ancestry in the genomic region on chromosome 2 containing the regulatory sequence in the MCM6 gene associated with lactase persistence (LP), a human adaptive trait tied to the cultural development of adult milk consumption. Overall, the AncestryGrapher toolkit facilitates the exploration, interpretation, and reporting of ancestry patterns in human populations. The AncestryGrapher toolkit is free and open source on https://github.com/alisi1989/RFmix2-Pipeline-to-plot. Supplementary data are available at Bioinformatics online.

The HLA profile and genetic affinities of three primitive Tamil-speaking endogamous groups: Kallars of Thanjavur, Piramalai Kallar and Vanniyar

BackgroundThe present study was aimed to study the frequencies of HLA-DRB1/-DQB1 alleles and haplotypes of three endogamous groups of Tamil Nadu state, South India. PCR-SSP typing of HLA-DRB1 and -DQB1 alleles were performed on 111 Kallars of Thanjavur, 80 Piramalai Kallar of Madurai and 119 Vanniyar. Genetic distances, neighbor-joining phylogenetic dendrograms and correspondence analysis have been performed.ResultsThe HLA class II alleles, DRB1*07 (25.2%), DRB1*15 (15.7%), DRB1*14 (11.7%) and DRB1*12 (9.90%) among Kallars of Thanjavur; DRB1*15 (28.7%), DRB1*04 (15.6%), DRB1*10 (14.3%), DRB1*13 (11.2%) and DRB1*03 (9.37%) among Piamalai Kallar and DRB1*15 (24.7%), DRB1*04 (15.9%), DRB1*07 (11.7%), DRB1*12 (11.3%) and DRB1*10 (10.0%) among Vanniyar were more frequent. Similarly, alleles DQB1*06 (31.0%), DQB1*02 (26.5%) and DQB1*05 (24.7%) among Kallars of Thanjavur; DQB1*05 (32.5%), DQB1*06 (31.8%), DQB1*02 (16.2%) and DQB1*03:02 (12.5%) among Piramalai Kallar and DQB1*05 (52.9%), DQB1*06 (22.6%) and DQB1*02 (11.3%) among Vanniyar were more frequent. We genotyped the two most frequent two-locus haplotypes, such as DRB1*15-DQB1*06 and DRB1*07-DQB1*02 for HLA-A/-B/–C alleles to identify the 5-locus extended haplotypes to extrapolate global affinities. We identified a number of five locus extended haplotypes among south Indian population with stronger global affinities. Further, we identified the presence of a highly unique extended haplotypes such as A*11-B*35-C*12-DRB1*07-DQB1*02 (HF:0.1458) in Kallars of Thanjavur, A*03-B*35-C*04-DRB1*15-DQB1*06 (HF:0.1833) in Piramalai Kallar and A*03-B*07-C*07-DRB1*15-DQB1*06 (HF: 0.1800) in Kallars of Thanjavur and (HF: 0.1081) in Vanniyar population.ConclusionsAllele distribution and haplotype analysis have demonstrated that the Kallars of Thanjavur, Piramalai Kallar and Vanniyar populations shared HLA alleles with other ethnic and other Indian populations, while showing population specific haplotypes. Analysis of population-specific distribution of HLA alleles is proved to be important in finding out the relatedness of the ethnic groups across continents. The extensive polymorphism of the HLA system also has useful application in the study of the origin, evolution and migration patterns of human populations.

0156 Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component analysis

Abstract Introduction The 24-hour rest and activity behaviors are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-hour rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status. Methods The current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey (NHANES, 2011-2014). We applied fPCA to derive profiles of the rest-activity cycle for overall, weekday and weekend activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health. Results We identified four distinct profiles (i.e., high amplitude, early rise, prolonged activity window, biphasic pattern) that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health. Conclusion Our study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status. Support (If Any)

Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component analysis

BackgroundThe 24-h rest and activity behaviors (i.e., physical activity, sedentary behaviors and sleep) are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-h rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status.MethodsThe current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey (NHANES, 2011–2014). Using 7-day 24-h actigraphy recordings, we applied fPCA to derive profiles for overall, weekday and weekend rest-activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health.ResultsWe identified four distinct profiles (i.e., high amplitude, early rise, prolonged activity window, biphasic pattern) that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health.ConclusionsOur study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status.

DNA sequence symmetries from randomness: the origin of the Chargaff's second parity rule.

Most living organisms rely on double-stranded DNA (dsDNA) to store their genetic information and perpetuate themselves. This biological information has been considered as the main target of evolution. However, here we show that symmetries and patterns in the dsDNA sequence can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. The randomness justifies the human codon biases and context-dependent mutation patterns in human populations. Thus, the DNA ‘exceptional symmetries,’ emerged from the randomness, have to be taken into account when looking for the DNA encoded information. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the selective biological processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.

The impact of genetic adaptation on chimpanzee subspecies differentiation.

Chimpanzees, humans' closest relatives, are in danger of extinction. Aside from direct human impacts such as hunting and habitat destruction, a key threat is transmissible disease. As humans continue to encroach upon their habitats, which shrink in size and grow in density, the risk of inter-population and cross-species viral transmission increases, a point dramatically made in the reverse with the global HIV/AIDS pandemic. Inhabiting central Africa, the four subspecies of chimpanzees differ in demographic history and geographical range, and are likely differentially adapted to their particular local environments. To quantitatively explore genetic adaptation, we investigated the genic enrichment for SNPs highly differentiated between chimpanzee subspecies. Previous analyses of such patterns in human populations exhibited limited evidence of adaptation. In contrast, chimpanzees show evidence of recent positive selection, with differences among subspecies. Specifically, we observe strong evidence of recent selection in eastern chimpanzees, with highly differentiated SNPs being uniquely enriched in genic sites in a way that is expected under recent adaptation but not under neutral evolution or background selection. These sites are enriched for genes involved in immune responses to pathogens, and for genes inferred to differentiate the immune response to infection by simian immunodeficiency virus (SIV) in natural vs. non-natural host species. Conversely, central chimpanzees exhibit an enrichment of signatures of positive selection only at cytokine receptors, due to selective sweeps in CCR3, CCR9 and CXCR6 -paralogs of CCR5 and CXCR4, the two major receptors utilized by HIV to enter human cells. Thus, our results suggest that positive selection has contributed to the genetic and phenotypic differentiation of chimpanzee subspecies, and that viruses likely play a predominate role in this differentiation, with SIV being a likely selective agent. Interestingly, our results suggest that SIV has elicited distinctive adaptive responses in these two chimpanzee subspecies.

Mycobacterial Evolution Intersects With Host Tolerance.

Over the past 200 years, tuberculosis (TB) has caused more deaths than any other infectious disease, likely infecting more people than it has at any other time in human history. Mycobacterium tuberculosis (Mtb), the etiologic agent of TB, is an obligate human pathogen that has evolved through the millennia to become an archetypal human-adapted pathogen. This review focuses on the evolutionary framework by which Mtb emerged as a specialized human pathogen and applies this perspective to the emergence of specific lineages that drive global TB burden. We consider how evolutionary pressures, including transmission dynamics, host tolerance, and human population patterns, may have shaped the evolution of diverse mycobacterial genomes.

Does the moon influence sleep in small-scale societies?

ObjectivesThe lunar cycle is expected to influence sleep-wake patterns in human populations that have greater exposure to the environment, as might be found in forager populations that experience few environmental buffers. We investigated this “moonlight” hypothesis in two African populations: one composed of hunter-gatherers (with minimal environmental buffering) and the other rural agriculturalists (with low-to-moderate environmental buffering). SettingResearch was conducted on Hadza hunter-gatherers from the Sengele community near Lake Eyasi in northern Tanzania and in Mandena, Madagascar, in a rural community of approximately 4000 farmers. ParticipantsThirty-one adult Hadza and 21 Malagasy adults were recruited. MeasurementsWe used the CamNtech Motionwatch 8 actigraph and generated data on an epoch-by-epoch, 1-minute basis. ResultsIn general support of the moonlight hypothesis, we uncovered an association between sleep-wake patterns and lunar cycle (ie., moonlight) for Hadza hunter-gatherers. However, the direction of the effect was opposite to what we predicted: as the potential for exposure to moonlight increased, activity generally shifted to a pattern of less nighttime activity and greater daytime activity. No significant effects were found in the Malagasy agriculturalists. ConclusionsThe proposal that human behaviors are linked with moon phase is a popular belief that persists despite the absence of consistent evidence. We provide the first direct evidence that lunar cycle is linked to sleep-wake pattern in a hunter-gatherer society, suggesting that moonlight does not inhibit sleep-wake patterns in the ways that electric lighting does.

Environmental Surveillance Reveals Complex Enterovirus Circulation Patterns in Human Populations.

BackgroundEnteroviruses are common human pathogens occasionally associated with severe disease, notoriously paralytic poliomyelitis caused by poliovirus. Other enterovirus serotypes such as enterovirus A71 and D68 have been linked to severe neurological syndromes. New enterovirus serotypes continue to emerge, some believed to be derived from nonhuman primates. However, little is known about the circulation patterns of many enterovirus serotypes and, in particular, the detailed enterovirus composition of sewage samples.MethodsWe used a next-generation sequencing approach analyzing reverse transcriptase polymerase chain reaction products synthesized directly from sewage concentrates.ResultsWe determined whole-capsid genome sequences of multiple enterovirus strains from all 4 A to D species present in environmental samples from the United Kingdom, Senegal, and Pakistan.ConclusionsOur results indicate complex enterovirus circulation patterns in human populations with differences in serotype composition between samples and evidence of sustained and widespread circulation of many enterovirus serotypes. Our analyses revealed known and divergent enterovirus strains, some of public health relevance and genetically linked to clinical isolates. Enteroviruses identified in sewage included vaccine-derived poliovirus and enterovirus D-68 stains, new enterovirus A71 and coxsackievirus A16 genogroups indigenous to Pakistan, and many strains from rarely reported serotypes. We show how this approach can be used for the early detection of emerging pathogens and to improve our understanding of enterovirus circulation in humans.

Characterization of the contribution of shared environmental and genetic factors to metabolic syndrome methylation heritability and familial correlations

BackgroundTransgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DNA methylation sites are inflated by environmental and genetic covariance within families is still unclear. We applied current methods to quantify the environmental and genetic contributors to the observed heritability and familial correlations of four previously associated MetS methylation sites at three genes (CPT1A, SOCS3 and ABCG1) using real data made available through the GAW20.ResultsOur findings support the role of both shared environment and genetic variation in explaining the heritability of MetS and the four MetS cytosine-phosphate-guanine (CpG) sites, although the resulting heritability estimates were indistinguishable from one another. Familial correlations by type of relative pair generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected, as would be consistent with the role of shared environmental factors in the inflation of our estimated correlations.ConclusionsOur work provides an interesting and flexible statistical framework for testing models of epigenetic inheritance in the context of human family studies. Future work should endeavor to replicate our findings and advance these methods to more robustly describe epigenetic inheritance patterns in human populations.

Population isolation predicts the severity of historical human epidemics

AbstractAimInfectious diseases are a major source of human mortality and have altered human history. Despite their importance, we lack a thorough understanding of why some historical epidemics were more deadly than others. For many organisms, geographically isolated populations (e.g., populations distant from the mainland) experience more severe epidemics, including after long periods of isolation. These patterns are likely to arise because geographical isolation reduces contact with infectious diseases, causing a corresponding naïvety and susceptibility to those pathogens. Here, we test for equivalent patterns in human populations, but over much longer time frames than have been considered previously.LocationGlobal, but with a particular focus on islands in the Americas and the Pacific.Time periodApproximately 1000–1920 CE.Major taxa studiedHumans and two of their pathogens: smallpox and influenza.MethodsWe used historical mortality data from populations afflicted with smallpox or Spanish flu to test for relationships between isolation, a suite of covariates (e.g., island size, population density) and epidemic severity (mortality rate).ResultsWe show that populations isolated from the mainland suffered greater mortality. Interestingly, New World populations suffered universally higher mortality during smallpox epidemics, probably because they became isolated before the appearance of smallpox, causing high levels of susceptibility. Conversely, by 1918 New World populations were predominantly Eurasian and thus historically exposed to influenza, causing low mortality.ConclusionHuman populations exhibit patterns of epidemics analogous to those in non‐human species; isolated populations tend to suffer more severe epidemics. However, historical contingencies can cause patterns to diverge from those typically observed among other free‐living species. Generally, we suggest that geography underlies the ecology and evolution of humans and their pathogens.

The interplay between individual social behavior and clinical symptoms in small clustered groups

BackgroundMixing patterns of human populations play a crucial role in shaping the spreading paths of infectious diseases. The diffusion of mobile and wearable devices able to record close proximity interactions represents a great opportunity for gathering detailed data on social interactions and mixing patterns in human populations. The aim of this study is to investigate how social interactions are affected by the onset of symptomatic conditions and to what extent the heterogeneity in human behavior can reflect a different risk of infection.MethodsWe study the relation between individuals’ social behavior and the onset of different symptoms, by making use of data collected in 2009 among students sharing a dormitory in a North America university campus. The dataset combines Bluetooth proximity records between study participants with self-reported daily records on their health state. Specifically, we investigate whether individuals’ social activity significantly changes during different symptomatic conditions, including those defining Influenza-like illness, and highlight to what extent possible heterogeneities in social behaviors among individuals with similar age and daily routines may be responsible for a different risk of infection for influenza.ResultsOur results suggest that symptoms associated with Influenza-like illness can be responsible of a reduction of about 40% in the average duration of contacts and of 30% in the daily time spent in social interactions, possibly driven by the onset of fever. However, differences in the number of daily contacts were found to be not statistically significant. In addition, we found that individuals who experienced clinical influenza during the study period were characterized by a significantly higher social activity. In particular, both the number of person-to-person contacts and the time spent in social interactions emerged as significant risk factors for influenza infection.ConclusionsOur findings highlight that Influenza-like illness can remarkably reduce the social activity of individuals and strengthen the idea that the heterogeneity in social habits among individuals can significantly contribute in shaping differences among the individuals’ risk of infection.

Dietary patterns in human populations from northwest Patagonia during Holocene: an approach using Binford’s frames of reference and Bayesian isotope mixing models

The goal of this paper is to assess the variation in the proportional contribution of diverse resources to the diet of human populations from northwest Patagonia (Argentina) throughout the Middle-Late Holocene. Particularly, we assessed the variation among three geographic areas and two periods. We first estimated the expected proportions of terrestrial animals and plants and aquatic resources for each area according to the Binford’s frames of references approach. A Bayesian mixing method was then applied to calculate the proportion of plants and animals in the diets from stable isotopes (δ13C and δ15N) of human bone collagen. The isotope values suggest that the composition of diets differed spatially and temporally. Diets of South Mendoza were mainly composed of terrestrial animals (Rhea-Lama and rodents) with a greater incorporation of C3 plants towards the later Late Holocene; in North Neuquen, Rhea and Lama represent a proportion of 0.84 of the diet consumed; and finally, the sample of Center Neuquen is the only one with high values of Araucaria in the diet. The isotopic values obtained for the three studied areas did not fit to the expectations of Binford’s model, North Neuquen being the area that departs most from the predicted proportions of terrestrial animals and plants and aquatic organisms in the diet. These findings open up new questions about the local conditions that influenced regional variation in the diet of prehistoric hunter-gatherers.

Modeling human population patterns on tree density

In order to evaluate the possible correlation between the tree density and the human population density, the forested area in Nav Asalem districtlocated in Guilan Province was selected. The descriptors of tree number and basal area per hectare as well as the stand density index were used to determine the tree density, which was conducted from a 2014 forest inventory including 62 cluster (558 plots) systematically scattered over 30 % of the forest area. In addition, to determine the density of the human population, circular buffers at intervals of 1 to 7 km from the center of each cluster was considered and population density of each layer was calculated using buffering functions. Statistical results showed that the average basal area, average number of trees and the average stand density index was 23.16 m 2 /ha, 243 per ha and 178.25 respectively and also different human population density in each buffer. Using Pearson correlation test indicated a significant negative correlation between the stand density index and basal area (DBH >15 cm) with human population density. There was no significant relationship between the number of trees per hectare and the human population density except at 7 km. This findings support studies regarding the disturbance has strong correlative with the number of residents per unit area at up to 7 km from clusters and greater control on anthropogenic interventions should be the main priority of sustainable forestry in Hyrcanian forests of northern Iran. Due to the existence of an effective relationship between the components of the tree density and human population in the forest, policy-makers and planners of natural resources could benefit management patterns appropriate to above components to achieve sustainable management.

Evaluating the Characteristics of Social Vulnerability to Wildfire: Demographics, Perceptions, and Parcel Characteristics.

A large body of research focuses on identifying patterns of human populations most at risk from hazards and the factors that help explain performance of mitigations that can help reduce that risk. One common concept in such studies is social vulnerability-human populations' potential exposure to, sensitivity from and ability to reduce negative impacts from a hazard. While there is growing interest in social vulnerability for wildfire, few studies have critically evaluated the characteristics that scholars often indicate influence social vulnerability to that hazard. This research utilizes surveys, wildfire simulations, and GIS data to test the relationships between select demographic, perceptual and parcel characteristics of property owners against empirically simulated metrics for wildfire exposure or wildfire-related damages and their performance of mitigation actions. Our results from Flathead County, MT, USA, suggest that parcel characteristics such as property value, building value, and the year structures were built explaining a significant amount of the variance in elements of social vulnerability. Demographic characteristics commonly used in social vulnerability analysis did not have significant relationships with measures of wildfire exposure or vulnerability. Part-time or full-time residency, age, perceived property risk, and year of development were among the few significant determinants of residents' performance of fuel reduction mitigations, although the significance of these factors varied across the levels of fuel reduction performed by homeowners. We use these and other results to argue for a renewed focus on the finer-scale characteristics that expose some populations to wildfire risk more than others.

Evaluating mtDNA patterns of genetic isolation using a re-sampling procedure: A case study on Italian populations

Background: A number of studies which have investigated isolation patterns in human populations rely on the analysis of intra- and inter-population genetic statistics of mtDNA polymorphisms. However, this approach makes it difficult to differentiate between the effects of long-term genetic isolation and the random fluctuations of statistics due to reduced sample size.Aim: To overcome the confounding effect of sample size when detecting signatures of genetic isolation.Subjects and methods: A re-sampling based procedure was employed to evaluate reduction in intra-population diversity, departure from surrounding genetic background and demographic stationarity in 34 Italian populations subject to isolation factors.Results: Signatures of genetic isolation were detected for all three statistics in seven populations: Pusteria valley, Sappada, Sauris, Timau settled in the eastern Italian Alps and Cappadocia, Filettino and Vallepietra settled in the Appenines. However, this study was unable to find signals for any of the statistics analysed in 19 populations. Finally, eight populations showing signals of isolation were found for one or two statistics.Conclusion: The analysis revealed that the use of population genetic statistics combined with re-sampling procedure can help detect signatures of genetic isolation in human populations, even using a single, although highly informative, locus like mtDNA.

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Abstract. Wildfires are by far the largest contributor to global biomass burning and constitute a large global source of atmospheric traces gases and aerosols. Such emissions have a considerable impact on air quality and constitute a major health hazard. Biomass burning also influences the radiative balance of the atmosphere and is thus not only of societal, but also of significant scientific interest. There is a common perception that climate change will lead to an increase in emissions as hot and dry weather events that promote wildfire will become more common. However, even though a few studies have found that the inclusion of CO2 fertilisation of photosynthesis and changes in human population patterns will tend to somewhat lower predictions of future wildfire emissions, no such study has included full ensemble ranges of both climate predictions and population projections, including the effect of different degrees of urbanisation.Here, we present a series of 124 simulations with the LPJ–GUESS–SIMFIRE global dynamic vegetation–wildfire model, including a semi-empirical formulation for the prediction of burned area based on fire weather, fuel continuity and human population density. The simulations use Climate Model Intercomparison Project 5 (CMIP5) climate predictions from eight Earth system models. These were combined with two Representative Concentration Pathways (RCPs) and five scenarios of future human population density based on the series of Shared Socioeconomic Pathways (SSPs) to assess the sensitivity of emissions to the effect of climate, CO2 and humans. In addition, two alternative parameterisations of the semi-empirical burned-area model were applied. Contrary to previous work, we find no clear future trend of global wildfire emissions for the moderate emissions and climate change scenario based on the RCP 4.5. Only historical population change introduces a decline by around 15 % since 1900. Future emissions could either increase for low population growth and fast urbanisation, or continue to decline for high population growth and slow urbanisation. Only for high future climate change (RCP8.5), wildfire emissions start to rise again after ca. 2020 but are unlikely to reach the levels of 1900 by the end of the 21st century. We find that climate warming will generally increase the risk of fire, but that this is only one of several equally important factors driving future levels of wildfire emissions, which include population change, CO2 fertilisation causing woody thickening, increased productivity and fuel load and faster litter turnover in a warmer climate.

Inference of sex-specific expansion patterns in human populations from Y-chromosome polymorphism.

Studying the current distribution of genetic diversity in humans has important implications for our understanding of the history of our species. We analyzed a set of linked STR and SNP loci from the paternally inherited Y chromosome to infer the past demography of 55 African and Eurasian populations, using both the parametric and nonparametric coalescent-based methods implemented in the BEAST application. We inferred expansion events in most sedentary farmer populations, while we found constant effective population sizes for both nomadic hunter-gatherers and seminomadic herders. Our results differed, on several aspects, from previous results on mtDNA and autosomal markers. First, we found more recent expansion patterns in Eurasia than in Africa. This discrepancy, substantially stronger than the ones found with the other kind of markers, may result from a lower effective population size for men, which might have made male-transmitted markers more sensitive to the out-of-Africa bottleneck. Second, we found expansion signals only for sedentary farmers but not for nomadic herders in Central Asia, while these signals were found for both kind of populations in this area when using mtDNA or autosomal markers. Expansion signals in this area may result from spatial expansion processes and may have been erased for the Y chromosome among the herders because of restricted male gene flow.

The interplay between human population dynamics and flooding in Bangladesh: a spatial analysis

Abstract. In Bangladesh, socio-economic and hydrological processes are both extremely dynamic and inter-related. Human population patterns are often explained as a response, or adaptation strategy, to physical events, e.g. flooding, salt-water intrusion, and erosion. Meanwhile, these physical processes are exacerbated, or mitigated, by diverse human interventions, e.g. river diversion, levees and polders. In this context, this paper describes an attempt to explore the complex interplay between floods and societies in Bangladeshi floodplains. In particular, we performed a spatially-distributed analysis of the interactions between the dynamics of human settlements and flood inundation patterns. To this end, we used flooding simulation results from inundation modelling, LISFLOOD-FP, as well as global datasets of population distribution data, such as the Gridded Population of the World (20 years, from 1990 to 2010) and HYDE datasets (310 years, from 1700 to 2010). The outcomes of this work highlight the behaviour of Bangladeshi floodplains as complex human–water systems and indicate the need to go beyond the traditional narratives based on one-way cause–effects, e.g. climate change leading to migrations.

Adaptive human CDKAL1 variants underlie hormonal response variations at the enteroinsular axis.

Recent analyses have identified positively selected loci that explain differences in immune responses, body forms, and adaptations to extreme climates, but variants that describe adaptations in energy-balance regulation remain underexplored. To identify variants that confer adaptations in energy-balance regulation, we explored the evolutionary history and functional associations of candidate variants in 207 genes. We screened single nucleotide polymorphisms in genes that had been associated with energy-balance regulation for unusual genetic patterns in human populations, followed by studying associations among selected variants and serum levels of GIP, insulin, and C-peptide in pregnant women after an oral glucose tolerance test. Our analysis indicated that 5′ variants in CDKAL1, CYB5R4, GAD2, and PPARG are marked with statistically significant signals of gene–environment interactions. Importantly, studies of serum hormone levels showed that variants in CDKAL1 are associated with glucose-induced GIP and insulin responses (p<0.05). On the other hand, a GAD2 variant exhibited a significant association with glucose-induced C-peptide response. In addition, simulation analysis indicated that a type 2 diabetes risk variant in CDKAL1 (rs7754840) was selected in East Asians ∼6,900 years ago. Taken together, these data indicated that variants in CDKAL1 and GAD2 were targets of prior environmental selection. Because the selection of the CDKAL1 variant overlapped with the selection of a cluster of GIP variants in the same population ∼11,800 to 2,000 years ago, we speculate that these regulatory genes at the human enteroinsular axis could be highly responsive to environmental selection in recent human history.