A test of the influence of continental axes of orientation on patterns of human gene flow

The geographic distribution of genetic variation reflects trends in past population migrations and can be used to make inferences about these migrations. It has been proposed that the east–west orientation of the Eurasian landmass facilitated the rapid spread of ancient technological innovations across Eurasia, while the north–south orientation of the Americas led to a slower diffusion of technology there. If the diffusion of technology was accompanied by gene flow, then this hypothesis predicts that genetic differentiation in the Americas along lines of longitude will be greater than that in Eurasia along lines of latitude. We use 678 microsatellite loci from 68 indigenous populations in Eurasia and the Americas to investigate the spatial axes that underlie population‐genetic variation. We find that genetic differentiation increases more rapidly along lines of longitude in the Americas than along lines of latitude in Eurasia. Distance along lines of latitude explains a sizeable portion of genetic distance in Eurasia, whereas distance along lines of longitude does not explain a large proportion of Eurasian genetic variation. Genetic differentiation in the Americas occurs along both latitudinal and longitudinal axes and has a greater magnitude than corresponding differentiation in Eurasia, even when adjusting for the lower level of genetic variation in the American populations. These results support the view that continental orientation has influenced migration patterns and has played an important role in determining both the structure of human genetic variation and the distribution and spread of cultural traits. Am J Phys Anthropol 2011. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88031/1/21533_ftp.pd

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios

Demographic Estimates from Y Chromosome Microsatellite Polymorphisms: Analysis of a Worldwide Sample

Polymorphisms in microsatellites on the human Y chromosome have been used to estimate important demographic parameters of human history. We compare two coalescent-based statistical methods that give estimates for a number of demographic parameters using the seven Y chromosome polymorphisms in the HGDP-CEPH Cell Line Panel, a collection of samples from 52 worldwide populations. The estimates for the time to the most recent common ancestor vary according to the method used and the assumptions about the prior distributions of model parameters, but are generally consistent with other global Y chromosome studies. We explore the sensitivity of these results to assumptions about the prior distributions and the evolutionary models themselves

Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies

Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum-likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method’s credible intervals for population size as a function of time cover 90% of true values across multiple demographic scenarios, enabling formal hypothesis testing about population size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of population size history over the past 150,000 years

Characterization of X-Linked SNP genotypic variation in globally distributed human populations

An analysis of X-linked genetic variation in human populations provides insights into population structure and demographic patterns

Global rise in human infectious disease outbreaks

To characterize the change in frequency of infectious disease outbreaks over time worldwide, we encoded and analysed a novel 33-year dataset (1980–2013) of 12 102 outbreaks of 215 human infectious diseases, comprising more than 44 million cases occuring in 219 nations. We merged these records with ecological characteristics of the causal pathogens to examine global temporal trends in the total number of outbreaks, disease richness (number of unique diseases), disease diversity (richness and outbreak evenness) and per capita cases. Bacteria, viruses, zoonotic diseases (originating in animals) and those caused by pathogens transmitted by vector hosts were responsible for the majority of outbreaks in our dataset. After controlling for disease surveillance, communications, geography and host availability, we find the total number and diversity of outbreaks, and richness of causal diseases increased significantly since 1980 (p < 0.0001). When we incorporate Internet usage into the model to control for biased reporting of outbreaks (starting 1990), the overall number of outbreaks and disease richness still increase significantly with time (p < 0.0001), but per capita cases decrease significantly (p = 0.005). Temporal trends in outbreaks differ based on the causal pathogen's taxonomy, host requirements and transmission mode. We discuss our preliminary findings in the context of global disease emergence and surveillance

Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure

Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables—sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample—on the “clusteredness” of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions